A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

Abstract

Background: The field of pharmacogenomics focuses on the way a person’s genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient’s genome, hence rendering its incorporation into clinical routine practice a realistic possibility. Results: The potential availability of a vast number of identified genetic variants in a clinical setting highlights the necessity of developing a method to evaluate and prioritize this information towards its exploitation in guiding medication or dosing scheme systematically and effectively. In this direction, the present study examines the development of a computational model that can classify new variants according to their possible effects on protein function, which in turn affects drug response, by using as a training set a dataset of functionally validated single nucleotide variants (SNVs) located in pharmacogenes. Conclusion: Overall, the proposed model holds promise to lead to an extremely useful variant prioritization and scoring tool with interesting clinical applications in pharmacogenomics.