Data Science, AI, and Machine Learning in Drug Development (2022) Pdf Free Download

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In recent years, the biopharmaceutical industry has faced an increasing productivity challenge. Despite many innovations in biomedical research that created a plethora of opportunities for detection, treatment, and prevention of serious illnesses, a high percent of candidate drugs shown to be promising in early research have failed in the late stage of clinical development. While the overall R&D spending has soared to an unsustainable level, the number of new drug approvals has declined significantly. Many companies that rely on blockbuster drugs to generate revenue growth are in a bind due to patent expirations and competition from generic drug and biosimilar makers. Meanwhile, rise in healthcare spending has resulted in increasing demands by payers and policy makers to demonstrate the value of medical products to justify payment. To be successful, companies not only need to improve the efficiency of drug-target discovery and clinical trials but also influence patients, prescribers, payers, and regulatory decisions with real-world data (RWD) to ensure better patient outcomes, accelerated approvals, and greater market access.

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