An Updated In Silico Prediction Method for Volumes of Systemic Circulation of 323 Disparate Chemicals for Use in Physiologically Based Pharmacokinetic Models to Estimate Plasma and Tissue Concentrations after Oral Doses in Rats

Abstract

Updated algorithms for predicting the volumes of systemic circulation (V 1 ), along with absorption rate constants and hepatic intrinsic clearances, as input parameters for physiologically based pharmacokinetic (PBPK) models were established to improve the accuracy of estimated plasma and tissue concentrations of 323 chemicals after virtual oral administrations in rats. Using ridge regression with an enlarged set of chemical descriptors (up to 99), the estimated input V 1 values resulted in an improved correlation coefficient (from 246 compounds) with the traditionally determined values. The PBPK model input parameters for rats of diverse compounds can be precisely estimated by increasing the number of descriptors.

Updated <i>in Silico</i> Prediction Methods for Fractions Absorbed and Key Input Parameters of 355 Disparate Chemicals for Physiologically Based Pharmacokinetic Models for Time-Dependent Plasma Concentrations after Virtual Oral Doses in Humans

“…We could successfully generate PBPK model input parameters for rat models calculated from a small number of chemical properties by machine-learning prediction tools. 26) Rat PBPK modeling 31) will also make increasingly important contributions to computational toxicology and facilitate assessment of the potential risks of industrial or food chemicals. In terms of human PBPK modeling, the previously used ridge regression approach 32) is a method for estimating the coefficients of multiple-regression models.…”

“…25) Based on these chemical descriptor sets, we applied the machine learning algorithm LightGBM 20) to Fa•Fg, ka, and V1 values under nested crossvalidation systems and establish new prediction systems based on 29, 11, and 12 in silicoderived descriptors, respectively. 26,27) Briefly, in our nested cross-validation approach, 90% of chemicals out of 355 chemicals were applied to inner cross-validation according to the reported methods. 13) The optimized parameters in 10 inner models were then applied to obtain the mean predictors in 10 outer sets.…”

Updated <i>in silico</i> prediction methods for fractions absorbed and absorption rate constants of 372 disparate chemicals for use in physiologically based pharmacokinetic models for estimating internal concentrations in rats

“…Although experimental time-series concentration data for non-pharmaceutical chemicals, unknowingly exposed to humans, are rarely available, the United States Environmental Protection Agency (EPA) has a public database of time-course concentration data in animals for approximately 200 environmentally-relevant chemicals (Sayre et al, 2020). We have shared the collected blood concentrations versus time data sets after oral administration in rats (Kamiya et al, 2021a(Kamiya et al, , 2021b with Wambaugh and his associates in Center for Computational Toxicology and Exposure, U.S. EPA (Cook et al, 2022). Our research group estimated pharmacokinetic parameters in rats derived from these corresponding plasma concentrationtime curves for simplified physiologically based pharmacokinetic (PBPK) models.…”

“…PBPK modeling, also known as PBK modeling in the European Union (Paini et al, 2019), that uses both the chemical properties of substances and the physiological properties of various organ systems has the potential to reduce animal testing by estimating internal chemical exposures. Simplified PBPK models with four organ systems have been established for a range of substances, as described Correspondence: Hiroshi Yamazaki (E-mail: hyamazak@ac.shoyaku.ac.jp) previously (Kamiya et al, 2021a(Kamiya et al, , 2021b.…”

“…Algorithms for predicting the input parameters for PBPK models, such as absorption rate constants (k a ), were previously established to facilitate modeling of the plasma (and tissue) concentrations of 323 chemicals after virtual oral administrations in rats (Kamiya et al, 2021a). In the previous study, the PBPK model input parameters for rats of diverse compounds could be precisely estimated with a modeling approach designed to integrate nested cross-validation using a ridge regression system with an enlarged set of up to 100 chemical descriptors (Kamiya et al, 2021a).…”