MIKKO SUOMINEN SIMPLE AND RAPID METHOD FOR

Transcription

MIKKO SUOMINEN
SIMPLE AND RAPID METHOD FOR MONITORING PHARMACEUTICALS IN
WASTEWATER
Master of Science Thesis
Examiners: Professor Helge
Lemmetyinen, Professor Tuula
Tuhkanen
Examiners and topic approved by
the Faculty Council of the
Faculty of Natural Sciences on
6th of February 2013
ii
TAMPERE UNIVERSITY OF TECHNOLOGY
Master’s Degree Programme in Environmental and Energy Engineering
SUOMINEN, MIKKO: Simple and rapid method for monitoring pharmaceuticals in
wastewater
Master of Science Thesis, 99 pages, 7 appendix pages
June 2013
Major: Chemistry
Inspectors: Prof. Helge Lemmetyinen; Prof. Tuula Tuhkanen
Keywords: Pharmaceuticals, HPLC, SPE, wastewater
ABSTRACT
Thousands of tons of pharmacologically active ingredients are used annually. The
compounds end up into the environment either directly or from wastewater treatment
plants. Also pharmaceutical factories generate point source emissions. Pharmaceuticals in
the environment have adverse and potentially unidentified effects and elimination of
pharmaceutical emissions at point sources is needed. To support this work, reliable
analytical methods capable of measuring pharmaceuticals in environmental matrices are
needed.
The aim of this Master’s thesis was to develop an HPLC-UV method for the
measurement of acetyl salicylic acid (ASA), ciprofloxacin (CPX), paracetamol (PCM),
sulfamethoxazole (SMX), diclofenac (DIC) and erythromycin (ERY) from wastewater. In
order to detect ERY derivatization was needed. Pretreatment of samples was optimized in
terms of sample pH. SPE recoveries and repeatabilities were determined and also
breakthrough of analytes was investigated. The aim was to achieve quantification limits of
0.05 mg/l.
Two separate methods were developed. A separate method for the derivatized ERY was
needed because the derivatization product was extremely hydrophobic. The HPLC method
for ASA, CPX, DIC, PCM and SMX used a 250 mm x 4.6 mm x 5 µm C18 column, a
gradient using 1 % acetic acid, 0.2 % triethylamine : ACN as mobile phases, a flow rate of
1 ml/min. ASA was detected at 275 nm and the rest of the compounds at 265 nm.
To retain ASA, SMX, PCM and DIC from wastewater C18 SPE sorbents were used and
sample pH adjusted to 2. For CPX strong cation exchange sorbents were used. ASA, DIC
and SMX didn’t show analyte breakthrough up to 200 ml but for PCM analyte
breakthrough occurred after 50 ml. Recoveries were 88.3 ± 3.6 % for ASA, 107.4 ± 1.1 %
for SMX and 86.9 ± 8.5 % for DIC using 100 ml sample volumes and 84.7 ± 4.6 % using
50 ml sample volume. Recovery of CPX was 77.8 ± 3.7 % using 100 ml sample volume.
Taking sample enrichment during SPE pretreatment into account, method detection
limits were 0.037 mg/l for PCM, 0.043 mg/l for ASA, 0.003 mg/l for SMX, 0.009 mg/l for
DIC and 0.048 mg/l for CPX using 1 ml as the final HPLC sample volume. Therefore
quantification at the 0.05 mg/l level could be done.
iii
Derivatization of ERY was carried out by evaporating the sample solvent and reacting
the residue with FMOC-Cl and phosphate buffer (pH 8.25) at 60 oC for 15 minutes. A 150
mm x 4.6 mm x 5 µm C8 column, isocratic elution with ACN:Milli-Q water 80:20 (v:v), a
flow rate of 2 ml/min and a detection wavelength of 265 nm were used. Linearity of the
HPLC method was fair (R2 = 0.927) and instrumental quantification limit was 9.6 µg of
ERY. ERY was extracted from wastewater at pH 10 using C18 SPE sorbents. The mean
recovery was 82.7 % ± 36.5 %. Breakthrough of ERY wasn’t investigated because of poor
derivatization repeatability. Taking sample enrichment into account, quantification of ERY
at the 0.05 mg/l level could be achieved by extracting approximately 230 ml of the sample.
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TAMPEREEN TEKNILLINEN YLIOPISTO
Ympäristö- ja energiatekniikan koulutusohjelma
SUOMINEN, MIKKO: Yksinkertainen ja nopea menetelmä
määrittämiseksi jätevedestä
Diplomityö, 99 sivua, 7 liitesivua
Kesäkuu 2013
Pääaine: Kemia
Tarkastajat: professori Helge Lemmetyinen; professori Tuula Tuhkanen
Avainsanat: Lääkeaineet, HPLC, SPE, jätevesi
lääkeaineiden
TIIVISTELMÄ
Sekä ihmisille että eläimille tarkoitettuja lääkkeitä käytetään vuosittain tuhansia tonneja.
Lääkeaineet päätyvät joko metaboliatuotteina tai alkuperäisessä muodossaan
jätevedenpuhdistamoille, joista ne kulkeutuvat edelleen puhdistamoiden purkuvesistöihin,
sillä lääkeaineet poistuvat huonosti jätevedenpuhdistamoiden käsittelyprosesseissa.
Lääkeaineiden hajoamattomuuteen ja vaikutuksiin ympäristössä on kiinnitetty huomiota
vasta muutaman vuoden ajan.
Myös lääketehtaiden jätevesiin on alettu kiinnittää enemmän huomiota. Alueilla, joilla
on laajamittaista tuotantoa, tehtaiden päästöt voivat muodostaa merkittävän osuuden
luonnossa havaittavista lääkeainepitoisuuksista. Lääkeaineiden suurista pitoisuuksista
ympäristössä raportoitiin Intian Hyderabadissa alueella, jolla toimii 90 lääketehdasta.
Lääketehtaat valmistavat rinnakkaisvalmisteita Euroopan ja Yhdysvaltojen markkinoille.
Esimerkiksi Ruotsissa markkinoilla olevista 242 lääkevalmisteesta 74 sisälsi yhdisteitä,
jotka on valmistettu Hyderabadissa.
Hyderabadin alueella lääketehtaiden jätevesiä käsittelevän puhdistuslaitoksen
käsitellystä jätevedestä mitattiin suuria siprofloksasiinipitoisuuksia. Pitoisuudet olivat
kolme kymmenen kertaluokkaa suurempia kuin mitä Microcystis aurengiosalle toksiset
pitoisuudet ovat. Toksisten vaikutusten lisäksi on havaittu merenelävien feminisaatiota ja
antibioottiresistenttiyden yleistymistä ympäristössä esiintyvien lääkeaineiden seurauksena.
Kenties kuitenkin suurin lääkeaineisiin liitetty uhkakuva liittyy ympäristössä esiintyvien
lääkeaineiden yhteisvaikutuksiin, josta käytetään nimitystä cocktail-efekti. Tällä
tarkoitetaan sitä, että lääkeaineet saattavat vaikuttaa ihmisten tai eläinten terveyteen
synergisesti. Lääkeaineiden mahdollisista yhteisvaikutuksista tiedetään toistaiseksi hyvin
vähän.
Jotta lääkeaineita voidaan analysoida ympäristönäytteistä, tulee näyte esikäsitellä, jonka
jälkeen näytteen sisältämät lukuisat yhdisteet tulee erottaa toisistaan. Esikäsittely tapahtuu
normaalisti suodattamalla näyte kiintoaineen poistamiseksi. Yhdisteiden erotus sen sijaan
tapahtuu kromatografisten menetelmien avulla.
Kromatografiassa näytteen yksittäiset yhdisteet voidaan erottaa toisistaan
kromatografikolonnin avulla. Näytteen molekyylit vuorovaikuttavat kromatografikolonnin
v
stationaarifaasin ja liikkuvan faasin kanssa. Mikäli liikkuva faasi on kaasu, puhutaan
kaasukromatografiasta, ja mikäli liikkuva faasi on neste, puhutaan nestekromatografiasta.
Lääkeaineilla on yleisesti ottaen suhteellisen pieni molekyylipaino, niissä on useampia
varauksellisia ryhmiä. Siten niiden haihtuvuus on suhteellisen alhainen. Näiden
ominaisuuksien seurauksena lääkeaineet erotetaan toisistaan tavallisesti korkean
suorituskyvyn nestekromatografian (eng. High Performance Liquid Chromatography,
HPLC) avulla. Lääkeaineiden rasvahakuisuuden vuoksi käytetään käänteisfaasi-HPLC:tä
(eng. reversed phase, RP) jossa kromatografikolonnin stationaarifaasina on rasvahakuinen
C18- tai C8-materiaali. Lääkeaineiden havaitsemiseen voidaan käyttää UV-detektoria, koska
se on halpa, yksinkertainen käyttää ja yleisesti saatavilla. UV-detektointi soveltuu
suurimmalle osalle lääkeaineita, sillä lääkeaineissa on tavallisesti UV-valoa absorboivia
funktionaalisia ryhmiä tai aromaattisuutta.
Lääkeaineiden analysoimiseksi ympäristöstä otetuista vesinäytteistä voidaan yrittää
eliminoida epäpuhtauksia, jotka ovat kemiallisesti määritettävien yhdisteiden kaltaisia ja
haittaavat siten analyysiä. Epäpuhtauksia voidaan vähentää kiinteäfaasiuuton (eng. Solid
Phase Extraction, SPE) avulla. SPE on tekniikka, joka kehitettiin 1970-luvulla ympäristö-,
biologisten ja teollisten näytteiden esikäsittelemiseksi.
SPE-käsittelyn aikana vesinäyte kaadetaan sorbentin (kiinteä faasi) läpi, joka sitoo
tutkittavat yhdisteet. Epäpuhtauksien eliminointi tapahtuu pesemällä sorbenttia sopivalla
liuottimella, joka liuottaa epäpuhtaudet mutta ei tutkittavaa yhdistettä. Lopulta yhdisteet
eluoidaan pieneen määrään sopivaa liotinta. Siten SPE:n avulla näyteen pitoisuutta voidaan
myös nostaa konsentroimalla suuri määrä vesinäytettä pieneen liuotintilavuuteen.
Käytettäessä neste-neste-uuttoa ongelmana on tavallisesti emulsionmuodostus, mutta
käytettäessä SPE:tä tätä ongelmaa ei ole. SPE:llä toistettavuus on myös parempi, sillä
käytettäessä neste-neste-uuttoa tarvitaan suuri määrä pieniä liuotinfraktioita yhdisteen
eristämiseksi. Koska yksittäisessä uutossa syntyy aina virhettä, on kasautunut virhe lopulta
merkittävä.
UV-detektointi ei suoraan sovellu kaikille lääkeaineille. Eritromysiini on tällainen
lääkeaine, ja toistaiseksi sen määrittämiseksi on olemassa vähän analyysimenetelmiä.
Eritromysiiniä tuotetaan paljon, ja suurten päästömäärien vuoksi on mahdollista, bakteerien
resistenttiys sitä kohtaan yleistyy. Vuonna 2012 tehdyssä kirjallisuuskatsauksessa
eritromysiini nostettiin suuriman riskin omaavaksi lääkeaineeksi.
Yhdisteet, jotka eivät suoraan absorboi UV-säteilyä, voidaan derivatisoida UVabsorbanssin lisäämiseksi. Tällöin tutkittavaan molekyyliin liitetään kemiallisesti UVsäteilyä absorboiva ryhmä. Derivatisointireagensseilta vaaditaan, että ne reagoivat
tutkittavan yhdisteen kanssa täydellisesti, jotta kvantifiointi olisi mahdollista.
Ympäristönäytteessä saattaa olla yhdisteitä, jotka myös reagoivat derivatisointireagenssin
kanssa ja siten kuluttavat reagenssia. Käyttämällä derivatisointireagenssia ylimäärin
voidaan varmistua, että tutkittava yhdiste reagoi mahdollisimman täydellisesti. Myös SPE-
vi
käsittelyn aikainen sorbentin pesu on tärkeää, sillä sen avulla voidaan häiritseviä yhdisteitä
vähentää.
Universal Corporation Limited (UCL) on Kenian Kikuyussa, lähellä Nairobia, sijaitseva
lääketehdas. UCL:llä on ollut joitakin ongelmia jätevedenkäsittelyn kanssa. Paikallinen
ympäristöviranomainen (National Environment Management Authority, NEMA) on
asettanut 0,05 mg/l rajan käsitellyn jäteveden lääkeainepitoisuudeksi kaikkien jätevedessä
olevien lääkeaineiden osalta. Ajoittain tähän arvoon ei ole päästy. Lääkeaineet häiritsevät
myös tehtaan jätevedenkäsittelylaitoksen aktiivilieteprosessia. Siten lääkeaineet haittaavat
epäsuorasti myös muiden vaatimusten (kiintoaine, biologinen hapenkulutus) saavuttamista.
Jätevedenkäsittelyprosessiin on suunnitteilla muutoksia, ja näiden muutosten
onnistumisen arvioimiseksi tarvitaan luotettavia analyysimenetelmiä. Erityisesti SPEesikäsittelyn luotettavuudesta halutaan varmistua. Lääkeaineiden analysoimisessa
jätevedestä käytetään poolittomia C18 sorbentteja. Koska lääkeaineet ovat yleisesti ottaen
ionisoituvia on näytteen pH:lla on suuri merkitys käytettäessä poolitonta
sorbenttimateriaalia. Mikäli näytteen pH on väärä, eivät lääkeaineet pidäty sorbentteihin ja
analyysitulokset ovat virheellisiä. Tehtaalla ei ole myöskään tällä hetkellä eritromysiinin
määrittämiseen käyttökelpoista meneltelmää
Tämä diplomityö suoritetiin yhteistyössä UCL:n kanssa. Kokeellisen työn alku
suoritettiin UCL:n tehtaalla Kenian Kikuyussa syksyllä 2012 suoritetun vaihtoopiskelujakson aikana. Työn kokeellista osuutta jatkettiin Tampereen teknillisellä
yliopistolla.
Työn tavoitteina oli kehittää yksinkertainen ja nopea menetelmä kuudelle lääkeaineelle
käyttämällä SPE:tä lääkeaineiden eristämiseen jätevedestä, HPLC:tä yhdisteiden
erottamiseksi toisistaan ja UV-detektointia yhdisteiden kvantifiointiin. Tutkittavaksi
lääkeaineiksi valittiin aspiriini, parasetamoli, siprofloksasiini, sulfametoksatsoli,
diklofenakki ja eritromysiini. Sorbenttimateriaaliksi valittiin C18, koska se on edullisin ja
siten sitä on mahdollista käyttää myös kehittyvissä maissa.
Vesinäytteiden esikäsittely haluttiin optimoida näytteen pH:n suhteen. Yhdisteiden
saannot sekä saantojen toistettavuudet haluttiin määrittää, jotta tulosten luotetavuutta
voitaisiin arvoioida. Lisäksi sorbenttien kapasiteettia haluttiin tutkia. Niin sanotun
läpimurron (eng. breakthrough) aikana tutkittava yhdiste ei enää pidäty sorbenttiin, ja
mikäli tätä ei oteta huomioon, saadaan analyysin tuloksena virheellisiä
lääkeainepitoisuuksia.
Menetelmiin haluttiin lisätä erillinen pesuvaihe, jonka aikana analyysiä häiritseviä
yhdisteitä voitaisiin poistaa näytteestä mahdollisimman tehokkaasti. Eritromysiinille tuli
kehittää derivatisointimenetelmä, jotta yhdisteen pitoisuus voitaisiin määrittää UVdetektorin avulla. Yhdisteiden määritysrajaksi lopullisissa menetelmissä haluttiin vähintään
0,05 mg/l, jotta NEMA:n asettaman rajan saavuttamista voitaisiin arvioida.
vii
Lopulta kehitettiin kaksi erillistä HPLC-menetelmää, sillä eritromysiinin
derivatisoimisen jälkeen osottautui, että derivatisointituote oli hyvin hydrofobinen, ja
yhdisteen eluoimiseksi tuli käyttää hyvin voimakasta ajoliosta. Aspiriinin, siprofloksasiinin,
parasetamolin, diklofenakin, ja sulfametoksatsolin määrittämiseksi käytettiin 250 mm x 4,6
mm x 5 µm C18-kolonnia, ajoliuoksina 1 % etikkahappoa ja 0,2 % trietyyliamiinia Milli-Qveteen liuotettuna sekä asetonitriiliä, 1 ml/min virtausnopeutta sekä 275 nm aallonpituutta
aspiriinin sekä 265 nm muiden yhdisteiden määrittämiseen. Menetelmä oli lineaarinen (R2
0,996) pitoisuusalueella 0,025 mg/l – 25 mg/l kaikkien lääkeaineiden osalta.
Kokeiden aikana havaittiin, että C18 sorbenttimateriaali ei sovellu siprofloksasiinin
eristämiseen, sillä yhdisteen amfoteerisen luonteen vuoksi yhdiste ei pidäty poolittomaan
C18-sorbenttimateriaaliin. Muille yhdisteille C18-sorbentti soveltuu, mutta pH:ssa 7
sulfametoksatsolin, aspiriinin ja parasetamolin saanto oli merkittävästi huonompi.
Säätämällä näytteen pH kahteen aspiriinin, diklofenakin, parasetamolin ja
sulfametoksatsolin saannot olivat kaikki hyväksyttäviä. Aspiriinin saanto oli 88,3 ± 3,6 %,
sulfametoksatsolin saanto oli 107,4 ± 1,1 % ja diklofenaakin saanto oli 99,8 ± 4,9 % 100
ml näytetilavuudella. Parasetamolin saanto 50 ml näytetilavuudelle oli 84,7 ± 4,6 %.
Siprofloksasiinin eristämiseksi käytettiin voimakkaita kationinvaihtosorbentteja (StrataX-C strong cation exchange sorbents). Näyte tehtiin happamaksi lisäämällä 20 µl vahvaa
fosforihappoa yhtä millilitraa näytettä kohti. Eluointiin käytettiin 5 %
ammoniumhydroksidia veteen liuotettuna. Siprofloksasiinin saanto oli 77,8 ± 3,7 %.
Analysoitaessa aspiriinia, diklofenaakkia ja sulfametoksatsolia näytettä voitiin rikastaa
ainakin 200 ml ilman että läpimurtoa havaittiin. Parasetamolilla havaittiin läpimurtoa jo 50
ml näytetilavuudella. Siten suurempia tilavuuksia ei voinut käyttää yhdisteen
rikastamiseksi. Siprofloksasiinia voitiin käyttää 20 ml 30 mg sorbenttimassalla, ja
käytettäessä 300 mg sorbenttia käytettävissä olevan tilavuuden oletettiin olevan 200 ml.
Kun SPE:n aikainen näytteen konsentrointi otettiin huomioon, menetelmän määritysrajaksi
laskettiin 0,037 mg/l parasetamolille, 0,043 mg/l aspiriinille, 0,003 mg/l
sulfametoksatsolille, 0,009 mg/l diklofenaakille sekä 0,048 mg/l siprofloksasiinille. Siten,
rikastamalla näytteet SPE:llä, lääkeaineet voitiin määrittää pitoisuuksista, jotka olivat alle
NEMA:n asettaman rajan.
Eritromysiinin
derivatisoimiseksi
tutkittiin
trimetyylibromosilaanin
ja
9fluorenyylimetyloksikarbonyylikloridin (FMOC-Cl) käyttöä. Jälkimmäistä käytettäessä
muodostui absorboiva tuote, joka voitiin havaita 265 nm aallonpituudella. Derivatisointi
tehtiin haihduttamalla liuotin paineilman avulla ja lisäämällä 100 µl 1 g/l FMOC-Clkantaliuosta ja 25 µl 50 mM kaliumdivetyfosfaattipuskuria, jonka pH oli 8,25.
Fosfaattipuskuri tarvittiin, jotta reaktiossa vapautuvat vetyionit neutraloituivat. pH:n
laskiessa alle seitsemän derivatisointireaktio pysähtyy. Seosta pidettiin 60 oC:ssa
vesihauteessa 15 minuuttia. Reaktion päättämiseksi seos jäähdytettiin juoksevan veden alla
ja seokseen lisättiin 25 µl fosfaattipuskuria. Reaktioseos analysoitiin suoraan HPLC:llä.
viii
Eritromysiiniderivaatan eluoimikseksi tarvittiin voimakas ajoliuos. Lopullisessa
menetelmässä käytettiin 80:20 asetonitriili:Milli-Q-seosta virtausnopeudella 2 ml/min, 150
mm x 4,6 mm x 5 µm C8 kolonnia ja 265 nm aallonpituutta detektoimiseen. Reaktiotuoteen
pinta-alasta muodostetun kalibraatiosuoran korrelaatiokerroin oli 0,927. Tavallisesti
korrelaatiokertoimen tulisi olla yli 0,996 jotta menetelmää voitaisiin pitää lineaarisena.
Siten menetelmää voidaan käyttää vain suuntaa-antavien tuloksien saamiseen.
Derivatisointireaktiossa liuottimien määrä oli 125 µl. Koska osa liuottimesta höyrystyi
ja tiivistyi koeputken seinämille, ja koska oli mahdollista, että tämä osuus oli erilainen eri
derivatisointikertojen välillä, aiheutui tästä vaihtelua derivaatan konsentraatioon. Tämä oli
todennäköisesti syynä derivatisointireaktion huonoon toistettavuuteen ja näkyi menetelmän
heikkona lineaarisuutena.
Eristettäessä eritromysiiniä C18 sorbenteilla näytteen pH tuli säätää arvoon 10. Kolme
100 ml näytettä, joissa eritromysiinin pitoisuus oli 0,73 mg/l eristettiin SPE:n avulla.
Käsittelyn saanto oli 82,7 ± 36,5 %. Virhettä saattoi aiheutua SPE-esikäsittelystä, mutta
todennäköisesti suurin virhelähde oli derivatisointivaiheen huono toistettavuus. Huonon
toistettavuuden takia eritromysiinin läpimurtoa SPE-esikäsittelyn aikana ei tutkittu.
Mahdollista läpimurtoa ei olisi ollut mahdollista luotettavasti erottaa derivatisoinnista
aiheutuvasta pitoisuuden vaihtelusta.
Eritromysiinin määritysrajaksi saatiin 9,6 µg eritromysiiniä (eristettynä
jätevesinäytteestä). Määritysraja on järkevintä ilmoittaa eritromysiinin massana ennen
derivatisointia, koska derivaatan massaa tai pitoisuutta lopullisessa näytteessä ei ollut
mahdollista määrittää. Määritysrajaan päästään rikastamalla 230 ml näytettä, jonka
pitoisuus eritromysiinin suhteen on 0,05 mg/l. Siten myös eritromysiiniä voidaan
kvantifioida pitoisuuksista, jotka vastaavat NEMA:n määrittämää rajaa 0,05 mg/l.
SPE-menetelmien optimoinnin aikana määritettiin optimaalinen pesuliuos eri
yhdisteille. Aspiriinin, parasetamolin, diklofenaakin ja sulfametoksatsolin tapauksessa
testatiin sorbentin pesua 2,5 % ja 5 % metanoliliuoksella. Piikkien pinta-aloissa ei ollut
merkittävää muutosta, joten 5 % metanoliliuos todettiin sopivaksi pesuliuokseksi. Vahvojen
kationinvaihtosorbenttien kohdalla käytettiin valmistajan suositettelemaa pesua 0,1 %
fosforihapolla. Koska saannot olivat hyviä, pesu todettiin soveltuvaksi.
Eritromysiiniderivaatalla testattiin pesua 10 %, 15 % ja 20 % metanoliliuoksilla.
Käytettäessä 15 % pesuliuosta saanto oli suurimmillaan, joten eritromysiiniderivaatalle
pesu 15 % metanolilla todettiin parhaaksi.
ix
PREFACE
This Master’s thesis was carried out at two locations. Initial experiments were conducted
during an exchange program in the fall of 2012 at a pharmaceutical factory called Universal
Corporation Ltd, which is located in Kikuyu, Kenya, near Nairobi. The exchange program
was funded by the Center of International Mobility, CIMO. The experiments were finished
in Finland at Tampere University of Technology in the spring of 2013.
