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

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

“…25) These parameter values were compared with the traditional in vivo-derived input parameters (Figure 1). When our previous machine-learning system 25) (in which Fa was derived from apparent permeabilities estimated from 17 chemical descriptors 21) using LightGBM, ka was derived from 65 chemical descriptors 25) using ridge regression, V1 was derived from 64 chemical descriptors 25) using ridge regression, and CLh,int was derived from 17 chemical descriptors 25) using LightGBM) was applied to the expanded panel of 355 chemicals, the correlation coefficients of the resulting Fa•Fg, ka, V1, and CLh,int values, respectively, were 0.28 (p < 0.01, Figure 1A), 0.36 (p < 0.01, 1B), 0.56 (p < 0.01, Figure 1C), and 0.85 (p < 0.01, Figure 1D). Moreover, the average absolute fold errors for Fa•Fg, ka, V1, and CLh,int were 2.02, 2.32, 2.18, and 2.56, respectively, for the previous estimation systems.…”

“…In a previous study, we calculated Fa values based on the in silico-generated Papp values 21) from the apical side to the basal side (referred to as A to B) as Fa = (logPapp A to B) 2.4 / (1.0 + (logPapp A to B) 2.4 ). Fg values were previously estimated from the gut extraction ratios as onetenth of the hepatic extraction ratios (in the well-stirred model).…”

“…19) By applying a light gradient boosting machine learning system (LightGBM 20) ), prediction accuracy was enhanced when estimating the apparent membrane permeability coefficients (Papp) across intestinal cell monolayers for 219 disparate chemicals using in silico-derived chemical descriptors. 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. 21) In our system, the intestinal availability (Fg) values were traditionally estimated from the gut extraction ratios as one-tenth of the hepatic extraction ratios. 25) PBPK models require a set of input parameters to generate output time-series kinetic data.…”

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

“…A wide range of in vitro and/ or in silico non-animal-based new approach methodolo-gies (NAMs) for chemical safety and risk assessments is currently being developed. 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).…”