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Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists.

During an investigation of drugs for improving the beta-cell response to glucose, we found that 4-cyclohexyl-4-oxobutyric acid selectively improved glucose-stimulated insulin release and glucose tolerance in both normal and diabetic rats. A series of 4-cycloalkyl-4-oxobutyric acids and related compounds were synthesized and evaluated for their effects on the glucose tolerance test and fasting euglycemia. This study elucidated the structural requirements for drug activity and determined that the optimum compound was 4-(trans-4-methylcyclohexyl)-4-oxobutyric acid 7 (JTT-608). This compound improved glucose tolerance from an oral dose of 3 mg/kg and did not change fasting euglycemia even at an oral dose of 30 mg/kg. Selective improvement of glucose-induced insulin secretion was observed in studies using neonatal streptozotocin rats (nSTZ rats) and perfused pancreases isolated from nSTZ rats.