By Peter Bannister and Alan Thomas Kohler on April 26, 2024
Pharmacogenomics (PGx), the study of how genetic profiles impact an individual’s responses to medication, has already begun to help healthcare providers (HCPs) optimize care through its capacity to preemptively enhance drug efficacy, minimize adverse side effects, and improve patient experiences. This rapidly growing field marries bioinformatics and pharmacology and represents a transformative new era of precision medicine and highly personalized treatments, one that serves patients by supporting clinicians to better predict therapeutic responses and more accurately optimize drug dosages.
But, with data-driven solutions come data-driven challenges, not the least of which is the size and complexity of the datasets that pharmacogenomics relies upon. The vastness of genomic data and patient responses to medical treatments requires a herculean human effort to analyze, and because distinguishing meaningful patterns (signal) from irrelevant data (noise) is such a significant challenge in large-scale data analysis, researchers may overlook vital connections between genetic information and patient drug responses.
AI has the potential to help PGx manage its data analysis challenges through its capacity to efficiently analyze enormous datasets and identify patterns and correlations that may otherwise remain obscured, aiding researchers and manufacturers in the production of new, more effective medications. Similarly to how AI is used in industries like aerospace for predictive maintenance (e.g., analyzing jet engine data), AI systems in healthcare can excel at cutting through the noise; that is, differentiating normal genetic variations from those that signify disease or predict drug responses, a process for human researchers that is analogous to finding a needle in a haystack. But, AI-driven PGx systems can also help patients directly. By using their patient’s genetic profile data HCPs can better predict individual responses to specific medications and help make informed treatment decisions that lead to better treatment outcomes.
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