Can Machine Learning Give Us Faster and Cheaper Clinical Trials?

In drug development, clinical trials carry a hefty price tag with a median cost of $48 million, or $41,413 per patient.[i] Usually, the more patients involved, the more expensive the trial. Crucially, biotech companies bringing novel therapeutics to market must recruit enough patients to adequately demonstrate efficacy and safety while reducing costs and minimizing time to market. Biotech and pharma executives frequently focus on streamlining operations such as patient recruitment and coordination of clinical sites. Yet, another compelling avenue for cost-savings surrounds the statistical methodology actually used to analyze the data once it is collected. Recently, such methodology has been extended to incorporate machine learning (ML) prediction models. This has led companies such as Unlearn, a San Francisco-based technology start-up, to offer tools and services that use ML to optimize clinical trials and, in their words, “advance artificial intelligence to eliminate trial and error i

In drug development, clinical trials carry a hefty price tag with a median cost of $48 million, or $41,413 per patient.[i] Usually, the more patients involved, the more expensive the trial. Crucially, biotech companies bringing novel therapeutics to market must recruit enough patients to adequately demonstrate efficacy and safety while reducing costs and minimizing time to market. Biotech and pharma executives frequently focus on streamlining operations such as patient recruitment and coordination of clinical sites. Yet, another compelling avenue for cost-savings surrounds the statistical meth