What is the Metabolism Module?
Metabolism plays a critical role in the bioavailability of drugs, food additives, agrochemicals, and industrial chemicals. It is also important in understanding drug-drug interactions (DDI). The cytochrome P450 enzymes (CYPs) are probably the most significant class of Phase I metabolizing enzymes, accounting for the majority of Phase I metabolic transformations of most drugs. Knowledge of the specific metabolites resulting from these transformations is often important in understanding toxicities, efficacy (in the case of prodrugs), and clearance, along with many other key aspects of drug pharmacokinetics. Thus, models for metabolite prediction can very useful in drug design and toxicity.
The Metabolism Module in ADMET Predictor allows you to predict:
- Cytochrome P450 sites of metabolism and metabolites for nine CYP isoforms
- CYP kinetic parameters (Km, Vmax, CLint) for the five major drug metabolizing CYPs
- CYP inhibition for the five major drug metabolizing CYPs
- Human and rat liver microsome unbound intrinsic clearance
- UGT substrate for nine UGT isoforms
Additional information on this module is provided below.
4.4.13 - The Metabolism Module in ADMET Predictor™ contains in silico models that classify compounds as substrates and/or inhibitors of the major CYP isoforms, while also predicting likely sites of metabolism and kinetic parameters (Km, Vmax, and intrinsic clearance). Classification models for phase II glucuronidation by UDP-glucuronosyltransferase are also included. This webinar describes the development of these models and how they can be applied to predict the disposition of drug candidates and assist with the lead optimization process. admet predictor
6.10.15 - Two new models, fraction unbound to liver microsomes and unbound human liver microsomal intrinsic clearance, were added to ADMET Predictor 7.2. The CYP kinetic parameter models were also re-built. Incorporation of these models into GastroPlus is also discussed. Finally, the set up for our KNIME workflow is demonstrated. admet predictor