Patient stratification and the use of real-world evidence in regulatory decision-making are two key areas where algorithms are having an impact on drug development. The two are linked: increased patient stratification makes it harder to recruit patients into randomized-controlled trials, increasing the pressure on drug developers to find alternative sources of evidence for showing efficacy. In addition to real-world evidence, we are also seeing the emergence of more efficient ‘master protocol trials’, where multiple targeted agents can be evaluated simultaneously. In this chapter, I will review these developments and investigate the limitations for AI in terms of demonstrating the efficacy of novel targeted agents.
Part of the book: Artificial Intelligence in Oncology Drug Discovery and Development