I would like to thank prof. Helge Lemmetyinen and prof. Tuula Tuhkanen for
supervising this Master’s thesis and providing me with this opportunity. I would also like to
thank the exchange program coordinators Outi Kaarela and Maarit Särkilahti for arranging
the practical matters. I would like to thank prof. Gachanja from Jomo Kenyatta University
of Agriculture and Technology for supervising my work in Kenya and for providing
invaluable advice and technical support at the beginning of this work.
I would like to thank the personnel at UCL who helped me during the practical work.
Especially I would like to thank Mr. Venkat Rama, Dr. Sonal Patel, Dr. Radiyah
Janoowalla and Mr. Kelvin Lugalia for practical support. Thanks to you everything that
was needed was at disposal. Just to name a few of the other persons who I’m indebted to at
UCL include Mr. Moses Sifuna, Mr. Benjamin Mutuku, Mr. Martin Ongas, Mr. Edward
Magowi, Ms. Virginia Wanjiku, Ms. Norah Maeharia, Mr. Livingstone Abueheri, Mr. Isaac
Kathoka and Mr. Amos Ngugi. The working atmosphere at UCL was carefree and I felt
myself most welcome.
Especially I would like to thank Pentti and Silvia Keskitalo for their hospitality during
my stay in Kenya. Thanks to you I didn’t experience practically any home sickness and
everyday matters both inside and outside of UCL went without problems.
I would like to thank MSc. Sanna Pynnönen who helped me to get started at TUT and
also for proofreading this Thesis. With Sanna we also had many good conversations that
were helpful with regard to this project. I would also like to thank associate professor
Alexander Efimov of Chemistry laboratory who helped me significantly throughout the
entire work. For the help regarding practical matters I’m indebted to Tea Tanhuanpää, Tarja
Ylijoki-Kaiste and Antti Nuottajärvi.
Finally, I would like to thank my friends and especially my parents Sirkka and Jarmo
and my sister Minna for all the support. This would not have been possible without you.
Tampere 24th of June, 2013
Mikko Suominen
x
TABLE OF CONTENTS
1
Introduction ..................................................................................................................... 1
2
Theoretical background .................................................................................................. 3
2.1
2.1.1
Problems related to pharmaceutical production in developing countries ........ 5
2.1.2
Problems related to pharmaceuticals in the environment ................................ 5
2.2
Environmental analyses of pharmaceuticals ........................................................... 8
2.2.1
HPLC method development ........................................................................... 10
2.2.2
Adjustment of mobile phase strength ............................................................. 12
2.2.3
Adjustment of mobile phase composition ...................................................... 12
2.2.4
Performance of an HPLC column .................................................................. 14
2.2.5
Detection and sensitivity ................................................................................ 15
2.2.6
Method validation .......................................................................................... 17
2.3
3
Pharmaceuticals in the environment........................................................................ 3
Sample pretreatment .............................................................................................. 19
2.3.1
Solid phase extraction .................................................................................... 21
2.3.2
Sorbent materials for non-polar compounds .................................................. 22
2.3.3
Sorbent materials for polar compounds ......................................................... 23
2.3.4
SPE pretreatment steps ................................................................................... 24
2.3.5
Recovery and breakthrough volume .............................................................. 25
2.4
Matrix effects ........................................................................................................ 26
2.5
Active ingredients included in the study ............................................................... 27
2.5.1
Sulfamethoxazole ........................................................................................... 28
2.5.2
Acetylsalicylic Acid ....................................................................................... 28
2.5.3
Diclofenac ...................................................................................................... 29
2.5.4
Ciprofloxacin HCl .......................................................................................... 30
2.5.5
Paracetamol .................................................................................................... 30
2.5.6
Erythromycin stearate .................................................................................... 31
Materials and methods .................................................................................................. 32
3.1
Site of the study ..................................................................................................... 32
3.2
Instrumentation used ............................................................................................. 34
xi
3.2.1
Instrumentation in Kenya ............................................................................... 34
3.2.2
Instrumentation in Finland ............................................................................. 35
3.3
Chemicals used ...................................................................................................... 36
3.4
Active ingredients studied ..................................................................................... 36
3.5
Calibration of the instrument ................................................................................. 38
3.5.1
Injector reproducibility................................................................................... 38
3.5.2
Detector linearity............................................................................................ 38
3.5.3
Injector precision/carryover ........................................................................... 38
3.5.4
Gradient linearity and accuracy ..................................................................... 39
3.6
Method development ............................................................................................. 39
3.6.1
Derivatization of erythromycin ...................................................................... 39
3.6.2
Optimization of resolution ............................................................................. 41
3.7
Method validation.................................................................................................. 42
3.7.1
Linear range ................................................................................................... 42
3.7.2
Limit of detection and limit of quantification ................................................ 42
3.7.3
Intraday and interday repeatability................................................................. 42
3.7.4
Method ruggedness ........................................................................................ 43
3.8
SPE pretreatment ................................................................................................... 43
3.8.1
Recoveries and breakthrough volumes .......................................................... 43
3.8.2
Determination of suitable SPE conditions ..................................................... 44
3.9
Method limit of quantification .............................................................................. 45
3.10 Wastewater samples .............................................................................................. 45
4
Results and discussion .................................................................................................. 47
4.1
Calibration results.................................................................................................. 47
4.1.1
Injector reproducibility................................................................................... 47
4.1.2
Detector linearity............................................................................................ 47
4.1.3
Injector precision/carryover ........................................................................... 48
4.1.4
Gradient linearity ........................................................................................... 48
4.2
Derivatization of ERY ........................................................................................... 49
4.2.1
TMBS derivatization reaction ........................................................................ 49
xii
4.2.2
4.3
FMOC derivatization reaction........................................................................ 50
Optimization of resolution ..................................................................................... 50
4.3.1
ASA, CPX, DIC, SMX and PCM .................................................................. 50
4.3.2
ERY ................................................................................................................ 52
4.4
Retention time, resolution, and peak quality parameters ...................................... 53
4.4.1
4.4.2
ERY ................................................................................................................ 54
4.5
Linear ranges ......................................................................................................... 56
4.5.1
4.5.2
ERY ................................................................................................................ 57
4.6
Instrumental limit of detection and limit of quantification ................................... 58
4.6.1
4.6.2
ERY ................................................................................................................ 60
4.7
Intraday and interday repeatability ........................................................................ 60
4.8
Method ruggedness ................................................................................................ 62
4.9
SPE pretreatment of ASA, CPX, DIC, PCM and SMX ........................................ 63
4.9.1
Recoveries using C18 sorbents........................................................................ 63
4.9.2
Breakthrough volume diagram for C18 sorbents ............................................ 65
4.9.3
Recoveries using strong cation exchange sorbents ........................................ 67
4.10 SPE pretreatment of ERY ...................................................................................... 68
4.11 Choice of wash solvent for SPE ............................................................................ 69
4.11.1
4.11.2
ERY ................................................................................................................ 69
4.12 Summary of optimal SPE cartridges and conditions ............................................. 70
4.13 Method limit of quantification .............................................................................. 71
4.13.1
ASA, CPX, DIC, PCM and SMX .................................................................. 71
4.13.2
ERY ................................................................................................................ 73
4.14 Wastewater samples .............................................................................................. 73
4.14.1
Separation of active ingredients from interferents ......................................... 73
4.14.2
Different concentration scenarios .................................................................. 75
xiii
5
Conclusions................................................................................................................... 77
6
References ..................................................................................................................... 78
Appendix A .......................................................................................................................... 84
Appendix B .......................................................................................................................... 85
Appendix C .......................................................................................................................... 86
Appendix D .......................................................................................................................... 87
Appendix E .......................................................................................................................... 88
Appendix F ........................................................................................................................... 89
xiv
LIST OF ABBREVIATIONS
ACN
Acetonitrile
amu
Atomic mass unit
API
Active pharmaceutical ingredient
ASA
Acetyl Salicylic Acid, Aspirin
Capacity factor, quantifies the selectivity between two analytes
BOD
Biological oxygen demand
COD
Chemical oxygen demand
CPX
Ciprofloxacin
C18
Octadecane, an aliphatic hydrocarbon with 18 carbon atoms
DIC
Diclofenac
EC/LC50
Effective/lethal concentration 50 %
ESI-TOF
Electrospray-Ionization Time-of-Flight
EU
European Union
FMOC-Cl
9-fluorenylmethyloxycarbonyl chloride, a derivatization reagent
GAA
Glacial acetic acid, an anhydrous form of acetic acid with an assay above
99.85 %
GC
Gas Chromatography
GMP
Good manufacturing practices. A certificate given accredit companies with a
given level of standards
HPLC/LC
High Performance Liquid Chromatography/Liquid Chromatography
HSA
Hexane sulfonic acid, an ion-pair reagent
k
Retention factor, describes the selectivity of method between two analytes
Ka
Equilibrium constant of a chemical reaction
Koc
Partition coefficient of a substance between organic carbon and water
xv
LLC
Liquid-liquid extraction, extraction method used to extract non-polar
compounds from polar solvents
LOD
Limit of detection, the concentration of analyte which can be detected
LogP
Logarithm of the ratio of neutral analyte concentrations in n-octanol and
water at equilibrium
LOQ
Limit of quantification, the concentration of analyte which can be quantified
reliably
N
Number of theoretical plates, describes the efficiency of a chromatographic
column
NEMA
The Kenyan regulatory authority (National Environment Management
Authority)
OECD
Organization for Economic Co-operation and Development
PCM
Paracetamol
PETL
Patancheru Enviro Tech Limited
pKa
Dissociation constant of an ionizable compound obtained as the negative
logarithm of the equilibrium constant describing the dissociation
PTF
Peak tailing factor, describes the quality of a peak in a chromatographic run
Rs
Resolution, describes how well two analytes are resolved during an HPLC
separation
SMX
Sulfamethoxazole
SPE
Solid phase extraction, a technique used to extract analytes from water
samples
TEA
Triethylamine, an amine modifier used in aqueous mobile phases to affect
separation of analytes
TMBS
Trimethylbromosilane, a derivatization reagent
t0
Dead time, describes the minimum time for a non-retained solute to elute
tr
Retention time, the time it takes an analyte to elute during a
chromatographic process
xvi
TSS
Total suspended solids
UCL
Universal corporation limited, a pharmaceutical company based in Kikuyu,
Kenya
UV/vis
UV/visible light, wavelength range of electromagnetic radiation from 190
nm to 800 nm
W
Width of a peak in a chromatogram
WWTP
Wastewater treatment plant
1
1 Introduction
Thousands of tons of pharmacologically active ingredients are used annually for human and
veterinary purposes (Dorival-García et al. 2013). Pharmaceuticals enter the environment in
treated wastewater from communal wastewater treatment plants since conventional
treatment methods are not capable to eliminate pharmaceuticals. As a result,
pharmaceuticals are released into the environment. (Fick et al. 2009)
Also drug factory wastewaters have been identified as significant point sources of
pharmaceutical emissions. In areas with intensive production pharmaceutical factories may
be the most significant sources of pharmaceuticals in the environment. (Fick et al. 2009) So
far there has been few reports on this matter but the subject is gaining increasing attention.
One of the best known cases dealing with high levels of pharmaceuticals in wastewaters
was reported in a study done in Hyderabad, India. 90 bulk drug manufacturers operate in
the area and a single wastewater treatment plant (WWTP) receives all the waters. (Fick et
al. 2009) In the treated wastewater of WWTP ciprofloxacin concentrations were three
orders of magnitude higher than the toxicity values for Microcystis aurengiosa. (Larsson et
al. 2007)
Pharmaceuticals in the environment have adverse effects. At least three different
problems have been observed. Steroidal estrogens have been found to cause feminization in
aquatic organisms. (Desbrow et al. 1998) Also toxic levels pharmaceuticals have been
detected in the environment (Triebskorn et al. 2004). The build-up of antibiotic resistant
bacteria has raised increased concern in recent years (Larcher & Yargeau 2011). However,
maybe the least known aspect of pharmaceuticals in the environment is the combined
effects that they may pose (Santos et al. 2010). In the environment many pharmaceuticals
are present simultaneously instead of single compounds (Ankley et al. 2007). The reported
cases of high pharmaceutical emissions into the environment together with the problematic
aspects regarding their presence in the environment act as an incentive for developing point
source treatment technologies.
The complex mixture of dissolved, suspended and non-aqueous matter present in
pharmaceutical wastewater poses challenges for the analysis of the compounds. During
sample pretreatment target molecules are separated from non-target compounds present in
the sample as well as possible. Target analytes may also need concentration if their
concentrations are below instrumental limits of detection. Both of these objectives can be
2
met using solid phase extraction (SPE) pretreatment. After sample pretreatment the sample
components are separated in a high performance liquid chromatographic (HPLC) run and
detected using a suitable detection method.
A pharmaceutical factory based in Kikuyu, Kenya, has had problems in meeting the
requirements set for the levels of pharmaceuticals in the wastewater effluent. The
requirement for the effluent quality is set by the Kenyan regulatory authority National
Environment Management Authority. The limit is 0.05 mg/l for all the active ingredients in
the treated wastewater.
At the time of this Master’s thesis there were four BSc theses under way at Tampere
University of Technology dealing with the elimination of pharmaceuticals in synthetic
wastewater. Precipitation, ozonation, activated carbon treatment and Fenton treatment of
pharmaceutical wastewater were studied. In order to estimate the effectiveness of the
measures taken to remove the active ingredients from the water applicable analytical
methods were needed.
At UCL the method for analyzing erythromycin in pharmaceutical formulations for
quality control purposes uses microbiological analysis. Therefore it cannot be applied for
the analysis of wastewater. The British Pharmacopoeia method for separating erythromycin
with HPLC takes 65 minutes and uses a high temperature which can be harmful for the
column. Therefore there isn’t an applicable method for analyzing erythromycin in the
wastewater.
Also improvements can be made to the present sample pretreatment procedure. At
present the sample pretreatment is not optimized in terms of pH. Also, the recoveries and
the breakthrough of the analytes during the SPE treatment have not been considered.
Finally, the solid phase extraction cartridges aren’t washed after sample loading. A washing
step may be beneficial in eliminating interferents in the final sample.
The aim of this Master’s thesis was to develop an analytical method for the analysis of
six pharmaceuticals. Sulfamethoxazole, acetylsalicylic acid, ciprofloxacin, diclofenac,
paracetamol and erythromycin were chosen based on their environmental risk quotients,
toxicity values or amounts produced. In the final method the active ingredients were
extracted from wastewater using solid phase extraction and separated using high
performance liquid chromatography. The compounds were detected using UV detection.
The aim was also that the HPLC methods would provide reliable results at the NEMA
limit 0.05 mg/l for all of the active ingredients. The performance of the solid phase
extraction pretreatment was studied. Information of the recoveries, reproducibilities and
possible breakthrough of the active ingredients were provided. A wash step was included in
order to remove as much of the interferents as possible.
3
2 Theoretical background
2.1 Pharmaceuticals in the environment
Thousands of tons of pharmacologically active ingredients are used annually for human and
veterinary purposes (Dorival-García et al. 2013). A major source of pharmaceuticals in the
environment is treated wastewater from communal wastewater treatment plants (WWTP).
The conventional treatment methods are not designed to remove pharmaceuticals and are
therefore insufficient in treating wastewaters containing pharmaceuticals. As a result,
pharmaceuticals are released into the environment. The chemicals enter the treatment plants
via human urine or feces as a result of incomplete metabolisation or as a result of
inappropriate disposal of unused drugs. (Fick et al. 2009) Depending on the chemical
nature of the compounds, up to 95 % of them are excreted as parent compounds or
metabolites. (Dorival-García et al. 2013) Levels up to micrograms per liter have been
measured in surface waters and sewage effluents all over the world. (Fick et al. 2009)
In addition to being released into the environment as dissolved in wastewater,
pharmaceuticals are also adsorbed onto activated sludge. For many active ingredients
sorption to sewage sludge is an important removal mechanism from wastewater. Such
active ingredients include certain antibiotics, antihypertensives, lipid regulators and
psychiatric drugs. When the removed sludge is applied as soil fertilizer, the adsorbed
pharmaceuticals may be desorbed. This represents an additional exposure route into the
environment. (Dorival-García et al. 2013)
Also drug factory wastewaters have been identified as significant contributors to total
pollution loading. In areas with intensive production pharmaceutical factories may have
even more significant impact on the environment than release after normal use and
excretion. (Fick et al. 2009) Therefore there is a demand for point source treatment before
allowing such streams to enter the environment or municipal sewage systems.
Estimating environmental effects of industrial chemicals is based on a number of
standard tests developed by the European Union (EU) and Organization for Economic Cooperation and Development (OECD). These tests are applicable especially in evaluating the
narcotic effects of industrial chemicals to living organisms. However, pharmaceuticals are
by nature biologically active, and therefore have a number of more specific modes of
action. Therefore it is likely that these tests are not best for evaluating possible harmful
effects of pharmaceuticals. (Stuer-Lauridsen et al. 2000)
Toxic effects of pharmaceuticals are not well known. Toxicity tests for some
nonmammalian species have been conducted but for many substances such information is
not available. Drugs that affect the reproduction and development of nontarget organisms
4
should receive strong focus. Such drugs include anticancer drugs that affect DNA,
progesterone receptor agonists, drugs that alter lipid synthesis such as statins and
compounds that inhibit a variety of cytochrome P450-mediated reactions such as conazoles.
The latter reactions are a key to many physiological processes. (Ankley et al. 2007)
Alterations of developmental and reproductional properties of organisms may be
affected by low levels of active ingredients present in the environment (Ankley et al. 2007).
In contrast to acute effects the pharmaceuticals may also have long term effects which are
chronic by nature. Sub-therapeutic levels can cause effects that accumulate over many
generations of aquatic organisms affecting the sustainability of the population. (Santos et
al. 2010) Even though the number of studies and experimental data on environmental
effects of pharmaceuticals are limited at present, it is likely that far more adverse effects of
pharmaceuticals in the environment will be identified in the future. More information will
be available as methods for impact assessment are developed. (Alder et al. 2006)
Estimating the health and ecological effects of a single pharmaceutical is not
necessarily adequate when the environment is concerned. Many pharmaceuticals are
present simultaneously and the pharmaceuticals may have a combined effect. This is
commonly referred to as the cocktail-effect. (Ankley et al. 2007) At present, such
synergistic effects are not well known as experimental data is not available (Santos et al.
2010).
The lifetimes of pharmaceuticals in the environment are less than that of traditional
environmentally problematic substances. However the discharge rates are often so high that
especially in small water bodies and streams with low flow rates they are practically
continuously present. Therefore non-target organisms may be exposed for prolonged times.
(Ankley et al. 2007)
A review by Verlicchi et al. (2012) reported the removal efficiencies, mass loads and
potential environmental risks of 118 pharmaceuticals belonging to 17 different therapeutic
classes. The environmental risk was characterized by the means of a risk quotient which
was calculated by comparing the measured average concentrations in wastewater and the
predicted no-effect concentrations. It was reported that some of the active ingredients posed
medium to high acute risk to aquatic life while all of the active ingredients posed a long
term risk due to chronic and mixture toxicities. In the study, three pharmaceuticals which
had the highest risk quotients were erythromycin, ofloxacin and sulfamethoxazole.
Pharmaceuticals can be characterized as being relatively large molecules with a
complex structure and generally as being ionisable with multiple ionization sites throughout
the molecule. As a consequence of these properties, pharmaceuticals may exist in
polymorphic states. A polymorphic state occurs when the molecule stacks in the solid state
in a particular way. Although identical in chemical composition, the chemical properties of
polymorphic pharmaceuticals may differ significantly from the usual solid state. The
bioavailability, solubility, dissolution rate among others may differ, and as a result
5
environmental concentrations may be significantly higher than what is predicted by water
solubility of normal solid state pharmaceuticals. (Alder et al. 2004)
2.1.1 Problems related to pharmaceutical production in developing
countries
The increasing levels of pharmaceuticals in the environment are especially distinctive in
developing countries where large quantities of bulk drugs are produced. Hyderabad, India,
is one of world’s largest production areas of generic active ingredients. In India and China
large amounts of generic pharmaceuticals are produced and exported to Europe and the
U.S. In Sweden, out of the 242 medicinal products on the market, 74 contained APIs
produced on the Hyderabad area. (Fick et al. 2009)
A study was conducted to monitor the levels of active ingredients in the wastewater
effluent of a local WWTP (Patancheru Enviro Tech Limited, PETL) in Patancheru, near
Hyderabad, India. The plant receives approximately 1 500 m3 of wastewater daily from 90
bulk drug manufacturers in the area. The concentration of ciprofloxacin in the wastewater
effluent was 14 mg/l and the concentration of cetirizine was 2.1 mg/l. Ciprofloxacin and
cetirizine were found in concentrations of micrograms per liter in several wells in the area
together with three other pharmaceuticals. The results therefore indicated that
pharmaceutical waste discharge can pollute ground waters over large areas. (Fick et al.
2009)
Larsson et al. (2007) reported even higher values of ciprofloxacin in the PETL
wastewater effluent in the Patancheru area. The concentration was up to 31 mg/l in the
treated effluent which is more than the maximum human therapeutic level in the plasma
and orders of magnitude higher than the EC50 toxicity values for Microcystis aurengiosa
(17µg/l) and Lemna minor (203 µg/l). Concentrations of five other fluoroquinolones
exceeded the toxicity values for plants, diatoms, blue green algae or other bacteria as well.
According to Larsson et al. (2007) even though it is generally thought that the high
price of pharmaceuticals in the market would encourage producers to generate as little
waste as possible the low production costs of bulk drugs in developing countries make it
unlikely that only trace amounts of active ingredients would be present in the wastewater.
This is because the value of the active ingredients rises only after they have reached the
final market. Also, the removal of active ingredients from the wastewater would require
significant investments. This may be the reason why emissions are tolerated.
2.1.2 Problems related to pharmaceuticals in the environment
Pharmaceuticals in the environment cause a number of direct or indirect problems. At least
three different kinds of problems related to pharmaceuticals in the environment have been
observed: feminization of marine organisms (Desbrow et al. 1998), direct toxicity effects
6
(Oaks et al. 2004) and the build-up of antibiotic resistance in bacteria (Fick et al. 2009). In
order to avoid such problems releasing large quantities of pharmaceuticals in the
environment should be prevented. This in turn would require enhanced source separation
activities. (Baquero et al. 2008)
Hormonal effects on aquatic organisms have been linked to certain APIs. Such
compounds include natural and synthetic steroidal estrogens which result in increased
estrogenic activity. For example 17 -ethynylestradiol causes vitellogenin synthesis and
feminization of rainbow trout fish at levels of 10 ng/l. (Desbrow et al. 1998)
Direct toxic effects include toxicity to micro-organisms. Toxicity of a given compound
is indicated as its concentration which leads to inhibition or mortality of half of the test
subjects during a given experiment. If inhibition is observed the term EC50 is used and
mortality is observed the term EC50 is used. The letters stand for lethal and effective.
(Oleszczuk & Hollert 2011)
Acute toxicity of pharmaceuticals is unlikely at environmental levels. However,
diclofenac, one of the most important active ingredients linked with toxic effects to
wildlife, has been found to cause cellular reactions in the liver, kidney and gills of rainbow
trout at environmentally relevant levels of 1 µg/l. At lower concentration levels, less than
100 µg/l, there have been signs in fish that can be interpreted as stress signals in order to
intensify detoxification and elimination of the foreign substances. The changes especially in
the kidney and the gills have been interpreted to be indicative of deterioration of the organs.
