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

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

“…In our previous study, human blood concentration versus time datasets for 212 disparate chemicals were generated using PBPK models with in silico-estimated input parameters. 25) To expand the scope of this machine learning-based system, we carried out a literature survey for reported in vivo pharmacokinetic datasets (used for verification of PBPK-generated data) and were able to add 143 chemicals (including 88 medicines from a new Japanese drug database, shown in Supplemental Table S1). Moreover, human blood concentrations versus time data of chemicals, the 143 additional chemicals, and the 10 secondary medicines were confirmed in a two-dimensional plane (with 25 partitions, Supplemental Fig.…”

“…S1) that represents the chemical space. Blood-to-plasma concentration ratios and liver (kidney)-to-plasma concentration ratios of the compounds were estimated from the plasma unbound fraction and octanol-water partition coefficient values generated using in silico tools, 25) as outlined in the Supplemental materials and methods. Simplified human PBPK models consisting of gut, liver, central, and kidney compartments were described previously.…”

“…Simplified human PBPK models consisting of gut, liver, central, and kidney compartments were described previously. 25) The necessary input parameters for PBPK models, i.e., fraction absorbed × intestinal availability (Fa•Fg), ka, V1, and CLh,int, are conventionally computed to achieve the best fit to reported human plasma concentrations. 25) The acceptability criterion for the predictive abilities of in silico systems was a threefold error (compared with in vivo values) in human PBPK-modeled plasma Cmax and AUC values after oral doses of 1.0 mg/kg per day.…”

“…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.…”

“…To improve the accessibility and feasibility of PBPK modeling, we previously generated PBPK model input parameters [i.e., absorption rate constants (ka), volumes of the systemic circulation (V1), and the hepatic intrinsic clearances (CLh,int)] in silico for a broad range of substances by applying a machine learning algorithm, 25) along with Fa•Fg values from estimated membrane permeabilities, as mentioned above. The increasing availability of pharmacokinetic datasets for substances is a determinant factor for validating updated in silico models for estimating input parameter values for human PBPK models.…”