Biobanks & Medication: Predicting Drug Response with Genetics
- A UCLA study introduces a novel framework leveraging biobanks to improve the prediction of patient responses to common medications and potential side effects. Published in Cell Genomics,the research...
- Michal Sadowski, the study's first author and a UCLA Bioinformatics Ph.D.
- The research team, led by UCLA's Noah Zaitlen and the University of Chicago's Andy Dahl, analyzed data from over 342,000 individuals in the UK Biobank.Thay investigated how genetic...
Decipher how your genes impact medication effectiveness! A groundbreaking UCLA study uses biobanks to revolutionize drug response prediction. The research team analyzed data from over 342,000 individuals, showing genetic variations can explain up to 9% of drug response variability.They probed four common drugs: statins, metformin, warfarin, and methotrexate, with the goal of improving personalized medicine. This approach offers insights into side effects and efficacy by using genetics to predict treatment outcomes. The study found that standard polygenic scores may underperform in clinical settings.News Directory 3 keeps you informed on the latest developments. Discover what’s next in the future of pharmacogenomics.
Biobanks Enhance Genetic Prediction of Drug Response, UCLA Study Finds
Updated june 09, 2025
A UCLA study introduces a novel framework leveraging biobanks to improve the prediction of patient responses to common medications and potential side effects. Published in Cell Genomics,the research highlights the value of large-scale genomic data in understanding the genetic factors influencing drug response.
Michal Sadowski, the study’s first author and a UCLA Bioinformatics Ph.D. candidate, noted that traditional pharmacogenomic studies often suffer from small sample sizes and high costs. Biobanks, containing sequenced genetic data from vast populations, offer a cost-effective option for analyzing drug response genetics. While not a replacement for randomized controlled trials, biobank data can significantly enhance future research and the use of genetics to predict treatment outcomes.
The research team, led by UCLA’s Noah Zaitlen and the University of Chicago’s Andy Dahl, analyzed data from over 342,000 individuals in the UK Biobank.Thay investigated how genetic makeups affected responses to four widely prescribed drugs: statins (high cholesterol), metformin (type 2 diabetes), warfarin (blood clots), and methotrexate (autoimmune diseases and cancer). The goal was to determine the extent to which genetic variation influences drug response variability and identify specific genes involved. This approach aims to improve personalized medicine by using genetics to predict treatment outcomes,offering insights into potential side effects and efficacy.
The study identified 156 genes potentially driving variations in statins’ impact on LDL cholesterol levels. genetic differences accounted for approximately 9% of the variation in drug response from person to person. The research also revealed that gene-drug interactions can affect the predictive power of polygenic scores, which estimate an individual’s risk for a trait or disease based on numerous genetic variants. The study found that standard polygenic scores might underperform in clinical settings because they include data from both statin users and non-users.
“we hope that in the future this will enable clinicians and patients to weigh the benefits and risks of a treatment in a more personalized way, and make more informed and timely decisions to embark on the treatment,” Sadowski said. “We expect that the analysis of biobank data will be most useful for widely prescribed drugs.”
What’s next
Future research is needed to enhance the reliability of inferences from biobank observational data and to better understand the limitations of genetic risk prediction in diverse populations. Further studies could reveal additional genetic associations and components of missing heritability through context-specific analyses of complex diseases.