(Triebskorn et al. 2004)
One of the most notable incidents related to diclofenac toxicity in wildlife occurred in
the 1990s at Keoladeo National Park, India. At the time oriental white-backed vulture
populations went through a loss of 95 %. Also in Pakistan, in the first decade of the 20th
century population declines of the oriental white-backed vulture between 34 – 95 % were
reported at three districts. Studies showed that the population declines were due to renal
failures after oral exposure of relatively high amounts of diclofenac. The renal failures of
the vultures lead to hyperuricaemia and subsequently to deposition of uric acid on and
within the internal organs. (Oaks et al. 2004)
The deceased specimen had fed on diclofenac treated livestock. Therefore it was
suggested that the high concentrations of diclofenac in the meat of deceased livestock had
been the origin of diclofenac in the vultures. To verify this theory, high oral doses of 2.5
mg/kg and low doses of 0.25 mg/kg were given to two test vultures. Both the high dose and
another of the low dose test subjects died from the doses. After this ten test subjects were
fed with diclofenac injected buffalo meat. The amounts consumed ranged between 0.8 – 1.0
mg/kg diclofenac and were enough to kill all of the test subjects. (Oaks et al. 2004)
In the study it was noted that the vultures were able to eliminate diclofenac from their
system and that diclofenac does not bioaccumulate. A specimen that was given a low dose
of diclofenac orally had eliminated it completely from the kidneys four weeks after
7
administration. No signs of renal lesions characteristic to renal failure were observed at
necropsy. Therefore the study suggested that diclofenac overdose is acute and dosedependent by nature. Low levels that are environmentally relevant are not high enough to
cause similar incidents that were observed in Pakistan. In Pakistan it is common habit that
deceased livestock is left for scavengers to remove. (Oaks et al. 2004)
Also other pharmaceuticals have been linked to toxicity in aquatic organisms.
Fluoroquinolone antibiotics have been identified as a significant group in regard of toxicity.
In a study conducted at the Hyderabad area, India, it was shown that plant effluent diluted
to 0.2 % of the initial concentration had levels of fluoroquinolone antibiotics that were
enough to cause a decrease of 70 % in body weight and body length of frog tadpoles.
(Carlsson et al. 2009) In a previous study the effluent was also shown to be toxic to certain
micro-organisms. (Larsson et al. 2007)
In addition to possible hormonal and toxic effects pharmaceuticals may pose another,
more indirect problem affecting also humans. In the recent years development of antibiotic
resistant bacteria has raised increased concern. This is due to increased levels of antibiotics
in the environment. An estimated consumption of antibiotics is from 100 000 to 200 000
tons annually. (Larcher & Yargeau 2011)
Macrolides such as tylosin and spiramycin have been found to induce resistance to
Streptococcus, Staphylococcus, clostridias and corynebacteria. It has been suggested that
also other macrolides such as erythromycin could possibly induce antibiotic resistance.
However direct toxicity effects of the compounds to humans are negligible. For example in
the case of erythromycin most serious complications at environmental levels include mild
gastrointestinal disturbances. (Edder et al. 2002)
There have been reports of resistant strains of bacteria acquiring resistance to other
groups of antibiotics after becoming resistant to a certain antibiotic. Therefore resistance to
for example penicillin can lead to resistance of other antibiotics as well. (Fick et al. 2009)
The presence of genotoxic pharmaceuticals in the environment may speed up the generation
of antibiotic resistant bacteria. (Larsson et al. 2007) Genotoxic pharmaceuticals that can
enhance antibiotic resistance in bacteria include for example ciprofloxacin. The mechanism
behind development of antibiotic resistance by genotoxic substances is horizontal gene
transfer of resistance between different species of bacteria. For ciprofloxacin this has been
observed to take place at concentrations as low as 5-10 µg/l. (Larsson et al. 2007)
There are occasions where pharmaceutical wastewater is treated using activated sludge
process. Such a procedure was reported in the pharmaceuticals production area in
Patancheru, India, where the combined waters of the factories in the area are being treated
in a single facility. (Fick et al. 2009) However, the use of activated sludge process poses
problems in such use since it enables the contact of bacteria and antibiotics. Also genotoxic
pharmaceuticals may be present which facilitates the buildup of antibiotic resistance.
8
Therefore it can be stated that activated sludge process may be intrinsically unsuitable for
treating wastewater in which pharmaceuticals are present. (Larsson et al. 2007)
2.2 Environmental analyses of pharmaceuticals
Determination of pharmaceuticals in environmental samples obtained from streams of
wastewater, sludge or sediment requires sophisticated analytical methods. In the sample,
the compound of interest is among many detectable compounds and other compounds
regarded as impurities. Therefore all of these compounds have to be separated from each
other before detection. (Alder et al. 2006)
A complex mixture of sample molecules is separated to individual components by an
analytical technique called chromatography. During the separation the sample mixture is
passed through a chromatographic column which is packed with a stationary phase. The
molecules move through the column together with a mobile phase and are separated based
on their different affinities towards the stationary and mobile phases. (Alder et al. 2006)
In general pharmaceuticals are hydrophobic but relatively polar molecules and have a
low molecular weight. Because of these properties, they are usually separated using either
gas chromatography (GC) or high performance liquid chromatography (HPLC). The
difference between these two separation methods is that in HPLC the mobile phase is liquid
and in GC the mobile phase is gaseous. (Alder et al. 2006)
Chromatographic separation methods are used together with many different detection
methods. The best choice for identification of active ingredients in environmental samples
is mass spectrometry (MS) which provides superior selectivity and sensitivity. However
due to its complexity and high price it may not be suitable for routine analysis. Other
available detectors include electron capture (EC) detector for GC and UV/visible
absorption or fluorescence detection for HPLC. These detectors are widely available, easy
to use and cheap. They do not provide effective selectivity for the sample molecules.
However they are most often used for routine analysis. (Alder et al. 2006)
In Fig. 2.1 an illustration of analytical methods applied in separation and detection of
pharmaceuticals in drinking water according to the WHO guideline “Pharmaceuticals in
drinking-water” is presented. (Cotruvo et al. 2012)
9
Figure 2.1 Analysis of pharmaceuticals in drinking water according to WHO (Cotruvo et
al. 2012).
Low volatility of pharmaceuticals limits the use of GC as a separation technique for
most pharmaceuticals. Low volatility is a result of strong interactions between the sample
molecules. In solution low volatility is due to interactions between sample and solvent
molecules due to effective solvation. When separating polar molecules with using GC,
derivatization of molecules is often needed in order to render them more volatile. To avoid
10
this procedure, polar samples are often analyzed using HPLC since pharmaceuticals can
usually be dissolved in some solvent. (Alder et al. 2006)
In HPLC many different modes are used based on the stationary phase used. These
modes can be used to separate for example polar, nonpolar, chiral and polymeric samples.
For the separation of pharmaceuticals two modes are used above all and include reversed
phase (RP) and normal phase (NP) HPLC. (Alder et al. 2006)
In RP-HPLC the stationary phase is non-polar, usually C18 bonded silica. The weak
mobile phase is polar, and usually contains water and stronger mobile phases are achieved
by using more hydrophobic solvents. In NP-HPLC, the stationary phase is polar, usually
unmodified silica. Non-polar solvents are used as weak solvents while the elution strength
can be increased by increasing solvent polarity. The first mode is more often used to
separate mixtures of pharmaceuticals. (Alder et al. 2006)
HPLC instrument consists of a mobile phase reservoir, a pump, an injector, a separation
column and a detector. The injector is either manual or automated, and it is used to inject
the sample with the use of a sampling loop. The sample is injected as a narrow pulse into
the mobile phase stream. Mobile phases are degassed before they enter into the
chromatographic column. This is because air bubbles affect both the separation process and
the detection. (Alder et al. 2006)
One of the most commonly used detection methods in HPLC, UV-Vis detection,
utilizes the ultra violet or visible light absorption of the compounds in the sample. The
sample concentration is directly proportional to the absorbance of the sample molecules
eluted at a given retention time. Other possible detection methods are based on refractive
index, fluorescence or electrochemistry of the analytes. (Alder et al. 2006)
2.2.1 HPLC method development
The first task during method development is to identify the problem at hand. Generally an
HPLC method can be planned for quantitative analysis, detection of compounds,
characterization of unknown components or purification. In environmental samples the task
is to separate given compounds from each other and quantify them. The planned method
should be such that it can be used in the target laboratory, which sets limitations for
equipment that can be used during method development. (Snyder et al. 1997)
High performance liquid chromatography method development involves finding out
best possible chromatographic conditions to allow sufficient resolution of target analytes.
The run should be performed during an acceptable run time. Parameters that can be
modified and affect the separation of analytes include type of column packing and choice of
mobile phase, the length and diameter of the column, mobile-phase flow rate, separation
temperature and sample volume. (Snyder & Kirkland 1979)
11
The retention of a peak during an HPLC run is characterized by retention factor k . The
retention factor of a peak is defined as
k
tr
t0
t0
,
(2.1)
where t r is the retention time of the compound of interest and t 0 is the column dead time.
Column dead time is dependent on the column dead volume and it is the minimum
retention time of any compound at a given flow rate. Usually an inert solvent molecule
gives a peak and its retention time is used as the column dead time. The signal appears
because of the absorption of the solvent. (Snyder et al. 1997)
The separation of the active ingredients provided by a method is described by resolution
Rs , which is defined as
Rs
2 t 2 t1
W1 W2 ,
(2.2)
where W1 and W2 are the peak widths of the adjacent peaks at the baseline and t1 and t 2
are the retention times of the corresponding peaks. Retention time is the time when a given
compound elutes out of the column. In the final method, resolution between target
compounds should be greater than 2 to ensure sufficient resolution. (Snyder et al. 1997)
From Eq. (2.2) it can be seen that in order to increase resolution the two peaks must
either be moved further apart from each other or the width of the peaks must be reduced.
(Snyder et al. 1997) The former is described by selectivity. Selectivity of a method can be
quantified by capacity factor
which is defined as
k1
,
k2
(2.3)
where k1 and k 2 are the retention factors of the compounds of interest. The latter can be
only affected by affecting the column conditions, which include flow rate, column length
and packing particle size. It is preferable to alter mobile phase composition, stationary
phase material and temperature during method development instead of column conditions.
This will ensure that resolution of selected analytes is achieved even though performance of
the column changes with time. (Snyder et al. 1997)
12
2.2.2 Adjustment of mobile phase strength
The first task for any method development is to adjust the mobile phase strength so that the
retention of analytes is acceptable. If the analytes of interest elute very early, it is difficult
to separate them from the solvent front and from each other. Run times greater than 20 min
are usually not practical and therefore not acceptable. (Snyder et al. 1997)
By changing the strength of the mobile phase, the retention times can be adjusted. A
strong solvent elutes the analytes early and a weak solvent increases the retention time.
Organic solvents are strong in reversed phase chromatography. Most used organic mobile
phases are methanol and acetonitrile, the latter being the stronger one. A weak mobile
phase under reversed phase conditions is a polar solvent such as water. By choosing an
appropriate mixture under isocratic conditions the retention times can usually be adjusted
so that the retention factors lie between 0.5 and 20. (Snyder et al. 1997)
2.2.3 Adjustment of mobile phase composition
The compounds may be resolved from each other after suitable solvent strength has been
determined. If this is not the case, mobile phase composition has to be adjusted. The
amount of organic solvent used can affect selectivity. A 5 % change in organic solvent
content can affect closely eluting peaks differently which can alter resolution. Another,
more powerful way is to use a different organic solvent altogether. Since analytes have
different solubilities in different solvents, this causes a change in retention. (Snyder et al.
1997)
The solubility of as analyte depends on its interactions with the solvent. Solubility is
affected by hydrogen-bonding or dipolar interactions. Organic solvents are usually slightly
acidic, basic or dipolar and the degree of how strongly these properties are expressed affect
analyte solubility. If, for example, a basic organic solvent is used, a change to a more
dipolar or acidic solvent may change the retention between closely eluting compounds. The
most common organic solvents used in HPLC methods are methanol, acetonitrile and
tetrahydrofurane. These solvents exhibit mostly acidic, dipolar, and basic properties,
respectively, and can be used interchangeably in order to induce selectivity differences.
(Snyder et al. 1997)
The retention of ionisable compounds is affected by pH if it changes over the pKa value
of the compound being separated. Ionic samples contain functional groups which undergo
dissociation and may be either acidic or basic. Acids become more polar and less retained
at pH values above their pKa whereas bases become less retained when pH is lower than
their pKa. (Snyder et al. 1997)
The change of pH of the mobile phase can be used to affect the retention of ionic
species. If pH is changed over the range pKa ± 1 during the run the polarity of the ionizable
species is changed which leads to faster elution. The retention factor k of a compound may
13
change by a factor of 10 if pH is varied over the pKa range of the sample. (Snyder et al.
1997)
On the other hand, if retention should stay constant during the entire run, the pH should
be kept constant. This can be achieved by using a buffer. A buffer is effective in
maintaining a constant pH around its pKa value, the effective range being pKa ± 1. The
amount of buffer used is an important aspect, 10 mM buffers may not maintain a constant
pH if the sample contains large amounts of buffered compounds at another pH range. The
change in pH from one run to another leads to irreproducible retention times. 50 mM buffer
concentrations may be too much because the buffer may not be soluble in the organic
solvent any more. Therefore a good compromise is 25 mM buffer solutions. (Snyder et al.
1997)
Ion-pair chromatography has been introduced to separate very polar, multiply ionized or
strongly basic compounds. With conventional aqueous or organic solvents these
compounds elute fast. In an ion-pair chromatographic system a counter ion is added into the
aqueous mobile phase. The counter ion is chosen so that it is opposite in charge to the
analytes of interest. (Snyder & Kirkland 1979)
A simplified example of the ion-pair chromatographic process is that initially the
sample molecule and the counter ion are only soluble to the aqueous phase, and after
combining the two the ion-pair formed is soluble in the organic phase. (Snyder & Kirkland
1979) Formation of such an ion pair results in a neutral entity that is retained well on the
non-polar stationary phase. Ion-pair reagents may also adsorb onto the stationary phase,
changing the retention behavior of the analyte on interest. In this case the non-polar portion
of the counter ion attaches to the stationary phase and leaves the polar part sticking out.
(Uesugi et al. 1997)
In addition to changing retention, the ion pair additives may improve peak shape.
(Kaiser et al. 2009) One possible cause of peak tailing in the case of basic compounds are
the interactions between the acidic silanols in the stationary phase and the protonated bases.
The cationic bases are interchanged with the protons of the silanols which affects retention.
Such silanol effects are reduced by covering them with an excess of the buffer cations such
as sodium, potassium or triethylammonium. (Snyder et al. 1997) Additives that can be used
include ammonium acetate, acetic acid and triethylamine (Kaiser et al. 2009).
An example of how conditions can affect resolution was presented in an article by
Kaiser et al. (2009) when they studied the separation of astaxanthin (AST) from lutein
(LUT). Using C30 stationary phase and a mixture of methanol and methyltertbutylether as
mobile phase, AST showed peak tailing and was poorly resolved from LUT when mobile
phase additives weren’t used. Addition of ammonium acetate allowed the separation of
LUT and AST. Also, peak area variations were eliminated between columns from different
manufacturers.
14
2.2.4 Performance of an HPLC column
The performance and specifications of an HPLC column are characterized by a set of
properties, which include plate number, asymmetry factor, tailing factor, selectivity or
capacity factor and column back pressure. There are many suppliers of columns and their
products may differ. Also the properties of a given column change as the column ages.
Therefore it is important that the column can be characterized since these properties affect
the output of the method. (Snyder et al. 1997)
Column performance is characterized by the number of theoretical plates N. Number of
theoretical plates is defined as
N
t
16 R
W
2
(2.4)
,
where t R (min) is the retention time of the peak and W (min) is the width of the peak at
baseline. Determining peak width at baseline may be subject to error due differences in
interpretation or software used to integrate, and an alternative is to use peak width at halfheight W½ . In this case the number of theoretical plates is defined as
N
t
5.54 R
W½
2
.
(2.5)
These properties are empirical and they are determined for a column using specified test
substances under defined conditions. The tests give results that can be compared to
predetermined values which can be used to assess the performance of the column. The
higher the value is the better the column performs and the narrower the peaks are. (Snyder
et al. 1997)
Peak asymmetry is a property that describes the shape of a chromatographic peak and
consequently the quality of the method. Columns and experimental conditions should
provide symmetrical peaks and this is one objective of method development. Poor peak
shape is often linked to loss of plate number, imprecise quantitation, undetected minor
bands and poor retention reproducibility. Peak asymmetry factor As is defined as (Snyder
et al. 1997)
15
As
B
,
A
(2.6)
where A is the portion of peak width between peak maxima and the left side edge of the
peak at 10 % of full peak height and B is the peak width between peak maxima and the
right edge of the peak at 10 % of full peak height. Good methods produce peaks with
asymmetry factors between 0.95 and 1.1 and an acceptable upper limit is 1.5. Another way
to describe peak shape is to use peak tailing factor PTF which is defined as
PTF
A B
,
2A
(2.7)
where the widths are measured at 5 % of full peak height. (Snyder et al. 1997)
A column back pressure less than 140 bar for a new column is desirable. As the column
ages the operation pressure can increase by a factor of 2 because of plugging of the column
by particulate matter. At lower pressures pumps, valves, autosamplers and seals last longer.
Also, columns tend to clog less and the overall reliability of the method is better. (Snyder et
al. 1997)
2.2.5 Detection and sensitivity
In most cases HPLC method development is carried out using UV-detection. UV-detectors
may be spectrophotometric variable-wavelength detectors or single-wavelength diode array
detectors. UV detection is suitable for sample detection unless the sample molecule has no
UV-absorbance, the concentration of the sample is too low for UV-detection, multiple
sample components cannot be separated from one another or structural information is
needed. (Snyder et al. 1997)
Most HPLC applications are carried out using wavelengths between 190 and 400 nm.
During method development, wavelengths suitable for the detection of each analyte of
interest are determined. If standards are available, the best way of selecting the detection
wavelength is to measure the UV-absorption spectrum in the mobile phase because the
solvent polarity and pH affect the absorbance maxima. The detector signal is proportional
to the molar absorptivity of the compound of interest. (Snyder et al. 1997)
In the case of trace analysis absorptivities greater than 1000 are usually required for
good results while compounds which have absorbances below 100 cannot be detected with
16
UV. Aromatic compounds generally have absorptivities above 1000 at wavelengths over
210 nm. Since pharmaceuticals generally are aromatic they can be detected using UV.
(Snyder et al. 1997)
Detection of analytes without chromophores is not possible with UV-detection. (Snyder
et al. 1997) Erythromycin (ERY) is an example of a chemical compound without
significant absorption. ERY absorbs best at wavelengths below 220 nm (G ówka &
Kara niewicz- ada 2007) and its molar absorptivity at 280 nm is only 50 (Toxnet 2011).
Ways to detect poorly UV absorbing compounds are the use of other detection modes or
derivatization. Detection methods which do not depend on UV absorbance of a compound
include electrochemical (EC) or mass-spectrometric (MS) detection. Derivatization of
poorly absorbing compounds involves exchanging poorly absorbing functional groups with
better absorbing ones. (Li et al. 2007)
Some derivatization reactions for ERY have been proposed in the literature. In one
approach derivatization ERY is done using trimethylbromosilane (TMBS). The structure of
the TMBS derivatization reagent is presented in Fig. 2.2.
Figure 2.2. The structure of trimethylbromosilane derivatization reagent
(http://www.sigmaaldrich.com/catalog/product/fluka/92337?lang=fi&region=FI).
In the procedure developed by Li et al. (2007) ERY was extracted from human plasma
using ethyl ether under alkaline conditions. After drying the extract, the residue was
dissolved in dichloromethane and reacted with TMBS. After terminating the reaction by
addition of water the organic layer was separated and dried. The residue was dissolved in
the mobile phase used later in the study
According to Li et al. (2007) the reaction between ERY and TMBS, the hydroxyl
groups of ERY are replaced with bromides. This is driven by the strong affinity between
silicon and the oxygen of the hydroxyl group. In the study only one reaction product was
reported using HPLC. The absorbance of this product at 275 nm was 1000 mAU suitable
for sensitive UV detection.
Also 9-fluorenylmethyl chloroformate (FMOC-Cl) has been proposed for the
derivatization of ERY. The structure of FMOC-Cl is presented in Fig. 2.3.
17
Fig 2.3. The structure of 9-fluorenylmethyloxychloroformate derivatization reagent
(http://www.sigmaaldrich.com/catalog/product/aldrich/160512?lang=fi&region=FI).
The biphenyl moiety in FMOC is optimal for UV detection, and its absorption
maximum is at 265 nm (G ówka & Kara niewicz- ada 2007). Derivatization reaction takes
place between amino or hydroxyl groups and the highly electrophilic carbonyl group of
FMOC-Cl (Clayden et al. 2001). In the case of erythromycin it is the hydroxyl group that
attacks the carbonyl group since free secondary or primary amines aren’t available. Base is
needed in order to remove proton from the hydroxyl group as it attacks the carbonyl group.
The chloride is an excellent leaving group and therefore carboxylic acid chlorides are
extreme reactive reagents in nucleophilic substitution reactions. (Clayden et al. 2001)
In a derivatization procedure ERY was extracted from human plasma. The dried extract
was dissolved in acetonitrile and derivatized with FMOC in acetonitrile at pH 7.5 buffered
with a phosphate buffer. The reaction time was 40 minutes in 60 oC. The reaction mixture
was directly analyzed with HPLC. (G ówka & Kara niewicz- ada 2007)
2.2.6 Method validation
Method validation is the final step of method development when the determined conditions
are tested and approved. Also the allowed variability of the conditions is determined in
order to see in which conditions the method is still reliable. Successful method validation
requires a well planned list of validation items in order to systematically determine the
usefulness of the method. Also, results that are considered as acceptable values should be
determined in advance. These values are often referred to as acceptance criteria. (Snyder et
al. 1997)
Items that should be defined during the validation of the method include specificity,
linearity, accuracy and precision. These are the most important parameters and should be
defined before even preliminary results are obtained using the method. Also limits of
detection (LOD) and quantification (LOQ), stability of samples, reagents and instruments,
ruggedness and robustness of the method can be determined. (Snyder et al. 1997)
Accuracy of the method is the closeness of the measured value to the true value. In
environmental samples, most relevant way to assess accuracy is to perform analyte
recovery tests in complex wastewater matrix. When a standard is spiked into the matrix, the
accuracy value reflects the recovery over the entire analytical procedure including sample
18
pretreatment and extraction. (Snyder et al. 1997) Also, possible variations in the peak area
response during the HPLC method are taken into account in the analyte recovery test.
(Kaiser et al. 2009) Another way to evaluate accuracy is to use the method of standard
addition. (Snyder et al. 1997)
Precision of a method is the consistency among individual test results when the method
is used to measure multiple samplings of a given sample. Precision is further divided into
three subcategories, which include repeatability, intermediate precision and reproducibility.
Repeatability is the precision of the method under a short period of time. It is the measure
of instrumental precision and often involves injecting a given sample ten or more times and
determining relative standard deviations of the peak areas. Acceptable repeatability of a
method is reflected if the relative standard deviation of the injections is less than 2 %.
Intermediate precision involves preparation of multiple standards and analyses done by
different analysts and instruments on different days. Reproducibility is the precision of the
method between different laboratories. (Snyder et al. 1997)
Linearity of a method reflects how well a straight line can be fitted into the calibration
plot of instrument response versus concentration of the sample. Such a calibration plot is
obtained by running standards of different concentrations and plotting the response versus
concentration. The data is then analyzed using linear least squares regression. The resulting
calibration graph can be used in determining concentrations of unknown samples.
Correlation coefficient of 0.999 is usually expected in order to regard the method as linear.
Range of a method is the concentration range of the sample, which still has adequate
accuracy, precision and linearity. (Snyder et al. 1997)
LOD is defined as the minimum concentration of the analyte that can be measured. In
order to make comparison of different methods possible, the signal is compared to the noise
of the instrument and usually a signal to noise ratio of 3 is used for LOD. (Snyder et al.
