Characterizing the Mutation Pathway for Integrase Inhibitor

• Activity Number: 256
• Type: Contributed
• Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Jing Zhao*+
• Companies: Merck Research Laboratories
• Keywords: Bayesian model selection ; Mutation Pathway ; Markov Chain Monte Carlo (MCMC)

Abstract

A wide variety of mutations in the viral genome of Human Immunodeficiency Virus Type-1 (HIV-1) are associated with reduced susceptibility to Antiretroviral Therapies (ARTs). Identifying pathways in which viral populations progress genotypically in the presence (or absence) of particular therapies, especially the new class Integrase Inhibitor may provide insight into the mechanism of resistance and help make more informed treatment decisions.

Our aim is to characterize statistically the dependency among viral genetic loci measured over time. We propose an exploratory Bayesian model selection procedure to identify Markov models that best describe this mutation process. This procedure is built on a 3-stage hierarchical mixture model for the Markov model uncertainty. This exploratory approach to characterizing the viral genetic pathways can provide interesting biological/clinical insight.