2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings.

OBJECTIVES: With the goal of facilitating the use of HIV-TRePS to optimize therapy in settings with limited healthcare resources, we aimed to develop computational models to predict treatment responses accurately in the absence of commonly used baseline data. METHODS: Twelve sets of random forest models were trained using very large, global datasets to predict either the probability of virological response (classifier models) or the absolute change in viral load in response to a new regimen (absolute models) following virological failure. Two 'standard' models were developed with all baseline variables present and 10 others developed without HIV genotype, time on therapy, CD4 count or any combination of the above. RESULTS: The standard classifier models achieved an AUC of 0.89 in cross-validation and independent testing. Models with missing variables achieved AUC values of 0.78-0.90. The standard absolute models made predictions that correlated significantly with observed changes in viral load with a mean absolute error of 0.65 log10 copies HIV RNA/mL in cross-validation and 0.69 log10 copies HIV RNA/mL in independent testing. Models with missing variables achieved values of 0.65-0.75 log10 copies HIV RNA/mL. All models identified alternative regimens that were predicted to be effective for the vast majority of cases where the new regimen prescribed in the clinic failed. All models were significantly better predictors of treatment response than genotyping with rules-based interpretation. CONCLUSIONS: These latest models that predict treatment responses accurately, even when a number of baseline variables are not available, are a major advance with greatly enhanced potential benefit, particularly in resource-limited settings. The only obstacle to realizing this potential is the willingness of healthcare professions to use the system.

Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response.

OBJECTIVE: Definitions of virological response vary from <50 up to 1000 copies of HIV-RNA/mL. Our previous models estimate the probability of HIV drug combinations reducing the viral load to <50 copies/mL, with no indication of whether higher thresholds of response may be achieved. Here, we describe the development of models that predict absolute viral load over time. METHODS: Two sets of random forest models were developed using 50,270 treatment change episodes from more than 20 countries. The models estimated viral load at different time points following the introduction of a new regimen from variables including baseline viral load, CD4 count, and treatment history. One set also used genotypes in their predictions. Independent data sets were used for evaluation. RESULTS: Both models achieved highly significant correlations between predicted and actual viral load changes (r = 0.67-0.68, mean absolute error of 0.73-0.74 log10 copies/mL). The models produced curves of virological response over time. Using failure definitions of <100, 400, or 1000 copies/mL, but not 50 copies/mL, both models were able to identify alternative regimens they predicted to be effective for the majority of cases where the new regimen prescribed in the clinic failed. CONCLUSIONS: These models could be useful for selecting the optimum combination therapy for patients requiring a change in therapy in settings using any definition of virological response. They also give an idea of the likely response curve over time. Given that genotypes are not required, these models could be a useful addition to the HIV-TRePS system for those in resource-limited settings.

2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings.

Objectives: Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping. Methods: Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system. Results: The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55-0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed. Conclusions: These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings.

An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype.

OBJECTIVES: Optimizing antiretroviral drug combination on an individual basis in resource-limited settings is challenging because of the limited availability of drugs and genotypic resistance testing. Here, we describe our latest computational models to predict treatment responses, with or without a genotype, and compare the potential utility of global and local models as a treatment tool for South Africa. METHODS: Global random forest models were trained to predict the probability of virological response to therapy following virological failure using 29 574 treatment change episodes (TCEs) without a genotype, 3179 of which were from South Africa and were used to develop local models. In addition, 15 130 TCEs including genotypes were used to develop another set of models. The 'no-genotype' models were tested with an independent global test set (n = 1700) plus a subset from South Africa (n = 222). The genotype models were tested with 750 independent cases. RESULTS: The global no-genotype models achieved area under the receiver-operating characteristic curve (AUC) values of 0.82 and 0.79 with the global and South African tests sets, respectively, and the South African models achieved AUCs of 0.70 and 0.79. The genotype models achieved an AUC of 0.84. The global no-genotype models identified more alternative, locally available regimens that were predicted to be effective for cases that failed their new regimen in the South African clinics than the local models. Both sets of models were significantly more accurate predictors of outcomes than genotyping with rules-based interpretation. CONCLUSIONS: These latest global models predict treatment responses accurately even without a genotype, out-performed the local South African models and have the potential to help optimize therapy, particularly in resource-limited settings.