1997) In mathematical form, the signal-to-noise ratio can be written as
S
N'
3,
(2.8)
where S is the peak height for the signal of the analyte and N’ is the peak height for the
noise. LOQ on the other hand is the concentration that can be reliably quantified.
Alternative definitions include concentrations that have a relative standard deviation less
than 3 %. (Snyder et al. 1997)
Specificity is the most important aspect of an analytical method. If the method isn’t
specific, accuracy, precision and linearity all are risked. There are two ways to achieve
specificity in an HPLC method. The first one is to use such conditions that resolution of all
compounds analyzed in a given method is achieved. Another way to achieve a specific
19
method is to use a detection method that responds only to some of the compounds. In this
case even a coeluting compound will not disturb the analysis. (Snyder et al. 1997)
Method ruggedness can be evaluated during method validation. Ruggedness is defined
as the reproducibility of the results when the conditions change slightly from the ones used
during method development. Such changes include different analysts, laboratories,
columns, instruments, sources of chemicals and so on. Method robustness, on the other
hand, is defined as the ability of the method to remain unaffected by slight variations in
method parameters. Changes in mobile phase composition or gradient, additives, column
temperature, flow rate, and so on, can be studied. Retention times and selectivities should
remain unchanged despite such variations. (Snyder et al. 1997)
It should be noted that method validation as method development itself depends on the
planned purpose of the method. Analytical methods for pharmaceutical formulations often
are for a limited number of compounds such as major components, degradation products
and trace impurities. (Snyder et al. 1997) In this case the objective of method development
can be limited to detection of these compounds only. However, in wastewater, varying set
of compounds is present at once, and all of them cannot be included in the development and
validation of the method.
Also the concentrations of the wastewaters may change significantly. Accuracy tests for
formulations of known composition include testing at 75 %, 100 % and 125 % of the
expected level of the analyte (Snyder et al. 1997) but in the case of environmental samples
linear range included in validation studies is more arbitrary. If the linear range is exceeded
the sample dilution is needed.
2.3 Sample pretreatment
Sampling and sample preparation can be regarded as the most important steps of the entire
analysis (Pavlovi et al. 2010). Environmental samples require pretreatment because of
their complex matrix before samples can be injected into the HPLC (Payán et al. 2010).
The first step in sample pretreatment is the elimination of particulate matter which if
present in the sample would have adverse effects on the HPLC column. Particulate
elimination can be carried out by filtration, centrifugation and sedimentation, filtration
being the most common. (Snyder et al. 1997)
Filtration of the sample can lead to losses of analytes adsorbed onto particulate matter
(Snyder et al. 1997). Depending on sample pH, ionizable analytes may be polar or nonpolar. Therefore if the filtered sample matrix has charged particles, adsorption of analytes
on the surface of the particles may occur. In Fig. 2.4 possible interactions between the
analytes and matrix components are presented (Schwarzenbach 2003).
20
Figure 2.4. Possible interactions between pharmaceuticals and the functional groups of
particulate matter present in the wastewater (Schwarzenbach 2003).
The interactions between the analyte and surface groups include electrostatic attraction,
formation of chemical bonds between for example between amine and surface carbonyl
groups and weaker interactions including hydrogen bonding (Schwarzenbach 2003). Since
most pharmaceuticals are readily soluble in organic solvents the analytes associated with
the particulate matter can be extracted with a strong organic solvent. The extract is then
added into the final sample in order to take the adsorbed fraction into account. (Snyder et
al. 1997)
After elimination of particulate matter, the sample may need to be enriched. Traditional
sample enrichment techniques include liquid-liquid extraction during which the sample
molecules are extracted from one phase to another. (Snyder et al. 1997) Sample clean-up
and preconcentration are included in this step. In this technique analyte loss is significant
because the extraction is inherently incomplete. Other, more recent techniques include use
of molecularly imprinted polymers, stir bar sorptive extraction, single drop microextraction
and hollow fiber liquid phase micro extraction. (Payán et al. 2010)
21
2.3.1 Solid phase extraction
Solid phase extraction (SPE) is a practical enrichment method. It was introduced in the
1970’s to provide an alternative to liquid-liquid extraction. (Bielicka-Daszkiewicz &
Voelkel 2009) The solid phase used to extract the analytes can be used in different formats.
The two most common devices include barrel shape phases inside a syringe and disk-like
structures in which the sorbent is inside two holders.
SPE is utilized both in scientific and industrial applications and include environmental,
biological and medical uses. The SPE procedure includes isolation of sample molecules,
pre-concentration, change of sample solvent and sample cleanup all in one step. When
compared to liquid-liquid extraction, SPE has a smaller demand for solvent and also
doesn’t have any problems with emulsion formation. (Bielicka-Daszkiewicz & Voelkel
2009)
Physical characteristics of the sorbent affect efficiency of the sorbent material and
include mass, surface area, particle size, pore size and pore volume (Bielicka-Daszkiewicz
& Voelkel 2009). It has been suggested that especially the specific surface area and the
mass are important parameters when the capacity is concerned. Surface area should be
taken into account when sorbent materials from different manufacturers are considered for
use. (Pavlovi et al. 2010)
The mass of sorbent should be such that it is enough to retain the analytes of interest
and also any additional impurities that compete for the sorbent material. It is possible that
the impurities interact stronger with sorbent material and are retained instead of the target
molecules. Too small mass of sorbent material leads to overload and or irreproducible
recoveries while too large amount of sorbent leads to excessive solvent demand and
possibly low recoveries. (Pavlovi et al. 2010)
In addition to physical parameters, also the sorbent material used has an effect on
parameters such as selectivity, affinity and capacity (Pavlovi et al. 2010). Materials that
can be utilized in SPE treatment include silica sorbents, polymeric sorbents and carbon
based sorbents e.g. activated carbon and graphitized carbon black (Bielicka-Daszkiewicz &
Voelkel 2009). In general there are great differences in performance between different
sorbent materials especially in retaining polar compounds. Therefore manufacturers have
developed different sorbent materials for retaining compounds of different chemical nature.
Usually an optimal SPE sorbent can be found to extract a given analyte. (Pavlovi et al.
2010) However, in environmental analyses where many compounds are to be detected in a
single procedure, compromises have to be made between compounds of different polarities.
The choice of SPE sorbent material usually involves a trial and error approach.
However, consideration of the interactions between the compounds to be isolated and the
sorbent material can help in choosing the starting point. (Bielicka-Daszkiewicz & Voelkel
2009) In the case of non-polar compounds non-polar sorbent material is a good starting
22
point. In the case of polar analytes, special stationary phases may be required, but in the
case of ionizable polar compounds the polarity can be affected by changing pH. (Pavlovi
et al. 2010)
2.3.2 Sorbent materials for non-polar compounds
Interactions between the analytes and the sorbent material have to be considered when
choosing suitable sorbent material. In the case of hydrophobic compounds this can be done
by considering the solubility of the sample molecule into the sorbent. (BielickaDaszkiewicz & Voelkel 2009) LogP value of the analyte indicates its solubility between
water and n-octanol. LogP is defined as
log P
coctanol
,
c water
(2.9)
where coctanol is the concentration of the analyte in n-octanol and cwater is the concentration
of the analyte in water under equilibrium conditions when the analyte is in its neutral form.
Octanol is used to represent the hydrophobic phase and therefore logP can be used in
estimating the lipophilicity of the analyte. (Alder et al. 2004)
From the equation defining logP it can be seen that compounds that are more soluble in
organic solvents than water have a positive logP value. Compounds that are more soluble in
aqueous media than organic solvents have a negative logP. Latter compounds are regarded
as polar and are poorly retained in hydrophobic, e.g. C18 SPE sorbent materials. (Pavlovi
et al. 2010)
Polarities of the compounds affect the mutual interactions between the compounds and
the sorbent and therefore the final extraction efficiency. In the case of ionizable compounds
polarity can be affected by adjusting pH.(Bielicka-Daszkiewicz & Voelkel 2009) pKa of a
chemical compound describes the pH below which acids get protonated and above which
bases get deprotonated. Both these states are neutral and more nonpolar than the ionized
forms. (Clayden et al. 2001)
pKa is defined as the negative log value of the equilibrium constant describing a
dissociation event. The equilibrium constant on the other hand describes the concentrations
between initial and final states when equilibrium has been reached. A chemical reaction can
be described with the use of a reaction equation. In case the reaction describes the
dissociation of an acid HA, the reaction can be written with the following equation
23
HA( aq )
H 2O
H 3O ( aq )
A ( aq ) .
(2.10)
The equilibrium constant of such a dissociation reaction is defined as the product of the
reaction product concentrations divided by the product of the reactant concentrations. Also,
the concentration of water during such dissociation events is large and to a large extent
constant. Therefore it can be included in the equilibrium constant. The equilibrium constant
for the reaction can be written as
Ka
(2.11)
H 3O A
.
AH
Taking the negative logarithm of the Eq. (2.11) we obtain the definition for pKa (Clayden et
al. 2001)
pK a
log H 3O
log
A
AH
pH
log
A
.
AH
(2.12)
From Eq. (2.12) it can be seen that when pH equals pKa, the concentrations of the
dissociated and neutral forms are the same. If the subtraction is 2, then the ratio of the
forms is 1/100. Therefore by adjusting pH below or above the pKa value the neutral form of
an acid or a base can be achieved and non-polar sorbent material used to retain the analytes.
(Clayden et al. 2001)
2.3.3 Sorbent materials for polar compounds
In the case of very polar hydrophilic compounds, whose logP values are below zero, the use
of polar sorbent materials may be the only option for sufficient retention. Many
manufacturers have designed sorbent materials to be used with polar compounds. Such
sorbent materials include modified styrene and polymeric resins.
Sorbent materials made of styrene modified with pyrrolidone groups are commercially
available. Such a sorbent is referred to as polymeric reversed phase sorbent material. The
retention mechanisms include hydrophobic, hydrogen-bonding and aromatic. A wide range
of mechanisms result in relatively good selectivity. In a study where eight selected
24
veterinary antibiotic pharmaceuticals were pretreated with this sorbent, seven had
recoveries near 100 % and the most polar compound having a logP value of -1.07 had a
recovery of 76.1 %. (Pavlovi et al. 2010)
Polymeric resin sorbents have been designed to retain cationic compounds. One
available sorbent material is modified with sulfonic groups. This structure has numerous
retention mechanisms including hydrophobic, dipole-dipole,
and strong cation
exchange. In Fig. 2.5 the structures of the polymeric reversed phase and strong cation
exchange materials are presented (Pavlovi et al. 2010).
Figure 2.5. Polymeric reversed phase (Strata-X) (a) and strong cation mixed mode (StrataX-C) (b) phases by Phenomenex.
The sulfonic acid group in the structure presented in Fig. 2.5 (b) acts as a strong cation
exchanger. The sulfonic acid group also causes some polarization in the neighborhood of
the aromatic ring due to inductive effect. This facilitates slight polar character. (Pavlovi et
al. 2010)
2.3.4 SPE pretreatment steps
The SPE pretreatment involves four steps. First the sorbent is conditioned with a suitable
solvent. Then the sample is applied or loaded onto the sorbent. The third step is to apply
some solvent onto the sorbent which removes interferents but does not desorb the analytes.
This is referred to as washing the sorbent. Finally the analytes are eluted out of the sorbent
with a solvent in which the analytes are readily soluble. (Snyder et al. 1997)
Conditioning of the sorbent is done for two purposes. Firstly an organic solvent
removes potential impurities from the sorbent that may have ended up in it during transport
or exposure in the laboratory. Secondly the organic solvent solvates the bed material. This
is important especially with C8 and C18 sorbents since dry sorbent materials have decreased
sample retention. Most common conditioning solvent is methanol, but acetonitrile may also
be used. Conditioning of the sorbent should be finished with application of water since the
hydrophobic analytes pass the sorbent if they are dissolved in methanol. (Snyder et al.
1997)
Next the sample is loaded onto the column. The sample may be introduced with a
syringe or pumped into the sorbent syringe especially in the case of environmental samples
25
larger than 50 ml. The sorbent material should not dry during the loading step since drying
out changes the absorption properties and affects retention. (Snyder et al. 1997)
Washing the sorbent material may be done using pure water, a buffer solution or
solution water and a small fraction of organic solvent. A successful wash solvent eliminates
as much of the interferents but leaves the analytes of interest onto the sorbent. Finally, the
analytes are eluted with a solvent that allows for complete recovery. A strong solvent may
be used if there aren’t any strongly bound interferents, otherwise weaker solvent may be
used. In case the elution solvent is too strong for the subsequent HPLC run it has to be
dried and the residue reconstituted to a suitable solvent. In case the elution solvent is
suitable for HPLC, it can be injected as is. (Snyder et al. 1997)
As a final statement, the performance of the SPE treatment depends not only on the
sorbent material but also on the solvents used during the pretreatment process. In addition
to chemical nature also the volumes of conditioning, washing and elution solvents used
affect recovery. Especially during elution a sufficient volume should be used. If sensitivity
is to be increased, the sample may be concentrated afterwards through evaporation and
reconstitution. (Bielicka-Daszkiewicz & Voelkel 2009)
2.3.5 Recovery and breakthrough volume
Recovery is defined as the ratio of sample that is loaded into the SPE sorbent and the
amount of sample that can be extracted. Breakthrough volume on the other hand is the
volume of sample after which the recovery starts to decline. (Hennion 1999) In the
literature breakthrough volume is defined as the volume of sample that can be loaded onto
the sorbent without the loss of analytes or the volume that can be loaded onto the SPE
sorbent and still obtain a given recovery. Depending on the reference, maximum recovery
is defined as 95 % to 100 %. (Bielicka-Daszkiewicz & Voelkel 2009)
When sample breakthrough occurs the sample is no longer retained as the sorbent bed
saturates because there are no longer free adsorption sites for the analytes (Snyder et al.
1997). Therefore breakthrough volume measurements are used for assessing the capacity of
a solid phase extraction sorbent. The performance of the sorbent and the breakthrough
volume depend on the concentration of the sample loaded onto the SPE sorbent,
temperature, flow rate and the kinetic properties of the sorbent material. Breakthrough
volume is reduced both on high and low flow rates. (Bielicka-Daszkiewicz & Voelkel
2009)
Breakthrough volume experiments are performed by passing different volumes of a
given sample through the sorbent and monitoring the recovery of the analyte (BielickaDaszkiewicz & Voelkel 2009). If a group of compounds are to be retained in a single step a
reasonable approach is to monitor the recovery of the analyte with poorest retention with a
given sorbent material (Pavlovi et al. 2010). The occurrence of sample breakthrough is
26
usually depicted by a diagram in which the recovery of analyte is displayed as a function of
the volume that has been loaded onto the sorbent. (Bielicka-Daszkiewicz & Voelkel 2009)
A general rule for the sorbent capacity is that 10 to 20 mg of analytes including
interferents can be retained per gram of sorbent packing (Snyder et al. 1997). Therefore
breakthrough can be thought of as a function of the mass of the analyte of interest
(Bielicka-Daszkiewicz & Voelkel 2009). It is necessary to include all of the compounds
present in real samples during breakthrough experiments because sorbent capacity and
breakthrough volume are affected by all the compounds in the sample. A high breakthrough
volume will guarantee that no sample loss takes place during sample loading. (Pavlovi et
al. 2010)
Repeatability of the extraction procedure is important since recovery is directly taken
into account when calculating the concentration of the pretreated sample. Poor specificites
of the recovery values are reflected as errors in final results. The specificities are usually
expressed as relative standard deviations of the given recovery value. (Pavlovi et al. 2010)
2.4 Matrix effects
Analysis of environmental samples differs from the analysis of for example pharmaceutical
formulations dissolved in a solvent. In environmental samples the analytes of interest are
present in a complex matrix and the signal caused by the analyte may be affected by this.
Such a phenomenon is referred to as matrix effect. Matrix effects include any kind of
changes in the analytical signal caused by the matrix. The impact on the signal can be a
reduction or an increase depending on the type of interference. (Harris 2007)
Pharmaceutical wastewater consists of a number of components which is referred to as
the matrix. In addition to the pharmaceutical active ingredients, other substances which are
used in the final pharmaceutical formulations are present in the wastewater. Such
substances are called excipients. Excipients are a vast group of compounds which may be
used for a number of purposes. (Haywood & Glass 2011)
Excipients can be used for example as carriers for the active ingredients. In this case
they should be inert by nature. They can be also used to dilute the drugs in case the active
ingredients are very potent and only a small amount is required per single dosage.
(Haywood & Glass 2011) Substances such as sugar, corn syrup, cocoa, lactose, calcium,
gelatin, talc, diatomaceous earth, alcohol and glycerin are used in palletizing and
encapsulating the final products and may be present in the wastewater. (Wang et al. 2006)
In case the production vessels are washed with something besides water also detergents
may be present.
Since there are numerous compounds present in the wastewater they may coelute during
HPLC separation if the compounds are of similar chemistry. Using UV detection coeluting
compounds lead to higher absorbances and consequently erroneously high concentrations.
27
(Vieno et al. 2006) If coeluting peaks are observed with during analysis of wastewater
samples, changing the flow rate to slower or using shallower gradients may improve
resolution. (Harris 2007)
In the case of wastewater it is likely that some active ingredients are present at large
concentrations. HPLC columns can only resolve a certain amount of compounds efficiently
and above certain concentrations of compounds analyzed asymmetry of the peaks and
changed retention times occur due to column overload. This leads to difficulties in
quantization. (Snyder & Kirkland 1979) This is a typical matrix effect when large sample
masses are used in an HPLC analysis. In general, reversed phase columns can handle 1-10
µg of sample per gram of silica. (Harris 2007)
During column overloading all of the stationary phase is occupied by the sample
molecules, and excess, unretained molecules elute earlier resulting in a tailed peak. In such
occasions the samples should be diluted. (Snyder & Kirkland 1979) Whether or not column
overloading is occurring can be determined by reducing the mass of sample by a factor of
ten. If retention times increase or if peaks become narrower, the mass of the sample has
been too large. By repeating the dilution process suitable masses which the column can
resolve can be found. (Harris 2007)
Peak splitting is a condition that can occur because of several reasons. In peak splitting,
usually all the peaks in the chromatogram are affected in a similar manner. The peak
signals are divided into parts, and the ratios of the divided parts are the same for all of the
peaks. Usually there is some physical obstacle before or inside the column that alters the
flow of the mobile phase leading to splitting. This is avoided by filtering the samples prior
to injecting. (Snyder & Kirkland 1979)
If ionic surfactants are present in the wastewater, they can form micelles in the column
or before the column resulting in peak splitting. In such case, high content of organic
mobile phase should be avoided. Injecting such surfactants into the HPLC column can be
avoided by using C18 SPE pretreatment. Surfactants arranged in the form of miscelles do
not adsorb onto the nonpolar sorbent material and are excluded from the final sample. Also
precipitation of buffers in the column with certain organic cosolvents especially at high
organic solvent concentrations leads to peak splitting. (Snyder & Kirkland 1979)
2.5 Active ingredients included in the study
The next section describes the uses, removal efficiencies during WWTP treatment,
biodegradabilities and environmental fates of the active ingredients included in the Thesis.
In Table 3.1 the structures, CAS-numbers, logP and pKa values and LD50 toxicity values of
the selected pharmaceuticals are presented.
28
2.5.1 Sulfamethoxazole
Sulfamethoxazole (SMX) is an antibacterial that is used to treat urinary tract infections,
acute pediatric middle ear infections, chronic bronchitis, enteritis, pneumonia and traveler’s
diarrhea. It is reported to be insoluble in ethyl ether and chloroform, 18.5 g/l in a 5:40
solution of methanol and acetone and readily soluble in hydrochloric acid and sodium
hydroxide through salt formation. It is readily soluble in water, water solubility being 610
mg/l. (Toxnet 2011)
Reported SMX removal rates at conventional wastewater treatment plants have ranged
between 0 and 90 %. Approximately 60 % of this has been achieved during the activated
sludge step. Reprted biodegradability values have been very varying. Some results have
indicated minimal biodegradability of SMX while others have claimed that significant
removal takes place. One study showed that the addition of a readily degradable carbon
source increased the removal 30 % while another study showed that the addition had no
effect at all. (Larcher & Yargeau 2011)
Larcher et al. (2011) conducted a study showing that SMX is not readily biodegradable.
Using seven strains of bacteria, only one, Rhodococcus equi, showed acceptable
biodegradability. The removal rates were 15 % without glucose and 29 % with glucose. The
removal rates with the six other bacteria ranged from 0 to 6.6 %.
Usually the degradation of synthetic chemicals is more complete in the presence of
more than one microbial species due to synergistic effects. The chemicals are mineralized
through complementary transformation reactions. However the removal efficiency by a
mixed population was 5 % at best after 300 h. This suggests that biodegradability in real
activated sludge treatment conditions may be worse than what might be inferred from
studies based on single bacterium studies. (Larcher & Yargeau 2011) The EC50 value of
SMX was reported to be 0.03 mg/l using the algae strain Synechococcus leopolensis leading
to inhibition of growth after 96 hours. (Toxnet 2011)
SMX will not adsorb onto suspended solid or sediment based on the Koc value of 72. In
general sulfonamide antimicrobials are not biodegradable and persist in the environment.
The compound has a bio concentration factor of 3 and therefore bioaccumulation is
insignificant. SMX does not undergo hydrolysis. (Toxnet 2011)
2.5.2 Acetylsalicylic Acid
Acetylsalicylic acid (ASA), also known as aspirin, is a non-steroidal anti-inflammatory
agent. Salicylates are used to relieve myalgia, musculoskeletal pain and other symptoms of
nonrheumatic inflammatory conditions. Salicylates are also indicated to relieve acute and
chronic rheumatoid arthritis. Low doses of acetyl salicylic acid are used widely in low
doses for their cardioprotective effects. (Toxnet 2011)
29
The solubilities of ASA are 200 g/l in methanol, 58.8 g/l in chloroform, 66.7 g/l in ether
and slightly less in anhydrous ether. Water solubility of ASA is 4.6 g/l. The pKa value 3.49
indicates that ASA exists almost completely in its anionic form in the environment and
does not adsorb onto soil containing organic carbon. ASA hydrolyses readily in soil and
water and has a physical half-life of 6.3 days at pH 7.4. It is not volatile based on its low
vapor pressure of 2.5 10-5 mmHg. However rather high concentrations of 0.34 µg/l have
been measured in the environment in surface waters. (Toxnet 2011)
ASA was most abundant pharmaceutical in a wastewater treatment influent made in a
study in Tokyo, Japan, and was detected at 7.6 µg/l level. This concentration was an order
of magnitude lower than the values reported in the Europe and USA. However, its removal
efficiency during activated sludge treatment process was high, about 95 %. Removal
through hydrolysis or microbial degradation to salicylic acid is the most likely degradation
pathway. (Nakada et al. 2006)
Molar absorptivity of ASA is only 59.2 1/M cm (Murtaza et al. 2011). For trace
analysis, usually molar absorptivities greater than 100 are required. Therefore ASA needs
to be preconcentrated before it can be detected using UV detection.
2.5.3 Diclofenac
Diclofenac (DIC) is a non-steroidal anti-inflammatory drug used to reduce inflammation,
pain, for example menstrual pain or dysmenorrheal. It works as an analgesic used in case of
arthritis or acute injury. (Zhang et al. 2008) Water solubility of DIC base is only 2.37 mg/l
and therefore it is usually administered as its sodium salt (Toxnet 2011).
