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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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    We developed F.O.R.W.A.R.D., a novel AI framework for drug development, to improve target prioritization and predict clinical trial success. This approach achieved 100% accuracy in predicting trial outcomes for Inflammatory Bowel Diseases.

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    Area of Science:

    • Biomedical Informatics
    • Computational Biology
    • Drug Discovery

    Background:

    • Target-based drug development is expensive and imprecise, with AI offering potential improvements.
    • Inflammatory Bowel Diseases (IBD) present a complex therapeutic challenge due to multifactorial origins.

    Purpose of the Study:

    • To introduce and validate F.O.R.W.A.R.D. (Framework for Outcome-based Research and Drug Development), a network-based target prioritization method.
    • To assess F.O.R.W.A.R.D.'s utility in predicting drug efficacy for Inflammatory Bowel Diseases.

    Main Methods:

    • F.O.R.W.A.R.D. utilizes real-world outcomes and a machine-learning classifier trained on transcriptomic data from clinical trials.
    • It defines a molecular signature of remission and integrates network connectivity to predict drug-target-gene interactions.
    • The approach was benchmarked against 210 clinical trials involving 52 targets.

    Main Results:

    • F.O.R.W.A.R.D. demonstrated a perfect predictive accuracy of 100% across diverse targets, mechanisms, and trial designs.
    • In-silico phase '0' trials indicated potential for informing trial design and re-evaluating failed drug candidates.

    Conclusions:

    • F.O.R.W.A.R.D. offers a powerful, data-driven approach to enhance drug discovery and development, improving precision and reducing costs.
    • The framework's adaptability to other therapeutic areas and its potential to guide clinical decision-making promise to transform R&D.