Trivariate Linear Regression and Machine Learning Prediction of Possible Roles of Efflux Transporters in Estimated Intestinal Permeability Values of 301 Disparate Chemicals

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

“…Blood concentrations versus time data sets after oral administration in rats (Kamiya et al, 2021a(Kamiya et al, , 2021b of the previously analyzed 323 primary and 10 secondary chemicals (Kamiya et al, 2021b) obtained by a literature survey underwent machine learning, along with 39 new additional chemicals, including medicines from a Japanese drug database (Supplemental Table S1). The variety of compounds in the chemical space (Kamiya et al, 2021b(Kamiya et al, , 2021c(Kamiya et al, , 2021a(Kamiya et al, , 2019 was previously evaluated for the examined chemicals in studies on intestinal permeability (Kamiya et al, 2021c, Shimizu et al, 2022 and rat pharmacokinetics (Kamiya et al, 2019(Kamiya et al, , 2021b(Kamiya et al, , 2021a. Traditionally, the necessary input parameters for PBPK models, i.e., F a •F g , k a , volume of the systemic circulation (V 1 ), and hepatic intrinsic clearance (CL h,int ), have been computed to give the best fit to reported/measured plasma concentrations using nonlinear regression analyses (Kamiya et al, 2021a(Kamiya et al, , 2021b, as briefly outlined in the Supplemental materials and methods.…”

“…By applying a light gradient boosting machine learning system (LightGBM), in silico prediction accuracy was enhanced when estimating the apparent membrane permeability coefficients (P app ) across intestinal cell monolayers for 219 disparate chemicals via an approach that incorporated in silico-derived chemical descriptors (Kamiya et al, 2021c). The possible roles of efflux transporters in the estimated intestinal permeability values of 301 chemicals were also predictable by machine learning (Shimizu et al, 2022). 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.…”

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

“…21) The likelihood of efflux transporters playing a role in the estimated intestinal permeability values of 301 chemicals was also predictable by machine learning. 22) For medicines, the Papp across human colorectal carcinoma cell line (Caco-2) monolayers generally correlates with the fraction absorbed (Fa) after oral intake. 23,24) Similarly, we previously calculated the Fa values of chemicals using in silico-estimated intestinal monolayer Papp values.…”