Abstract
Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.
Highlights
The emergence of drug resistant mutants remains a major obstacle to long-term treatment success of highly active antiretroviral therapy (HAART) against human immunodeficiency virus (HIV)-1 [1,2]
We illustrate our method by predicting fitness characteristics of mutant genotypes for two different antiretroviral therapies with the drugs zidovudine and indinavir
Our model extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure
Summary
The emergence of drug resistant mutants remains a major obstacle to long-term treatment success of highly active antiretroviral therapy (HAART) against HIV-1 [1,2]. Mathematical models of in vivo viral infection dynamics have provided critical insights into HIV-1 disease and therapy by disentangling viral and target cell dynamics [3,4], quantifying drug class specific effects on viral load decay [5,6] and elucidating general principles of antiretroviral therapy [7,8] Their utility in studying the emergence of drug-specific mutations and resistance, is limited by the availability of realistic mutation landscapes. Statistical models of mutational pathways have been used to understand the evolution of drug-resistance in vivo, for example, by estimating evolutionary landscapes of viral mutations based on in vivo data [12,13,14,15], establishing genotype–phenotype maps [16] and predicting individual treatment outcomes [17,18] These approaches, do not integrate details of the viral infection dynamics and the specific actions of different drug classes
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