Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

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.