Worldwide Medical and Safety, Pfizer Inc, New York, NY.
Pfizer Research and Development, Pfizer Inc, New York, NY.
JCO Clin Cancer Inform. 2024 Oct;8:e2400087. doi: 10.1200/CCI.24.00087. Epub 2024 Oct 1.
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.
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.
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 43 years; < .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.
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.
癌症药物开发仍然是一个关键但具有挑战性的过程，影响着数百万患者及其家庭。我们采用生物医学信息学和人工智能 (AI) 方法，评估了定义用于癌症患者的首创类药物的监管和转化研究格局。
这是一项对 2018 年至 2022 年期间美国食品和药物管理局 (FDA) 批准的所有新型首创类药物的回顾性观察性研究，按癌症药物与非癌症药物进行分层。一个利用互操作性标准和 ChatGPT 的生物医学信息学管道对 FDA、美国国立卫生研究院和世界卫生组织提供的公共数据库进行了整合和分析。
在 2018 年至 2022 年期间，FDA 总共批准了 247 种新型药物，其中 107 种（43.3%）为涉及新生物靶点的首创类药物。在这些首创类药物中，有 30 种（28%）治疗方法适用于癌症患者，包括 19 种（63.3%）实体瘤和其余 11 种（36.7%）血液癌。成功获得 FDA 批准的首创类癌症药物之前，平均有 68 篇基础、临床和其他相关转化科学的文献，肿瘤相关治疗的基于靶点的研究中位年限少于非癌症相关治疗（33-43 年；<.05）。总体而言，94.4%的首创类药物至少有 25 年与目标相关的研究论文，而 85.5%的首创类药物至少有 10 年的转化研究出版物。
新型首创类癌症治疗方法的特点是具有多样化的临床适应症、个性化的分子靶点、依赖于加速的监管途径以及反映这一复杂格局的转化研究指标。生物医学信息学和 AI 提供了可扩展的数据驱动方法，可以评估甚至解决药物开发管道中的重要挑战。