The biodegradability of DIC was studied in a modified OECD 301C closed bottle
biodegradability test and it was noted that the concentration in DIC concentration did not
change significantly in 28 days. In addition, one of the two major degradation products
formed from DIC in higher organisms such as rat, diclofenac- -O-acyl glucuronide was
converted back to DIC. Another major degradation product, 4’-hydroxy diclofenac, was
broken down into an unknown product. (Lee et al. 2012)
DIC is not biodegradable and therefore is not eliminated during activated sludge
process. The small removal of DIC during activated sludge process is due to sorption, and
can be estimated from its logP value which has been estimated to lie between 1.90 and 3.74
(Lee et al. 2012). However, sorption that would have practical significance is not expected
to take place since its distribution coefficient between water and activated sludge is 16
l/kgss. The minimum value in order for sorption to take place is 500 l/kgss. DIC cannot be
eliminated using air stripping because of its low Henry’s law coefficient of 4.79 10-7. A
minimum value for successful air stripping is more than 3 10-3. (Zhang et al. 2008)
30
2.5.4 Ciprofloxacin HCl
Ciprofloxacin (CPX) is an anti-infective agent which inhibits nucleic acid synthesis in
target organisms. It is used in adults for the treatment of bone and joint infections. It is also
used to treat and to reduce the progression of inhalational anthrax after confirmation of
exposure to aerosolized B. anthracis spores. (Toxnet 2011)
CPX is a molecule that has a naphthyridine ring which has two nitrogen atoms and a
quinoline ring that has one nitrogen atom. CPX also has a carboxylic acid group at the C3
position of the molecule. Because of both acidic and basic functional groups, the molecule
exhibits both properties and these are affected by the choice of solvent. (Varanda et al.
2006) The pKa value of the protonated amino group on the piperazinyl ring is 8.74 while
the pKa of the carboxylic acid group is 6.09. (Toxnet 2011)
Water solubility of CPX is 30 g/l (Snyder & Kirkland 1979). The solubility of CPX
hydrochloride is greater in water than its hydrogen free form because of the presence of a
charge. The solubility order of CPX HCl is water > ethanol > 2-propanol and acetone. The
solubility in acetone is less than 20 mg/l. (Varanda et al. 2006)
CPX in aqueous solution is susceptible to photodegradation. It has a Koc value of
61,000 which suggests that in the environment it is immobilized onto soil. Using the OECD
closed bottle biodegradation test, 0 % of CPX degraded during 40 days which indicates that
it does not undergo biodegradation in water or soil. Bioconcentration factor of 3 suggests
that bioaccumulation is low. (Toxnet 2011) In a study conducted in Bangkok, Thailand,
removal efficiencies of CPX were reported to be between 40 and 89 % the average being
64.2 % (Tewari et al. 2013). A study focusing on the different removal routes stated that
sorption onto activated sludge is its the main removal route during biological treatment
(Dorival-García et al. 2013).
2.5.5 Paracetamol
Paracetamol (PCM) is a non-narcotic analgesic which is used to reduce moderate pain and
fever. It provides symptomatic relief from pain, but does not affect the cause of the pain as
opposed to salicylates or non-steroidal anti-inflammatory drugs. Other uses are the
treatment of headache, moderate myalgia, arthralgia, chronic pain due to cancer, mild
osteoarthritis and postpartum and postoperative pain. (Toxnet 2011)
The solubility of PCM is 14 g/l in water at 25 oC. It is also readily soluble in most
organic solvents used in the laboratory such as methanol, ethanol, methylene dichloride,
ethyl acetate, but does not dissolve in aliphatic alkanes. pH of a saturated PCM solution is
between 5.5 and 6.5. (Toxnet 2011)
PCM is not expected to adsorb onto soil or sediment based on the relatively low Koc
value of 41 and as a consequence it is very mobile in the environment. Degradation by
hydrolysis does not take place in environmental conditions. It is categorized as readily
31
biodegradable. A bioconcentration factor of 3 suggests minimal bioaccumulation. (Toxnet
2011)
Yu et al. (2011) studied the biodegradation and other removal pathways of PCM during
wastewater treatment. Other possible pathways included biosorption, hydrolysis and
volatilization. Biodegradation was determined as the removal difference between
biologically active and inhibited activated sludge and the removal by biosorption as the
difference between that of inhibited sludge and control without biological material. The
results indicated that PCM removal without biological material was negligible and
biosorption and biodegradation were very efficient. 100 % biosorption occurred in 8 days
and 25 % removal based solely on biodegradation was achieved in 2 days. Also, in a
desorption test, it was shown that less than 50 % of the sorbed PCM will desorb from the
sludge. (Yu et al. 2011)
2.5.6 Erythromycin stearate
Erythromycin (ERY) is a macrolide antibiotic. It is used both for human and veterinary
purposes. In humans, it is used for treatment of anthrax, acne vulgaris, community-acquired
pneumonia, oititus media in combination with four other active ingredients, as an
alternative for penicillins and sulfonamides to treat recurrent rheumatic fever and for the
treatment of mild to moderately severe infections of the upper and lower respiratory tract
infections caused by Streptococcus pneumoniae. (Toxnet 2011)
ERY is freely soluble in alcohols, acetone chloroforms and acetonitrile while water
solubility is only 1.44 mg/l. Vapor pressure of ERY is only 2.12 10-25 mmHg at 25 oC.
Therefore ERY is not volatile. (Toxnet 2011)
In a paper studying removal of antibiotics during waste water treatment through
adsorption onto the sludge and biodegradation it was reported that anhydro-erythromycin
could not be eliminated with either of the mechanisms. Therefore it was stated that ERY
cannot be eliminated from wastewater during aerobic biological treatment process. (Li &
Zhang 2010) In a OECD closed bottle test which studied the biodegradation of antibiotics it
was concluded that only 3 % of ERY degraded during both 14 and 28 days. Results below
5 % may also be accounted by experimental error. (Alexy et al. 2004). In the study
assessing environmental risks of pharmaceuticals after secondary wastewater treatment,
ERY got the highest risk quotient of the studied 118 pharmaceuticals (Verlicchi et al.
2012).
32
3 Materials and methods
The experimental part of the Master’s Thesis was started in a pharmaceutical factory based
in Kikuyu, Kenya called Universal Corporation Limited. The experiments were carried out
during a three and a half month period in the fall of 2012. During this period the
chromatographic method for the separation of ASA, DIC, CPX, PCM and SMX was
optimized. The experiments were continued in Finland at Tampere University of
Technology. The performance of the SPE cartridges was studied and the pretreatment
developed.
After completion of the method for the first group of compounds method for the
detection of ERY in wastewater was developed. This was started by carrying out the
derivatization of a pure stock solution. After this the derivatization of SPE pretreated
sample in Milli-Q water was studied. Finally the SPE method was optimized in order to
wash the sample from the interferents and also to elute the compound out of the sorbent.
3.1 Site of the study
Universal Corporation Limited (UCL) is a pharmaceutical factory based in Kikuyu, Kenya.
The company produces over 100 preparations for human use. The preparations include
tablets, capsules, ointments, creams and powders. UCL was founded in 1996 under the
name Universal pharmacy (K) Ltd. In 2006 the company got its present name Universal
Corporation Ltd. (Universal Corporation Limited 2013)
Initially UPKL had tablet, suspension and syrup production lines. In 2003 the company
started also exporting its products to Somalia. The present production facilities where
commissioned in 2005 and presently UCL exports its products to 12 African countries.
UCL Ltd. has been accredited with good manufacturing practices (GMP) certificate by the
local authority, Pharmacy and Poisons Board of Kenya, and an international quality
compliance statement. (Universal Corporation Limited 2013)
The factory’s operation produces two kinds of wastewater fractions which are
combined. The toilets and a kitchen produce sanitary wastewaters. Pharmaceutical
wastewaters are generated during washing of the production containers. Creams, syrups and
suspensions are produced in the liquid department and subsequently about 2000 to 3000
liters of wastewater are generated once or twice a week. In the granulation department
about 1000-2000 liters of wastewater is generated twice a week and in the coating
department 1000 to 2000 liters of wastewater is generated once a week. (Rama 2012)
A wastewater treatment plant is in use at UCL for treating the pharmaceutical
containing wastewater which was originally constructed by a Finnish company called
33
Galvatek Oy. The plant utilizes chemical and biological treatment steps. The layout of the
pharmaceutical factory wastewater treatment plant is presented in Fig. 3.1.
Figure 3.1. The layout of wastewater treatment plant at UCL.
In Fig. 3.1 the mixing of the two wastewater fractions and the steps they go through before
this are presented. The wastewater from washing the production containers is held in three
25 m3 tanks where mixing of the pharmaceuticals is thought to occur. The mixing is needed
in order to equalize the concentration differences resulting from different stages of the
washing process. (Rama 2012)
After mixing the water passes to a container, where settling takes place and different
aluminum based precipitation chemicals are added to boost the settling process. Typically
either a local product called Rapid Floc or aluminium sulphate are used. Theoretically the
active ingredients are eliminated during this step but it is likely that the wastewater matrix
lowers the removal efficiency. The matrix consists mostly of the excipients. Used
excipients include fumed silica, aluminum sulphate, benzoic acid, butanol, calcium
carbonate, castor oil, croscarmellose sodium, disodium ethylenediaminetetraacetic acid,
gelatin, glucose syrup, glycerin, hard paraffin wax, hydropropyl cellulose, lactose
monohydrate, liquid paraffin, maize starch, methyl paraben, microcrystalline cellulose,
sodium starch glycolate, sorbitol, turpentine oil and xanthan gum. (Rama 2012)
Next in the process is mixing the process water with the sanitary wastewaters. Before
mixing, the sanitary waters pass a fine screen, which has a gap size of 1 mm. Mixing of the
process waters and sanitary waters is ensured in an aerated balancing tank. After proper
34
mixing, the water goes through a biological filter and the activated sludge process. Last
step, which at the moment is bypassed, is the use of an activated carbon filter. The treated
wastewater is percolated through a filtration field into the soil. (Rama 2012)
The effluent quality is being monitored for water quality parameters such as chemical
oxygen demand (COD), biological oxygen demand (BOD), total suspended solids (TSS),
total nitrogen, total phosphorus and so on. At times TSS and BOD values have been too
high and have not met the requirements of local authorities. This is likely because the
pharmaceuticals in the wastewater are toxic to most bacteria and therefore hinder the
activated sludge process. This lowers the removal of organic matter. In addition to the
indirect problems caused by the pharmaceuticals in the wastewater also the high residual
concentrations of pharmaceuticals are a problem and have not met the requirement 0.05
mg/l set by NEMA. (Rama 2012)
3.2 Instrumentation used
During the experimental part of this work, experiments were done in Kenya and in Finland.
Next the instruments used in both the locations are presented.
3.2.1 Instrumentation in Kenya
The manufacturer of the used HPLC instrument was Thermo Separation Products. The
system consisted of a SpectraSYSTEM P2000 pump, SpectraSYSTEM AS3000 automatic
injector, SpectraSYSTEM UV1000 single wavelength UV detector, a vacuum degasser and
two mobile phase lines with a double pumping system. The software used was TSP HPLC
30150 and the interface connecting the hardware and the software was SpectraSYSTEM
SN4000 interface. The column used for ASA, CPX, DIC, PCM and SMX was Discovery
HS C18 250mm x 4.6 mm x 5 µm by Supelco CO.
Samples were weighed with an analytical scale (Mettler Toledo, AE200, ID 01087,
Quality Spectrum Systems, Ltd). Glassware were dried in an oven (Series 8000 drying
oven, Termaks, Norway) before sample preparation. Stocks were kept in a refrigerator
(Caterina, LG) with a temperature range from 3 to 8 oC.
HPLC mobile phases were always filtered through a 0.45 µm glass fiber filter before
introducing them into the HPLC instrument, and sonicated for 30 minutes. If the mobile
phases had been prepared prior to the day they were used, they were sonicated for 15
minutes. The pH values of the mobile phases in case of acidic or basic solvents were
checked with a pH211 pH meter by Hanna instruments (Rhode Island, USA).
Wastewater samples were first filtered through Nylon 66 membranes, whose
dimensions were 0.45 µm x 47 mm (Kobian House, Nairobi, Kenya) after which they were
filtered through 0.2 µm x 47 mm membrane filters (Type MV, Macherey-Nagel GmbH &
Co., Düren, Germany) to avoid clogging during the SPE pretreatment step. HPLC samples
35
were filtered through 0.45 µm x 25 mm Chromofil Xtra MV-45/25 cellulose mixed ester
filters (Macherey-Nagel GmbH & Co., Düren, Germany) to avoid introducing particulate
matter into the HPLC columns. Varian C18 SPE cartridges by Agilent Technologies were
used for SPE pretreatment.
3.2.2 Instrumentation in Finland
In Finland, an Agilent Technologies (Series 1100) HPLC system was used. The system
consisted of a HP series 1100 G1322A degasser, G1311A Quad pump,, G1316A Colcom
column thermostat, G1315A diode array detector and a Agilent Series 1100 G1313A ALS
autosampler. The software used was HP Chemstation (LC1100) ver. B.01.03. For the
analysis of ASA, CPX, DIC, PCM and SMX, a Phenomenex Luna C18(2) 100 A 250 mm x
4.6 mm x 5 µm column was used. For the derivatized ERY a Hewlett-Packard Zorbax
Eclipse XDB-C8 150 mm x 4.6 mm x 5 µm column was used.
For ASA, CPX, DIC, PCM and SMX Cronus 1 000 mg/ 12 ml C18 cartridges (Jaytee
BioSciences Ltd., Kent, UK) were used. Also Phenomenex Strata-X-C (Polymeric Strong
Cation) 30 mg/1 ml cartridges (Phenomenex Inc., Varlose, Denmark) were tested. For ERY
Isolute C18 (EC) 500mg/ 3 ml sorbents (Biotage AB, Uppsala, Sweden) were used. During
SPE pretreatment a Supelco Visiprep 24 vacuum manifold was used.
The manufacturer of the mass spectrometer used to identify the ERY derivatization
product was a Waters and the model of the instrument was LCT Premier XE. The
instrument used an ESI ion source and a TOF detector. The software used to process data
was MassLynx V4.1.
The filters used to filter the mobile phases were GH Polypro 47 mm 0.2 µm hydrophilic
PP membranes (Life Sciences, Del Valle, Mexico). Filters used to filter the HPLC samples
were 0.45 µm i.d. 25 mm Nylon membrane syringe filters by VWR international
(Darmstadt, Germany). The filters used to filter waste water samples were 47 mm i.d. 0.45
µm glass microfiber filters by Whatman International Ltd. (Maidstone, England).
Sample pH was adjusted using a WTW pH 340 pH Meter whose accuracy was 0.01 pH
units. For weighing the samples a Mettler Toledo XS204 (Mettler Toledo Inc, Ohio, USA)
analytical grade scale (accuracy 0.001 g) was used. For the production of Milli-Q water, a
Milli-Q Plus Ultra-Pure Water System (resistivity of water produced at least 18.2 M cm)
was used. During sample preparation a Vortex Genie 1 Touch Mixer (Scientific Industries
Inc, New York, USA) was used.
Derivatization of ERY was carried out in 5.0 ml skirted polypropylene test tubes
(VWR, Radnor, PA, USA). A IKA-HEIZBAD HB-250 water bath (Janke & Kunkel GmbH
& CO.KG, Staufen, Germany) was used to heat the reaction mixture to the required
temperature. The derivative was transferred into glass inserts compatible with standard
HPLC vials (shell style, volume 250 µl) by Supelco (Bellefonte, PA, USA). For
36
transferring the derivatized ERY disposable open jet Pasteur pipettes (150 mm, 250 µl)
were used (VWR International/Merck, New Jersey, USA).
3.3 Chemicals used
In Kenya HPLC grade MeOH, analytical grade glacial acetic acid, ammonia solution,
analytical grade ammonium acetate, HPLC grade 1-hexanesulfonic acid sodium
monohydrate and analytical grade triethylamine was purchased from Rankem, RFCL
limited, New Delhi, India. HPLC grade ACN was purchased from Sigma-Aldrich Chemie
GmbH, Steinheim, Germany. Fresh mobile phases were prepared at least once a week.
In Finland HPLC grade ACN, MeOH, TEA, FMOC-chloride and TMBS were
purchased from Sigma-Aldrich Chemie GmbH (Steinheim, Germany). Hydrochloric acid
(assay over 37 %), diethyl ether (anhydrous, assay over 99.5 %) and potassium dihydrogen
phosphate (99.5 – 100.5 %) were purchased from Merck (Darmstadt, Germany). Glacial
acetic acid (99-100 %) Ortho-phosphoric acid (assay over 85 %) and dichloromethane
(assay over 99.5 %) were purchased from J.T. Baker (Denventer, Netherlands).
3.4 Active ingredients studied
The active ingredients for which analytical methods were developed were chosen because
of their toxicities, their environmental risk quotients (Verlicchi et al. 2012) or amounts
produced. In Table 3.1 the structures, CAS-numbers, water solubilities, logP, pKa and
toxicity values of the studied pharmaceuticals are presented. (Toxnet 2011)
37
Table 3.1. Structures,
tructures, CAS-numbers, logP, pKa and toxicity values of the pharmaceuticals
included in the method development (Toxnet 2011).
Name
structure
CAS number
logP
pKa
LD50 (mg/kg)
ASA
50-78-2
1.4
3.49
200 (rat)
ERY
114-07-8
3.06
8.88
426 (rat)
PCM
103-90-2
0.4
9.38
2400 (rat)
CPX
85721-33-1
0.26
6.09;
8.62
over 5000 (rat)
DIC
15307-86-5
3.9
4.15
240 (mouse)
SMX
723-46-6
0.7
1.6; 5.7
6370 (rat)
Stock solutions used during method development were prepared from working reference
standards produced by Universal Corporation Limited. The pharmaceuticals were dissolved
in appropriate solvents. ASA, DIC, PCM and SMX were dissolved
dissolv in methanol and CPX
was dissolved in water since its solubility in methanol is poor.
In case of SMX,, DIC and CPX the stocks were renewed at least once a month. The
compounds are chemically stable and therefore their stability was not considered to be an
issue. ASA and PCM stocks were prepared once a week.
38
3.5 Calibration of the instrument
In Kenya calibration of the ThermoSeparation Products HPLC instrument was carried out
using a Standard Operation Procedure before the experiments were started. The procedure
which was used in Kenya was approved for use in a Quality Control Laboratory of UCL
and the procedures have been approved by Q.C. Managers and authorized by the Quality
Assurance Head. In Finland servicing was carried out by the maintenance division of the
manufacturer.
3.5.1 Injector reproducibility
In the method for testing the TSP HPLC injector reproducibility a 250 x 4.6 mm x 5 µm C18
column was used. The flow rate was 1 ml/min, and 100% HPLC methanol was used as
mobile phase. The signal was detected at a wavelength of 254 nm. As the sample, 0.5 %
toluene in methanol was injected.
Four different concentrations of the sample were injected, each six replicates. The
concentrations injected were 1 µl, 3 µl, 7 µl and 10 µl. From the areas produced by the
toluene sample, the relative standard deviations were calculated for each concentration, and
the injector reproducibility was considered to be sufficient if the relative standard deviation
for each concentration was less than 1 %.
3.5.2 Detector linearity
In the method for determining the TSP HPLC detector linearity the same column as for the
injector reproducibility test was used. The flow rate was 1 ml min-1, and the mobile phase
used was ACN:H2O 15:85 (v:v). Injection volume was 20 µl. The signal was detected at
272 nm. Caffeine samples of five different concentrations, 0.005 mg/l, 0.025 mg/l, 0.125
mg/l, 0.250 mg/l and 0.5 mg/l in the mobile phase were injected each triplicate. From the
data points of the first, second and third injections were drawn. The detector response was
considered to be linear if the correlation coefficient was more than 0.996.
3.5.3 Injector precision/carryover
The carryover of the injector was determined by injecting a blank sample consisting of the
mobile phase once and then injecting the caffeine standard whose concentration was 125
mg/l six times. After this a blank was injected again. The injector was considered to be
precise and the carryover insignificant if the relative standard deviation of the six caffeine
injections was less than 2 % and the caffeine signal of the last blank was less than 3 % of
the last caffeine injection signal.
39
3.5.4 Gradient linearity and accuracy
The performance of the pumps was studied by doing a gradient linearity calibration. In the
method the same column as in the previous calibration measurements was used, methanol
was used as the sample, and the mobile phase in line A was 6 mg/l methyl paraben in
methanol and in line B was methanol. The gradient used to test the pump performance is
illustrated in Fig 3.2.
Figure 3.2. The gradient used to test the gradient performance of the TSP HPLC pump.
The composition of the mobile phase was changed rapidly, in 0.1 minutes, by 20 %. The
ideal response to the change would be a sharp change into the new methyl paraben
concentration during the 0.1 min shift with a following plateau indicating a constant ratio of
mobile phases.
3.6 Method development
3.6.1 Derivatization of erythromycin
Erythromycin (ERY) is poorly detectable using UV detection. In the literature, other more
usable detection methods for detecting ERY have been reported. These detection methods
include electrochemical detection or mass spectrometry combined with HPLC (G ówka &
Kara niewicz- ada 2007) or the use of flame ionization in combination with gas
chromatography (Kanfer et al. 1998).
40
Electrochemical detection is a relatively rare detection method and is not available at
TUT or UCL Ltd. Mass spectrometric detector is expensive and if used as a detector
separate from HPLC it doesn’t produce quantitative information. Also, it is incompatible
with older instruments using high mobile phase flow rates and operating at lower pressures
since the presence of solvent molecules have to be eliminated before the analyte enter the
mass spectrometer. (Harris 2007)
Gas chromatographic methods for the separation of ERY have been proposed. The
problem with ERY using gas chromatography is the limited volatility of the large molecule.
At least one article reported the use of a complex derivatization mixture containing N,Obis-(trimethylsilyl)-acetamide, N-trimethylsilylimidazole, and trimethylchlorosilane in
pyridine (Tsuji & Robertson 1971). The use of such a complex mixture is rather expensive
and prone to errors. Also only a few articles have been published since the 80’s (Kanfer et
al. 1998). Therefore the use of gas chromatography for separating ERY was given up.
However, information on the presence and concentrations of ERY in wastewater should
be possible to be determined since as presented in the theoretical background chapter of this
work, ERY has been shown to have a significant environmental risk quotient and is
considered therefore to be a risk for the environment. ERY is also produced in large
quantities and therefore there should be a way to determine its removal efficiency.
Because of the aforementioned reasons, the derivatization for UV detection was
pursued. Two approaches presented in the literature review were chosen for closer
examination. The derivatization using trimethylbromosilane (TMBS) reported to lead to the
formation of a UV absorbing brominated derivative was chosen (Li et al. 2007). Also,
derivatization using fluorenylmethyloxycarbonyl chloride (FMOC-Cl) leading to the
formation of a UV absorbing FMOC-derivative was chosen for further studying.
In a separate article (Sastre Toraño & Guchelaar 1998) it was reported that using the
FMOC derivatization column performance lowered after multiple injections. It was
suggested that remaining FMOC molecules react irreversibly with the column silanol
groups resulting in peak broadening, front tailing and peak doubling. In addition, the
derivatized macrolide is stable only for approximately four hours. Also the time required to
complete the reaction favored the TMBS approach. (Li et al. 2007) The FMOC
derivatization was reported to take 40 minutes to complete whereas the TMBS
derivatization was reported to complete in ten minutes. Therefore the TMBS approach was
evaluated first.
Both of the chosen derivatization procedures were first done using pure stock solutions
to test if the derivatization reaction would lead to the formation of a UV-absorbing
compound. In case the derivatization would be successful, the applicability of it in case of
wastewater would be further investigated. In both of the articles the original sample was a
biological fluid so the initial extraction would need to be modified.
41
Derivatization of ERY was first attempted using the method proposed by Li et al.
(2007). In their study prior to the derivatization, the analyte was extracted from rat plasma.
In order to minimize sources of error, also the extraction step was repeated although pure
stock solution was used. 150 µl of ERY and 20 µl of 0.25 M NaOH solution were mixed
and the analyte then extracted with ethyl ether. The organic phase was separated and dried
and the residue was dissolved in 600 µl of dichloromethane. 50 µl of TMBS was added and
the reaction was allowed to take place for 10 minutes at 0 oC. The reaction was terminated
by adding 200 µl of water after which the organic layer was separated and dried. The
residue was dissolved in the mobile phase.
