Research ArticlesPrediction of Human Pharmacokinetics from Animal Data and Molecular Structural Parameters using Multivariate Regression Analysis: Oral Clearance
Section snippets
INTRODUCTION
In the process of developing new drugs, it is very important to predict the pharmacokinetic profile of a drug in human for decisions on appropriate dosage and the best clinical trial design. In our previous paper,1 we reported a new regression equation for predicting human clearance after intravenous administration, using animal data for clearance and some molecular structural parameters, and showed that the regression equation had better predictive performance than allometric approaches that
Data
CLPO data for rats (CLPO,rat; range, 0.300–27778 mL/min/kg), dogs (CLPO,dog; range, 0.150–1598 mL/min/kg), and humans (CLPO,man; range, 0.150–3767 mL/min/kg) after oral administration of 87 drugs were obtained from the literature.6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60.
RESULTS
MLR analysis led to the selection of five descriptor variables as statistically significant in the best combination; they are, CLPO,rat × MW, CLPO,rat × Ha, CLPO,dog × Ha, c log P × c log P, and Ha × Ha. The values of q2 and rRMSE for the MLR model are 0.597 and 0.523, respectively.
PLS analyses were performed using five basic descriptors and 15 quadratic term descriptors. The standard PLS analysis with those 20 descriptors suggests that a one‐component PLS model is the best model according to
DISCUSSION
Predicting the pharmacokinetic profile of a drug in human is of great interest, and CLPO,man is one of the most important pharmacokinetic parameters. In the present study, we aimed to develop a method for predicting CLPO,man and tested the predictability of our method by comparing it with allometric approaches.
Judging from global indices such as q2 and RMSE, shown in Table 2, the predictive performance of the regression analysis methods is better than that of the allometric approaches, and the
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