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa.

BACKGROUND: Selecting the optimal combination of HIV drugs for an individual in resource-limited settings is challenging because of the limited availability of drugs and genotyping. OBJECTIVE: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa. METHODS: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load, details of failing and previous antiretroviral drugs, drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative's (RDI's) models used these data to predict the probability of a viral load < 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs. RESULTS: The models achieved accuracy (area under the receiver-operator characteristic curve) of 0.72 when predicting response to therapy, which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic. CONCLUSION: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI's models have the potential to optimise treatment selection and reduce virological failure in different patient populations, without the use of a genotype.

An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes.

OBJECTIVES: The optimal individualized selection of antiretroviral drugs in resource-limited settings is challenging because of the limited availability of drugs and genotyping. Here we describe the development of the latest computational models to predict the response to combination antiretroviral therapy without a genotype, for potential use in such settings. METHODS: Random forest models were trained to predict the probability of a virological response to therapy (<50 copies HIV RNA/mL) following virological failure using the following data from 22,567 treatment-change episodes including 1090 from southern Africa: baseline viral load and CD4 cell count, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. The models were assessed during cross-validation and with an independent global test set of 1000 cases including 100 from southern Africa. The models' accuracy [area under the receiver-operating characteristic curve (AUC)] was evaluated and compared with genotyping using rules-based interpretation systems for those cases with genotypes available. RESULTS: The models achieved AUCs of 0.79-0.84 (mean 0.82) during cross-validation, 0.80 with the global test set and 0.78 with the southern African subset. The AUCs were significantly lower (0.56-0.57) for genotyping. CONCLUSIONS: The models predicted virological response to HIV therapy without a genotype as accurately as previous models that included a genotype. They were accurate for cases from southern Africa and significantly more accurate than genotyping. These models will be accessible via the online treatment support tool HIV-TRePS and have the potential to help optimize antiretroviral therapy in resource-limited settings where genotyping is not generally available.

Potential impact of a free online HIV treatment response prediction system for reducing virological failures and drug costs after antiretroviral therapy failure in a resource-limited setting.

OBJECTIVE: Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. METHODS: The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. RESULTS: The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%). CONCLUSIONS: Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings.

The use of computational models to predict response to HIV therapy for clinical cases in Romania.

INTRODUCTION: A major challenge in Romania is the optimisation of antiretroviral therapy for the many HIV-infected adults with, on average, a decade of treatment experience. The RDI has developed computational models that predict virological response to therapy but these require a genotype, which is not routinely available in Romania. Moreover the models, which were trained without any Romanian data, have proved most accurate for patients from the healthcare settings that contributed the training data. Here we develop and test a novel model that does not require a genotype, with test data from Romania. METHODS: A random forest (RF) model was developed to predict the probability of the HIV viral load (VL) being reduced to <50 copies/ml following therapy change. The input variables were baseline VL, CD4 count, treatment history and time to follow-up. The model was developed with 3188 treatment changes episodes (TCEs) from North America, Western Europe and Australia. The model's predictions for 100 independent TCEs from the RDI database were compared to those of a model trained with the same data plus genotypes and then tested using 39 TCEs from Romania in terms of the area under the ROC curve (AUC). RESULTS: When tested with the 100 independent RDI TCEs, the AUC values for the models with and without genotypes were 0.88 and 0.86 respectively. For the 39 Romanian TCEs the AUC was 0.60. However, when 14 cases with viral loads that may have been between 50 and 400 copies were removed, the AUC increased to 0.83. DISCUSSION: Despite having been trained without data from Romania, the model predicted treatment responses in treatment-experienced Romanian patients with clade F virus accurately without the need for a genotype. The results suggest that this approach might be generalisable and useful in helping design optimal salvage regimens for treatment-experienced patients in countries with limited resources where genotyping is not always available.

The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool.

