Accuracy, precision, and consistency of expert HIV type 1 genotype interpretation: an international comparison (The GUESS Study)

We should not stop considering HIV drug resistance testing at failure of first-line antiretroviral therapy

HIV drug resistance is a major global hurdle to successful and sustained antiretroviral therapy. Global guidelines recommend testing for antiretroviral drug resistance and results are used to inform treatment regimen design for patients at different stages of therapy. Several clinical trials investigated optimal regimens after failure of first-line antiretroviral therapy, yielding data that advanced knowledge and informed care. However, further interpretation of data from these studies questioned the benefit of antiretroviral drug resistance testing for cases in which first-line treatment is not effective and, furthermore, that relying on the results of antiretroviral drug resistance testing could be misleading and unnecessary. In this Viewpoint, which is largely focused on high-income settings, we review these data, reflect on the potential problems with their interpretation, and call for caution in their extrapolation. Without negating the importance of the data, and recognising the varied circumstances related to HIV drug resistance testing in different global settings, we advise caution before changing current practice and recommendations. We believe that we should not universally stop considering HIV drug resistance testing at failure of first-line antiretroviral therapy.

Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data

Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions.

Infection with the human immunodeficiency virus (HIV) currently cannot be cured. It can however be contained through treatment with a combination of several anti-viral drugs. Yet, during treatment resistance can occur which leads to drugs becoming ineffective. Through a combination of drugs, this resistance can be deferred indefinitely. The optimal combination of drugs depends on the specific strain of HIV with which the patient is infected. Previously, methods have been developed that predict a personalized treatment regimen based on the genetic sequence (genotype) of the virus via the use of computer modeling, corner stone of the methods is drug affinity prediction. Here we have applied proteochemometric modeling which takes this genetic information into account, but also includes chemical description of the drugs that are now clinically available. We show that this combined technique performs better than models that only include genetic information. Our approach leads to personalized treatment predictions with a higher reliability compared to the current state of the art. In addition, we include a reliability measure which allows each prediction to be assessed for reliability. Finally we describe mutations of the HIV genome that were not previously described in literature and lead to resistance to treatment.

Clinical management of HIV drug resistance

Combination antiretroviral therapy for HIV-1 infection has resulted in profound reductions in viremia and is associated with marked improvements in morbidity and mortality. Therapy is not curative, however, and prolonged therapy is complicated by drug toxicity and the emergence of drug resistance. Management of clinical drug resistance requires in depth evaluation, and includes extensive history, physical examination and laboratory studies. Appropriate use of resistance testing provides valuable information useful in constructing regimens for treatment-experienced individuals with viremia during therapy. This review outlines the emergence of drug resistance in vivo, and describes clinical evaluation and therapeutic options of the individual with rebound viremia during therapy.

Connection domain mutations N348I and A360V in HIV-1 reverse transcriptase enhance resistance to 3'-azido-3'-deoxythymidine through both RNase H-dependent and -independent mechanisms

Thymidine analogue-associated mutations (TAMs) in reverse transcriptase (RT) of the human immunodeficiency virus type 1 (HIV-1) cause resistance to 3'-azido-3'-deoxythymidine (AZT) through excision of the incorporated monophosphate. Mutations in the connection domain of HIV-1 RT can augment AZT resistance. It has been suggested that these mutations compromise RNase H cleavage, providing more time for AZT excision to occur. However, the underlying mechanism remains elusive. Here, we focused on connection mutations N348I and A360V that are frequently observed in clinical samples of treatment-experienced patients. We show that both N348I and A360V, in combination with TAMs, decrease the efficiency of RNase H cleavage and increase excision of AZT in the presence of the pyrophosphate donor ATP. The TAMs/N348I/A360V mutant accumulates transiently formed, shorter hybrids that can rebind to RT before the template is irreversibly degraded. These hybrids dissociate selectively from the RNase H-competent complex, whereas binding in the polymerase-competent mode is either not affected with N348I or modestly improved with A360V. Both connection domain mutations can compensate for TAM-mediated deficits in processive DNA synthesis, and experiments with RNase H negative mutant enzymes confirm an RNase H-independent contribution to increased levels of resistance to AZT. Moreover, the combination of diminished RNase H cleavage and increased processivity renders the use of both PP(i) and ATP advantageous, whereas classic TAMs solely enhance the ATP-dependent reaction. Taken together, our findings demonstrate that distinct, complementary mechanisms can contribute to higher levels of excision of AZT, which in turn can amplify resistance to this drug.

Should we include connection domain mutations of HIV-1 reverse transcriptase in HIV resistance testing

The author discusses a new study that found a common mutation in the connection domain of reverse transcriptase, a mutation that confers resistance to two antiretroviral drug classes.

Minor-drug-resistant HIV populations and treatment failure

Treatment efficacy in those infected with HIV is eventually compromised by the development of resistance to antiretroviral drugs. To delay resistance in patients it is critical to better understand drug-resistance mechanisms and to accurately detect drug-resistant mutations prior to treatment. Minor-drug-resistant viruses are present in chronically infected patients, treatment-failure patients and those recently infected with resistant viruses through transmission. They are often present at levels below 20%, therefore conventional genotypic and phenotypic assays cannot detect them. Accumulating data indicate that minor-resistant viruses that are present before treatment can lead to drug resistance and poor treatment responses in patients. Detailed characterization of such minor-resistant populations using highly sensitive assays may have a profound impact on the treatment of HIV-infected individuals.

Incorporating drug-resistance measurements into the clinical management of HIV-1 infection

Testing for resistance to antiretrovirals is considered to be standard of care and is widely used in the management of human immunodeficiency virus (HIV)-infected persons. Despite the widespread use of resistance testing, the clinician still faces a number of challenges when applying these technologies in the optimal management of antiretroviral therapy. Both genotype and phenotype tests require interpretation, and available interpretative algorithms for genotypes and resistance cutoffs for phenotypes are incomplete and evolving. Even experts in HIV resistance do not completely agree in their interpretation of genotypes. Moreover, discordant results between genotypes and phenotypes are a common source of confusion when both tests are used to evaluate resistance in a patient. Finally, newer indicators, such as replication capacity, are clinically available and appear to have prognostic value, but how this in vitro measure should be used in the management of antiretroviral therapy remains to be fully defined.