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Using Big Data and AI to predict postmarketing requirements

When a new medicine is approved, there is a less than 25% chance that the FDA or the EMA will impose, as part of conditional approval, studies conducted to satisfy post-marketing requirements.

In the past, sponsors either postponed or canceled some mandated postmarketing studies. This is no longer possible because regulatory authorities are scrutinizing the successful completion of such studies and the timely delivery of the data. On 16 November 2018, FDA Commissioner Dr. Scott Gottlieb published a statement on “the FDA’s efforts to hold industry accountable for fulfilling critical post-marketing studies of the benefits, safety of new drugs.” The statement recognized that the “timely conduct of these studies is a major area of policy focus inside the FDA.

If drug developers were able to predict the chances that a new medicine will face postmarketing requirements, they would be able "to act early in the development cycle and either mitigate the risk by collecting additional data in the early phases of development or plan ahead for a commitment that will be acceptable to the authorities."

This is exactly what PRA set out to achieve. Using  real-world solutions, big data and AI, PRA looked at the profiles of all drugs approved in the past 10 years and through analysing the data, were able to predict with a reasonable accuracy the probability that the FDA or the EMA will impose postmarketing studies for a specific drug in development.

This report from PRA featured on BioProcess International explores the trends in Real-World Study design and postmarketing commitments in the EU and US, and what we can learn from big data.

Download the Trends in Real-World Study Design report here

Download report

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