Literature DB >> 28964990

High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling.

Cory L Strope1, Kamel Mansouri2, Harvey J Clewell3, James R Rabinowitz4, Caroline Stevens5, John F Wambaugh4.   

Abstract

Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes such as protein binding, tissue partitioning, and apparent volume of distribution at steady state (Vdss). Here, estimates of ionization equilibrium constants (i.e., pKa) were analyzed for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment. Results revealed broad differences in the ionization of pharmaceutical chemicals and chemicals with either near-field (in the home) or far-field sources. The utility of these high-throughput ionization predictions was evaluated via a case-study of predicted PK Vdss for 22 compounds monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES). The chemical distribution ratio between water and tissue was estimated using predicted ionization states characterized by pKa. Probability distributions corresponding to ionizable atom types (IATs) were then used to analyze the sensitivity of predicted Vdss on predicted pKa using Monte Carlo methods. 8 of the 22 compounds were predicted to be ionizable. For 5 of the 8 the predictions based upon ionization are significantly different from what would be predicted for a neutral compound. For all but one (foramsulfuron), the probability distribution of predicted Vdss generated by IAT sensitivity analysis spans both the neutral prediction and the prediction using ionization. As new data sets of chemical-specific information on metabolism and excretion for hundreds of chemicals are being made available (e.g., Wetmore et al., 2015), high-throughput methods for calculating Vdss and tissue-specific PK distribution coefficients will allow the rapid construction of PK models to provide context for both biomonitoring data and high-throughput toxicity screening studies such as Tox21 and ToxCast. Published by Elsevier B.V.

Entities:  

Keywords:  High throughput; Ionization; PBPK; Volume of distribution; pKa

Mesh:

Substances:

Year:  2017        PMID: 28964990      PMCID: PMC6055917          DOI: 10.1016/j.scitotenv.2017.09.033

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


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4.  Influence of soil pH on the sorption of ionizable chemicals: modeling advances.

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Journal:  Environ Toxicol Chem       Date:  2008-10       Impact factor: 3.742

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4.  Open-source QSAR models for pKa prediction using multiple machine learning approaches.

Authors:  Kamel Mansouri; Neal F Cariello; Alexandru Korotcov; Valery Tkachenko; Chris M Grulke; Catherine S Sprankle; David Allen; Warren M Casey; Nicole C Kleinstreuer; Antony J Williams
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