Related Experiment Video
Updated: Jun 11, 2025
Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
Published on: February 7, 2021
Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of
Jay G Ronquillo1,2, Brett South1,2, Prakash Naik2
1Worldwide Medical and Safety, Pfizer Inc, New York, NY.
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.
Insights
Developing novel cancer drugs requires extensive research, with first-in-class cancer treatments averaging 33 years of target research. Biomedical informatics and AI aid in navigating this complex drug development pipeline.
Area of Science:
- Biomedical Informatics
- Artificial Intelligence
- Drug Development
- Oncology Research
Background:
- Cancer drug development is a complex and lengthy process.
- First-in-class drugs represent significant therapeutic advancements.
- Biomedical informatics and AI offer novel approaches to analyze drug development.
Purpose of the Study:
- To assess the regulatory and translational research landscape of successful first-in-class cancer drugs.
- To analyze the characteristics of novel first-in-class drugs approved by the FDA.
- To leverage biomedical informatics and AI for evaluating cancer drug development.
Main Methods:
- Retrospective observational study of FDA-approved novel first-in-class drugs (2018-2022).
- Stratification of drugs by cancer versus noncancer indications.
- Utilized a biomedical informatics pipeline with ChatGPT for data analysis from public databases.
Main Results:
- 30 out of 107 first-in-class drugs (28%) were for cancer treatment.
- Oncology drugs required a median of 33 years of target research, compared to 43 years for non-cancer drugs.
- 94.4% of first-in-class drugs had at least 25 years of target research publications.
Conclusions:
- First-in-class cancer treatments are characterized by diverse targets and expedited regulatory pathways.
- Translational research metrics are crucial indicators in the complex cancer drug landscape.
- Biomedical informatics and AI provide scalable, data-driven solutions for drug development challenges.
Related Concept Videos
02:57Targeted Cancer Therapies
There are several types of targeted therapies against...
01:11Clinical Trials: Overview
02:50Combination Therapies and Personalized Medicine
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
01:26Drug Discovery: Overview