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The octanol/water partition coefficient, P (logP), is a hydrophobicity index and is one of the determining factors of the pharmacokinetics of chemical compounds. LogP values obtained from in silico software, open chemistry databases, and in vitro liquid chromatography retention factors may vary. Some chemicals (boscalid, etoxazole, and permethrin) have up to four-order-magnitude differences in in silico/in vitro P values. This study aimed to evaluate the effects of logP values of these three compounds, along with bisphenol A, 1,2-dibromobenzene, tetrabromobisphenol A, trazodone, and triazolam, on the input parameters and output plasma/hepatic concentration–time profiles of simple physiologically based pharmacokinetic (PBPK) models. Although the blood-to-plasma concentration ratios (~0.9–0.6) were slightly affected by variations in logP values, logarithmic plasma unbound fraction values and liver-to-plasma partition coefficients (Kp,h) were, respectively, inversely and linearly correlated with logP values (Kp,h was stable at ~6.7 for logP > 4). LogP was among the input parameters for previously established machine learning systems; consequently, the resulting logarithmic intrinsic clearance values were correlated with logP values in the range 2–8. However, the bioavailability, absorption rate constants, and volumes of distribution were not affected. PBPK-modeled plasma and hepatic maximum concentrations and areas under the concentration–time curves after virtual oral doses were mostly within ~0.5- to ~2-fold ranges, except for substances with low in vitro logP values, e.g., etoxazole and permethrin. These results suggest that in silico logP values are generally suitable for pharmacokinetic modeling; nevertheless, caution is needed for compounds with low in vitro logP values of ~2. [ABSTRACT FROM AUTHOR]

The octanol/water partition coefficient P (logP) is a hydrophobicity index and is one of the determining factors for the pharmacokinetics of orally administered substances because it influences membrane permeability. To illustrate the wide-ranging variety of compounds in the chemical space, a two-dimensional data plot consisting of 25 blocks was previously proposed based on a substance’s in silico chemical descriptors. The logP values of approximately 200 diverse chemicals (test plus reference compounds covering all 25 blocks of the chemical space) were estimated experimentally using retention times in reverse-phase liquid chromatography; these values were compared with those of authentic reference compounds with established logP values (available for 17 of 60 reference substances in the Organization for Economic Co-operation and Development Test Guideline 117). The logP values of 140 of 165 chemicals successfully estimated using four different mobile phase conditions (pH 2, 4, 7, and 10 for molecular forms) correlated significantly with those calculated using the in silico packages ChemDraw and ACD/Percepta (r > 0.72). Although substances that neighbored authentic compounds in the chemical space had precisely correlated logP values estimated experimentally and in silico, some compounds that were more distant from authentic substances showed lower logP values than those estimated in silico. These results indicate that additional authentic reference materials with wider ranging chemical diversity and their logP values from reverse-phase liquid chromatography should be included in the international test guidance to promote simple and reliable estimation of octanol/water partition coefficients, which are important determinant factors for the pharmacokinetics of general chemicals. [ABSTRACT FROM AUTHOR]

Physiologically based pharmacokinetic (PBPK) modeling has the potential to estimate internal chemical exposures. Algorithms for predicting the input parameters for PBPK modeling, such as absorption rate constants (ka), were previously reported for 323 chemicals in rats. In this study, a currently updated system for estimating the fraction absorbed x intestinal availability of compounds, along with a modified estimation system that generates ka values, is reported, based on the previously analyzed 323 primary compounds, 10 secondary compounds, and 39 additional substances. The in silico estimation of input parameters for PBPK models (i.e., fraction absorbed x intestinal availability and ka) was adapted for an updated panel of 372 chemicals using machine learning algorithms based on between 16 and 18 in silico- calculated chemical properties. Simplified human PBPK models were then used to calculate virtual areas under the plasma concentration-time curve (AUC) based on two sets of input parameters, i.e., traditionally derived values from in vivo data and those calculated in silico using the current updated machine learning systems. The AUC data sets were well correlated; the current correlation coefficient increased from 0.61 to 0.82 (p < 0.01, n = 372). Therefore, the above-described computational methods constitute a new alternative approach that could contribute to chemical safety evaluations. [ABSTRACT FROM AUTHOR]