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Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance.

ABSTRACT: We propose a systematic approach for a better understanding of how HIV viruses employ various combinations of mutations to resist drug treatments, which is critical to developing new drugs and optimizing the use of existing drugs. By probabilistically modeling mutations in the HIV-1 protease or reverse transcriptase (RT) isolated from drug-treated patients, we present a statistical procedure that first detects mutation combinations associated with drug resistance and then infers detailed interaction structures of these mutations. The molecular basis of our statistical predictions is further studied by using molecular dynamics simulations and free energy calculations. We have demonstrated the usefulness of this systematic procedure on three HIV drugs, (Indinavir, Zidovudine, and Nevirapine), discovered unique interaction features between viral mutations induced by these drugs, and revealed the structural basis of such interactions.

SUBMITTER: Zhang J

PROVIDER: S-EPMC2824344 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance.

Zhang Jing J Hou Tingjun T Wang Wei W Liu Jun S JS

Proceedings of the National Academy of Sciences of the United States of America 20100111 4

We propose a systematic approach for a better understanding of how HIV viruses employ various combinations of mutations to resist drug treatments, which is critical to developing new drugs and optimizing the use of existing drugs. By probabilistically modeling mutations in the HIV-1 protease or reverse transcriptase (RT) isolated from drug-treated patients, we present a statistical procedure that first detects mutation combinations associated with drug resistance and then infers detailed interac ...[more]

PMID: 20080674

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Project description:BackgroundAssessing the prevalence of HIV-1 drug-resistance and the mutation patterns associated with resistance in the geographical regions implementing free antiretroviral therapy (ART) in China is necessary for preventing the spread of resistant strains and designing the regimens for the subsequent therapies with limited resources.MethodsPlasma samples in different cities/prefectures were collected at Yunnan Provincial Hospital of Infectious Disease from January 2010 to December 2011. Genotyping of drug-resistant individuals was conducted using an in-house assay on plasma samples. Viral load, CD4 T cell counts and demographic data were obtained from medical records and an administered questionnaire.ResultsA total of 609 pol sequences (515 ART-failure and 94 therapy-naïve individuals) derived from 664 samples were obtained. The prevalence of drug-resistance was 45.1% in the ART-failure individuals. Of these, 26.8% harbored HIV strains dually resistant to nucleoside reverse transcriptase inhibitors and non-nucleoside reverse transcriptase inhibitors, and 14.8% harbored HIV strains resistant to only one drug category. Mutations such as M184V/I, K103N, V106A, Y181C and G190A were common among the ART-failure individuals, and the frequencies of M184V/I, K103N and V106A were 28.2%, 19.2%, and 22.1%, respectively. The percentages of individuals exhibiting intermediate or high-level resistance to 3TC, FTC, EFV and NVP drugs were 28.4%, 28.2%, 37.3%, and 37.5%, respectively. Factors such as ethnicity, transmission route, CD4 counts, viral load and the duration of ART were significantly correlated with development of drug resistance in the ART-failure individuals.ConclusionsThe high prevalence of HIV drug-resistance observed among the ART-failure individuals from 2010 to 2011 in Yunnan province should be of increasing concern in regions where the implementation of ART is widespread. Education about the risk factors associated with HIV drug resistance is important for preventing and controlling the spread of HIV drug-resistant strains.

Project description:BACKGROUND: Population transmission models of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use simplistic assumptions--typically constant, homogeneous rates--to represent the short-term risk and long-term effects of drug resistance. In contrast, within-host models of drug resistance allow for more detailed dynamics of host immunity, latent reservoirs of virus, and drug PK/PD. Bridging these two levels of modeling detail requires an understanding of the "levers"--model parameters or combinations thereof--that change only one independent observable at a time. Using the example of accidental tenofovir-based pre-exposure prophyaxis (PrEP) use during HIV infection, we will explore methods of implementing host heterogeneities and their long-term effects on drug resistance. RESULTS: We combined and extended existing models of virus dynamics by incorporating pharmacokinetics, pharmacodynamics, and adherence behavior. We identified two "levers" associated with the host immune pressure against the virus, which can be used to independently modify the setpoint viral load and the shape of the acute phase viral load peak. We propose parameter relationships that can explain differences in acute and setpoint viral load among hosts, and demonstrate their influence on the rates of emergence and reversion of drug resistance. The importance of these dynamics is illustrated by modeling long-lived latent reservoirs of virus, through which past intervals of drug resistance can lead to failure of suppressive drug regimens. Finally, we analyze assumptions about temporal patterns of drug adherence and their impact on resistance dynamics, finding that with the same overall level of adherence, the dwell times in drug-adherent versus not-adherent states can alter the levels of drug-resistant virus incorporated into latent reservoirs. CONCLUSIONS: We have shown how a diverse range of observable viral load trajectories can be produced from a basic model of virus dynamics using immunity-related "levers". Immune pressure, in turn, influences the dynamics of drug resistance, with increased immune activity delaying drug resistance and driving more rapid return to dominance of drug-susceptible virus after drug cessation. Both immune pressure and patterns of drug adherence influence the long-term risk of drug resistance. In the case of accidental PrEP use during infection, rapid transitions between adherence states and/or weak immunity fortifies the "memory" of previous PrEP exposure, increasing the risk of future drug resistance. This model framework provides a means for analyzing individual-level risks of drug resistance and implementing heterogeneities among hosts, thereby achieving a crucial prerequisite for improving population-level models of drug resistance.

Dataset Information

Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance.

Publications

Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance.

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