Drug metabolism is a cornerstone of drug discovery and development, and understanding the enzymes involved in these processes is essential for designing safe, effective, and commercially viable pharmaceuticals. Among these enzymes, cytochrome P450s (also known as CYPs or P450s) play a central role, making them a primary focus in drug discovery research.
CYPs are a ubiquitous superfamily of heme-containing monooxygenases responsible for approximately 70–80% of observed drug metabolism. Their extensive involvement in metabolising a wide array of compounds presents both challenges and opportunities for drug development:
The P450 family is far from simple, with numerous isoforms contributing to drug metabolism. Key isoforms such as CYP3A4, CYP2D6, CYP2C9, and CYP1A2 dominate metabolic pathways, but each isoform has unique substrate specificities and varying prevalence in different individuals. This complexity demands sophisticated approaches to predict and study their activity.
One effective approach to understanding cytochrome P450 involvement in drug metabolism is the use of predictive models. For example, Optibrium’s WhichP450 model helps identify the P450 isoforms most likely responsible for metabolising a given compound. Represented as a visual pie chart, this model provides insights into potential routes of clearance.
A practical application of this model is illustrated in scenarios where metabolism is predicted to be predominantly mediated by a single polymorphic isoform, such as CYP2D6. Such predictions flag compounds at higher risk of variability in exposure due to genetic differences, enabling researchers to prioritise experimental validation through methods like reaction phenotyping experiments.
While predictive models provide valuable insights, experimental validation remains critical. Techniques such as CYP phenotyping experiments with recombinant enzymes allow researchers to confirm which isoforms are responsible for drug clearance. These experiments validate computational predictions, ensuring robust data to guide further development.
Cytochrome P450 enzymes are at the heart of drug metabolism research due to their profound impact on pharmacokinetics, safety, and efficacy. By leveraging predictive models and experimental techniques, researchers can identify potential risks early, design safer drugs, and maximise their chances of success in the clinic. With tools like Optibrium’s WhichP450 model, available within StarDrop and Semeta, navigating the complexity of P450 isoforms becomes a more manageable and insightful process, ultimately leading to better outcomes in drug discovery.
CEO and Company Director
Matt holds a Master’s in Computation from the University of Oxford and a PhD in Theoretical Physics from the University of Cambridge. He led teams developing predictive ADME models and advanced decision-support tools for drug discovery at Camitro (UK), ArQule Inc., and Inpharmatica. In 2006, he took charge of Inpharmatica’s ADME business, overseeing experimental services and the StarDrop software platform. After Inpharmatica’s acquisition, he became Senior Director of BioFocus DPI’s ADMET division and, in 2009, led a management buyout to establish Optibrium.
Watch Optibrium CEO Matt Segall and Principal Scientist Mario Öeren as they explore groundbreaking new quantum mechanics and machine learning models which go beyond P450s and provide insights on a broad range of enzymes involved in drug metabolism.
In this example we will explore the feasibility of pursuing a fast-follower for Buspirone, a 5-HT1A ligand used as an anti-anxiolytic therapeutic, which has a known liability due to rapid metabolism by CYP3A4.
Interpreting metabolite-ID experiments; determining the right species for animal studies; providing optimisation suggestions for your medicinal chemistry colleagues to overcome…
