Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of First-in-Class Oncology Drugs in the United States, 2018-2022.

Abstract

Purpose: Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer.

Methods: This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO.

Results: Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications.

Conclusion: Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.