Predicting sorption of pharmaceuticals and personal care
products onto soil and digested sludge using artificial neural
networks

J 2009

Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks

HAVEL, Josef

Základní údaje

Originální název

Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks

Název česky

Predikce adsorpce farmak a produktů osobní péče v půdách pomocí neuronových sítí

Autoři

HAVEL, Josef (203 Česká republika, garant)

Vydání

The Analyst : an international analytical science journal [vol. 120 (1995)- Cambridge, The Royal Society of Chemistry, 2009, 0003-2654

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10406 Analytical chemistry

Stát vydavatele

Irsko

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 3.272

Kód RIV

RIV/00216224:14310/09:00036970

Organizační jednotka

Přírodovědecká fakulta

UT WoS

000264482300005

Klíčová slova česky

Adsorpce farmaka osobní péče umělé neuronové sítě

Klíčová slova anglicky

Sorption pharmaceuticals personal care products artificial neural networks

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 25. 10. 2009 16:00, prof. RNDr. Josef Havel, DrSc.

Anotace

ORIG CZ

V originále

Abstract: A comprehensive analytical investigation of the sorption behaviour of a large selection of over-the-counter, prescribed pharmaceuticals and illicit drugs to agricultural soils and freeze-dried digested sludges is presented. Batch sorption experiments were carried out to identify which compounds could potentially concentrate in soils as a result of biosolid enrichment. Analysis of aqueous samples was carried out directly using liquid chromatography-tandem mass spectrometry (LC-MS/MS). For solids analysis, combined pressurised liquid extraction and solid phase extraction methods were used prior to LC-MS/MS. Solid-water distribution coefficients (K-d) were calculated based on slopes of sorption isotherms over a defined concentration range. Molecular descriptors such as log P, pK(a), molar refractivity, aromatic ratio, hydrophilic factor and topological surface area were collected for all solutes and, along with generated Kd data, were incorporated as a training set within a developed artificial neural network to predict Kd for all solutes within both sample types. Therefore, this work represents a novel approach using combined and cross-validated analytical and computational techniques to confidently study sorption modes within the environment. The logarithm plots of predicted versus experimentally determined Kd are presented which showed excellent correlation (R-2 > 0.88), highlighting that artificial neural networks could be used as a predictive tool for this application. To evaluate the developed model, it was used to predict K-d for meclofenamic acid, mefenamic acid, ibuprofen and furosemide and subsequently compared to experimentally determined values in soil. Ratios of experimental/predicted K-d values were found to be 1.00, 1.00, 1.75 and 1.65, respectively.

Česky

Analytické studium adsorpce farmak a produktů osobní péče v půdách a sedimentech

Citovat

HAVEL, Josef. Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks. The Analyst : an international analytical science journal [vol. 120 (1995)-. Cambridge: The Royal Society of Chemistry, 2009, roč. 2009, č. 134, s. 663-670. ISSN 0003-2654.

@article{851532,
   author = {Havel, Josef},
   article_location = {Cambridge},
   article_number = {134},
   keywords = {Sorption pharmaceuticals personal care products artificial neural networks},
   language = {eng},
   issn = {0003-2654},
   journal = {The Analyst : an international analytical science journal [vol. 120 (1995)-},
   title = {Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks},
   volume = {2009},
   year = {2009}
}

TY  - JOUR
ID  - 851532
AU  - Havel, Josef
PY  - 2009
TI  - Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks
JF  - The Analyst : an international analytical science journal [vol. 120 (1995)-
VL  - 2009
IS  - 134
SP  - 663-670
EP  - 663-670
PB  - The Royal Society of Chemistry
SN  - 00032654
KW  - Sorption pharmaceuticals personal care products artificial neural networks
N2  - Abstract: A comprehensive analytical investigation of the sorption behaviour of a large selection of over-the-counter, prescribed pharmaceuticals and illicit drugs to agricultural soils and freeze-dried digested sludges is presented. Batch sorption experiments were carried out to identify which compounds could potentially concentrate in soils as a result of biosolid enrichment. Analysis of aqueous samples was carried out directly using liquid chromatography-tandem mass spectrometry (LC-MS/MS). For solids analysis, combined pressurised liquid extraction and solid phase extraction methods were used prior to LC-MS/MS. Solid-water distribution coefficients (K-d) were calculated based on slopes of sorption isotherms over a defined concentration range. Molecular descriptors such as log P, pK(a), molar refractivity, aromatic ratio, hydrophilic factor and topological surface area were collected for all solutes and, along with generated Kd data, were incorporated as a training set within a developed artificial neural network to predict Kd for all solutes within both sample types. Therefore, this work represents a novel approach using combined and cross-validated analytical and computational techniques to confidently study sorption modes within the environment. The logarithm plots of predicted versus experimentally determined Kd are presented which showed excellent correlation (R-2 > 0.88), highlighting that artificial neural networks could be used as a predictive tool for this application. To evaluate the developed model, it was used to predict K-d for meclofenamic acid, mefenamic acid, ibuprofen and furosemide and subsequently compared to experimentally determined values in soil. Ratios of experimental/predicted K-d values were found to be 1.00, 1.00, 1.75 and 1.65, respectively.
ER  -

HAVEL, Josef. Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks. \textit{The Analyst : an international analytical science journal [vol. 120 (1995)-}. Cambridge: The Royal Society of Chemistry, 2009, roč.~2009, č.~134, s.~663-670. ISSN~0003-2654.

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