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

摘要

Highlights•We have replaced a proprietary human variability model with an open-source EPA tool for high throughput risk prioritization.•Introduced the ionizable atom type (IAT), a high-throughput method for assessing the effects of ionization on compound PK.•Identified broad differences in the ionization of chemicals intended for pharmaceutical use, near-, and far-field sources.•pKa was estimated for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment.•Explored the pKa prediction uncertainty for 22 NHANES chemicals using IATs and how errors in predictions impact PK models.Graphical abstractDisplay OmittedAbstractChemical 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 Vdssfor 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 Vdsson predicted pKausing 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 Vdssgenerated 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 Vdssand 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.