King's College London

Research portal

The use of molecular descriptors to model pharmaceutical uptake by a fish primary gill cell culture epithelium

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1576-1584
Number of pages9
JournalEnvironmental science & technology
Volume53
Issue number3
Early online date27 Dec 2018
DOIs
Publication statusPublished - 5 Feb 2019

Documents

King's Authors

Abstract

Modelling approaches, such as Quantitative Structure-Activity Relationships (QSARs) use molecular descriptors to predict the bioavailable properties of a compound in biota. However, these models have mainly been derived based on empirical data for lipophilic neutral compounds and may not predict the uptake of ionizable compounds. The majority of pharmaceuticals are ionizable and freshwaters can have a range of pH values that will affect speciation. In this study we assessed the uptake of 10 pharmaceuticals (acetazolamide, beclomethasone, carbamazepine, diclofenac, gemfibrozil, ibuprofen, ketoprofen, norethindrone, propranolol and warfarin) with differing modes-of action and physicochemical properties (pKa, logS, logD, logKow, molecular weight (MW) and polar surface area (PSA)) by an in vitro primary fish gill cell culture system (FIGCS) for 24 h in artificial freshwater. Principal component analysis (PCA) and partial least squares (PLS) regression was used to determine the molecular descriptors that influence the uptake rates. Ionizable drugs were taken up by FIGCS and a strong positive correlation was observed between logS and a negative correlation observed between pKa, logD, MW and the uptake rate. This approach shows that models can be derived based on physicochemical properties of pharmaceuticals and using an in vitro gill system to predict uptake of other compounds. There is a need for a robust and validated model for gill uptake that could be used in a tiered risk assessment to prioritize compounds for experimental testing.

Download statistics

No data available

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454