TL;DR:
- Optibrium, a leader in drug discovery solutions, published a groundbreaking study on predicting drug metabolism using quantum mechanics and machine learning.
- Unpredictable metabolism can lead to drug candidate failures; early prediction of metabolic routes is crucial.
- The study introduces the WhichEnzyme™ model, accurately identifying enzymes likely to metabolize drug candidates.
- The model combines with existing ones, including regioselectivity models and the WhichP450 model.
- This combined approach offers high precision in identifying experimentally observed metabolites, surpassing other methods.
- Researchers can now identify compounds with improved metabolic stability and safety profiles.
- The study reflects six years of focused research, culminating in precise, isoform-specific regioselectivity models.
Main AI News:
In the dynamic world of pharmaceuticals, where success is measured in precision and safety, Optibrium emerges as a beacon of innovation. The leading developer of software and AI solutions for drug discovery has unveiled a groundbreaking study, ‘Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning,’ published in Xenobiotica. This research heralds a new era in early drug discovery by revolutionizing the prediction of metabolic pathways and metabolites.
The significance of this innovation cannot be overstated, as unexpected metabolic reactions can derail late-stage drug candidates or necessitate the withdrawal of approved drugs. To enhance a drug’s chances of success, it is imperative to predict the dominant routes of metabolism in the early stages of development.
The paper’s journey begins with the introduction of Optibrium’s WhichEnzyme™ model, a marvel of predictive accuracy that identifies the enzyme families most likely to metabolize a drug candidate. Building on this foundation, the research team integrates this groundbreaking model with Optibrium’s previously established models. These models encompass regioselectivity models for pivotal Phase I and Phase II drug-metabolizing enzymes, leveraging quantum mechanical simulations and machine learning to forecast metabolic sites and resulting metabolites. Furthermore, the WhichP450 model, designed to predict the specific Cytochrome P450 isoform(s) responsible for a compound’s metabolism, contributes to the comprehensive approach.
The culmination of these combined model outputs is a pioneering method for identifying the most probable routes of metabolism and the associated metabolites, aligning closely with experimental observations. This method stands out with its remarkable sensitivity in identifying experimentally reported metabolites, surpassing other prediction methods in precision for in vivo metabolite profiles. It empowers researchers to pinpoint compounds characterized by heightened metabolic stability and enhanced safety profiles, serving as the cornerstone for Optibrium’s recently launched StarDrop Metabolism module.
Dr. Mario Öeren, Principal Scientist at Optibrium, expressed the culmination of six years of dedicated research in this statement: “Our latest study embodies a practical model that enables users to forecast metabolic pathways for a diverse range of drug-like compounds. Through meticulously curated datasets and our signature reactivity-accessibility approach, we have crafted precise, isoform-specific regioselectivity models for the pivotal Phase I and II enzyme families.” Optibrium’s commitment to innovation in drug discovery shines brightly, illuminating a path toward safer and more efficacious pharmaceuticals.
Conclusion:
Optibrium’s innovative approach to drug metabolism prediction empowers pharmaceutical companies to make more informed decisions in drug development, ultimately reducing the risk of late-stage failures and withdrawals. This breakthrough has the potential to revolutionize the pharmaceutical market by enhancing the efficiency and safety of drug discovery processes, potentially leading to the development of safer and more effective drugs.
