| Title: | Prediction of drug response using pharmacogenomics-driven machine learning approach |
| Lead Institution: | Yongin Severance Hospital |
| Principal investigator: | Professor Sunga Bae |
Application 85037
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About
Precision medicine tailors diagnoses and treatment options on an individual patient basis. This is a paradigm shift from choosing a treatment based on aggregate therapeutic efficacy (i.e., percentage of patients achieving the desired therapeutic benefit in clinical trials). Individualized treatment is greatly needed because not all patients will respond to a particular drug, and drug response varies among patients. Reduction of costs in assaying patients' genetic makeup (genome) among other biological measures has accelerated advances in precision medicine. However, there are only a few well-characterized systemic diseases for which dramatically improved treatment outcomes have been achieved by using genomics data in routine clinical settings. To expand the benefits of individualized medicine to include other widely prevalent complex diseases (e.g., mental health disorders, migraines etc.), a key question to address is: "Can a clinician's assessment of disease severity, augmented with patient-specific biological measures help identify a therapeutic agent (e.g., drug) and dose with the highest likelihood of achieving the desired therapeutic benefit?" The answer to that question requires "intelligence" from high-quality data, rich clinical insights/annotations from domain experience (e.g., physicians, biologists), and analytical approaches to combine heterogeneous patient-specific measures to generate "artificial intelligence." The aims of this study are to identify and validate genetic variants by data-driven pharmacogenomic approach on drug responses and adverse events, using UK Biobank data. Developing this systematic prediction framework that is optimized for pharmacogenetic assessments can help each individual achieve an optimal therapeutic response, avoid therapeutic failures, and minimize drug-induced toxicity. The estimated duration of our project is more than 3 years.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8874 | Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data | Changho Han (+6) | 2024 | iScience |
| 11939 | Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange | Dukyong Yoon (+6) | 2024 | Journal of Medical Internet Research |
Enabling scientific discoveries that improve human health