AI Revolutionizes Cancer Treatment: Precision Medicine’s New Frontier
Artificial intelligence is rapidly transforming cancer care, offering unprecedented opportunities in diagnostics, drug development, and personalized treatment. AI’s ability to analyze vast datasets is paving the way for more effective and targeted therapies, promising a new era of precision oncology.
The Rise of Precision Oncology
Precision medicine has advanced significantly, beginning in the 1990s with the identification of biomarkers to guide treatment decisions. Initially, patients relied on chemotherapy and radiation. These methods often led to serious side effects, highlighting the need for more tailored approaches.
Mohan Uttarwar, CEO of 1Cell.Ai, said, “We believe every oncology solution should be precision-based.” Technological advancements, like DNA sequencing and sophisticated computational models, enabled scientists to identify cancer subtypes and stratify patients accurately.
AI’s Role in Drug Discovery
AI is a pivotal tool in precision oncology and drug development, capable of analyzing immense datasets to identify patterns and predict outcomes. This technology is changing how pharmaceutical research and development functions. AI allows companies to transition to data-driven methods rather than relying solely on intuition.
Arun Krishna, head of U.S. oncology at AstraZeneca, acknowledged the profound change AI has brought to drug discovery. Companies can use AI to search cancer genomes for specific mutations, identify patients most likely to respond to treatment, and design better therapies, like antibody-drug conjugates.
Drugmakers can use their extensive biochemical datasets to predict the potency and potential toxicities of molecules, as well as possible drug interactions. Animal testing, a time-consuming and labor-intensive process, could be minimized or replaced by AI, a move recognized by the FDA. The agency plans to phase out animal testing for certain therapies in favor of AI and human organoid models.
Examples of AI in Action
Several biopharma companies are integrating AI into their processes. AstraZeneca has invested over $1 billion in AI partnerships. For Krishna, predictive AI in drug discovery is “the holy grail.” AI can now identify potentially useful molecules much faster than traditional methods, reducing the time from months or years to around 30 days.
AstraZeneca has used AI to better classify lung cancer patients, using an AI model to determine which patients are more likely to respond to treatment. This effort yielded an AI-derived biomarker, TROP2-QCS. Patients with this biomarker saw a 43% decrease in the risk of disease progression or death when treated with Dato-DXd versus docetaxel.
Jared Christensen, vice president at Pfizer, mentioned that his company is developing the next generation of tools to use in preclinical and clinical development. Novartis has partnered with Generate:Biomedicines to utilize its AI platform for designing new medicines. They put down $65 million up-front.
Future Trends and Challenges
The potential of generative AI is remarkable. In August 2023, Insilico Medicine brought the first drug fully created with generative AI into Phase II clinical trials. The biotech reported positive results in idiopathic pulmonary fibrosis. Generate:Biomedicines collected $273 million for its pipeline of protein therapeutics targeting various diseases.
According to Ofer Sharon, generative AI has the capacity to “redefine the pace and scope of innovation” in drug development. Uttarwar agrees that generative AI will dramatically reduce drug discovery time and costs. He and Sharon agree that the next frontier for precision oncology is multi-omics, which integrates information from the genome, transcriptome, proteome, and metabolome.
“Genomic alterations tell part of the story, but proteins reflect what’s actually happening in real time within the tumor microenvironment,”
—Ofer Sharon, CEO of OncoHost
The rising integration of AI will be facilitated by “growing regulatory acceptance of AI-defined biomarkers” and an industry push to integrate AI tools into workflows and trial designs, according to Sharon. The global AI in oncology market is projected to reach $4.9 billion by 2028, growing at a CAGR of 22.5% from 2021 to 2028 (Mordor Intelligence).
Challenges remain, especially regarding data management and trust. AI models depend on the quality of their training data. Better data standardization is needed across the drug development pipeline to ensure AI models are properly trained, so the insights are actionable. Sharon stresses the need for transparency in AI decision-making.
Sharon believes 2025 will be a pivotal year, with the first AI-discovered or designed oncology therapeutics entering human trials, signifying a paradigm shift in therapy development. The future of cancer treatment is undoubtedly intertwined with artificial intelligence.
