SHERIDAN, WYOMING – July 8, 2025 – A confluence of artificial intelligence and genomics is creating a critical inflection point for the pharmaceutical industry—offering a long-awaited opportunity to reduce adverse drug reactions, improve clinical outcomes, and restore public trust in a sector burdened by scrutiny and skepticism.
As adverse drug reactions remain among the top five causes of death in the U.S.—with more than 150,000 fatalities annually and billions in associated healthcare costs—pharmacogenetics is emerging as a strategic imperative rather than a research niche. But until recently, scaling this precision approach to drug prescription had remained elusive due to technical limitations. Today, AI-enabled analysis of vast, real-world genomic datasets is transforming that landscape.
A Technology Shift With Strategic Implications
Industry adoption of pharmacogenetics has long been hampered by complexity. Drug response is shaped not by individual genes, but by intricate interactions between multiple biological and environmental variables. Traditional tools simply couldn’t process the volume or depth of this data. Now, advanced AI platforms are bridging the gap.
As Allan Gobbs, co-founder of the pharmacogenetics startup PGxAI, explains: “I have a front-row seat to how advanced AI platforms can integrate vast, multilayered datasets encompassing genomics, transcriptomics, epigenetics and even diet and microbiome profiles. These systems can learn from diverse patient populations around the world and adapt to account for differences in drug metabolism. Just as importantly, they improve over time as they ingest real-world outcomes from patients who follow pharmacogenetic guidance.”
Real-World Data Enables Real-Time Personalization
What was once theoretical is now operational. Major healthcare networks—including the Department of Veterans Affairs, Mount Sinai, Mayo Clinic, and Massachusetts General Hospital—are aggregating patient DNA with clinical records to inform treatment at scale. Notable initiatives include:
- The VA’s Million Veteran Program, one of the largest genomic biobanks globally.
- Mount Sinai’s Million Health Discoveries Program, launched with Regeneron, which is sequencing one million patients and integrating their genomic data with electronic health records.
- Mayo Clinic’s expansion of its biobank via collaboration with the All of Us Research Program.
These efforts are now feeding real-world datasets into AI engines capable of delivering actionable insights at the point of care.
“Today’s models can process enormous volumes of data in real time, providing clinical insights at the point of care,” Gobbs notes, highlighting the leap from delayed consensus-based recommendations to dynamic, evidence-informed guidance.
Overcoming Industry Inertia
Despite the technological readiness, industry-wide adoption of pharmacogenetic-guided prescribing remains uneven. Concerns persist that such precision could shrink pharmaceutical market segments by limiting one-size-fits-all prescriptions. Yet early use cases, particularly in cardiology, psychiatry, and neurology, show the opposite.
“For high-cost therapies, improved precision doesn’t just enhance outcomes; it accelerates adoption, increases efficiency and offers a much-needed path to restoring public trust,” says Gobbs.
He continues, “Even blockbuster drugs like GLP-1 agonists carry serious risks when used broadly—including pancreatitis, gallbladder complications, kidney impairment and potential thyroid tumors. Early user data show that nearly one in three patients carry genetic variants linked to increased risk. As these drugs scale, wide adoption must go hand in hand with personalization.”
The broader market impact is becoming clear. “Take warfarin, for example. Patients with certain genetic variants face a dramatically increased risk of serious bleeding if treated with standard doses of the drug. In psychiatry and neurology, where patient responses can vary dramatically, precision tools offer equally transformative potential.”
A New Era for Drug Development
Beyond clinical care, pharmacogenetics is reshaping pharmaceutical R&D economics. AI systems can now process decades of clinical and genetic data, creating efficiencies in identifying trial participants most likely to respond to therapies.
“The promise doesn’t end with today’s medications. AI-driven pharmacogenetics could transform the clinical trial process itself, enabling researchers to identify likely responders to experimental therapies with far greater accuracy and increase candidate drugs’ chances of success. It could even help revive drugs previously deemed failures by isolating genetic subgroups that would benefit, turning past losses into future breakthroughs.”
Moreover, the groundwork is already in place. “Indeed, over the past decade, collection of genetic data during clinical trials has quietly become standard practice across the pharmaceutical industry. Most major companies now routinely obtain DNA samples from trial participants and conduct pharmacogenomic analyses as part of early and late-stage drug development.”
“What was missing until now was the ability to interpret that data at scale. With AI systems finally able to process it, the industry has reached a turning point in the potential to realize the promise of pharmacogenetics—both in drug development and clinical practice. Whether Big Pharma seizes this opportunity or lets it slip by will define the decade ahead.”