Machine Learning Prediction of the Three Main Input Parameters of a Simplified Physiologically Based Pharmacokinetic Model Subsequently Used to Generate Time-Dependent Plasma Concentration Data in Humans after Oral Doses of 212 Disparate Chemicals.

Physiologically based pharmacokinetic (PBPK) modeling has the potential to play significant roles in estimating internal chemical exposures. The three major PBPK model input parameters (i.e., absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearances) were generated in silico for 212 chemicals using machine learning algorithms. These input parameters were calculated based on sets of between 17 and 65 chemical properties that were generated by in silico prediction tools before being processed by machine learning algorithms. The resulting simplified PBPK models were used to estimate plasma concentrations after virtual oral administrations in humans. The estimated absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearance values for the 212 test compounds determined traditionally (i.e., based on fitting to measured concentration profiles) and newly estimated had correlation coefficients of 0.65, 0.68, and 0.77 (p < 0.01, n = 212), respectively. When human plasma concentrations were modeled using traditionally determined input parameters and again using in silico estimated input parameters, the two sets of maximum plasma concentrations (r = 0.85, p < 0.01, n = 212) and areas under the curve (r = 0.80, p < 0.01, n = 212) were correlated. Virtual chemical exposure levels in liver and kidney were also estimated using these simplified PBPK models along with human plasma levels. These results indicate that the PBPK model input parameters for humans of a diverse set of compounds can be reliability estimated using chemical descriptors calculated using in silico tools.

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.

Updated algorithms for predicting the volumes of systemic circulation (V1), 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 V1 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.

Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning.

For medicines, the apparent membrane permeability coefficients (Papp) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro Papp values of 29 industrial chemicals were found to have an inverse association with their reported no-observed effect levels for hepatotoxicity in rats. In the current study, we expanded our influx permeability predictions for the 90 previously investigated chemicals to both influx and efflux permeability predictions for 207 diverse primary compounds, along with those for 23 secondary compounds. Trivariate linear regression analysis found that the observed influx and efflux logPapp values determined by in vitro experiments significantly correlated with molecular weights and the octanol-water distribution coefficients at apical and basal pH levels (pH 6.0 and 7.4, respectively) (apical to basal, r = 0.76, n = 198; and basal to apical, r = 0.77, n = 202); the distribution coefficients were estimated in silico. Further, prediction accuracy was enhanced by applying a light gradient boosting machine learning system (LightGBM) to estimate influx and efflux logPapp values that incorporated 17 and 19 in silico chemical descriptors (r = 0.83-0.84, p < 0.001). The determination in vitro and/or prediction in silico of permeability coefficients across intestinal cell monolayers of a diverse range of industrial chemicals/food components/medicines could contribute to the safety evaluations of oral intakes of general chemicals in humans. Such new alternative methods could also reduce the need for animal testing during toxicity assessment.