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Cognitive systems for drug discovery

Developing new therapeutics today cost in excess of $2 billion and can take more than a decade before making it to the patient. Of the drugs that enter phase 1 clinical trials approximately 10 percent result in new drug applications (NDAs). These high levels of attrition are due largely to a combination of poor safety profiles and inadequate efficacy. Such poor performance is a result of our limited understanding of disease etiology, how drugs perturb cellular pathways, and an inability to accurately predict off target drug interactions.

We are addressing the issue of high attrition during drug discovery by developing predicative methods for identifying drugs with a high risk of causing tissue injury. Specifically, we are building models for drug induced liver injury (DILI). DILI has been identified as one of the primary reasons for clinical trial failure for several compounds across drug classes and disease indications. Our approach takes advantage of machine learning and natural language processing tools to mine and analyze biomedical data across a range of structured and unstructured data warehouses (Figure CS-1). This work builds on our high-throughput in vitro platform for screening drug toxicity and leverages the high performance computing capabilities through the Rensselaer Center for Computational Innovation (CCI) and the Cognitive and Immersive Systems Lab (CISL).

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Figure CS-1. Computational/experimental paradigm for drug discovery, which includes cognitive systems for exploring new dimensions of biomedical information.