OBJECTIVE: The optimum selection and sequencing of combination antiretroviral therapy to maintain viral suppression can be challenging. The HIV Resistance Response Database Initiative has pioneered the development of computational models that predict the virological response to drug combinations. Here we describe the development and testing of random forest models to power an online treatment selection tool. METHODS: Five thousand, seven hundred and fifty-two treatment change episodes were selected to train a committee of 10 models to predict the probability of virological response to a new regimen. The input variables were antiretroviral treatment history, baseline CD4 cell count, viral load and genotype, drugs in the new regimen, time from treatment change to follow-up and follow-up viral load values. The models were assessed during cross-validation and with an independent set of 50 treatment change episodes by plotting receiver-operator characteristic curves and their performance compared with genotypic sensitivity scores from rules-based genotype interpretation systems. RESULTS: The models achieved an area under the curve during cross-validation of 0.77-0.87 (mean = 0.82), accuracy of 72-81% (mean = 77%), sensitivity of 62-80% (mean = 67%) and specificity of 75-89% (mean = 81%). When tested with the 50 test cases, the area under the curve was 0.70-0.88, accuracy 64-82%, sensitivity 62-80% and specificity 68-95%. The genotypic sensitivity scores achieved an area under the curve of 0.51-0.52, overall accuracy of 54-56%, sensitivity of 43-64% and specificity of 41-73%. CONCLUSION: The models achieved a consistent, high level of accuracy in predicting treatment responses, which was markedly superior to that of genotypic sensitivity scores. The models are being used to power an experimental system now available via the Internet.

HIV and hepatitis virus resistance to antivirals: review of data from the XIX International HIV and Hepatitis Virus Drug Resistance Workshop and curative strategies.

The XIX International HIV and Hepatitis Virus Drug Resistance Workshop offered scientists, clinical investigators, physicians and others an opportunity to present study results selected in a rigorous peer-review process and to discuss those data in an open forum. In 2010, Workshop organizers expanded the programme to include hepatitis B and C viruses, reasoning that workers in all three fields could benefit from shared experience, positive and negative. Slide sessions at the 2010 Workshop focused on hepatitis virus resistance to current and experimental antivirals; epidemiology of HIV resistance; HIV pathogenesis, fitness and resistance; resistance to new antiretrovirals; markers of response to HIV entry inhibitors; HIV persistence, reservoirs and elimination strategies; application of new viral sequencing techniques; and mechanisms of HIV drug resistance. This article summarizes all slide presentations at the Workshop.

Clinical evaluation of the potential utility of computational modeling as an HIV treatment selection tool by physicians with considerable HIV experience.

The HIV Resistance Response Database Initiative (RDI), which comprises a small research team in the United Kingdom and collaborating clinical centers in more than 15 countries, has used antiretroviral treatment and response data from thousands of patients around the world to develop computational models that are highly predictive of virologic response. The potential utility of such models as a tool for assisting treatment selection was assessed in two clinical pilot studies: a prospective study in Canada and Italy, which was terminated early because of the availability of new drugs not covered by the system, and a retrospective study in the United States. For these studies, a Web-based user interface was constructed to provide access to the models. Participating physicians entered baseline data for cases of treatment failure and then registered their treatment intention. They then received a report listing the five alternative regimens that the models predicted would be most effective plus their own selection, ranked in order of predicted virologic response. The physicians then entered their final treatment decision. Twenty-three physicians entered 114 cases (75 unique cases with 39 entered twice by different physicians). Overall, 33% of treatment decisions were changed following review of the report. The final treatment decisions and the best of the RDI alternatives were predicted to produce greater virologic responses and involve fewer drugs than the original selections. Most physicians found the system easy to use and understand. All but one indicated they would use the system if it were available, particularly for highly treatment-experienced cases with challenging resistance profiles. Despite limitations, the first clinical evaluation of this approach by physicians with substantial HIV-experience suggests that it has the potential to deliver clinical and economic benefits.

Progress in basic and clinical research on HIV resistance: report on the XVIII International HIV Drug Resistance Workshop.

