Credit: Shuo | stock.adobe.com Artificial intelligence (AI) has had an undeniable impact on society in the past few years, having fundamentally changed how people from diverse industrial sectors do their jobs day to day. Drug discovery has been the focus of pharma attention.
Last month alone, we have seen Insilico announce its lead candidate has met its primary endpoints and is in the process of designing a Phase IIb trial, while a DeepMind spinout, Isomorphic Labs, has just issued £182 million in new shares. It’s also worth looking at the impact of AI for trial management and clinical efficacy, an area still in its infancy, as we think this could grow into an even bigger force, fundamentally transforming how we approach drug development.
There are many areas in which AI could improve speed, reduce complexity, and consequently improve efficiency in workflows and costs. From design and trial start-up to conduct and analysis, there is enormous potential for applications of AI within clinical trials to have a profound impact on human health.
One of the best areas for AI to improve clinical research is in site feasibility and selection. Picking the right sites optimized for the eligibility criteria and trial specifications is perhaps the single most important decision impacting the success or failure of the trial because of the causal impact on enrollment rates.
Trials that don’t enroll quickly enough risk failure to provide the statistical power needed to conclude the drug under investigation is safe and effective, and this remains a top cause of trial failure today. AI prediction engines, which use large language models (LLMs) to read a protocol, understand the context, and select the “best” sites for the protocol, are starting to become available.
Yet these engines are, at best, on par (and sometimes worse) than human performance on the same task. The reason is that there is a lack of available site performance data aligned with the key metrics that would help drive successful predictions.
The key to overcoming this is existing data that can be used for training. We use data pipelines that aggregate KPIs and raw data from past protocols, […]
AI Already Starting to Deliver Faster, Safer, More Effective Clinical Trials
