Lhasa and Optibrium improve predictive modelling for drug-metabolising enzymes

Research shows how novel predictive models develop drug design and reduce toxicity

Optibrium and Lhasa – developers of software and AI solutions for drug discovery and development – have announced the publication of a peer-reviewed study in the Journal of Medicinal Chemistry.

The paper, Predicting Regioselectivity of AO, CYP, FMO and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning, describes how the team used existing experimental results, in combination with quantum mechanics and machine learning, in order to build predictive models for drug metabolism.

The study will subsequently underpin the development of new capabilities which better determine the metabolic fate of drug candidates and streamline the preclinical drug discovery process.

Unexpected metabolism can cause the failure of many late-stage drug candidates, or even the withdrawal of approved drugs, making metabolism prediction essential for potential drug candidates.

Current predictive models of metabolism usually target the human cytochrome P450 (CYP) enzyme family, due to its well-characterised role in the metabolism of drug-like compounds. There is, however, an increasing need to predict metabolism for other enzymes, such as human aldehyde oxidates, flavin-containing monooxygenases and Uridine 5’-diphospho glucuronosyltransferases (UGTs).

The study also demonstrates novel predictive models for these enzymes, while extending the existing model for CYP metabolism to preclinical species. Meanwhile, expanding the portfolio of predictive models beyond CYPs will allow drug discovery scientists to establish a compound’s metabolic fate more accurately. This will help to design better drugs and identify toxicity earlier in the project.

Dr Matthew Segall, chief executive officer of Optibrium, explained: “A huge congratulations to our team on this achievement. Here at Optibrium, we are always looking to innovate, and we pride ourselves on the scientific rigour behind our portfolio. The research will deliver powerful new capabilities to our StarDrop platform, strengthening our mission to push the boundaries of what’s possible within the computer-aided drug discovery space.”

Dr Mario Öeren, principal scientist at Optibrium, commented: “We are delighted to see our research published in The Journal of Medicinal Chemistry. Combining quantum mechanical simulations and machine learning has allowed us to successfully expand predictive models of metabolism to new enzymes – a unique undertaking which addresses some of the key preclinical challenges of today.

“We are confident that this research’s demonstrated ability to predict metabolism across a broad range of different metabolic enzymes will provide an invaluable resource for scientists approaching drug discovery,” he added.

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