Abstract 3565: Personalized Drug Discovery Process (PDDP): An AI-driven, sustainable, and equitable solution for cancer drug discovery

Abstract Background: Personalized medicine has improved survival rates for lung cancer patients with common targetable alterations. However, developing drugs for rare or ultra-rare molecular alterations is likely to become economically unsustainable, potentially creating unequal access to personalized therapies for patients with these mutations. Methods: We formulate a new mathematical model and use Monte Carlo simulations to calculate pharma companies’ projected profits from targets that affect an increasingly smaller fraction of the world population, considering the costs of R&D and Randomized Clinical Trials (RCTs) and the probability of drug approval. We then use similar methods to calculate projected profits for an alternative, feasible, AI-driven approach called the Personalized Drug Discovery Process (PDDP), which has four steps: target identification, protein synthesis, drug matching or repurposing, and bioassay synthesis. Findings: At the current annual price for targeted therapies of around $200, 000/patient and RCT costs of $100, 000 per subject, the financial incentives for further drug discovery under the current approval paradigm evaporate for targets affecting less than 0.5% of the lung cancer population, even if Phase 0 and I R&D costs are as low as $100 million/target. To ensure proper incentives for Pharma companies to innovate even for targets affecting less than 0.5% of lung cancer patients, targeted therapies would require margins of over $828, 000/year per patient over manufacturing costs, implying global societal costs of over $1.45 trillion/year. PDDP is instead projected to be sustainable, even accounting for the required investments in infrastructure. At peak, we calculate PDDP will cost $1, 250 per lung cancer patient, net of drug manufacturing costs. Pharma companies will invest in PDDP and hence foster further research if successful personalized drugs yield margins between $25, 000 - $90, 000/year per patient. Conclusions: PDDP is already a feasible and sustainable drug discovery process. This can create a paradigm shift in drug discovery by testing the process instead of specific drugs. By contrast, under current regulatory agency requirements, the drug discovery and development process is unsustainable for targets with a frequency of less than 0.5%. This approach promotes innovation and equity in access for all patients with any identifiable target mutation Citation Format: Pietro Veronesi, Adekunle Odunsi, Marciano Siniscalchi, Marina Chiara Garassino. Personalized Drug Discovery Process (PDDP): An AI-driven, sustainable, and equitable solution for cancer drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3565.