Proof of principle concept for the analysis and functional prediction of rare genetic variants in the CYP2C19 and CYP2D6 genes.

Background: Variations in pharmacogenes that regulate drug absorption, distribution, metabolism, and excretion (ADME) contribute to approximately 20-30% of interindividual differences in drug response. While many common variants are successfully utilized in clinical settings to predict individual drug responses, a significant portion of the genetic basis underlying this variability remains unidentified. This includes rare variants, which are estimated to account for 4-6% of drug response variability.

Results: To comprehensively elucidate the functional consequences and molecular mechanisms of rare variants, we conducted in vitro enzyme expression studies combined with in silico structure-function analyses. We selected 11 rare variants in the CYP2C19 and CYP2D6 genes identified among participants within the Estonian Biobank. Variant cDNAs were heterologously expressed in HEK-293 cells, and detailed enzyme activity analyses were performed. The experimental results were further validated against average scores from five optimized in silico prediction models: LRT, Mutation Assessor, PROVEAN, VEST3, and CADD. To explore structure-activity relationships, we performed in silico docking of substrates into available 3D enzyme structures. Our findings reveal that most of the rare genetic variants caused significant functional alterations, including: (i) Likely impairments in substrate transport to the active site due to narrowing of access channels; (ii) Changes in catalytic rates; and (iii) Potential effects on substrate extrusion rates from the active site. The in silico prediction tools accurately anticipated the functional impact of 6 out of the 11 variants (54%).

Conclusions: Evaluating the functionality of rare variants will become increasingly essential as rapid and cost-effective whole-genome sequencing technologies continue to advance. Our results highlight the need for further refinement of in silico prediction models, particularly those leveraging 3D crystal enzyme structures, to enhance the accuracy of functional predictions for rare genetic variants.