Extrapolation of Hepatic Concentrations of Industrial Chemicals Using Pharmacokinetic Models to Predict Hepatotoxicity

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.

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

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.

Physiologically Based Pharmacokinetic Models Predicting Renal and Hepatic Concentrations of Industrial Chemicals after Virtual Oral Doses in Rats

Recently developed high-throughput in vitro assays in combination with computational models could provide alternatives to animal testing. The purpose of the present study was to model the plasma, hepatic, and renal pharmacokinetics of approximately 150 structurally varied types of drugs, food components, and industrial chemicals after virtual external oral dosing in rats and to determine the relationship between the simulated internal concentrations in tissue/plasma and their lowest-observed-effect levels. The model parameters were based on rat plasma data from the literature and empirically determined pharmacokinetics measured after oral administrations to rats carried out to evaluate hepatotoxic or nephrotic potentials. To ensure that the analyzed substances exhibited a broad diversity of chemical structures, their structure-based location in the chemical space underwent projection onto a two-dimensional plane, as reported previously, using generative topographic mapping. A high-throughput in silico one-compartment model and a physiologically based pharmacokinetic (PBPK) model consisting of chemical receptor (gut), metabolizing (liver), central (main), and excreting (kidney) compartments were developed in parallel. For 159 disparate chemicals, the maximum plasma concentrations and the areas under the concentration-time curves obtained by one-compartment models and modified simple PBPK models were closely correlated. However, there were differences between the PBPK modeled and empirically obtained hepatic/renal concentrations and plasma maximal concentrations/areas under the concentration-time curves of the 159 chemicals. For a few compounds, the lowest-observed-effect levels were available for hepatotoxicity and nephrotoxicity in the Hazard Evaluation Support System Integrated Platform in Japan. The areas under the renal or hepatic concentration-time curves estimated using PBPK modeling were inversely associated with these lowest-observed-effect levels. Using PBPK forward dosimetry could provide the plasma/tissue concentrations of drugs and chemicals after oral dosing, thereby facilitating estimates of nephrotoxic or hepatotoxic potential as a part of the risk assessment.