In Silico Prediction of Input Parameters for Simplified Physiologically Based Pharmacokinetic Models for Estimating Plasma, Liver, and Kidney Exposures in Rats after Oral Doses of 246 Disparate Chemicals

Abstract

Recently developed computational models can estimate plasma, hepatic, and renal concentrations of industrial chemicals in rats. Typically, the input parameter values (i.e., the absorption rate constant, volume of systemic circulation, and hepatic intrinsic clearance) for simplified physiologically based pharmacokinetic (PBPK) model systems are calculated to give the best fit to measured or reported in vivo blood substance concentration values in animals. The purpose of the present study was to estimate in silico these three input pharmacokinetic parameters using a machine learning algorithm applied to a broad range of chemical properties obtained from several cheminformatics software tools. These in silico estimated parameters were then incorporated into PBPK models for predicting internal exposures in rats. Following this approach, simplified PBPK models were set up for 246 drugs, food components, and industrial chemicals with a broad range of chemical structures. We had previously generated PBPK models for 158 of these substances, whereas 88 for which concentration series data were available in the literature were newly modeled. The values for the absorption rate constant, volume of systemic circulation, and hepatic intrinsic clearance could be generated in silico by equations containing between 14 and 26 physicochemical properties. After virtual oral dosing, the output concentration values of the 246 compounds in plasma, liver, and kidney from rat PBPK models using traditionally determined and in silico estimated input parameters were well correlated (r ≥ 0.83). In summary, by using PBPK models consisting of chemical receptor (gut), metabolizing (liver), excreting (kidney), and central (main) compartments with in silico-derived input parameters, the forward dosimetry of new chemicals could provide the plasma/tissue concentrations of drugs and chemicals after oral dosing, thereby facilitating estimates of hematotoxic, hepatotoxic, or nephrotoxic potential as a part of risk assessment.

Metabolic profiles for the pyrrolizidine alkaloid neopetasitenine and its metabolite petasitenine in humans extrapolated from rat <i>in vivo</i> and <i>in vitro</i> data sets using a simplified physiologically based pharmacokinetic model

“…In this study, neopetasitenine and its deacetylated metabo-lite petasitenine were selected as model substances. The previously reported in silico machine learning system (Kamiya et al, 2021) yielded important pharmacokinetic parameters (k a , V 1 , and CL h,int ) of neopetasitenine and petasitenine based on their chemical descriptors (Table 1). This approach was used because of the absence of experimental pharmacokinetic data in rats.…”

“…Pharma- (Kamiya et al, 2020b); the value in parentheses was determined experimentally and confirmed after the in silico estimation. b Initial values for PBPK modeling for the absorption rate constant (k a ), volume of the systemic circulation (V 1 ), and hepatic intrinsic clearance (CL h,int ) were estimated by in silico machine learning methods with a dataset of 246 chemicals, as described previously (Kamiya et al, 2021). cokinetic parameters such as k a , the apparent volume of distribution (V/F), and the oral clearance (CL) were preliminary calculated for neopetasitenine and petasitenine using one-compartment models (Table 2), based on the rat plasma data obtained in the present study.…”

“…The initial parameter values for the PBPK model, i.e., the absorption rate constant (k a ), the volume of the systemic circulation (V 1 ), and the hepatic intrinsic clearance (CL h,int ), were estimated using in silico machine learning methods with a training dataset of 246 chemicals, as described previously (Kamiya et al, 2021). This was done because of the absence of experimental pharmacokinetic data in rats.…”

Pharmacokinetics of primary oxidative metabolites of thalidomide in rats and in chimeric mice humanized with different human hepatocytes

“…A simplified rat PBPK model consisting of chemical receptor (gut), metabolizing (liver), excreting (kidney), and central compartments was set up as described elsewhere (Kamiya et al, 2020b(Kamiya et al, , 2021. The molecular weights (258, 274, and 274), octanol-water partition coefficients (clogP; 0.528, -0.138, and 0.402), plasma unbound fractions (f u,p ; 0.588, 0.615, and 0.237), and blood-plasma concentration ratios (R b ; 0.893, 0.885, and 0.904) of thalidomide, 5′-hydroxythalidomide, and 5-hydroxythalidomide, respectively, were used as described previously (Nishiyama et al, 2015;Yamazaki et al, 2012).…”

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

“…20) The input parameters for PBPK models are generally based on in vivo data; however, to investigate the feasibility of making such modeling more accessible in real-time, we generated PBPK model input parameters in silico for a broad range of chemicals using a machine learning algorithm without reference to in vivo studies. 21,22) The values of the three major PBPK model input parameters, i.e., the absorption rate constant (k a ), the volume of the systemic circulation (V 1 ), and the hepatic intrinsic clearance (CL h,int ), for this selection of chemicals were previously generated in silico for rats. 21) Therefore, the aim of the present study was to establish in silico models for predicting k a , V 1 , and CL h,int values (for use in human PBPK models) based on a number of chemical descriptors.…”