Comparable long-term efficacy of Lopinavir/Ritonavir and similar drug-resistance profiles in different HIV-1 subtypes

Spectrum of Atazanavir-Selected Protease Inhibitor-Resistance Mutations

Ritonavir-boosted atazanavir is an option for second-line therapy in low- and middle-income countries (LMICs). We analyzed publicly available HIV-1 protease sequences from previously PI-naïve patients with virological failure (VF) following treatment with atazanavir. Overall, 1497 patient sequences were identified, including 740 reported in 27 published studies and 757 from datasets assembled for this analysis. A total of 63% of patients received boosted atazanavir. A total of 38% had non-subtype B viruses. A total of 264 (18%) sequences had a PI drug-resistance mutation (DRM) defined as having a Stanford HIV Drug Resistance Database mutation penalty score. Among sequences with a DRM, nine major DRMs had a prevalence >5%: I50L (34%), M46I (33%), V82A (22%), L90M (19%), I54V (16%), N88S (10%), M46L (8%), V32I (6%), and I84V (6%). Common accessory DRMs were L33F (21%), Q58E (16%), K20T (14%), G73S (12%), L10F (10%), F53L (10%), K43T (9%), and L24I (6%). A novel nonpolymorphic mutation, L89T occurred in 8.4% of non-subtype B, but in only 0.4% of subtype B sequences. The 264 sequences included 3 (1.1%) interpreted as causing high-level, 14 (5.3%) as causing intermediate, and 27 (10.2%) as causing low-level darunavir resistance. Atazanavir selects for nine major and eight accessory DRMs, and one novel nonpolymorphic mutation occurring primarily in non-B sequences. Atazanavir-selected mutations confer low-levels of darunavir cross resistance. Clinical studies, however, are required to determine the optimal boosted PI to use for second-line and potentially later line therapy in LMICs.

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

Although combination antiretroviral 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. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained 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. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.

There is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Australia and western and central Europe, 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 compare the temporal ordering of lopinavir resistance mutations in HIV-1 subtypes B versus C. To this end, we combine the Hidden Conjunctive Bayesian Network (H-CBN) model with an approximate inference scheme enabling comparisons of larger networks. We show that the development of resistance to lopinavir differs significantly between subtypes B and C, such that findings based on subtype B sequences can not always be applied to sybtype C. The described methodology is suitable for comparing different subgroups in the context of other evolutionary processes.

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

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.

HIV-1 second-line failure and drug resistance at high-level and low-level viremia in Western Kenya

OBJECTIVE

Characterize failure and resistance above and below guidelines-recommended 1000 copies/ml virologic threshold, upon second-line failure.

DESIGN

Cross-sectional study.

METHODS

Kenyan adults on lopinavir/ritonavir-based second-line were enrolled at AMPATH (Academic Model Providing Access to Healthcare). Charts were reviewed for demographic/clinical characteristics and CD4/viral load were obtained. Participants with detectable viral load had a second visit and pol genotyping was attempted in both visits. Accumulated resistance was defined as mutations in the second, not the first visit. Low-level viremia (LLV) was detectable viral load less than 1000 copies/ml. Failure and resistance associations were evaluated using logistic and Poisson regression, Fisher Exact and t-tests.

RESULTS

Of 394 participants (median age 42, 60% women, median 1.9 years on second-line) 48% had detectable viral load; 21% had viral load more than 1000 copies/ml, associated with younger age, tuberculosis treatment, shorter time on second-line, lower CD4count/percentage, longer first-line treatment interruption and pregnancy. In 105 sequences from the first visit (35 with LLV), 79% had resistance (57% dual-class, 7% triple-class; 46% with intermediate-to-high-level resistance to ≥1 future drug option). LLV was associated with more overall and NRTI-associated mutations and with predicted resistance to more next-regimen drugs. In 48 second-visit sequences (after median 55 days; IQR 28-33), 40% accumulated resistance and LLV was associated with more mutation accumulation.

