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High-Throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling
Citation:
Strope, C., K. Mansouri, H. Clewell, J. Rabinowitz, C. Stevens, AND J. Wambaugh. High-Throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, Netherlands, 615:150-160, (2018). https://doi.org/10.1016/j.scitotenv.2017.09.033
Impact/Purpose:
This manuscript describes the importance of predicting chemical ionization for properly describing the pharmacokinetics of environmental chemicals. Whether a chemical in human tissue or the environment is neutral or positively or negatively charged, greatly determines how the chemicals distributes within the body. Here, computer models have been used to predict ionization for thousands of chemicals, allowing comparison between pharmaceuticals and environmental chemicals, as well as more appropriate pharmacokintic modleing.
Description:
Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes, such as protein binding, tissue partitioning, and apparent volume of distribution (Vd). Here we report estimates of equilibrium constants for ionization (i.e., pKa) for 8132 pharmaceuticals and 24281 other compounds to which humans might be exposed in the environment. We identify broad differences in the ionization of chemicals intended for pharmaceutical use, chemicals with near field (in the home) sources, and chemicals with far field sources. We demonstrate the utility of these high throughput ionization predictions by evaluating the impact of chemical ionization on predicted PK Vd for 22 compounds that are monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control National Health and Nutrition Examination Survey (NHANES). Methodologies in the PK literature for the prediction of compound distribution into specific tissues or the whole body (e.g., Vd) require specific information on physico-chemical behavior. Hydrophobicity/lipophilicity (partitioning of chemical between water and octanol (i.e. lipid)) drives partitioning of neutral compounds into and charged/zwitterionic compounds away from adipose tissues. However, the biological tissue constituents have differing affinities for charged compounds. Since many chemicals can become ionized either positively or negatively at physiological pHs, this will alter how chemicals may distribute into biological material. Here we estimate the chemical distribution ratio between water and tissue using predicted ionization states characterized by pKa. We determine probability distributions corresponding to ionizable atom types (IATs) from which we then draw using Monte Carlo methods to analyze the sensitivity of predicted Vd on predicted pKa. As new datasets 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 Vd 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.
URLs/Downloads:
DOI: High-Throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling
https://doi.org/10.1016/j.scitotenv.2017.09.033