Session 4: QM P450 Predictions

Predicting regioselectivity and lability of cytochrome P450 metabolism using quantum mechanical simulations

Matthew Segall






Matthew Segall, Jonathan Tyzack, Peter Hunt



[1] J.P. Jones et al. (2002) Drug Metab. Dispos. 30(1): 7-12
[2] G. Cruciani et al. (2005) J. Med. Chem. 48(22): 6970-6979
[3] M. Hennemann et al. (2009) ChemMedChem 4(4): 657-669
[4] P. Rydberg et al. (2010) ACS Med. Chem. Lett. 1(3): 96-100
[5] J. Zaretzki et al. (2011) J. Chem. Inf. Model. 51(7): 1667-1689
[6] StarDrop, Optibrium Ltd, Cambridge, UK,


Many computational methods have been developed that predict the regioselectivity of metabolism by drug metabolising isoforms of the Cytochrome P450 class of enzymes (P450) [1-5].

Here we describe recent developments to a method for predicting P450 metabolism that combines quantum mechanical (QM) simulations to estimate the reactivity of potential sites of metabolism on a compound with a ligand-based approach to account for the effects of orientation and steric constraints due to the binding pockets of different P450 isoforms.

These new developments include modelling reaction pathways for epoxidation and developing models for an extended range of P450 isoforms. The resulting models achieve accuracies of 85-90% on independent test sets.

The metabolites resulting from oxidation at each potential site of metabolism can also be predicted, to guide experimental metabolite identification and highlight potentially active, reactive or toxic metabolites for further investigation.

While valuable, predicting the relative proportion of metabolite formation at different sites on a compound is only a partial solution to designing more stable compounds.

The advantage of a quantum mechanical approach is that it provides a quantitative estimate of the reactivity of each site, from which additional information can be derived regarding the vulnerability of each site to metabolism in absolute terms.

One such measurement is the site lability, as calculated by StarDrop™ [6], which is a measure of the efficiency of the product formation step.

This is an important factor influencing the rate of metabolism and we will illustrate how this provides valuable guidance regarding the potential to redesign compounds to overcome issues due to rapid P450 metabolism.