Predictive P450 Toxicology: Metabolism and Polymorphisms

Moltech Corporation, Palo Alto CA

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): Toxic and carcinogenic side effects of drug administration are often due to their cytochrome P450 (CYP450) metabolism. It has been estimated that 1-in 15 hospital admissions is due to adverse drug effects. In addition, prescribed drugs account for 106,000 deaths and 2.2 million adverse reactions. Both interindividual and population subgroup variations in metabolism of drugs and xenobiotics have profound clinical consequences. The ability to predict metabolism at the earliest stage of drug discovery would accelerate the pace by which efficacious agents lacking toxicity are developed. This approach also takes advantage of the growing pharmacogenetics knowledge pertaining to both genotypic and phenotypic effects on drug metabolism. Rapid progress in our understanding of the underlying principles of CYP450 metabolism has resulted from the convergence of structural and molecular biology with theoretical methods, capable of predicting the geometric and thermodynamic determinants of competitive metabolism. This has enabled us to predict the principal products of CYP450 metabolism of drugs by a combination of ligand-P450 configurational sampling, based on rapid flexible docking, and electronic determinants of metabolism by the active heme species of P450s. Critical appraisals of this approach indicate a robust ability to predict major metabolites of test drugs, including toxic products, such as N-acetyl-p-benzoquinoneimine derived from CYP2E1 acetaminophen metabolism. This initial appraisal will be extended to test predictions of an assembled large 3D structural drug database with associated metabolic and kinetic data. The overall goal of this Phase I SBIR is twofold: 1) to critically assess a method capable of making metabolism predictions of drugs, by the major mammalian CYP450 isoforms, on the timescale of minutes per lead compound using a desktop computer, and 2) the extension of this method to predict the effects of single nucleotide polymorphisms (SNPs) on metabolism. This dual approach will establish an in silico predictive toxicology product developed with corporate partnership in a phase II application, that will be invaluable, even at the earliest stages of molecular conception, to the drug discovery process.