The other ERY derivatization procedure reported by Glowka et al. (2007) using
FMOC-Cl was also carried out. However, the methodology was adopted from another
article which used a derivatization time of 15 minutes. (Farshchi et al. 2009)
The initial extraction procedure was skipped and the stock was derivatized. 50 µl of the
ERY-stearate stock in MeOH was dried in a 5 ml polypropylene test tube under stream of
compressed air. 100 µl of 1 g/l FMOC solution dissolved in acetonitrile and 25 µl of 50
mM phosphate buffer at pH 8.25 were added into the test tube with the residue. The
reaction mixture was mixed with a vortex shaker for 10 seconds. Then the test tubes were
put in a water bath at 60 oC. After 15 minutes the reaction was stopped by cooling down the
test tube under running tap water and adding 25 µl of the phosphate buffer. The reaction
mixture was injected directly into the HPLC system.
3.6.2 Optimization of resolution
Conditions which enabled the separation of compounds within a group were sought for. As
a starting point, two articles written in Tampere University of Technology (TUT) were
adopted. In the papers the active ingredients were divided into two groups, acidic
(Lindqvist et al. 2005) and basic and neutral (Vieno et al. 2006) pharmaceuticals. Initially
the six active ingredients were divided in two groups. CPX, PCM and ERY were classified
as neutral or basic active ingredients and DIC, ASA and SMX were labeled as acidic
compounds.
For ERY a method using 10 % of 40 mM ammonium acetate adjusted to pH 5.5 with
formic acid and varying percentages of methanol and deionized water as mobile phases was
used (Hilton & Thomas 2003). The retention time of ERY using this method was supposed
to be around 22 minutes. Lack of a visible peak suggested that a derivatization step was
needed.
42
3.7 Method validation
3.7.1 Linear range
The linear range of the method for the five compounds was studied. Concentrations for the
linearity study were 25 mg/l, 5 mg/l, 2.5 mg/l, 0.25 mg/l and 0.025 mg/l. 0.05 mg/l is the
NEMA limit for the active ingredients in the wastewater that is being discharged into the
environment, so the method has to be able to detect at least this concentration.
2.5 mg/l is the concentration of the pretreated wastewater sample having the initial
concentration of 0.05 mg/l when 100 ml of the wastewater is applied during the SPE phase,
the final sample volume is 2 ml and a 100 % recovery is assumed. Therefore 25 mg/l was
considered to be a high enough concentration as an upper limit, since the water samples can
be diluted in case the concentrations of active ingredients in the samples are higher than
this.
For ERY masses from 2 µg to 70 µg were derivatized and linearity of the derivatization
products was considered. The smallest mass was calculated based on calculating the mass
that could be theoretically extracted from a wastewater sample if 100 ml of water at 0.05
mg/l level would be extracted and the recovery would be 50 %. This would result in a mass
of 2.5 µg.
3.7.2 Limit of detection and limit of quantification
The limits of detection and quantification were calculated using Eq. (2.8). Ratios of 3 and
10 were used for detection and quantification limits, respectively. Peak heights of the
signals were used. Peak height for noise was obtained from parts of the chromatogram
where there were no apparent peaks. The peak heights for the active ingredient of interest
measured in mAU were plotted as a function of active ingredient concentration. The limits
were then calculated from this equation for peak heights which equaled the noise height
multiplied by 3 for detection limit and 10 for quantification limit.
3.7.3 Intraday and interday repeatability
For ASA, CPX, DIC, SMX and PCM intraday and interday repeatabilities were studied.
Intraday repeatability was studied by injecting a series of 2.5 mg/l, 10 mg/l and 25 mg/l
pooled standard samples each three times within the same day. The relative standard
deviation was then calculated to see if the peak areas remained the same.
Intraday repeatability was evaluated by injecting the same 10 mg/l sample on the
subsequent day three times to see if the method would give the same peak areas. The
repeatability was determined by calculating the relative error between the two day values
for each of the active ingredients.
43
3.7.4 Method ruggedness
The method for ASA, CPX, DIC, SMX and PCM was tested for its robustness in terms of
sensitivity to changes in gradient and temperature. The temperature was changed by
increasing or decreasing it from 22 oC by 2 oC. The gradient was changed by increasing or
decreasing the final ACN content after every slope by 2 %.
The effects of these changes were taken into account by considering the change in
retention times and by calculating the relative change in retention factor. This would
provide qualitative information on changes in the method performance in case of changes
in operational parameters.
3.8 SPE pretreatment
3.8.1 Recoveries and breakthrough volumes
Recoveries using different sample pH values and sample volumes were determined for C18
SPE sorbents. Each sample volume was tested at three different pHs, namely 2, 7 and 10.
Samples were prepared by spiking a known amount of the active ingredients in Milli-Q
water and by comparing the response to standard response without the SPE step. The
prepared solutions had 4 mg/l of ASA and 0.5 mg/l of all other active ingredients present.
The choice 0.5 mg/l was an agreement between the NEMA limit 0.05 mg/l which is
required for the treated wastewater and the reported real wastewater effluent concentrations
lying in the mg/l level. The higher concentration for ASA was necessary since the detection
limit for ASA is lower.
In addition to an acidic and a neutral pH also pH 10 was included because in the HPLC
guide by Snyder et al. (1997) it was suggested that amphoteric compounds such as CPX are
most polar at pH ranges between their two pKa values. In this state both of the functional
groups are ionized but at pHs above the higher pKa value only the acidic group is ionized
and the molecule is less polar. Therefore the possibility of retention of CPX at this pH was
investigated.
Prior to sample loading the C18 sorbents were always washed with 10 ml of MeOH and
conditioned with 10 ml of Milli-Q water. Elution of the retained compounds was always
carried out with 2 ml of MeOH. Prior to this the sorbent was dried for 5 min after loading
of the sample.
Sample volumes of 25 ml, 50 ml, 100 ml and 200 ml were loaded onto the SPE sorbents
to gain knowledge about possible breakthrough of analytes. Volumes greater than 200 ml
are not reasonable in case of real samples because of the need of time consuming. The
amount of the SPE cartridges was limited and therefore only duplicate trials were
performed.
44
Phenomenex Strata-X-C sorbents were also tested since it became obvious during the
recovery tests that C18 wasn’t suitable for retaining all the active ingredients. This sorbent
only retains cations and therefore the pH of the sample was adjusted to acidic by using 20
µl of strong phosphoric acid per 1 ml of the sample. Prior to sample loading the sorbent
was washed with 1 ml of MeOH and conditioned with 1 ml of MQ-water. 10 or 20 ml of
the acidified sample was loaded into the sorbent, after which it was washed with 1 ml of 5
% phosphoric acid. The sorbent was allowed to dry with the suction on for three minutes.
The sample volumes that were loaded onto the sorbent were smaller since the mass of
the sorbent was only 30 mg. The rationale behind this was that if real wastewater samples
were loaded onto the sorbent larger sorbent masses would be used. Therefore the obtained
results could be applied to real conditions by multiplying the loadable sample volume and
the mass of sorbent by ten.
For C18 sorbents graphs of peak areas versus amount loaded onto the sorbent were
plotted. From the graph occurrence of possible breakthroughs were identified.
Breakthrough is considered to take place when the recovery of the analyte starts to deviate
from linearity.
3.8.2 Determination of suitable SPE conditions
Based on the recovery experiments suitable sample load volumes and sample pH values
were determined. Washing was examined since it may reduce interfering components and
different percentages of methanol in deionized water were studied. The MeOH content to
be used was chosen based on the logP value of the most polar compound in every group.
According to the manufacturer, 10 % MeOH can be used for every logP unit of the most
polar compound.
Because PCM is the most polar compound and its logP is 0.46 a starting point for
testing the wash could be a 4.6 % MeOH solution during the analysis of ASA, CPX, DIC,
PCM and SMX. Based on this consideration, 2.5 % and 5 % MeOH solutions were
evaluated for washing of the SPE sorbent.
The logP value of ERY is 3.06 so the starting point according to the manufacturer’s
instruction for the wash solution would be 30 % MeOH. Therefore 25 %, 20 %, 15 % and
10 % MeOH solutions were tested.
Eluting the sample from the strong cation exchanger was optimized using different
elution solutions and volumes. A wash with 0.1 % phosphoric acid was recommended by
the manufacturer. This was tested to see if acceptable results would be obtained.
45
3.9 Method limit of quantification
Method limits of quantification taking into account the instrumental limits of quantification
and the sample preconcentration during SPE pretreatment were calculated. The equation
used to calculate the overall limit of quantification is
LOQ m
LOQ i Ve
Vs
,
(3.1)
where LOQ m (mg/l) is the method limit of quantification, LOQi (mg/l) is the instrumental
limit of quantification, Ve (ml) is the elution volume of the sample, Vs (ml) is the volume
of sample loaded onto the SPE sorbent and
is the recovery of the sample during the SPE
pretreatment.
3.10 Wastewater samples
Samples were filtered through a 0.45 µm glass microfiber filter (i.d. 47 mm) before SPE to
remove particulate matter. The apparatus used for sample filtration is presented in Fig. 3.3.
Figure 3.3. The filtration system used to filter particulate matter.
46
In the apparatus used the suction is created using running water. The vacuum facilitates the
passage of sample through the filter. Real wastewater samples are typically extremely rich
in suspended matter and the filter paper had to be changed several times during the
filtration of 500 ml.
SPE pretreatment was done with the aid of a vacuum manifold. This allows for the SPE
treatment of multiple samples simultaneously. The vacuum inside the glass chamber is
usually –200 mmHg. By adjusting the stopcock the flow rate could be controlled. The
apparatus is presented in Fig. 3.4.
Figure 3.4. The SPE manifold used during SPE pretreatment of the samples.
Wastewater samples brought to Finland from UCLwere analyzed in order to estimate the
performance of the method. The wastewater samples were taken from the final effluent
after all of the treatment phases had been applied to the wastewater. The HPLC
chromatograms were used to qualitatively assess the capability of the method to resolve
target analytes from interferents.
47
4 Results and discussion
4.1 Calibration results
4.1.1 Injector reproducibility
Three different volumes of the toluene standard were injected each 6 times to see the
reproducibility of the injector. The results are presented in Table 4.1.
Table 4.1. Average peak areas and relative standard deviations for six injections using 3, 7
and 10 µl injection volumes.
Sample volume (µl)
3
7
10
Average of peak area
(mAU*min)
1901361
3778473
6070497
Relative standard deviation (%)
0.51
0.20
0.18
The injector was considered to be linear because the relative standard deviation was below
1 % for all the injection volumes.
4.1.2 Detector linearity
Five different concentrations of a caffeine standard were injected three times each. Three
separate graphs of peak area versus the concentration of the standard for the first, second
and third injections were plotted. The plots are presented in Fig 4.1.
48
Peak area (mAu*min)
30000000
y = 5E+07x + 352712
R² = 0.9979
20000000
10000000
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Concentration of standard (mg/l)
Detector linearity graph 1
Figure 4.1. The calibration graphs for three separate caffeine standard series.
The equation and the correlation coefficients were the same for the three standard series.
Since the correlation coefficients were above 0.996 for all the standard series, the detector
was considered to be sufficiently linear.
4.1.3 Injector precision/carryover
In order to assess carryover of analytes in the injector, a series of injections was performed.
First a blank was injected, then six injections using a 125 mg/l caffeine standard were
performed, and finally a blank was injected.
The relative standard deviation of the six standard injections was 0.19 % so the
injections were reproducible. The carryover of caffeine in the final blank was 0.40 % of the
average for the six standard injections. Since it was below 3 % it was concluded that the
carryover is tolerable.
4.1.4 Gradient linearity
The chromatogram of the gradient linearity test is presented in Appedix I. From the
chromatogram it can be seen that the mobile phase settled to the new value only at the end
of the ten minute constant mobile phase composition step. This indicated relatively poor
pump performance. The maximum height of the each step of the methyl paraben signal at
the end of the 10 minute step was used as the value for the step. The ratio of the
corresponding step signal height and the 100 % methyl paraben signal height was
calculated, and a graph of the signal height ratios vs. methyl paraben percentage was
plotted. The plot is presented in Fig. 4.2.
49
14
y = 0.1317x + 0.1143
R² = 0.9957
Peak height
12
10
8
6
4
2
0
0
20
40
60
80
100
percentage of methyl paraben
Gradient linearity
Figure 4.2. The calibration graph of the gradient performance.
The pump performance was considered to be sufficient if the linear fit had a correlation
coefficient greater than 0.996. The value 0.9957 rounds up to 0.996 and therefore the pump
performance was considered acceptable.
4.2 Derivatization of ERY
4.2.1 TMBS derivatization reaction
The mixture that was obtained by derivatizing ERY with TMBS in dichloromethane at 0 oC
for 10 minutes was run using HPLC. The gradient run used MeOH and Milli-Q water with
10 % AcONH4 adjusted to pH 5.5 with formic acid in both as mobile phases. Also blanks
were prepared by reacting 50 µl of the TBMS reagent alone or stearic acid plus TMBS in
dichloromethane. The chromatogram of the HPLC run is presented in Appendix B.
There are peaks appearing at 9 min, 10 min and 16 min in both the derivatization
product and the blanks. According to Li et al. (2007) the derivatization with TMBS leads to
a single UV absorbing ERY derivatization product which could be detected using HPLC at
275 nm. However the results indicate that the derivatization reaction lead to formation of
multiple products but none of the belonged to the derivatized ERY. Also, the possibility of
the derivative eluting after the 25 min run was ruled out. Therefore it was concluded that
the TMBS procedure didn’t lead to the formation of an absorbing ERY species and this
derivatization method was abandoned.
50
4.2.2 FMOC derivatization reaction
For the FMOC-derivatized ERY no peaks were observed that also didn’t appear in the
blank mixture which was made by reacting the FMOC-Cl in acetonitrile and the phosphate
buffer alone. However, close examination of an article on the same subject (Edder et al.
2002) indicated that the derivatization reaction stops at pH below 7 and is stable in the pH
range 7-8.5. Since ERY-stearate was used as source or ERY, the solution had to be made
neutral because the pH of the solution was approximately 5.5. Using 0.25 NaOH the pH of
the stock was adjusted to 7.
After pH adjustment, the derivatization was carried on again, and the reaction products
analyzed. A peak appeared at 23 to 25 minutes using a gradient consisting of acetonitrile
and water. The identity of the product was confirmed by derivatizing different amounts of
ERY. The response increased linearly it was concluded that the peak belonged to the
derivatized ERY. The chromatograms of the runs are presented in Appendix B.
The product was also confirmed using mass spectrometry. The fraction eluting at 23-25
min was separated after the detector and analyzed. It was confirmed that the peak contained
monosubstituted FMOC-ERY since there was a peak at 956.25 amu. There were no signals
for the di- or trisubstituted product. The mass spectrum is presented in Appendix C.
Shangguan et al. stated in their article that the byproducts in the FMOC-Cl reaction are
the unreacted FMOC-Cl, 9-fluorenylmethyloxycarboxylic acid which is the hydrolysis
product of FMOC-Cl and fluorescent alcohol which results from the decarboxylation of
FMOC. (Shangguan et al. 2001) The byproducts elute earlier than the derivatized ERY and
therefore didn’t require removal from the reaction mixture. However, a peak of the
derivatization reagent indicated that an excess has been used. For derivatizing
environmental samples excess of the derivatization reagent should be used in order to
achieve reproducible derivatization.
4.3 Optimization of resolution
4.3.1 ASA, CPX, DIC, SMX and PCM
In the method for acidic compounds initially 10 mM ammonium hydroxide at pH 10 and
ACN were used as mobile phases. The gradient lead to very large noise peaks. It is possible
that the high pH lead to dissolution of the silica from the C18 column and this caused the
noise peaks.
Throughout the method development using ammonium hydroxide the main problem
was the insufficient separation of ASA and SMX. The retention times were too close to
each other, and the resolution was generally below 1 between these compounds. Since it is
generally accepted that C18 columns should be used at the pH range 2-7.5 the method using
ammonium hydroxide as a mobile phase was given up.
51
After the method using ammonium hydroxide as the mobile phase was rejected, the
main focus was put on the method using a 1 % glacial acetic acid (GAA) solution and ACN
as the mobile phases (Vieno et al. 2006). Because of its high UV absorbance acetic acid
isn’t suitable for wavelengths below 220 nm. Without derivatization ERY has no
chromophores and doesn’t absorb above 220 nm. The derivatized ERY is very hydrophobic
and requires the use of a strong mobile phase. Therefore ERY wasn’t included in this
method.
Separation of SMX and ASA turned out to be most problematic and sufficient
resolution between the two active ingredients using 1 % GAA and ACN in different ratios
wasn’t achieved. The next step was to introduce an ion pair reagent in the aqueous phase.
First monohydrous hexanesulphonic acid (HSA) was tried out. HSA had little effect on the
retention times. This is likely because HSA is a strong acid that is deprotonated at almost
every pH. Therefore it is in anionic form and since ASA also is partially anionic, and SMX
is neutral, HSA didn’t change their retention behavior.
The next step was to introduce triethylamine (TEA) into the aqueous mobile phase. A
percentage of 0.2 by volume was adopted from standard operation procedure for separating
SMX from trimethoprim. By adding TEA an acceptable resolution between ASA and SMX
was achieved. The retention time for ASA was 4.97 min and for SMX 6.7 min. Also the
resolutions between all the other compounds were acceptable.
Retention time for PCM was 4.9 min and 14.2 min for SMX. There were no compounds
eluting between 4.9 min and 14 min. Also for a wastewater sample there were practically
no signals between 4 and 14 minutes. Therefore the ACN percentage was changed to
increase earlier. At 4 minutes, the composition changed to 60 % in three minutes. This had
the effect of eluting the peaks at 14 min earlier, increasing the resolution between 14 and 18
minutes.
The method was modified by extending the runtime to 22 minutes because DIC eluted
at 18.7 min which was during the last 2 minutes of the run. The gradient used is presented
in Table 4.2.
Table 4.2. The gradient used in the developed method. Mobile phase B is acetonitrile.
Time (min)
0
4
7
13
16
19
21
25
Percentage of B (%)
20
20
60
60
95
95
20
20
52
The ACN percentage was increased into 95 % for three minutes at 16 minutes time. This
was done in order to make sure all of the compounds would elute during the run, resulting
in minimal carryover. At 21 minutes the percentage of B is decrease back to 20 and kept
there for three minutes before the start of the next run
4.3.2 ERY
As stated, elution of ERY required the use of a strong mobile phase. Therefore ERY was
analyzed with a separate chromatographic method. Initially, eluting the derivatized ERY
with a gradient using MeOH, Milli-Q water and 40 mM ammonium acetate adjusted to pH
5.5 with formic acid was used. This lead to the elution of a peak whose area increased when
the amount of ERY being derivatized was increased. Soon it became obvious, however, that
the peak didn’t elute during the run, but eluted from previous injections and eluted as a
carryover peak. Therefore the mobile phase composition was changed in order to elute the
derivative at a reasonable retention time.
In the final method for the separation of ERY a 15 cm C8 column was used. The
compound was eluted in isocratic conditions and the mobile phase consisted of 80 % ACN
in water. A flow rate of 2 ml/min was used. The peak belonging to the derivatized ERY
was well resolved from the other derivatization products present in the solution.
The final method uses a strong mobile phase and a large flow rate. This is not very
economical, since 16 ml of ACN is used up per run. However, such strong elution
conditions were needed to elute all of the compounds in a single run.
The method development process was relatively slow since the use of too weak a
solvent led to various carryover peaks. Therefore identification of peaks usig a UV detector
was challenging after changes in the run had been made. Finally, the strong mobile phase
eliminated the problem of interfering carryover peaks. In the final method the identity of
the peaks was confirmed by mass spectrometry.
53
4.4 Retention time, resolution, and peak quality parameters
The separation of the analytes is presented in Fig. 4.3.
180
160
Signal (mAu)
140
120
100
80
60
40
20
0
0
5
10
15
Retention Time (min)
20
25
PCM, CPX, ASA, SMX, DIC 10 mg/l
Figure 4.3. The separation of PCM (4.19 min), CPX (5.05 min), ASA (9.48 min), SMX
(10.05 min) and DIC (16.38 min) using the developed method.
In the picture it can be clearly seen that the active ingredients chosen for this method are
sufficiently resolved. Results for retention time, resolution and number of theoretical plates
for ASA, PCM, CPX, DIC and SMX are presented in Table 4.3. The parameters were
obtained using a standard whose concentration was 2.5 mg/l in all of the active ingredients.
Table 4.3. Retention times, resolutions and numbers of theoretical plates for ASA, PCM,
CPX, DIC and SMX.
retention time (min)
resolution
number of theoretical plates
PCM
4.19
6.17
22462
CPX
5.16
6.17
10403
ASA
9.48
30.15
206094
SMX
10.06
6.58
194026
DIC
16.34
40.17
87526
At UCL Ltd. the minimum allowed value for the plate number for quality control of
finished active ingredients is not less than 1,000. All of the active ingredients in this
method therefore meet this requirement and the method provides sufficient resolution
between the defined analytes.
54
In the case of real wastewater samples unidentified compounds are bound to be present
since wastewaters consist of numerous compounds. If such compounds are chemically
similar they elute at similar retention times. In order to be able to separate such compounds
the performance of the column (which can be often estimated by the number of injections
performed with a given column) should be sufficient to produce peaks that are narrow
enough.
If columns with a good performance cannot be used, one way to improve resolution is
to increase the runtime. This can be done by decreasing mobile phase flow rate or by
making the gradients less steep. However, method validation wasn’t done with such
extended run times because of time and reagent constraints.
Asymmetry factors (As) and peak tailing factors (PTF) were calculated using Eq. (2.6)
and (2.7). The results are presented in Table (4.4).
Table 4.4. Asymmetry factors and peak tailing factors for ASA, CPX, DIC, SMX and PCM
and the A and B values used to calculate them.
A (5%) (min)
B (5 %) (min)
A (10 %) (min)
B (10 %) (min)
PTF
As
ASA
0.039
0.065
0.036
0.056
1.333
1.556
CPX
0.177
0.217
0.139
0.149
1.113
1.072
DIC
0.124
0.262
0.108
0.204
1.556
1.889
SMX
0.030
0.100
0.034
0.076
2.152
2.235
PCM
0.064
0.087
0.055
0.072
1.180
1.309
Asymmetry factors should lie between 0.95 and 1.5. Peaks for ASA, CPX and PCM are
close to this but the peaks for DIC and SMX are a bit worse. However the peaks are sharp
and quantization can be done using the produced peaks. At UCL, peak tailing factors
should lie between 0.8 and 2. This requirement is fulfilled for all the peaks except SMX
which exceeds this value slightly.
4.4.2 ERY
The retention time of the derivatized ERY in the final method is 5.6 min. All of the other,
less hydrophobic compounds present in the derivatization mixture eluted earlier, the last
one at 2.2 min. However, the gap between the derivatized ERY and the last derivatization
byproduct offers some separation capability of derivatized compounds present in
wastewater. A chromatogram using the developed method for the separation of ERY is
presented in Fig. 4.4.
55
Signal (mAu)
2000
1500
1000
500
0
0
2
4
6
8
10
Retention time (min)
Figure 4.4. Chromatogram of derivatized ERY.
The derivatization reagent eluting at 1,6 min absorbs intensively. The large peak shows that
the reagent was largely in excess. In Fig. 4.5 the lower part of the chromatogram is zoomed
to highlight the peak belonging to the ERY derivative.
95
Signal (mAu)
75
55
35
15
-5
0
1
2
3
4
5
6
7
Figure 4.5. The chromatogram of derivatized ERY zoomed to lower absorbances.
