Pharmacogenomics of HIV Therapy

$741,228R01FY2017AINIH

Vanderbilt University Medical Center, Nashville TN

Investigators

Linked publications & trials

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Abstract

DESCRIPTION (provided by applicant): Access to safe and effective antiretroviral therapy (ART) is a cornerstone in the global struggle against HIV/AIDS, which affects approximately 1 million individuals in the US and 34 million worldwide. There is marked interindividual variability in response to ART regarding drug toxicity, virologic efficacy, and immune recovery. Virtually every antiretroviral is affected by drug absorption, distribution, metabolism, and elimination (ADME), and genetic polymorphisms in ADME genes are known to have functional effects, as do off-target genes. Large effect sizes with ADME and off-target genes often reveal associations with small sample sizes. A challenge in quantifying the impact of human genetic variants on HIV treatment response is that associations are often context dependent. There is great opportunity to accelerate the pace and scope of pharmacogenomic discovery. An exciting new high impact strategy is the phenome-wide association study (PheWAS), which asks whether genetic polymorphisms are associated with one or more clinical traits (i.e. phenotypes) across the entire phenome . PheWAS is largely unbiased regarding phenotypes, and is ideal for interrogating large numbers of context-dependent associations. Large, prospective, randomized clinical trials data offer a unique window to genotype-phenotype associations. This proposal will, for the first time, apply PheWAS to data from prospective clinical trials, and will emphasize context- dependent associations. This will be involve >5,500 individuals who initiated ART in prospective, randomized trials of the AIDS Clinical Trials Group. We will apply a phenome-wide strategy to discover novel associations between genetic polymorphisms, particularly in known ADME genes, and HIV treatment response phenotypes in data from prospective, randomized clinical trials. We will refine and replicate associations, both within and beyond HIV treatment trials datasets. We will also apply simulation modeling and cost-effectiveness analysis to assess the clinical utility of upfront genetic tests to inform antiretroviral prescribing. Our vision is t identify associations that are sufficiently robust to translate into clinical care, and to impact HIV/AIDS worldwide.

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