Over nearly two decades, the International HIV Drug Resistance Workshop has become the leading forum for new research on viral resistance to agents developed to treat infection with HIV. The XVIII workshop featured work on HIV type-1 (HIV-1) persistence, reservoirs and elimination strategies; resistance to HIV-1 entry inhibitors (including a comparison of genotyping versus phenotyping to determine HIV-1 coreceptor use before treatment with CCR5 antagonists); polymerase domain resistance to reverse transcriptase inhibitors (including hepatitis B virus and HIV-1 resistance to lamivudine, and emergence of the K65R mutation in HIV-1 subtypes B and C); connection and RNase H domain resistance to reverse transcriptase inhibitors (including the effect of mutations in those domains on response to efavirenz and etravirine); resistance to hepatitis C virus and HIV-1 protease inhibitors; resistance to the integrase inhibitor raltegravir; global resistance epidemiology (including models to predict response to second-line antiretrovirals in resource-poor settings); and the role of minority resistant variants (including the effect of such variants on prevention of mother-to-child transmission of HIV-1). This report summarizes data from the oral abstract presentations at the workshop.

A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy.

OBJECTIVE: HIV treatment failure is commonly associated with drug resistance and the selection of a new regimen is often guided by genotypic resistance testing. The interpretation of complex genotypic data poses a major challenge. We have developed artificial neural network (ANN) models that predict virological response to therapy from HIV genotype and other clinical information. Here we compare the accuracy of ANN with alternative modelling methodologies, random forests (RF) and support vector machines (SVM). METHODS: Data from 1204 treatment change episodes (TCEs) were identified from the HIV Resistance Response Database Initiative (RDI) database and partitioned at random into a training set of 1154 and a test set of 50. The training set was then partitioned using an L-cross (L=10 in this study) validation scheme for training individual computational models. Seventy six input variables were used for training the models: 55 baseline genotype mutations; the 14 potential drugs in the new treatment regimen; four treatment history variables; baseline viral load; CD4 count and time to follow-up viral load. The output variable was follow-up viral load. Performance was evaluated in terms of the correlations and absolute differences between the individual models' predictions and the actual DeltaVL values. RESULTS: The correlations (r(2)) between predicted and actual DeltaVL varied from 0.318 to 0.546 for ANN, 0.590 to 0.751 for RF and 0.300 to 0.720 for SVM. The mean absolute differences varied from 0.677 to 0.903 for ANN, 0.494 to 0.644 for RF and 0.500 to 0.790 for SVM. ANN models were significantly inferior to RF and SVM models. The predictions of the ANN, RF and SVM committees all correlated highly significantly with the actual DeltaVL of the independent test TCEs, producing r(2) values of 0.689, 0.707 and 0.620, respectively. The mean absolute differences were 0.543, 0.600 and 0.607log(10)copies/ml for ANN, RF and SVM, respectively. There were no statistically significant differences between the three committees. Combining the committees' outputs improved correlations between predicted and actual virological responses. The combination of all three committees gave a correlation of r(2)=0.728. The mean absolute differences followed a similar pattern. CONCLUSIONS: RF and SVM models can produce predictions of virological response to HIV treatment that are comparable in accuracy to a committee of ANN models. Combining the predictions of different models improves their accuracy somewhat. This approach has potential as a future clinical tool and a combination of ANN and RF models is being taken forward for clinical evaluation.

Application of artificial neural networks for decision support in medicine.

The emergence of drug resistant pathogens can reduce the efficacy of drugs commonly used to treat infectious diseases. Human immunodeficiency virus (HIV) is particularly sensitive to drug selection pressure, rapidly evolving into drug resistant variants on exposure to anti-HIV drugs. Over 200 mutations within the genetic material of HIV have been shown to be associated with drug resistance to date, and complex mutational patterns have been found in HIV isolates from infected patients exposed to multiple antiretroviral drugs. Genotyping is commonly used in clinical practice as a tool to identify drug resistance mutations in HIV from individual patients. This information is then used to help guide the choice of future therapy for patients whose drug regimen is failing because of the development of drug resistant HIV. Many sets of rules and algorithms are available to predict loss of susceptibility to individual antiretroviral drugs from genotypic data. Although this approach has been helpful, the interpretation of genotypic data remains challenging. We describe here the development and application of ANN models as alternative tools for the interpretation of HIV genotypic drug resistance data. A large amount of clinical and virological data, from around 30,000 patients treated with antiretroviral drugs, has been collected by the HIV Resistance Response Database Initiative (RDI, www.hivrdi.org) in a centralized database. Treatment change episodes (TCEs) have been extracted from these data and used along with HIV drug resistance mutations as the basic input variables to train ANN models. We performed a series of analyses that have helped define the following: (1) the reliability of ANN predictions for HIV patients receiving routine clinical care; (2) the utility of ANN models to identify effective treatments for patients failing therapy; (3) strategies to increase the accuracy of ANN predictions; and (4) performance of ANN models in comparison to the rules-based methods currently in use.