The ERY peak at 5.6 min was obtained using a sample in which 36.6 µg of ERY has been
derivatized. The peak is well resolved from other derivatization byproducts. The number of
theoretical plates for the ERY peak is 3987 which is acceptable. Using Eq. (2.6) an
asymmetry factor of 1.55 can be calculated for ERY (A = 0.161, B = 0.249) which slightly
56
exceeds the limit of 1.5 but is still acceptable. Peak tailing factor for ERY is 2.41 (A =
0.273, B = 1.044) which also exceeds the upper limit of 2 set for PTF but can be still
regarded as acceptable.
4.5 Linear ranges
Pooled samples of the active ingredients in the presented concentrations were prepared.
Each sample was injected three times, and the average peak area of the three injections was
used to plot the linearity graph. The method was found to be linear for each of the active
ingredients at the concentration range from 0.025 mg/l to 25 mg/l. The peak areas versus
analyte concentrations are presented in Fig. 4.6.
1200
Peak area (mAU*min)
1000
800
600
400
200
0
-200
0
5
10
15
20
25
30
Concentration of analyte (mg/l)
PCM
CPX
SMX
DIC
ASA
Figure 4.6. Graphs of peak areas versus concentrations of analytes for ASA, CPX, DIC,
SMX and PCM.
Detection of SMX is the most sensitive while detection of ASA is least sensitive. For ASA,
also concentrations 50 mg/l and 100 mg/l are included in the formation of the calibration
graph, but also the concentration range 0-30 mg/l is displayed. Otherwise the lower
concentrations would be less clear.
In Table 4.5 the equations of the external calibration graphs are presented.
57
Table 4.5. Calibration graph equations and regression coefficients for the active
ingredients studied.
calibration graph
equation
y = 3.025x - 7.306
y = 30.811 - 16.237
y = 13.759 + 0.041
y = 16.655 - 8.415
y = 40.937 - 9.823
API
ASA
CPX
DIC
PCM
SMX
R2
0.998
0.999
0.994
0.998
1.000
The external calibration graph equations can be used to calculate the concentrations of
unknown samples when they are analysed with the method. Different definitions of highly
linear methods exist, but regression coefficients larger than 0.996 are generally considered
highly linear. The linear regression value of DIC only is less than this, but the value is still
acceptable.
4.5.2 ERY
Five different volumes of the erythromycin stock solution were evaporated and derivatized,
and the products analyzed with HPLC. The stock concentration used was 1464 mg/l of
ERY in methanol. In Fig. 4.7 the peak areas as a function of ERY mass before
derivatization are presented.
300.00
Peak Area (mAU*min)
250.00
200.00
150.00
100.00
50.00
0.00
0.00
0.02
0.04
0.06
0.08
Mass of ERY derivatized (mg)
Figure 4.7. The calibration graph of the derivatized ERY.
The results indicate that the method is moderately linear between 2 and 70 µg of ERY
being derivatized. The equation of the calibration graph is y = 3746.9x - 16.827 and the
58
regression coefficient is R² = 0.9269. The poor regression coefficient is likely to result from
the variability of the derivatization reaction.
The variability may be a result of the procedure used to carry out the derivatization. A 5
ml test tube is used as the container during the derivatization. Since the volume of solvent
is only 125 µl and since some of the solvent ends up in the walls of the container due to
evaporation and condensation, the volume of the solvent changes from run to run. If the
mass of ERY is 70 µg, depending on whether 10 µl or 20 µl of solvent is lost due to
evaporation, the change ERY concentration is approximately 10 % relative to the original
concentration. Differences in evaporated solvent volume are bound to affect also the
concentration of the derivative.
Errors may arise also from different temperatures during the derivatization since the
accuracy was only ± 2 oC. The derivatization reaction may also not stop completely after
the designated time, and something should be added into the reaction mixture that would
completely use up the derivatization reagent. Because only microliters of the stock to be
derivatized were used, errors during the measurement might have affected the final result.
The accuracy of the pipettes used may have a significant effect at such small volumes.
Also, some degradation of the product may occur which may change the mass of the
derivative.
4.6 Instrumental limit of detection and limit of quantification
Limits of detection (LOD) and quantification (LOQ) for the instrument were calculated
according to Eq. (2.8). At this point it should be noted that instrumental LODs and LOQs
are lower than the LODs and LOQs of the overall method since SPE pretreatment of the
samples increases the concentrations of the samples.
The noise peak heights were obtained from the chromatograms where no apparent
peaks were observed. For ASA noise height was 0.14 mAU and for the rest of the
compounds the noise height was 0.18 mAU. A part of a chromatogram is presented in Fig.
4.8 to demonstrate the magnitude of the noise compared to the signal for ASA.
59
0.9
Signal (mAu)
0.7
0.5
0.3
0.1
-0.1 7.5
8
-0.3
8.5
9
9.5
10
Retention Time (min)
Figure 4.8. Illustration of instrumental noise. Noise is a result of the variation of the
baseline with time.
The variation in baseline stability can be seen in Fig. 4.8. The peak at 8.85 min belongs to
an impurity and the peak belonging to ASA is at 9.5 min. The area of the impurity is only
5.2 % of the ASA peak at 9.5 min.
Peak heights were determined for all of the compounds and plotted against the
respective concentrations. The linear equations of the plots were obtained. From these
equations the concentrations equaling 10 N’ or 3 N’ were calculated for all the active
ingredients. Limits of detection and limits of quantification are presented in Table 4.6.
Table 4.6. Instrumental limits of detection and quantification for ASA, CPX, DIC, SMX and
PCM.
LOQ (mg/l)
LOD (mg/l)
PCM
1.56
0.70
CPX
1.45
0.87
SMX
0.61
0.32
DIC
1.74
0.47
ASA
7.40
3.64
The limits are for 10 µl sample volumes. If 20 µl sample volumes were used, the limits
would be roughly two times lower. This was not done however, since in Finland the peaks
produced by the method were highly tailed using 20 µl sample volumes. The reason for
peak tailing was the use of a column that was not used when the ferrules were fit into the
capillaries of the HPLC. This caused extra volume in between the column and the capillary.
In this dead volume sample mixing takes place and results in distorted peaks. Such an event
is referred to as extra column effect in the literature. Tailing was avoided by using a lower
sample volume. Because this was discovered only at the very end of the method
development the experiments weren’t carried out again.
60
Instrumental LOD and LOQ are for samples injected into the HPLC. Overall LOQs of
the method are lower when sample preconcentration is applied. If for example sample is
concentrated 50 times during sample pretreatment, the method limits are 50 times lower.
4.6.2 ERY
From the chromatogram of the derivatized ERY, a noise signal height of 0.1 was
determined. The peak heights of the derivatized ERY samples were plotted as a function of
the sample masses being derivatized and a sensitivity graph was plotted. From the equation,
sample volumes corresponding to 10 N’ or 3 N’ were determined. Using this
methodology a LOD of 6.5 µg and LOQ of 9.6 µg of ERY were obtained. The limits are
represented as mass of ERY to be derivatized because the mass or concentration of the
derivatization product aren’t known.
The water solubility of ERY is low, approximately 1.22 mg/l. Because of this, a
minimum of approximately 9.8 ml of the sample has to be passed though the SPE sorbent
to yield a detectable mass of ERY. Passing 9.8 ml of the sample with a concentration of
1.22 mg/l would result in a mass of 9.6 µg assuming a recovery of 80 % during the SPE
step. For lower concentrations higher volumes have to be applied. For example for 0.122
mg/l a ten time volume of 98 ml has to be loaded to obtain a quantifiable mass.
4.7 Intraday and interday repeatability
The repeatabilities of the method for ASA, CPX, DIC, PCM and SMX were determined to
evaluate the reliability of the method. For ERY this was not done, because the
reproducibility of the derivatization procedure was poor. The variability of the method
could not have been distinguished from the variability of the derivatization procedure.
For intraday repeatability, the series of 2.5 mg/l, 10 mg/l and 25 mg/l samples were
injected three times. The results of these injections are presented in Table 4.7.
61
Table 4.7. The intraday repeatability of the method for PCM, CPX, ASA, SMX and DIC.
Sample concentration (mg/l)
PCM
CPX
ASA
SMX
DIC
2.5
10
25
2.5
10
25
2.5
10
25
2.5
10
25
2.5
10
25
Average of peak
area (mAU*min)
57.4
201.6
528.1
64.5
322.1
778.8
6.5
24.4
58.8
173.7
610.1
1622.2
42.3
149.9
404.2
RSD (%)
1.6
0.6
0.4
2.0
0.9
0.6
11.9
9.1
12.1
0.4
0.2
0.4
2.3
0.9
0.6
The repeatability is good for all other compounds besides ASA. The relative standard
deviation varies between 9.1 % and 12.1 % for the 2.5 mg/l and 25 mg/l samples. The
reason might partly be due to the poor sensitivity of ASA and the peak area might be most
affected by the small variations in the baseline resulting in large errors.
However one possible explanation is that because of the long run time, some
decomposition of ASA into salicylic acid took already place. Because the run time is 22
min, there was approximately two hours between the first and the third injection of the
same sample. Examination of the results showed that the peak size had already decreased
significantly and a peak for salicylic acid had appeared. Therefore the poor repeatability is
not due to the method but rather the decomposition of the sample.
In order to evaluate the interday repeatability, the 10 mg/l sample was injected three
times on the following day. The results are presented in Table 4.8.
62
Table 4.8. The average peak areas of the 10 mg/l sample on the second day and the relative
change to previous day.
PCM
CPX
ASA
SMX
DIC
Average peak area (mAU*min)
202.2
317.4
7.2
614.9
152.9
relative change to previous day (%)
0.3
1.4
70.6
0.8
2.1
The results indicate that the interday repeatability is adequate for PCM, CPX, SMX and
DIC. However for ASA the change is enormous. As for the variability during the
experiments of the first day, the change is likely because of the decomposition of ASA.
During the final injection, the peak area was only 25 % of the initial peak area for the 10
mg/l sample. The result highlights that the analysis of ASA needs to be done quickly after
sample preparation.
4.8 Method ruggedness
For ASA, CPX, DIC, PCM and SMX ruggedness of the method was evaluated. The
gradient was changed so that after every slope the percentage of the organic solvent was
either increased or decreased by 2%. In Table 4.9 the change in retention time as a function
of changes in operational parameters is presented.
Table 4.9. Change of retention times in minutes for the studied active ingredients when the
operational temperature or gradient are affected.
Change in conditions
Increase in T by 2 oC
Decrease in T by 2 oC
Increase in gradient slope
Decrease in greadient slope
PCM
(min)
0.01
0.01
0.01
0.00
CPX
(min)
0.03
0.05
0.04
0.04
ASA
(min)
0.01
0.02
0.05
0.06
SMX
(min)
0.02
0.02
0.08
0.10
DIC
(min)
0.17
0.00
0.92
0.64
It is clear from the results that the changes in the parameters affect the separation of the
analytes only slightly. Only for DIC the change in retention time is noticeable when the
gradient is changed. In case of wastewater, changes in retention time greater than 1 min
may result from column overloading. Therefore sample pretreatment and optimization of
total mass of analytes in the sample may are more important than slight changes in
operational parameters.
In Table 4.10 the relative changes in retention factors are presented as a function of
changes in operational parameters.
63
Table 4.10. Relative changes in retention factors for the studied active ingredients when the
operational temperature or gradient are affected.
Change in conditions
PCM (%)
CPX (%)
ASA (%)
SMX (%)
Increase in T by 2 oC
Decrease in T by 2 oC
Increase in gradient slope
Decrease in greadient slope
0.4
0.4
0.4
0.0
0.7
1.1
0.9
0.9
0.0
0.1
0.2
0.2
0.1
0.1
0.3
0.4
DIC
(%)
0.2
0.0
1.2
0.7
By considering the retention factors the differences between early and late eluting
compounds are smaller. For all of the analytes the change in retention factor is less than 2
% which indicates that the changes affect the separation performance only slightly. The
results indicate that the method is relatively free from interferences caused by changes in
temperature and gradient performance.
4.9 SPE pretreatment of ASA, CPX, DIC, PCM and SMX
4.9.1 Recoveries using C18 sorbents
In Table 4.11 the recoveries for the active ingredients using 1000 mg Cronus C18 sorbents
at different sample loading pHs using load volumes of 25, 50, 100 and 200 ml are
presented. The results for all of the active ingredients are presented to show the significance
of sample pH to extraction efficiency.
64
Table 4.11. The recoveries of the analytes using 1000 mg Cronus C18 sorbents at different
pH values.
pH
2
7
10
volume (ml)
50
50
100
100
200
200
50
50
50
100
100
100
200
200
200
50
50
100
100
200
200
PCM
82.0
87.5
47.9
50.1
25.8
21.6
17.2
17.8
19.7
9.5
9.9
9.8
4.5
5.2
5.0
11.6
6.3
5.2
CPX
7.3
8.5
11.5
1.3
6.3
5.5
7.2
56.7
4.4
5.4
25.6
56.0
22.6
90.3
6.0
53.9
21.6
31.5
51.9
ASA
82.8
80.6
86.0
90.7
87.0
83.4
-
SMX
94.1
101.1
106.6
108.2
102.5
102.5
23.0
37.1
37.6
7.1
18.7
17.6
7.7
9.6
10.8
3.9
7.8
1.7
2.7
0.8
0.7
DIC
81.6
92.1
103.3
96.4
100.0
105.7
111.3
117.2
109.9
111.7
114.6
106.3
115.2
121.0
117.7
115.8
118.6
118.5
104.3
116.6
42.0
The recoveries in Table 4.11 have been calculated by dividing the mass eluted from the
SPE by the mass that was theoretically loaded into the SPE. The mass that was eluted was
calculated by converting the peak areas from the chromatograms into corresponding
concentrations using external calibration curves and by multiplying these concentrations by
the elution volume. The theoretical mass loaded into the SPE was calculated by multiplying
the concentration of the synthetic wastewater after spiking with the volume loaded into the
SPE. A dash indicates that there was no apparent peak for the compound in the
chromatogram.
For PCM it can be seen that breakthrough occurs at every pH. Recovery is largest at
small sample volumes and decreases at increasing sample volume. At pH 7 and 10 the
recoveries are generally below 20 % even at small sample volumes. At pH 2 recovery of
PCM increases to 84.5 %. The pKa of PCM is 9.38 and could be regarded slightly acidic.
The pH of a saturated aqueous PCM solution is around 6 (Toxnet 2011). At pH values
above the pKa value the hydroxyl group in the aromatic ring is deprotonated and the
molecule forms relatively stable phenoxide anions due to resonance. Therefore pH
65
adjustment to acidic transforms all of the molecules into neutral form and recovery is
improved.
At pH 2 recovery of PCM decreases from 84.5 % to 49 % when volume of sample is
increased from 50 ml to 100 ml. Because increasing the sample volume lowers the recovery
larger sample volumes cannot be used to increase sensitivity of the method.
In the case of ASA, there was no distinct peak at any other pH than 2. The pKa of ASA
is 3.49. Therefore it exists as an ionized compound above this pH which isn’t retained
efficiently with C18. At pH 2 no observable breakthrough occurs and samples at least up to
200 ml can be loaded onto the SPE sorbent without loss of analyte. The mean value of the
presented values for the five recoveries at sample volumes 100 ml and 200 volumes is 85.1
%.
In case of SMX, recoveries at pH 7 and 10 are generally lower than 20 %. At larger
sample volumes breakthrough occurs. However, at pH 2 the recoveries are near 100 % and
independent of sample volume up to 200 ml. The mean for the recoveries for 50 ml, 100 ml
and 200 ml sample volumes is 102.5 %. Therefore it can be stated that recovery of SMX is
100 % when pH is adjusted to 2.
The retention of DIC seems to be independent of sample pH. This is supported by the
fact that logP value of DIC is 3.9 which is the highest for the analytes included in this
method. If the occasional deviations from the general trend of almost complete recovery are
neglected, it can be stated that the retention of DIC is independent of sample volume and
pH and that recovery is 100 % at any sample volume up to 200 ml.
In case of CPX the results are difficult to interpret. The recoveries seem to have large
variations. At pH 2, the recoveries are below 10 % and therefore this pH cannot be used for
extracting CPX. At pH 7 recoveries seem to increase with increasing sample volume but
deviations are unacceptably large. At pH 10 the trend is not even that clear.
The high recoveries at times can be possibly explained with other retention mechanism
besides adsorption onto the sorbent. At large sample volumes, the pores of the sorbent may
become clogged and lead to physical entrapment of the analytes. Also, adsorption onto the
polar parts of the more non-polar molecules may occur which leads to retention of CPX.
Whatever the explanation is it seems that CPX cannot be extracted using C18 sorbents and
therefore reliable results cannot be obtained.
4.9.2 Breakthrough volume diagram for C18 sorbents
In Fig. 4.9 the breakthrough volume graph is depicted by presenting the recoveries as a
function of the applied sample during SPE treatment.
66
Recovery of analyte (%)
120
100
80
60
40
20
0
40
90
140
190
Volume of loaded sample (ml)
ASA recovery
PCM recovery
CPX recovery pH 7
CPX recovery pH 10
SMX recovery
DIC recovery
Figure 4.9. Breakthrough volume graph for ASA, CPX, DIC, SMX and PCM.
In Fig. 4.9 the recoveries of ASA, SMX, DIC and PCM for samples whose pH has been
adjusted to 2 are presented. It can be seen that ASA, SMX and DIC can be loaded onto the
SPE cartridge without breakthrough. PCM undergoes distinct breakthrough and the
recovery is optimal at 50 ml sample volumes. Recovery of CPX is low both at pH 7 and 10
Most importantly the standard deviations of these recoveries are unacceptably large. In
Table 4.12 the recoveries and relative standard deviations for CPX at pH 7 and 10 are
presented.
Table 4.12. Recoveries and relative standard deviations for CPX at pH 7 and 10. The
number of parallel samples is 3 for pH 7 and 2 for pH 10.
Volume (ml)
50
100
200
CPX (pH 7)
6.4 ± 13.4
22.2 ± 135.0
34.8 ± 53.2
CPX (pH 10)
48.1 ± 123.9
37.7 ± 60.6
41.7 ± 34.6
Examination of Table 4.12 shows that while the recoveries at pH 10 seem to be constant,
the variations between extractions are high. This supports the statement that C18 sorbents
cannot be used to extract CPX.
In Table 4.13 the average recoveries and relative standard deviations for ASA, PCM,
DIC and SMX are presented.
67
Table 4.13. Recoveries and relative standard deviations for ASA, PCM, DIC and SMX at
pH 2. The number of parallel samples is 2.
Volume (ml)
50
100
200
PCM
84.7 ± 4.6
49.0 ± 3.1
23.9 ± 12.4
ASA
81.7 ± 1.8
88.3 ± 3.6
85.2 ± 2.9
SMX
97.6 ± 5.0
107.4 ± 1.1
102.5 ± 0.0
DIC
86.9 ± 8.5
99.8 ± 4.9
102.9 ± 4.0
In general the reproducibilities are good. For DIC the RSD of the two 50 ml samples is
quite large and may result from incomplete elution during the SPE treatment. For PCM the
RSD for the two 200 ml samples is large. At this point breakthrough of analytes occurs
which causes poor reproducibility.
4.9.3 Recoveries using strong cation exchange sorbents
In an attempt to increase the recovery of CPX polymeric sorbents were tested. Ten StrataX-C sorbent samples obtained from Phenomenex were used to evaluate the capability of
this material. Elution was carried out with 1 to 2 ml of 5 % ammonium hydroxide in either
MeOH or water. The results of the experiments are presented in Table 4.14.
Table 4.14. Recoveries for strong cation exchange sorbents using different elution solvents
and sample volumes.
Volume (ml)
Elution solvent
10
1 ml of 5 % NH4OH in MeOH
1 ml of 5 % NH4OH in H2O
20
PCM
(%)
132.2
138.2
130.3
111.4
105.7
114.6
130.3
116.8
108.3
111.9
CPX
(%)
41.6
37.6
44.7
56.1
54.3
61.1
79.8
75.8
75.5
76.7
ASA
(%)
86.6
119.2
76.3
-
SMX
(%)
97.2
84.1
101.9
89.1
89.2
90.4
107.9
96.2
104.4
102.2
DIC
(%)
96.2
102.9
95.2
4.6
3.0
From Table 4.14 it can be seen that CIP can be retained more efficiently with cation
exchange materials and most importantly the results are reproducible. When ammonium
hydroxide in MeOH was used for elution the recovery was around 40 %. Ammonium
hydroxide is used to render the molecule less cationic and therefore to detach it from the
sorbent.
The recovery of CIP could be increased by using ammonium hydroxide in water instead
of MeOH. This is because CPX is readily soluble in water. At low pH its solubility in water
68
may be even greater. A recovery value around 56 % could be achieved by changing the
elution solvent. By increasing the elution volume to 2 ml recovery could be enhanced even
further to above 75 %. Therefore cationic exchange sorbents are suitable for retaining CIP.
Also for SMX and PCM elution with water leads to good results as can be expected from
their water solubility values. However DIC and ASA have to be eluted using ammonium
hydroxide in MeOH.
In order to simplify sample loading polymeric reversed phase sorbents could be used.
When such a material is used, there is no need for pH adjustment. In the literature
recoveries greater than 80 % for CPX and SMX have been reported. (Garcia-Ac et al.
2009) In order to extract all of these analytes in a single procedure, a single SPE cartridge
could be eluted first with the ammonium hydroxide in water and after this with ammonium
hydroxide in MeOH.
4.10 SPE pretreatment of ERY
Three 100 ml samples were passed through C18 sorbents. Instead of extensive breakthrough
experiments only a single sample load volume was considered. Possible breakthrough
could not have been distinguished from the variability caused by the poor repeatability of
the derivatization procedure. It was assumed that the retention behavior would be
comparable to that of DIC. This is justifiable based on the similar logP values of ERY and
DIC. For DIC, the recovery was independent of sample volume up to 200 ml and similar
behavior of ERY was assumed.
The pKa of ERY is 8.9 and the pKa belongs to the amine group. Therefore above this
pH ERY mostly nonpolar. In order to make sure that the retention would be optimal, pH
was adjusted to 10 during sample loading. The sample concentration was 0.73 mg/l. The
recoveries are presented in Table 4.15.
Table 4.15. Peak areas, masses calculated from these areas and recoveries with average
and RSD values for three samples spiked in Milli-Q water.
Sample
1
2
3
average
RSD (%)
Peak area
(mAU*min)
116.3
240.4
273.5
210.1
39.5
Mass of ERY derivatized
(mg)
35.5
68.7
77.5
60.6
36.5
Recovery
(%)
48.5
93.8
105.8
82.7
36.5
From the results it can be seen that the recoveries are highly variable, possibly due to the
variability of the derivatization procedure. Therefore the error is not a result of the SPE
pretreatment step only. However there is no way to verify the recoveries of the SPE step
69
alone without the derivatization and the errors of these two steps accumulate. A relative
standard deviation of 36.5 % is too high for accurate quantitation. However it is the best
alternative for the estimation of ERY concentrations in wastewater.
Determination of ERY in waste water always requires SPE pretreatment. This is
because the derivatization requires that the solvent is eliminated. This is practical to
perform only for readily evaporating solvents such as MeOH. SPE enables the change from
one solvent to another.
4.11 Choice of wash solvent for SPE
Washing of the SPE sorbent with 2.5 % and 5 % MeOH during sample pretreatment was
carried out. The results are presented in Table 4.16.
Table 4.16. Recoveries of analytes relative to unwashed sorbents using 2.5 % MeOH and 5
% MeOH as wash solvents.
Wash solvent
2.5 % MeOH
5 % MeOH
PCM (%)
100.6
104.8
ASA (%)
96.1
93.3
SMX (%)
109.3
99.6
DIC (%)
99.5
99.3
From Table 4.16 it can be seen that the recoveries are quite independent of the wash step
even when 5 % MeOH is used. Therefore a wash with 5 % MeOH is suitable.
