Suitable albumin concentrations for enhanced drug oxidation activities mediated by human liver microsomal cytochrome P450 2C9 and other forms predicted with unbound fractions and partition/distribution coefficients of model substrates

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

For medicines, the apparent membrane permeability coefficients (P_app) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro P_app values of 29 industrial chemicals were found to have an inverse association with their reported no-observed effect levels for hepatotoxicity in rats. In the current study, we expanded our influx permeability predictions for the 90 previously investigated chemicals to both influx and efflux permeability predictions for 207 diverse primary compounds, along with those for 23 secondary compounds. Trivariate linear regression analysis found that the observed influx and efflux logP_app values determined by in vitro experiments significantly correlated with molecular weights and the octanol-water distribution coefficients at apical and basal pH levels (pH 6.0 and 7.4, respectively) (apical to basal, r = 0.76, n = 198; and basal to apical, r = 0.77, n = 202); the distribution coefficients were estimated in silico. Further, prediction accuracy was enhanced by applying a light gradient boosting machine learning system (LightGBM) to estimate influx and efflux logP_app values that incorporated 17 and 19 in silico chemical descriptors (r = 0.83-0.84, p < 0.001). The determination in vitro and/or prediction in silico of permeability coefficients across intestinal cell monolayers of a diverse range of industrial chemicals/food components/medicines could contribute to the safety evaluations of oral intakes of general chemicals in humans. Such new alternative methods could also reduce the need for animal testing during toxicity assessment.

Determination and prediction of permeability across intestinal epithelial cell monolayer of a diverse range of industrial chemicals/drugs for estimation of oral absorption as a putative marker of hepatotoxicity

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Permeability values of 90 industry chemicals were measured by a Caco-2 system.

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A multivariate prediction equation for permeability of chemicals was proposed.

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Chemical permeability coefficients were inversely associated with hepatic NOELs.

Apparent permeability coefficients (P_app) across a human intestinal epithelial Caco-2 cell monolayer were measured for a range of industrial/drug chemicals. A predictive equation for determining in vitro P_app values of fifty-six substances was set up using multivariate regression analysis based on in silico-estimated physicochemical properties (molecular weights and water distribution coefficients for apical and basal pH environments) (r = 0.77, p <  0.01). Predicted logP_app values of a secondary set of 34 compounds were correlated with the measured values. Under the medicinal logP_app values associated with their reported fraction absorbed, a significant inverse non-linear correlation was found between the logarithmic transformed values of observed P_app values and reported hepatic no-observed-effect levels of industrial chemicals (r = –0.55, p <  0.01, n = 29). In vitro determination and/or in silico prediction of permeability across intestinal cells could be effective for estimating oral absorption as a putative indicator for hepatotoxicity.

Plasma and Hepatic Concentrations of Chemicals after Virtual Oral Administrations Extrapolated Using Rat Plasma Data and Simple Physiologically Based Pharmacokinetic Models

Only a small fraction of chemicals possesses adequate in vivo toxicokinetic data for assessing potential hazards. The aim of the present study was to model the plasma and hepatic pharmacokinetics of more than 50 disparate types of chemicals and drugs after virtual oral administrations in rats. The models were based on reported pharmacokinetics determined after oral administration to rats. An inverse relationship was observed between no-observed-effect levels after oral administration and chemical absorbance rates evaluated for cell permeability ( r = -0.98, p < 0.001, n = 17). For a varied selection of more than 30 chemicals, the plasma concentration curves and the maximum concentrations obtained using a simple one-compartment model (recently recommended as a high-throughput toxicokinetic model) and a simple physiologically based pharmacokinetic (PBPK) model (consisting of chemical receptor, metabolizing, and central compartments) were highly consistent. The hepatic and plasma concentrations and the hepatic and plasma areas under the concentration-time curves of more than 50 chemicals were roughly correlated; however, differences were evident between the PBPK-modeled values in livers and empirically obtained values in plasma. Of the compounds selected for analysis, only seven had the lowest observed effect level (LOEL) values for hepatoxicity listed in the Hazard Evaluation Support System Integrated Platform in Japan. For these seven compounds, the LOEL values and the areas under the hepatic concentration-time curves estimated using PBPK modeling were inversely correlated ( r = -0.78, p < 0.05, n = 7). This study provides important information to help simulate the high hepatic levels of potent hepatotoxic compounds. Using suitable PBPK parameters, the present models could estimate the plasma/hepatic concentrations of chemicals and drugs after oral doses using both PBPK forward and reverse dosimetry, thereby indicating the potential value of this modeling approach in predicting hepatic toxicity as a part of risk assessments of chemicals absorbed in the human body.