Challenges and opportunities associated with rare-variant pharmacogenomics br

Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.

Automated summary

Recent advances in NGS have identified thousands of rare pharmacogenetic variations with unknown functional effects, but accurate interpretation at the individual patient level remains challenging. Strategies involving experimental assays, AI, and machine learning combined with population-scale biobank projects can facilitate the interpretation of NGS data for personalized medicine.