When strong cation exchange sorbents were used, the wash with 0.1 % phosphoric acid
was included in the pretreatment as per the manufacturer’s instructions. Since this
procedure lead to acceptable recoveries (presented in the previous paragraph) this was
considered suitable.
4.11.2 ERY
Samples whose volumes were 50 ml and concentration 1.46 mg/l of ERY were loaded onto
the SPE and washed with 2 ml of different MeOH solutions. The eluate was then
evaporated and derivatized using the sample procedure as for the standards. In Table 4.17
the peak areas and the percent recoveries relative to a derivatized sample without SPE
pretreatment are presented.
70
Table 4.17. Peak areas and recoveries relative to a derivatized sample without SPE
pretreatment using wash solutions containing varying amounts of MeOH.
25 %
20 %
15 %
10 %
Peak area (mAU*min)
580
13172
25000
12873
Recovery relative to an untreated sample (%)
0.9
20.3
38.6
19.9
The recoveries of the SPE pretreatment are poor. This is likely because the concentration of
the sample was at the limit of water solubility of ERY which in the literature was reported
to be 1.22 mg/l. Also, the water solubility is usually defined for a neutral sample and
because the sample pH was adjusted to 10, the molecules were partially nonpolar. This
could have further decreased the water solubility. Because the conditions were the same for
all of the samples, the results can still be used to see which wash solvent gives the best
recovery.
The recovery is best for the sample which has been washed with 2 ml of 15 % MeOH.
The recommendation of the manufacturer which suggests the use of a 30 % MeOH for
washing seems unsuitable because even for a 25 % MeOH was solution the result seems
poor. A wash with 15 % MeOH was chosen to be applied.
4.12 Summary of optimal SPE cartridges and conditions
The pH of the synthetic wastewater solution without pH adjustment was approximately 4.8.
Without pH adjustment C18 was optimal for DIC, and tolerable for PCM and SMX. For
ASA the recoveries were low, around 20%. CPX wasn’t retained onto C18 sorbents at any
pH.
When pH was adjusted to 2, recovery of ASA increased significantly, from
approximately 20 % to 85 %. Also the recovery of PCM was increased significantly from
17,5 % to 84.5 % when pH was adjusted to 2. The recovery of SMX was slightly improved.
Therefore, it was concluded that sample loading in case of PCM, ASP and SMX should be
carried out at pH 2. In case of DIC, the recovery was acceptable at pH 2 but it was slightly
better when either pH 7 or 10 was used. However, the change wasn’t that dramatic and
sample loading can be carried out also at pH 2. In Appendix D a working instruction for the
analysis of PCM, ASA, SMX and DIC from waste water using C18 SPE sorbents is
presented.
In case of CPX the recovery seemed irreproducible at pH 2, 7 and 10. Therefore it was
concluded that CPX cannot be efficiently retained using C18 at any pH. In order to improve
the retention of CPX a polymeric Strata-X-C sorbent was tested. The sample was acidified
using 20 µl of strong phosphoric acid per ml of sample. When the sorbent was eluted with 2
71
ml of 5 % ammonium hydroxide in water the recovery of CPX was 75 %. Therefore it was
concluded that a strong cation exchanger was suitable for retaining CPX. In Appendix E a
working instruction for the analysis of PCM, ASA, SMX and DIC from waste water using
Strata-X-C SPE sorbents is presented.
For ERY, C18 sorbents are suitable for retaining the analyte. However pH adjustment to
10 is needed in order to render the molecule neutral and ensure that the retention is optimal.
2 ml of methanol is sufficient in eluting the sample from the sorbents. In Appendix F a
working instruction for the analysis of ERY from waste water using C18 SPE sorbents is
presented.
Using C18 sorbents there was no difference in the recovery of DIC, ASA, SMX and
PCM when a wash with 2 ml of either 2.5 % MeOH or 5 % MeOH solutions was applied.
Therefore wash with 5 % MeOH was seen fit. For washing the strong cation exchange
sorbent, a wash with 2 ml of 0.1 % phosphoric acid was seen to be best. During SPE
pretreatment of ERY wash with 2 ml of 15 % MeOH leads to acceptable retention while
eliminating some interferents.
4.13 Method limit of quantification
One of the main objectives of this Thesis was to develop a method which could provide
information on concentrations of the active ingredients at the NEMA limit 0.05 mg/l.
Instrumental limits of quantification were presented in paragraph 4.6. By taking into
account concentration of samples during SPE pretreatment overall limits of quantification
can be calculated.
4.13.1 ASA, CPX, DIC, PCM and SMX
In case ASA, DIC and SMX C18 sorbents can be used to preconcentrate the sample
significantly since breakthrough of analytes isn’t observed for sample volumes up to 200
ml. For PCM the recovery falls as larger amounts than 50 ml of the sample are loaded onto
the column. Therefore C18 has limited ability to preconcentrate PCM. In Table 4.18 the
theoretical limits of quantification for the entire method are presented.
The method limits of quantification were calculated using Eq. (3.1). In the calculations
it is assumed that the recoveries of analytes at concentrations similar to the method limit of
detection and at 0.5 mg/l concentration are the same. HPLC sample volume of 1 ml is
achieved by evaporating the SPE eluate and dissolving the residue in 1 ml MeOH. In this
case the method LOQ values are less than the NEMA limit. Also for ASA it is possible that
even larger sample volumes can be used than 200 ml which would also lead to a higher
method LOQ.
72
Table 4.18. Instrumental LOQs, applicable sample volumes during SPE pretreatment, used
elution volumes, recoveries and theoretical method LOQs for the active ingredients
retainable with C18 sorbents.
Active
ingredient
PCM
ASA
SMX
DIC
Instrumental
LOQ (mg/l)
1.56
7.40
0.61
1.74
Applicable
sample (ml)
50
200
200
200
HPLC sample
volume (ml)
1
1
1
1
Recovery
(%)
84.7
85.1
100.0
96.5
Method LOQ
(mg/l)
0.037
0.043
0.003
0.009
Using strong cation exchange sorbents also preconcentration of CPX is possible. Sample
volumes used were only 10 to 20 ml using these sorbents since only 30 mg sorbents were
used. However, at this point it is assumed that multiply the sorbent mass by ten also the
applicable sample can be multiplied by ten while the recovery stays the same.
Ammonium hydroxide in MeOH was used only with 10 ml sample volumes. Therefore
it is impossible to say if breakthrough of analytes takes place at larger sample volumes and
if they can be used to preconcentrate the sample to the quantifiable concentration.
Sufficient elution of ASA and DIC requires the use of ammonium hydroxide in MeOH and
therefore they may not be detectable at the NEMA limit suing strong cation exchange
sorbents.
Ammonium hydroxide in water was used as elution solvent for 10 ml and 20 ml sample
volumes. While the recovery of PCM decreased slightly using 20 sample volume, no
significant breakthrough of the analytes was observed up to 20 ml. Therefore it can be
stated that PCM, CPX and SMX can be concentrated using 20 ml sample volume with a 30
mg sorbent. The method limits of detection for PCM, CPX and SMX using strong cation
exchange sorbents are presented in Table 4.19.
Table 4.19. Instrumental LOQs, applicable sample volumes during SPE pretreatment, used
elution volumes, recoveries and theoretical method LOQs for PCM, CPX and SMX using
strong cation exchange sorbents.
PCM
CPX
SMX
Instrumental
LOQ (mg/l)
1.56
7.40
0.61
Applicable
sample (ml)
200
200
200
Elution
volume (ml)
1
1
1
Recovery
(%)
116.8
76.9
102.7
Method LOQ
(mg/l)
0.008
0.048
0.003
In the calculation of the method LOQ values it was assumed that the applicable volume is
200 ml if a 300 mg sorbent is used.
73
4.13.2 ERY
For ERY breakthrough experiments weren’t carried out. However, during the evaluation of
recovery three 100 ml sample were extracted with C18. Concentrations of the samples were
0.73 mg/l and the average recovery of the three extractions was 82.7 %.
Mass of ERY to be derivatized leading to a quantifiable signal is 9.6 µg. To obtain such
a mass, 192 ml of 0.05 mg/l sample should be passed through the sorbent. Taking the
recovery into account, the required sample volume is approximately 230 ml. Because the
logP values and water solubilities of ERY and DIC are similar, it is likely that ERY
behaves like DIC during the C18 SPE pretreatment and therefore C18 could be used to
concentrate ERY.
4.14 Wastewater samples
4.14.1 Separation of active ingredients from interferents
Figure 4.10 illustrates the capability of the developed method to resolve PCM, CPX, ASA,
SMX and DIC from interferents in a wastewater sample.
9.5
Signal (mAu)
7.5
5.5
Waste water
sample
3.5
1.5
-0.5 2
4
6
8
10
12
14
16
Figure 4.10. A typical chromatogram of a wastewater sample with interferents present.
For this particular wastewater sample the beginning of the chromatogram is quite crowded.
PCM eluting at 4.18 min is not completely resolved from another analyte. CPX is fully
74
resolved. A peak appears at 9.32 min, is fully resolved and belongs very likely to ASA. A
peak appearing at 10.06 min belongs very likely to SMX. The peak shape is poor, partly
because of the subtracted baseline of a blank whose shape didn’t completely match with the
baseline of this particular run. At 16.2 min a small peak appears which is very likely DIC.
In Fig. 4.11 the beginning of the chromatogram is highlighted.
9.5
Signal (mAu)
7.5
Waste
water
sample
5.5
3.5
1.5
-0.5 2
2.5
3
3.5
4
4.5
5
5.5
6
Figure 4.11. Beginning of the chromatogram for a wastewater sample.
In Fig. 4.11 the incomplete resolution of PCM (4.18 min) from another compound (4.10
min) can be seen more clearly. To get an approximate peak area the peaks can be separated
using a vertical separator. Fairly good information on the analyte concentration can be
obtained.
Because of the variability of the composition of the pharmaceutical wastewater there
are likely to be tens of different active ingredients present in the wastewater at some point
or another. Also if advanced oxidation processes such as Fenton treatment or ozonation
would be applied for the wastewater, the compounds would be very likely only partially
degraded. Such degradation products could be similar in structure with the parent
compound. Therefore they would also elute at similar retention time and the separation
would be incomplete.
If better resolution is needed the chromatograms could be run using smaller flow rates.
If the poorly eluting compounds elute during a gradient, the gradient slope may be made
shallower to increase resolution. For example for samples whose chromatograms are like
the one presented in Fig. 4.11 the beginning could be rerun using a smaller flow rate.
75
4.14.2 Different concentration scenarios
As opposed to pharmaceutical preparations the concentrations of active ingredients in the
wastewater are unknown. Therefore the sample pretreatment should be adjusted to suit the
nature of the water. Three different scenarios can be identified.
In the first scenario, the concentrations are high for all the active ingredients. In this
case no SPE pretreatment is needed while sample dilution may be needed. This was the
case for all of the analyses done for measuring SMX in the wastewater. After dilution the
sample must be filtered before it can be introduced into the HPLC system. Water is an
optimal diluent for HPLC because it is a very weak solvent and does not cause any peak
broadening.
In the second scenario, all of the concentrations are low and approximately on the same
concentration level. This may be the case for effectively treated wastewater. In this case the
SPE treatment should be carried out normally to enrich the analytes.
The third and the most difficult scenario is that the concentrations are different for
different active ingredients. In Fig. 4.12 a chromatogram of such a sample is presented.
990
Signal (mAU)
790
590
SPE treated waste
water
390
Raw waste water
190
-10
0
5
10
15
Figure 4.12. A chromatogram of a SPE treated sample and the same sample without SPE
enrichment .
In Fig. 412 a common scenario can be seen. The signal from SMX at 10 minutes is large
and results in not only a tall but also a broad peak. The peak overlaps with the peak for
ASA which would appear at 9.3 min. From the chromatogram of the SPE treated sample,
quantification of ASA is for this reason impossible. On the other hand, in the sample in
which SPE treatment hasn’t been applied the levels of ASA are too low to be detected.
Because the sensitivities differ ASA requires enrichment to be detectable. Therefore the
76
analysis of ASA in the presence of SMX is difficult and an extraction procedure specific
for ASA which eliminates SMX should be applied.
At pH values above the pKa of ASA (3.49), ASA exists as ionic species and is not
retained by C18 whereas the retention of SMX is greater. Therefore in order to selectively
enrich ASA the pH should be adjusted to 5 and for example 200 ml of wastewater passed
through C18 sorbent. The effluent containing ASA is collected while at least a part of SMX
has been retained into the sorbent. Finally, the pH of the effluent is adjusted to 2, and the
procedure repeated. This leads to retention of ASA with less SMX present which allows for
better detection of ASA in the final sample.
77
5 Conclusions
The objective of combining six different compounds in a single pretreatment and HPLC
analysis was not achieved. ERY required a separate HPLC method because the
derivatization product was highly hydrophobic. Due to different polarities of the remaining
analytes they couldn’t be extracted in a single SPE step using C18 sorbents. Extraction of
CPX required the use of a strong cation exchange sorbent.
For the remaining compounds, ASA, PCM, SMX and DIC C18 SPE pretreatment was
suitable. The pretreatment required that sample pH was adjusted to 2. The recoveries were
found to be high and reproducible. Using a strong cation exchange sorbent, also CPX could
be extracted efficiently and reproducibly. For SMX, DIC and ASA recovery remained
independent of sample volume up to 200 ml. In case of PCM breakthrough was observed
after a 50 ml sample volume. Using strong cation exchange sorbents, CPX didn’t show
breakthrough up to 200 ml of sample.
By concentrating the samples during SPE pretreatment the analytes could be quantified
in wastewater at concentrations which are below the NEMA limit 0.05 mg/l. However this
could not be verified experimentally using a real wastewater matrix since wastewater
without the active ingredients present wasn’t available.
Derivatization of ERY was carried out using FMOC-chloride and phosphate buffer at
o
60 C. The derivatization mixture was analysed directly with HPLC. The method showed
poor linearity possibly due to the poor reproducibility of the derivatization procedure.
ERY was extracted from wastewater using C18 sorbents at pH 10. The 36.5 % relative
standard deviation of SPE recovery suggests that the results are not accurate but rather
approximate. By applying 230 ml of sample whose concentration is 0.05 mg/l of ERY a
mass which is detectable using the developed method can be obtained. Therefore
quantification of ERY is also possible at the NEMA limit.
Washing solvent that could be used during sample loading was optimized for all of the
compounds. For ASA, PCM, SMX and DIC, a wash with 5 % MeOH was found to be
suitable. For strong cation exchange sorbents a wash with 0.1 % phosphoric acid could be
applied and for ERY a wash with 15 % MeOH turned out to be best.
Separation of the target analytes from interferents during a HPLC run may be very
challenging. The composition of the sample keeps changing on a weekly basis depending
on production. A universal chromatographic method capable of separating all the
compounds is practically impossible to develop. Ways to improve separation of closely
eluting interferents are decreasing mobile phase flow rate and making the gradients
shallower. Because of different sensitivities, selective concentration of the sample may be
needed in order to yield sufficient concentrations of poorly absorbing analytes.
78
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Appendix A
Chromatogram of the gradient performance test using methyl paraben and methanol as
mobile phases.
85
Appendix B
Chromatograms of the TMBS derivatization products monitored at 275 nm. The first
chromatogram belongs to the reaction mixture where ERY was present. In the second
chromatogram only stearic acid, a constituent of ERY stearate, was derivatized. The third
one is for the derivatization reagent alone. There are no additional peaks in the derivatized
ERY using TMBS.
Signal (mAU*min)
120
100
80
60
40
20
0
0
5
10
15
20
25
20
25
20
25
ERY TMBS derivative
350
Signal (mAU*min)
300
250
200
150
100
50
0
-50 0
5
10
15
Stearic acid TMBS derivative
Signal (mAU*min)
80
60
40
20
0
0
5
10
15
Blank TMBS derivative
86
Appendix C
Mass spectrum of the FMOC-Cl derivatization product of ERY. The upper spectrum
belongs to the isotope model of FMOC derivatized ERY. Below is the actual derivative
which was separated after HPLC and run using MS.
87
Appendix D
Analysis of PCM, ASA, SMX and DIC from waste water using C18 SPE sorbents
Conditioning of C18 SPE sorbents: Connect the sorbent to a vacuum pump and pour 10
ml of methanol and 10 ml of distilled water through the sorbent.
Sample preparation: pH adjustment: Filter 100 ml of the waste water first through a 0,45
um filter and then through a 0,2 um filter. Before the SPE pretreatment adjust the sample
pH to 2 with hydrochloric acid.
SPE loading: Pour 100 ml of the sample through the preconditioned C18 sorbent with a
flow rate of approximately 5 ml/min. After passing the sample through the sorbent pour 2
ml of 5 % MeOH through the sorbent (wash). Dry the sorbent under the vacuum for 15
min. Elute the sample from the sorbent using 2 ml of MeOH. Filter the dissolved sample
through a 0,45 um filter using a syringe and transfer into an HPLC vial for analysis.
Chromatographic conditions
Column: C18 250 x 4,6 mm, 5 um, Flow rate: 1 ml/min
Gradient: See Table 4.2.
Detection wavelength: 265 nm for DIC, SMX and PCM, 275 nm for ASA
Injection volume: 20 ul
Diluent (for standards): MeOH
Oven temperature: Ambient
Mobile phase: line A: DI-water : glacial acetic acid : triethylamine 988:10:2 (v:v), Line B:
ACN
Retention times: PCM 4.1 min, ASA 9.3 min, SMX 10.2 min, and DIC 16.8 min (+/- 10%
for each retention time).
Recoveries of active ingredients: ASA: 88.4 %, PCM 49.0 %, SMX 100.0 % DIC 100.0
%
Standard preparation for external calibration curves and calculation of wastewater
concentration: To obtain the calibration curve, prepare three standards of concentrations
25 mg/l, 10 mg/l and 2,5 mg/l dissolved in the diluent. Measure the peak areas by HPLC
each triplicate. Calculate the average of the peak areas and plot the peak areas as a function
of the concentrations. The equation of this plot is the external calibration curve which is
used to calculate the concentrations in the SPE pretreated samples.
Concentrations of active ingredients in wastewater samples are calculated using the
concentrations in SPE pretreated samples. The concentrations in the water samples are
calculated by taking the concentration (divide by 100 ml/ 2 ml = 50) and recovery during
SPE pretreatment (divide by the recovery, 0.884 for ASA for example) into account.
88
Appendix E
Analysis of PCM, CPX, ASA, SMX and DIC from wastewater using Strata-X-C sorbents
Conditioning of Strata-X-C SPE sorbents: Connect the sorbent to a vacuum pump and
pour 10 ml of methanol and 10 ml of distilled water through the sorbent.
pH to acidic by adding 2 ml of phosphoric acid into the sample (20 µl per 1 ml of sample).
SPE loading: Pour 100 ml of the sample through the preconditioned sorbent with a flow
rate of approximately 5 ml/min. After passing the sample through the sorbent pour 2 ml of
0.1 % phosphoric acid through the sorbent (wash). Dry the sorbent under the vacuum for 3
min. Pour 2 ml of 5 % NH4OH in water (elution of CPX and SMX) or 2 ml of 5 % NH4OH
in MeOH (elution of PCM, ASA and DIC) through the sorbent. Filter the dissolved sample
through a 0,45 um filter using a syringe and transfer into an HPLC vial for analysis.
Column: C18 250 x 4,6 mm, 5 um, Flow rate: 1 ml/min
Gradient: See Table 4.2.
Detection wavelength: 265 nm for CPX, DIC, SMX and PCM, 275 nm for ASA
Injection volume: 20 ul
Mobile phase: line A: DI-water : glacial acetic acid : triethylamine 988:10:2 (v:v), Line B:
ACN
Retention times: PCM 4.1 min, CPX 5.1 min, ASA 9.3 min, SMX 10.2 min, and DIC 16.8
min (+/- 10% for each retention time).
Recoveries of active ingredients: ASA: 94.0 %, PCM 100.0 %, CPX 77.8 %, SMX 94.4
% DIC 98.1 %
Standard preparation for external calibration curves and calculation of waste water
concentration: To obtain the calibration curve, prepare three standards of concentrations
25 mg/l, 10 mg/l and 2,5 mg/l dissolved in the diluent. Measure the peak areas by HPLC
each triplicate. Calculate the average of the peak areas and plot the peak areas as a function
of the concentrations. The equation of this plot is the external calibration curve which is
used to calculate the concentrations in the SPE pretreated samples.
Concentrations of active ingredients in wastewater samples are calculated using the
concentrations of the SPE pretreated samples. The concentrations in the water samples are
calculated by taking the preconcentration (divide by 100 ml/ 2 ml = 50) and recovery
during SPE pretreatment (divide by the recovery, 0.94 for ASA for example) into account.
89
Appendix F
Analysis of ERY from wastewater using C18 SPE sorbents
Reagent preparation: Prepare 10 ml of 1 g/l stock of FMOC-Cl in ACN and 10 ml of 50
mM of potassium dihydrogen phosphate buffer at pH to 8.25 and 10 ml of 50 mM of
potassium dihydrogen phosphate buffer at pH to 7.0 (adjustment of pH using sodium
hydroxide).
Conditioning of C18 SPE sorbents: Connect the sorbent to a vacuum pump and pour 10
ml of methanol and 10 ml of distilled water through the sorbent.
pH to 10 with 0.25 M NaOH.
SPE loading: Pour 100 ml of the sample through the preconditioned C18 sorbent with a
flow rate of approximately 5 ml/min. After passing the sample through the sorbent pour 2
ml of 15 % MeOH through the sorbent (wash). Dry the sorbent under the vacuum for 15
min. Elute the sample from the sorbent using 2 ml of methanol. Filter the dissolved sample
through a 0,45 um filter using a syringe filter. Pass 1 ml of MeOH through the filter and
combine the fractions. Add 20 µl of 50 mM potassium dihydrogen phosphate buffer at pH 7
to the eluate. Mix with a Vortex shaker for 10 seconds. Dry the eluate under compressed air
stream until the test tube is dry.
Derivatization of ERY: Add 100 µl of the FMOC-Cl solution and 25 µl of the potassium
dihydrogen phosphate (pH 8.25) buffer into the evaporated sample. Cover the test tube with
parafilm to avoid evaporation of solvent. Mix for 10 seconds with a Vortex mixer and put
into a water bath at 60 oC for 15 minutes (exact time). After 15 min, cool the test tube under
running water and add 25 µl of phosphate buffer at pH 8.25. Shake with a Vortex mixer for
10 seconds. Put the sample in a 150 µl insert fitted inside an HPLC vial. Store the sample at
5 - 8 oC unless analyzed directly.
Column: C18 150 x 4,6 mm, 5 um or C8 150 x 4,6 mm, 5 um Flow rate: 2 ml/min
Elution: Isocratic; ACN : DI water 80 : 20 (v:v) for 10 min
Detection wavelength: 265 nm, Injection volume: 10 µl
Retention times: The derivative is identified based on its retention time. The retention time
is 5.6 min using a C8 column and 10.9 min using a C18 column. Quantification of actives
is done using an external calibration curve.
Recovery: ERY: 82.7 %
Standard preparation for external calibration curves and calculation of wastewater
concentration: To obtain the calibration curve, prepare a stock solution of approximately
90
500 mg/l of ERY in MeOH. Adjust pH to above 7 with 0.25 M NaOH. Calculate the
volume needed to obtain masses of 5 µg, 10 µg and 50 µg of ERY. Evaporate the solvent
and derivatize the standard as described in the “derivatization of ERY” paragraph. Run the
samples and form the plots of peak areas versus mass of ERY. The equation of this plot is
used to calculate the mass of ERY before derivatization.
mass derivatized = ((signal – constant)/slope)
The concentrations in the water samples are calculated by dividing the mass that has been
derivatized by the sample volume (100 ml) and the recovery of the SPE step (0.827).
concentration in wastewater= derivatized mass/ sample volume/recovery

MIKKO SUOMINEN SIMPLE AND RAPID METHOD FOR

Transcription

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