Comparable Long-Term Efficacy of Lopinavir/Ritonavir and Similar Drug-Resistance Profiles in Different HIV-1 Subtypes

Grossman, Zehava; Schapiro, Jonathan; Levy, Itzchak; Elbirt, Daniel; Chowers, Michal; Riesenberg, Klaris; Olstein-Pops, Karen; Shahar, Eduardo; Istomin, Valery; Asher, Ilan; Gottessman, Bat-Sheva; Shemer, Yonat; Elinav, Hila; Hassoun, Gamal; Rosenberg, Shira; Averbuch, Diana; Machleb-Guri, Keren; Kra-Oz, Zipi; Radian-Sade, Sara; Rudich, H; Ram, Daniela; Maayan, Shlomo; Agmon‐Levin, Nancy; Sthoeger, Zev

doi:10.1371/journal.pone.0086239

Cited by 10 publications

(14 citation statements)

References 65 publications

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“…We use the H-CBN2 method to infer evolutionary pathways to lopinavir resistance in HIV-1 subtypes B and C confirming previous knowledge on frequently observed patterns of resistance-conferring mutations in response to lopinavir treatment [33,34].…”

Section: Introductionsupporting

confidence: 67%

“…For subtype B, we find strong support for a mutation at residue 46 as an initial event ( Fig 6). The inferred posets can explain previously observed mutation patterns, such as M46I+I54V alone or in combinations with L76V or V82A in subtype B [33], as well as M46I+I54V+V82A and L10F+M46I+I54V+L76V+V82A in subtype C [34].…”

Section: Comparison Of Drug Resistance-associated Mutational Pathwayssupporting

confidence: 60%

“…Even though there are descriptive analyses of subtype-specific PI mutation profiles [23,33,34,44], to our knowledge, this study is the first comparative analysis of pathways of accumulating mutations over time in different HIV-1 subtypes. In addition to a more systematic approach to investigating mutation patterns, the number of observations in our study is greater than in any of the previous studies, which ranged from 88 to 165 patients.…”

Section: Discussionmentioning

confidence: 99%

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Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Posada-Céspedes

Zyl

Montazeri

et al. 2020

Preprint

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Although combination antiretoviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Here, we present a methodology for the comparison of mutational pathways in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational pathways from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models on a large number of resistance mutations and develop a statistical test to assess differences in the inferred mutational pathways between two groups. We apply this method to the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional data set of South African individuals living with HIV-1 subtype C, as well as a genotype data set of subtype B infections derived from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. Our results also show that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Furthermore, the maximum likelihood mutational networks for subtypes B and C share only 7 edges (Jaccard distance 0.802) and imply many different evolutionary pathways. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational pathways between any two groups.Author summaryThere is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Western Europe and Australia, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we study lopinavir resistance pathways in HIV-1 subtypes B versus C, but the methodology can be generally applied to compare the temporal ordering of genetic events in different subgroups.

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Section: Introductionsupporting

confidence: 67%

Section: Comparison Of Drug Resistance-associated Mutational Pathwayssupporting

confidence: 60%

Section: Discussionmentioning

confidence: 99%

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Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Posada-Céspedes

Zyl

Montazeri

et al. 2020

Preprint

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“…Although differences in drug susceptibility exist between subtypes, recent large international studies assessing effectiveness of various ART regimens have not demonstrated increased treatment failures in specific subtypes, although there have been instances of delayed CD4 cell count recovery [ 33 , 34 , 35 , 36 ] attributed to viral subtype. For instance, in a study conducted across Thailand, Hong Kong, Japan, Taiwan, and South Korea, 1036 individuals, of whom 778 harbored CRF01_AE virus and 258 individuals harbored sub-type B, were followed for 1547 person-years.…”

Section: Effect Of Hiv Subtypes On Antiretroviral Therapy Outcomesmentioning

confidence: 99%

“…There was no difference in virologic response between subtypes. In a study from Israel, the authors noted that the perceived differences in treatment outcomes amongst various subtypes might actually be due to differences in quality of care provided across economic and geographic boundaries [ 33 ]. In their population, they showed that although there was a significant difference in rise of CD4 count by subtype, (175 in subtype B versus 98 in subtype C, p < 0.001), no such difference existed in viral loads or resistance rates.…”

Section: Effect Of Hiv Subtypes On Antiretroviral Therapy Outcomesmentioning

confidence: 99%

Impact of Human Immunodeficiency Virus Type-1 Sequence Diversity on Antiretroviral Therapy Outcomes

Langs-Barlow

Paintsil

2014

Viruses

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Worldwide circulating HIV-1 genomes show extensive variation represented by different subtypes, polymorphisms and drug-resistant strains. Reports on the impact of sequence variation on antiretroviral therapy (ART) outcomes are mixed. In this review, we summarize relevant published data from both resource-rich and resource-limited countries in the last 10 years on the impact of HIV-1 sequence diversity on treatment outcomes. The prevalence of transmission of drug resistant mutations (DRMs) varies considerably, ranging from 0% to 27% worldwide. Factors such as geographic location, access and availability to ART, duration since inception of treatment programs, quality of care, risk-taking behaviors, mode of transmission, and viral subtype all dictate the prevalence in a particular geographical region. Although HIV-1 subtype may not be a good predictor of treatment outcome, review of emerging evidence supports the fact that HIV-1 genome sequence-resulting from natural polymorphisms or drug-associated mutations-matters when it comes to treatment outcomes. Therefore, continued surveillance of drug resistant variants in both treatment-naïve and treatment-experienced populations is needed to reduce the transmission of DRMs and to optimize the efficacy of the current ART armamentarium.

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Gag-protease coevolution shapes the outcome of lopinavir-inclusive treatment regimens in chronically infected HIV-1 subtype C patients

Marie

Gordon

2019

Bioinformatics

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Motivation Commonly, protease inhibitor failure is characterized by the development of multiple protease resistance mutations (PRMs). While the impact of PRMs on therapy failure are understood, the introduction of Gag mutations with protease remains largely unclear. Results Here, we utilized phylogenetic analyses and Bayesian network learning as tools to understand Gag-protease coevolution and elucidate the pathways leading to Lopinavir failure in HIV-1 subtype C infected patients. Our analyses indicate that while PRMs coevolve in response to drug selection pressure within protease, the Gag mutations added to the existing network while specifically interacting with known Lopinavir failure PRMs. Additionally, the selection of mutations at specific positions in Gag-protease suggests that these coevolving mutational changes occurs to maintain structural integrity during Gag cleavage. Supplementary information Supplementary data are available at Bioinformatics online.

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Comparable Long-Term Efficacy of Lopinavir/Ritonavir and Similar Drug-Resistance Profiles in Different HIV-1 Subtypes

Cited by 10 publications

References 65 publications

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Impact of Human Immunodeficiency Virus Type-1 Sequence Diversity on Antiretroviral Therapy Outcomes

Gag-protease coevolution shapes the outcome of lopinavir-inclusive treatment regimens in chronically infected HIV-1 subtype C patients

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