Data-centric feedback loops for next-generation immunotherapy development.

Despite explosive growth in biomedical data generation, driven largely by genomics, and in computational capabilities, the probability that a candidate entering phase I ultimately reaches approval has remained stubbornly low over the past decades. This paradox points to a central bottleneck not in data generation, but in converting biological and clinical data into decisions that govern progression, redesign or termination. Here we argue that drug development should be reframed from a linear pipeline into an iterative learning system driven by continuous data feedback. We outline a data-centric framework in which high-dimensional, multimodal molecular and perturbation data, particularly single-cell and spatial readouts, are used to iteratively refine disease models, therapeutic hypotheses, molecular designs and patient stratification strategies across discovery and clinical stages. Using immunotherapies as a proof-of-concept domain, we propose that single-cell molecular readouts from therapeutic perturbations can both de-risk development and deepen mechanistic understanding of immune responses in humans. Finally, we draw parallels to reinforcement learning, in which human molecular and clinical data provide the feedback signal that updates mechanistic models and guides the design of subsequent interventions. Embracing this paradigm offers a path towards more mechanistically grounded, context-aware therapies with higher translational success.