Clinical implications of resistance to antiretrovirals: new resistance technologies and interpretations.

Understanding resistance to antiretroviral therapy plays an ever more crucial role in managing HIV infection as new agents - including several in new antiretroviral classes - promise better control of multidrug-resistant virus in the developed world. Yet these new drugs have different, and often complex, resistance profiles. At the same time, resistance has assumed a key role in developing countries as access to additional antiretrovirals expands in the face of first-line regimen failures. Every year the International HIV Drug Resistance Workshop gathers leading investigators and resistance-savvy clinicians to share unpublished, peer-reviewed research on the mechanisms, pathogenesis, epidemiology, and clinical implications of resistance to licensed and experimental antivirals. The 2007 workshop, held on 12-16 June, proved particularly notable for its exploration of resistance to two new antiretroviral classes, integrase inhibitors and CCR5 antagonists, as well as to agents that control hepatitis C virus (HCV) infection. This report summarizes most oral presentations from the workshop and many posters.

Broad advances in understanding HIV resistance to antiretrovirals: report on the XVII International HIV Drug Resistance Workshop.

The 2008 International HIV Drug Resistance Workshop explored six topics on viral resistance: new antiretrovirals; clinical implications; epidemiology; new technologies and interpretations; HIV pathogenesis, fitness, and resistance; and mechanisms of resistance. The last of these topics provided a forum for new work on resistance of hepatitis B and C viruses, which were also explored in two poster sessions. Much work focused on resistance to the two most recent antiretroviral classes (integrase inhibitors and CCR5 antagonists), a new set of entry inhibitor candidates and one new class represented by the maturation inhibitor bevirimat. Other research explored two novel non-nucleoside reverse transcriptase inhibitors, etravirine and IDX899. Epidemiological work analysed rates of transmitted resistant virus, multiclass resistance in antiretroviral-experienced patients and a heightened resistance risk in injecting drug users regardless of adherence. New research on resistance technologies involved an enhanced assay for HIV-1 coreceptor determination and improved gene-based tools for predicting coreceptor use. In the pathogenesis arena, a small study of intensification shed light on the likely source of residual viraemia in patients on successful antiretroviral therapy. A large study in Mozambique correlated the timing of infant infection with selection, transmission and persistence of nevirapine resistance mutations. Mechanistic research explored resistance to the integrase inhibitor raltegravir, K65R-mediated resistance to tenofovir and the role of connection domain mutations in resistance to zidovudine.

The development of artificial neural networks to predict virological response to combination HIV therapy.

INTRODUCTION: When used in combination, antiretroviral drugs are highly effective for suppressing HIV replication. Nevertheless, treatment failure commonly occurs and is generally associated with viral drug resistance. The choice of an alternative regimen may be guided by a drug-resistance test. However, interpretation of resistance from genotypic data poses a major challenge. METHODS: As an alternative to current interpretation systems, we have developed artificial neural network (ANN) models to predict virological response to combination therapy from HIV genotype and other clinical information. RESULTS: ANN models trained with genotype, baseline viral load and time to follow-up viral load (1154 treatment change episodes from multiple clinics), produced predictions of virological response that were highly significantly correlated with actual responses (r2 = 0.53; P < 0.00001) using independent test data from clinics that contributed training data. Augmented models, trained with the additional variables of baseline CD4+ T-cell count and four treatment history variables, were more accurate, explaining 69% of the variance in virological response. Models trained with the full input dataset, but only those data involving highly active antiretroviral therapy (three or more full-dose antiretroviral drugs in combination), performed at an intermediate level, explaining 61% of the variance. The augmented models performed less well when tested with data from unfamiliar clinics that had not contributed data to the training dataset, explaining 46% of the variance in response. CONCLUSION: These data indicate that ANN models can be quite accurate predictors of virological response to HIV therapy even for patients from unfamiliar clinics. ANN models therefore warrant further development as a potential tool to aid treatment selection.

