Precision Pharmacogenetics and Genotype Class Based Prediction of Mavacamten Response in Obstructive Hypertrophic Cardiomyopathy

Overview

This research study, aims to understand why a specific heart medication called mavacamten works better for some people with hypertrophic cardiomyopathy (HCM) than for others. We believe the answer might be in our genes. The study focuses on two key areas: 1. The specific gene causing HCM:The study will investigate whether the type of gene causing the condition in a person influences how well mavacamten works for them. 2. Each individual carry a certain gene that helps metabolise and process medication (otherwise known as pharmacogenetics). Our research will closely examine a gene called CYP2C19 to see if a person's natural processing speed (slow, normal, or fast) affects the medicine's performance. The study will also look for rare genetic variations that standard tests might miss.

The PRO-Gene Mava study is a prospective, observational cohort study designed to investigate the genetic and pharmacogenomic determinants of response to mavacamten in adults with obstructive hypertrophic cardiomyopathy (oHCM). While mavacamten, a cardiac myosin inhibitor, has demonstrated efficacy in reducing left ventricular outflow tract (LVOT) obstruction, significant inter-individual variability in clinical response exists. This study is predicated on two primary hypotheses: 1. Genotype-Dependent Efficacy: Pre-clinical data suggest mavacamten's mechanism of action may be more effective in normalising hypercontractility driven by thick-filament sarcomeric variants (e.g., MYH7) compared to thin-filament variants (e.g., TNNT2, TNNI3), which primarily increase myofilament calcium sensitivity. This study will test this hypothesis in a real-world clinical setting. 2. Pharmacogenomic Variability: Mavacamten is metabolised predominantly by CYP2C19. The Summary of Product Characteristics (SmPC) recommends dose adjustments for known poor metabolizers (PMs). However, standard clinical genotyping panels typically only assess common loss-of-function alleles (e.g., \*2, \*3), potentially misclassifying patients with rare alleles. Furthermore, the clinical impact on intermediate (IM) and ultra-rapid (UM) metabolizers is not well-characterised.The study aims to bridge this knowledge gap by integrating deep genetic data with longitudinal clinical outcomes.