Oncology Dose Optimization: Tailored Approaches to Different Molecular Classes

Oncology dose optimization during the era of chemotherapy focused on identifying the maximum tolerated dose (MTD) for registrational trials, often resulting in significant toxicity. The advent of molecular targeted drugs and immunotherapies offers the potential to achieve similar efficacy with lower doses and fewer side effects, as maximal efficacy is often reached at doses below the MTD. Recent FDA guidance outlines expectations for improving dose optimization in oncology drug development. This review presents a framework for tailored dose optimization by categorizing oncology molecules into four distinct classes based on their mechanisms of action and clinical activities: small molecule targeted therapies and antibody‐drug conjugates (Class 1), large molecule antagonists (Class 2), cancer immunotherapy agonists (Class 3), and molecules with limited or no single‐agent activity (Class 4). Unique dose optimization considerations for each class are discussed, supported by illustrative case examples. To enhance robust dose decision‐making and optimize patient resource utilization, we propose using proof of activity as a gate for initiating dose expansion with one or multiple dose levels. This review emphasizes the importance of integrating all relevant preclinical data, disease knowledge, and clinical measurements and highlights the essential role of quantitative pharmacology and statistical modeling in optimizing doses.