This peer-reviewed paper in Xenobiotica describes a new method to determine the most likely experimentally-observed routes of metabolism and metabolites based on our WhichP450™, regioselectivity and new WhichEnzyme™ model.
The paper builds on previous metabolism prediction research, developing the capabilities available in our StarDrop Metabolism module.
Summary
Unexpected metabolism can lead to late-stage drug candidate failure. Early in silico prediction of the dominant routes of metabolism is therefore vital to improve a drug’s chance of success.
In this paper, we describe the development of our new WhichEnzyme model. This model tells the user which enzymes are most likely to metabolise their compound.
By training heuristics on a combination of the outputs of the new WhichEnzyme model, our WhichP450 and regioselectivity models, we developed a new method to determine the most likely routes of metabolism and metabolites to be observed experimentally. This method delivers high sensitivity, with great success in identifying experimentally reported metabolites. It also demonstrates higher precision than other methods for predicting in vivo metabolite profiles.
Find out more about predicting Phase I and II metabolism
Read the full paper on the journal webpage, and discover our new method for predicting routes of Phase I and II metabolism, or get in touch with us if you’re interested in seeing a copy of the pre-print.
If you would like to discover more of our metabolism research, take a look at our recent J. Med. Chem. paper, which describes how to predict the regioselectivity of AO, CYP, FMO and UGT metabolism. Alternatively, you can watch our webinar with authors Matt D. Segall and Mario Öeren on the integrated prediction of Phase I and II metabolism.
More drug metabolism resources
Predicting regioselectivity of cytosolic SULT metabolism for drugs
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
Perfecting the use of imperfect QSAR models
In this webinar, we examine the effective use of QSAR modelling in drug discovery and discuss a variety of pain points for medicinal chemists in knowing when a model can be trusted and how to avoid common pitfalls.
