In this presentation we will describe recent developments to a method for predicting Cytochrome 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.
The resulting models achieve accuracies of 85-90% on independent test sets across multiple P450 isoforms.
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 QM 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, which is a measure of the efficiency of the product formation step and an important factor influencing the rate of metabolism.
We will illustrate how this provides valuable guidance to redesign compounds and overcome issues due to rapid P450 metabolism, using practical examples from lead optimisation projects.
Matthew Segall
Optibrium Limited
In this presentation we will describe recent developments to a method for predicting Cytochrome 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.
The resulting models achieve accuracies of 85-90% on independent test sets across multiple P450 isoforms.
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 QM 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, which is a measure of the efficiency of the product formation step and an important factor influencing the rate of metabolism.
We will illustrate how this provides valuable guidance to redesign compounds and overcome issues due to rapid P450 metabolism, using practical examples from lead optimisation projects.
Matt is CEO of Optibrium. He has a Master of Science in computation from the University of Oxford and a Ph.D. in theoretical physics from the University of Cambridge. As Associate Director at Camitro (UK), ArQule Inc. and then Inpharmatica, he led a team developing predictive ADME models and state-of-the-art intuitive decision-support and visualization tools for drug discovery. In January 2006, he became responsible for management of Inpharmatica's ADME business, including experimental ADME services and the StarDrop software platform. Following acquisition of Inpharmatica, Matt became Senior Director responsible for BioFocus DPI's ADMET division and in 2009 led a management buyout of the StarDrop business to found Optibrium, which develops software for small molecule design, optimisation and data analysis. Matt has published over 30 peer-reviewed papers and book chapters on computational chemistry, cheminformatics and drug discovery.