Predicting intrinsic clearance for drugs and drug candidates metabolized by aldehyde oxidase

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Metabolism

Background:

• Drug metabolism by aldehyde oxidase (AO) frequently leads to clinical trial failures.
• Allometric scaling and in vitro human data often underestimate AO-mediated drug clearance.
• Accurate prediction of drug clearance is crucial for successful drug development.

Purpose of the Study:

• To develop the first in silico models for predicting in vitro and in vivo human intrinsic clearance for AO-metabolized drugs.
• To identify key chemical descriptors influencing AO-mediated clearance.
• To assess the predictive accuracy of computational models compared to traditional methods.

Main Methods:

• Development of quantitative structure-activity relationship (QSAR) models using two chemical descriptors.
• In silico prediction of intrinsic clearance for eight model drugs.
• Comparison of model predictions with experimental in vitro and in vivo human data.

Main Results:

• The developed in silico models accurately predict human intrinsic clearance for AO-metabolized drugs using only two descriptors.
• These models explain a significant portion of the variance in drug clearance data.
• In vivo computational models demonstrated superior prediction of human intrinsic clearance compared to preclinical in vitro animal testing.

Conclusions:

• Aldehyde oxidase (AO) mediated drug metabolism rates are amenable to accurate computational prediction.
• In silico models can guide drug discovery by predicting pharmacokinetics early in development.
• These predictive tools hold potential for improving the success rate of drugs in clinical trials.