HIV-1 Drug Resistance: Where do Polymorphisms Fit in?

Theys, Kristof; Abecasis, Ana; Vandamme, Anne‐Mieke

doi:10.2217/fmb.13.10

Cited by 12 publications

(16 citation statements)

References 19 publications

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“…The proportion of polymorphisms varies between and within subtypes, and polymorphisms that are occurring naturally may contribute to the evolution of HIV‐1 drug resistance [Svicher et al, ; Theys et al, ]. In HIV‐1B infection, polymorphisms strongly correlate with virological failure and influence polymerase replication capacity [Garriga et al, ; Ng et al, ].…”

Section: Discussionmentioning

confidence: 99%

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Limited increase in primary HIV‐1C drug resistance mutations in treatment naïve individuals in Ethiopia

Huruy

Maier

Mulu

et al. 2015

Journal of Medical Virology

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Antiretroviral drug resistance is a major challenge for management and control of HIV-1 infection worldwide and particularly in resource limited countries. The frequency of primary drug resistance mutations (DRMs) and of naturally occurring polymorphisms was determined in 83 antiretroviral treatment (ART) naïve Ethiopian individuals infected with HIV-1, consecutively enrolled in 2010. In all individuals HIV-1C was found. The median (interquartile range) of CD4(+) T-cell count and viral load were 100 (49-201) cells/μl and 44,640 (12,553-134,664) copies/ml, respectively. Protease (PR) and reverse transcriptase (RT) genes of HIV-1 RNA were amplified and sequenced. The proportion of primary DRM to any drug class, using the World Health Organization mutation lists, was 7.2% (6/83), thus exceeding the WHO threshold limit of 5%. Three individuals (3.6%) had non-nucleoside reverse transcriptase inhibitor (NNRTI) mutations, two individuals (2.4%) had protease inhibitor mutations, and one (1.2%) had mutations associated with two drug classes (nucleoside reverse transcriptase inhibitor and NNRTI). In addition, the frequency of polymorphisms in the PR and RT genes was higher compared with previous studies in Ethiopian as well as worldwide isolates. Hence, genotypic drug resistance testing as part of routine management of individuals seems reasonable even in resource limited countries prior to treatment in order to allow proper choice of ART.

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Section: Discussionmentioning

confidence: 99%

“…In HIV‐1B infection, polymorphisms strongly correlate with virological failure and influence polymerase replication capacity [Garriga et al, ; Ng et al, ]. However, according to some reports the clinical significance of polymorphisms remains controversial [Taylor et al, ; Theys et al, ].…”

Section: Discussionmentioning

confidence: 99%

Limited increase in primary HIV‐1C drug resistance mutations in treatment naïve individuals in Ethiopia

Huruy

Maier

Mulu

et al. 2015

Journal of Medical Virology

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“…In population genetics, a polymorphism is defined as a substitution that is present in more than one percent of the population [26,28]. The role HIV-1 polymorphisms play in chemotherapeutic success remains controversial [23,24,29–31]. Certain polymorphisms may tend to accumulate during chemotherapy while also being present in drug-naïve patients, albeit with a reduced frequency [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…The role HIV-1 polymorphisms play in chemotherapeutic success remains controversial [23,24,29–31]. Certain polymorphisms may tend to accumulate during chemotherapy while also being present in drug-naïve patients, albeit with a reduced frequency [22,23]. Polymorphisms present at baseline may influence the drug susceptibility of an HIV-1 variant [26,30–32].…”

Section: Introductionmentioning

confidence: 99%

Using drug exposure for predicting drug resistance – A data-driven genotypic interpretation tool

et al. 2017

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Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs.

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“…These variants served as input for the BN learning procedure. In general, resistance-associated variants are expected to be associated to treatment, while treatment-associated variants do not always confer resistance (Theys et al, 2013).…”

Section: Identification Of Treatment-associated Variantsmentioning

confidence: 99%

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning

Cuypers

Libin

Schrooten

et al. 2017

Infection, Genetics and Evolution

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Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naïve and PIexperienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PInaïve patients who cleared the virus during PI therapy and from PI-naïve patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach.

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HIV-1 Drug Resistance: Where do Polymorphisms Fit in?

Cited by 12 publications

References 19 publications

Limited increase in primary HIV‐1C drug resistance mutations in treatment naïve individuals in Ethiopia

Limited increase in primary HIV‐1C drug resistance mutations in treatment naïve individuals in Ethiopia

Using drug exposure for predicting drug resistance – A data-driven genotypic interpretation tool

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning

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