CONCLUSION

High resistance upon second-line failure exists at levels above and below guideline-recommended virologic-failure threshold, impacting future treatment options. Optimization of care should include increased viral load monitoring, resistance testing and third-line ART access, and consideration of lowering the virologic failure threshold, though this demands further investigation.

[Genetic diversity of human immunodeficiency viruses and antiretroviral therapy]

The lecture is devoted to the analysis of the state-of-the-art of the impact of genetic diversity of human immunodeficiency (HIV) viruses on the pattern of infection and the efficiency of antiretroviral therapy (ART). It provides brief information on the origin and evolution of HIV and on the current classification of their genetic variants. The molecular epidemiological situation of HIV infection in Russia and nearby states and the major molecular HIV variants that are dominant in these countries, as well as their origin and prevalence trends are characterized. How the diversity of HIV can affect the efficiency of diagnosis, the transmission of the virus, and the pattern of HIV pathogenesis are briefly reviewed. The comparative data available in the world's scientific literature on these topics are given. More detailed attention is given to the possible causes of varying therapeutic effects against different HIV subtypes, as well as to the specific features of the formation and phenotyping manifestation of ART drug resistance mutations. There is evidence for the necessity of forming a unified follow-up system for treated HIV-infected patients during ART scaling, including in an effort to evaluate the impact of the specific features of the HIV genome on the efficiency of treatment regimens used in Russia.

Human Immunodeficiency Virus Type 1 Drug Resistance Mutations Update

As treatment options coalesce around a smaller number of antiretroviral drugs (ARVs), data are emerging on the drug resistance mutations (DRMs) selected by the most widely used ARVs and on the impact of these DRMs on ARV susceptibility and virological response to first- and later-line treatment regimens. Recent studies have described the DRMs that emerge in patients receiving tenofovir prodrugs, the nonnucleoside reverse transcriptase inhibitors efavirenz and rilpivirine, ritonavir-boosted lopinavir, and the integrase inhibitors raltegravir and elvitegravir. Several small studies have described DRMs that emerge in patients receiving dolutegravir.

A Population-Structured HIV Epidemic in Israel: Roles of Risk and Ethnicity

Background

HIV in Israel started with a subtype-B epidemic among men who have sex with men, followed in the 1980s and 1990s by introductions of subtype C from Ethiopia (predominantly acquired by heterosexual transmission) and subtype A from the former Soviet Union (FSU, most often acquired by intravenous drug use). The epidemic matured over the last 15 years without additional large influx of exogenous infections. Between 2005 and 2013 the number of infected men who have sex with men (MSM) increased 2.9-fold, compared to 1.6-fold and 1.3-fold for intravenous drug users (IVDU) and Ethiopian-origin residents. Understanding contemporary spread is essential for effective public health planning.

Methods

We analyzed demographic and virologic data from 1,427 HIV-infected individuals diagnosed with HIV-I during 1998–2012. HIV phylogenies were reconstructed with maximum-likelihood and Bayesian methods.

Results

Subtype-B viruses, but not A or C, demonstrated a striking number of large clusters with common ancestors having posterior probability ≥0.95, including some suggesting presence of transmission networks. Transmitted drug resistance was highest in subtype B (13%). MSM represented a frequent risk factor in cross-ethnic transmission, demonstrated by the presence of Israeli-born with non-B virus infections and FSU immigrants with non-A subtypes.

Conclusions

Reconstructed phylogenetic trees demonstrated substantial grouping in subtype B, but not in non-MSM subtype-A or in subtype-C, reflecting differences in transmission dynamics linked to HIV transmission categories. Cross-ethnic spread occurred through multiple independent introductions, with MSM playing a prevalent role in the transmission of the virus. Such data provide a baseline to track epidemic trends and will be useful in informing and quantifying efforts to reduce HIV transmission.

Impact of human immunodeficiency virus type-1 sequence diversity on antiretroviral therapy outcomes

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.