The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients

Abstract

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.

Nucleoside reverse-transcriptase inhibitor cross-resistance and outcomes from second-line antiretroviral therapy in the public health approach: an observational analysis within the randomised, open-label, EARNEST trial

“… 19 , 20 , 21 The effect of resistance mutations on viral replication fidelity might also protect the protease inhibitor by limiting the development of new mutations to escape drug pressure, thus increasing regimen durability. 22 , 23 Pharmacokinetic considerations might enhance these benefits; NRTIs, such as tenofovir and lamivudine, have long intracellular half-lives that might help to maintain viral suppression when protease inhibitor levels are low (with late or occasional missed doses) and this effect might be independent of predicted activity. This potential effect could explain why we observed that the NRTIs were capable of matching the suppression obtained with raltegravir (a drug with a relatively short intracellular half-life) in combination with a protease inhibitor.…”

An Evolutionary Model-Based Approach To Quantify the Genetic Barrier to Drug Resistance in Fast-Evolving Viruses and Its Application to HIV-1 Subtypes and Integrase Inhibitors

“…Apart from a documented subtype B INSTI resistance pathway attributed to differential codon usage at position 140 (17,(21)(22)(23), the role of baseline nucleotide variation on INSTI resistance development has been less systematically investigated. The probabilistic models of resistance evolution that previously quantified the genetic barrier for the historical drug classes require sequence data of various subtypes from treatmentexperienced patients (4)(5)(6), which are limited to date in the context of INSTIs (19,24). Alternatively, the genetic barrier can be estimated by the number and type of required nucleotide substitutions to evolve from a wild-type virus to a resistant mutant (25).…”

A Framework for Inferring Fitness Landscapes of Patient-Derived Viruses Using Quasispecies Theory

“…In clinical settings, viral fitness is also of great interest. Disease progression, the formation of escape mutants, and ultimately treatment failure depend on viral fitness ( Clavel and Hance 2004 ; Beerenwinkel et al 2013 ).…”