A recent assessment of the success of the drug development process put the percentage of phase I programs that make it to approval at just 13.8%. That’s not great, and while It’s not as bad as some earlier estimates, nonetheless points to a crisis of attrition within the industry. Netramark, a Toronto-based start-up, claim they have a solution to that crisis. Leveraging various machine-learning based methods from machine learning, Netramark aim to give new life to failing and failed drugs by revealing specific sub-populations that treatments may prove successful in, whilst reducing clinical trial size to enable savings for industry. It’s an exciting idea, and we recently spoke to Netramark founder Dr. Joseph Geraci to discover more about the company’s technology, and how machine learning and quantum computing may make drug development easier for clinicians, scientists, and patients.