This episode focuses on dose escalation, a critical process in early-phase clinical trials used to determine the Maximum Tolerated Dose (MTD) of a new drug. We explain how researchers gradually increase the dosage given to small groups of patients (cohorts) while carefully monitoring for any adverse effects. The discussion explores the specific decision criteria used to determine when to escalate the dose and when to stop, based on the severity and frequency of side effects. Real-world case studies, such as the development of romidepsin for cutaneous T-cell lymphoma, are used to illustrate the challenges and complexities of this process. The importance of adhering to regulations from the FDA and ICH is emphasized throughout the episode.

Beyond the technical aspects of dose escalation, the episode explores the role of artificial intelligence (AI) in drug development. We discuss how AI and machine learning can be used to analyze large amounts of data from pre-clinical and clinical trials, potentially identifying safety signals earlier and predicting optimal dosing regimens. The ethical considerations and challenges associated with using AI in this context are also discussed, including the importance of data quality and human oversight. Finally, the episode highlights the success story of imatinib (Gleevec), a groundbreaking treatment for chronic myeloid leukemia (CML), and how dose escalation studies played a critical role in its development.