The Prediction of Human Pharmacokinetic Parameters from Preclinical and In Vitro Metabolism Data

• Published in: The Journal of pharmacology and experimental therapeutics 1997-10, Vol.283 (1), p.46-58
• Main Authors: Obach, R S, Baxter, J G, Liston, T E, Silber, B M, Jones, B C, MacIntyre, F, Rance, D J, Wastall, P
• Format: Article
• Language: English
• Subjects: Animals; Biological Availability; Half-Life; Humans; Metabolic Clearance Rate; Pharmacokinetics; Retrospective Studies
• ISSN: 0022-3565; 1521-0103

We describe a comprehensive retrospective analysis in which the abilities of several methods by which human pharmacokinetic parameters are predicted from preclinical pharmacokinetic data and/or in vitro metabolism data were assessed. The prediction methods examined included both methods from the scientific literature as well as some described in this report for the first time. Four methods were examined for their ability to predict human volume of distribution. Three were highly predictive, yielding, on average, predictions that were within 60% to 90% of actual values. Twelve methods were assessed for their utility in predicting clearance. The most successful allometric scaling method yielded clearance predictions that were, on average, within 80% of actual values. The best methods in which in vitro metabolism data from human liver microsomes were scaled to in vivo clearance values yielded predicted clearance values that were, on average, within 70% to 80% of actual values. Human t 1/2 was predicted by combining predictions of human volume of distribution and clearance. The best t 1/2 prediction methods successfully assigned compounds to appropriate dosing regimen categories ( e.g., once daily, twice daily and so forth) 70% to 80% of the time. In addition, correlations between human t 1/2 and t 1/2 values from preclinical species were also generally successful (72–87%) when used to predict human dosing regimens. In summary, this retrospective analysis has identified several approaches by which human pharmacokinetic data can be predicted from preclinical data. Such approaches should find utility in the drug discovery and development processes in the identification and selection of compounds that will possess appropriate pharmacokinetic characteristics in humans for progression to clinical trials.