Artificial Intelligence for Clinical Trial Design
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Suboptimal patient selection and recruiting techniques, paired with the inability to monitor and coach patients effectively during clinical trials, are two of the main causes for high trial failure rates.
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High failure rates of clinical trials contribute substantially to the inefficiency of the drug development cycle, in other words the trend that fewer new drugs reach the market despite increasing pharma R&D investment. This trend has been observed for decades and is ongoing.
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AI techniques have advanced to a level of maturity that allows them to be employed under real-life conditions to assist human decision-makers.
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AI has the potential to transform key steps of clinical trial design from study preparation to execution towards improving trial success rates, thus lowering the pharma R&D burden.
Clinical trials consume the latter half of the 10 to 15 year, 1.5–2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates.
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