Behind the scenes, artificial intelligence and biomedical informatics are fundamentally reshaping how scientists navigate the complex journey from laboratory discovery to life-saving medicine.
In the high-stakes race to develop new cancer drugs, a quiet revolution is underway. Behind the scenes, artificial intelligence (AI) and biomedical informatics are fundamentally reshaping how scientists navigate the complex journey from laboratory discovery to life-saving medicine. For patients awaiting new treatments, particularly those with rare cancers who have few options, this technological shift promises to accelerate the delivery of innovative therapies.
Consider this: between 2018 and 2022, the U.S. Food and Drug Administration (FDA) approved 247 novel drugs, with an impressive 43% classified as "first-in-class" medications featuring entirely new biological targets 5 . Among these groundbreaking treatments, 30 were dedicated to fighting cancer, offering new hope to patients with both solid tumors and blood cancers 5 .
What few outside research circles realize is that technologies like ChatGPT and sophisticated data analysis pipelines are now helping researchers decode the very blueprint of successful drug development, analyzing decades of research data to identify what separates promising discoveries from dead ends 5 .
Before diving into how AI is transforming cancer research, let's clarify what we mean by "artificial intelligence" in this context. In medicine, AI refers to computer systems that can perform tasks that typically require human intelligence—such as recognizing patterns, making predictions, and drawing insights from massive datasets 3 . Machine learning, a subset of AI, enables computers to learn automatically from data without being explicitly programmed for every scenario 3 .
Computer systems that perform tasks requiring human intelligence, such as pattern recognition and prediction from complex datasets.
The science of collecting, organizing, and analyzing biomedical data from genetic sequences to clinical trial results.
When we talk about "biomedical informatics," we're referring to the science of collecting, organizing, and analyzing biomedical data—from genetic sequences to clinical trial results. The integration of AI with biomedical informatics creates a powerful toolkit that can process information at a scale and speed far beyond human capability, identifying subtle connections that might escape even the most attentive researcher 5 .
"Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline" 5 .
What does AI reveal about the path to successful cancer drug development? A groundbreaking study conducted an informatics and AI-guided assessment of the regulatory and translational research landscape for first-in-class oncology drugs approved between 2018-2022 5 . The researchers employed a sophisticated biomedical informatics pipeline that leveraged interoperability standards and AI tools to integrate and analyze public databases from the FDA, National Institutes of Health, and World Health Organization 5 .
Solid tumor treatments among first-in-class cancer drugs
Median publications preceding FDA approval
Years of target-based research before approval
| Category | Number of Drugs | Percentage |
|---|---|---|
| Total First-in-Class Drugs | 30 | 100% |
| Solid Tumor Treatments | 19 | 63.3% |
| Blood Cancer Treatments | 11 | 36.7% |
| Program | Purpose |
|---|---|
| Fast Track | Facilitates development and expedites review of drugs for serious conditions |
| Breakthrough Therapy | Expedites development and review for drugs showing substantial improvement |
| Accelerated Approval | Allows approval based on surrogate endpoints likely to predict clinical benefit |
| Priority Review | Shortens FDA review timeline from 10 months to 6 months |
The research also highlighted how regulatory pathways have adapted to accelerate promising treatments. Expedited programs were used extensively for serious conditions, with 65% of novel drug approvals in 2022 utilizing one or more of these programs 1 . These include Fast Track, Breakthrough Therapy, Priority Review, and Accelerated Approval—all designed to bring vital medications to patients faster 1 .
To understand how AI is transforming our approach to cancer drug development, let's examine the pivotal experiment conducted by researchers to analyze first-in-class oncology drugs.
The team identified all novel drugs receiving FDA approval between 2018-2022, focusing on first-in-class medications that work through entirely new biological mechanisms compared to existing treatments 5 .
They divided these drugs into cancer versus non-cancer categories to enable comparative analysis 5 .
The researchers created a sophisticated computational pipeline that incorporated interoperability standards and AI tools similar to ChatGPT to integrate information from multiple public databases 5 .
The system analyzed what the study called "translational research metrics"—essentially quantifying the research journey from basic scientific discovery to clinical application by examining publication histories, clinical trials, and regulatory documents 5 .
When the data was processed and analyzed, several compelling patterns emerged that illustrate the complex landscape of cancer drug development:
| Research Phase | Cancer Drugs | Non-Cancer Drugs |
|---|---|---|
| Target-Based Research (Median Years) | 33 years | 43 years |
| Minimum Years of Target-Related Research | 25+ years for 94.4% of drugs | 25+ years for 94.4% of drugs |
| Translational Research Publications | At least 10 years for 85.5% of drugs | At least 10 years for 85.5% of drugs |
Years of target-based research before cancer drug approval
Median scientific publications preceding FDA approval
Perhaps most importantly, the research demonstrated that "biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline" 5 . By mapping the successful pathway of first-in-class drugs, the study provides a roadmap that could help future researchers navigate the complex journey more efficiently.
What does it take to conduct such cutting-edge research? Today's cancer informatics researcher relies less on traditional lab equipment and more on sophisticated digital tools and data resources.
FDA approval databases provide crucial information about drug applications, clinical trial results, and approval pathways 5 .
Platforms like PubMed give researchers access to millions of scientific papers, enabling tracking of research evolution 5 .
Systems like ChatGPT help process vast amounts of unstructured text data, identifying patterns and connections 5 .
Universal translators of the digital research world, allowing different systems to communicate effectively 5 .
Custom-built software frameworks that automate data gathering, cleaning, analysis, and visualization 5 .
As AI systems become more sophisticated, their potential to reshape cancer care expands. Researchers are already developing AI that can predict the activity of genes at the cellular level, potentially opening new paths for understanding the mutations that cause cancers to occur 4 . Other systems, like the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, can detect signs of 19 different tumor types and predict a patient's survival potential across different cancers 4 .
Perhaps most excitingly, AI is beginning to help identify which patients might benefit most from specific treatments. The National Institutes of Health developed LORIS (logistic regression-based immunotherapy-response score), which can predict which cancer patients might benefit best from certain immunotherapy treatments 4 . This is particularly valuable since immunotherapy, which uses the body's immune system to target cancer cells, is less invasive than traditional chemotherapy but only effective for a subset of people 4 .
As these technologies continue to evolve, they promise not only to accelerate drug development but to create a future where cancer treatment becomes increasingly personalized, matching the right therapy to the right patient at the right time. Though AI will never replace the critical judgment of physicians and researchers, it is becoming an indispensable partner in the fight against cancer—helping decode the complex language of cancer at a scale and speed never before possible.