Key reports from the XV International HIV Drug Resistance Workshop 2006.

The XV International HIV Drug Resistance Workshop recorded advances in basic and clinical science of HIV resistance to antiretrovirals as well as new findings on resistance by hepatitis B virus (HBV) and hepatitis C virus (HCV). In the clinical arena, attendees learned of four cases of resistance to lopinavir/ritonavir monotherapy, correlation between low-frequency pretreatment mutations and failure of a first antiretroviral regimen, emergence of non-nucleoside-related mutations in 20% of patients interrupting a suppressive nonnucleoside regimen, and evolution of mutations conferring resistance to an HIV entry inhibitor that is being studied as a vaginal microbicide. New data reported from the POWER 1, 2 and 3 salvage trials suggested that there is a close correlation between darunavir (TMC114) phenotypic susceptibility, the number of baseline protease inhibitor-related resistance mutations and virological response. Scientists exploring the mechanisms of resistance reported of mutations in the carboxy-terminal domain of reverse transcriptase that may further resistance to zidovudine, novel mutations that may contribute to resistance of both nucleoside and non-nucleoside reverse transcriptase inhibitors, and a mechanism that HCV and HIV may share to resist antiviral therapy.

The XIVth International HIV Drug Resistance Workshop, Quebec City, Canada, June 2005.

This report summarizes research advances that further our understanding of the evolution, mechanisms and clinical impact of HIV drug resistance presented at the XIVth International HIV Drug Resistance Workshop held in Quebec City, Canada from June 7-11, 2005. The topics that were discussed included the clinical implications of resistance in mother-to-child transmission, breakthroughs in technologies for studying resistance, resistance to new antiretroviral agents, mechanisms of HIV drug resistance, epidemiological trends, and HIV fitness and pathogenesis.

Influence of naturally occurring insertions in the fingers subdomain of human immunodeficiency virus type 1 reverse transcriptase on polymerase fidelity and mutation frequencies in vitro.

The fingers subdomain of human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) is a hotspot for nucleoside analogue resistance mutations. Some multi-nucleoside analogue-resistant variants contain a T69S substitution along with dipeptide insertions between residues 69 and 70. This set of mutations usually co-exists with classic zidovudine-resistance mutations (e.g. M41L and T215Y) or an A62V mutation and confers resistance to multiple nucleoside analogue inhibitors. As insertions lie in the vicinity of the dNTP-binding pocket, their influence on RT fidelity was investigated. Commonly occurring insertion mutations were selected, i.e. T69S-AG, T69S-SG and T69S-SS alone, in combination with 3'-azido-2',3'-deoxythymidine-resistance mutations M41L, L210W, R211K, L214F, T215Y (LAG(AZ) and LSG(AZ)) or with an alternate set where A62V substitution replaces M41L (VAG(AZ), VSG(AZ) and VSS(AZ)). Using a lacZalpha gapped duplex substrate, the forward mutation frequencies of recombinant wild-type and mutant RTs bearing each of the above sets of mutations were measured. All of the mutants displayed significant decreases in mutation frequencies. Whereas the dipeptide insertions alone showed the least decrease (4.0- to 7.5-fold), the VAG series showed an intermediate reduction (5.0- to 11.4-fold) and the LAG set showed the largest reduction in mutation frequencies (15.3- and 16.3-fold for LAG(AZ) and LSG(AZ), respectively). Single dNTP exclusion assays for mutants LSG(AZ) and LAG(AZ) confirmed their large reduction in misincorporation efficiencies. The increased in vitro fidelity was not due to excision of the incorrect nucleotide via ATP-dependent removal. There was also no direct correlation between increased fidelity and template-primer affinity, suggesting a change in the active site that is conducive to better discrimination during dNTP insertion.