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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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.

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., (© The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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.

Objectives: Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs., Methods: Random forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure., Results: The models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania., Conclusions: We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

In the absence of widespread access to individualized laboratory monitoring, which forms an integral part of HIV patient management in resource-rich settings, the roll-out of highly active antiretroviral therapy (HAART) in resource-limited settings has adopted a public health approach based on standard HAART protocols and clinical/immunological definitions of therapy failure. The cost-effectiveness of HIV-1 viral load monitoring at the individual level in such settings has been debated, and questions remain over the long-term and population-level impact of managing HAART without it. Computational models that accurately predict virological response to HAART using baseline data including CD4 count, viral load and genotypic resistance profile, as developed by the Resistance Database Initiative, have significant potential as an aid to treatment selection and optimization. Recently developed models have shown good predictive performance without the need for genotypic data, with viral load emerging as by far the most important variable. This finding provides further, indirect support for the use of viral load monitoring for the long-term optimization of HAART in resource-limited settings.

Two large, independent human immunodeficiency virus type 1 resistance databases containing >7700 reverse-transcriptase (RT) sequences were used to analyze the epidemiology of amino acid substitutions at codons 44 and 118, which confer moderate lamivudine resistance in the presence of zidovudine resistance. As expected, E44A/D and V118I mutations were strongly associated with M41L, D67N, L210W, and T215Y but also with other mutations, including K43E/N/Q, T69D, V75M, H208Y, R211K, and K219R. Both E44D and V118I were more frequently associated with stavudine and didanosine than with zidovudine and lamivudine treatment. However, selection of E44A/D and V118I was also detected in association with a switch to other nucleoside RT inhibitors, including zalcitabine and abacavir. Site-directed mutagenesis confirmed that 44D and 118I can decrease phenotypic susceptibility not only to lamivudine but also to most other nucleoside analogues, particularly stavudine and abacavir. Thus, substitutions at RT codons 44 and 118 have broad implications in nucleoside RT inhibitor resistance in the setting of several nucleoside-associated mutations.

Human immunodeficiency virus type 1 (HIV-1) strains dually resistant to zidovudine and lamivudine (3TC) may arise during zidovudine-3TC combination therapy. The objective of this cross-sectional study (n = 43 patients) was to test the association between therapy response (clinical and immunologic) to zidovudine-3TC and the level of phenotypic zidovudine resistance and zidovudine resistance-associated genotype of 3TC-resistant isolates. Other variables included were baseline CD4+ cell count, baseline Centers for Disease Control and Prevention (CDC) classification, virus load, and time receiving zidovudine. Phenotypic resistance was assessed using a recombinant virus assay. Genotypic analysis was based on population sequencing of plasma HIV-1. In a univariate analysis using a logistic regression model, it was found that therapy response was significantly associated with phenotypic and genotypic zidovudine resistance, baseline CD4+ cell count, and virus load. After adjustment for all variables, phenotypic resistance to zidovudine remained the only significantly associated factor, independent of baseline CD4+ cell count, baseline CDC classification, and virus load.

A randomized controlled study was done to determine whether initial combination therapy with zidovudine and zalcitabine or zidovudine and didanosine would delay the emergence of zidovudine-resistant virus. Human immunodeficiency virus (HIV)-1-infected patients with <300 CD4 cells/mm3 and <4 weeks of prior zidovudine therapy were randomized to zidovudine, zidovudine plus zalcitabine, or zidovudine plus didanosine. Combination therapy did not delay the emergence of zidovudine-resistant virus isolates. However, combination therapy resulted in a significant increase in CD4 cells through 72 weeks compared with zidovudine monotherapy and a greater and more sustained decline in serum HIV-1 RNA. Although this trial was not designed as a clinical end-point study, patients assigned to zidovudine plus didanosine combination therapy experienced a significant delay in time to first AIDS-defining event or death compared with those assigned to zidovudine monotherapy.

During zidovudine therapy, human immunodeficiency virus type 1 (HIV-1) acquires a distinctive set of mutations that diminish the sensitivity of the virus to this drug in vitro. An AIDS patient is described who, while being treated with zidovudine, transmitted HIV-1 bearing a drug resistance mutation to a young woman who had never received zidovudine treatment. DNA sequencing of HIV-1 proviruses confirmed that these 2 persons shared HIV genetic variants, including a mutation at codon 70 in the reverse transcriptase gene associated with reduced in vitro sensitivity to zidovudine. This mutation persisted in the woman > 1 year in the absence of antiretroviral therapy. HIV-1 with genetic markers of zidovudine resistance can be transmitted heterosexually, but it is uncertain whether dissemination of drug-resistant virus will substantially reduce the usefulness of this drug.

The order of appearance in the reverse transcriptase gene of four mutations implicated in the development of resistance to zidovudine was investigated by selective polymerase chain reaction. Serial human immunodeficiency virus isolates were studied from 18 initially asymptomatic individuals who had been treated with zidovudine for 2 years. Most subjects had similar patterns. The first mutation occurred transiently at codon 70; its disappearance was paralleled by the appearance of a mutation at codon 215. Subsequently, in some individuals, the mutation at codon 70 reappeared. During the 2 years of treatment, no mutations developed at codon 219 and only one at codon 67, suggesting that most individuals developed only partly resistant virus. This was confirmed by plaque-reduction assay. Six subjects progressed to AIDS within the 2-year study period, confirming that the development of highly resistant isolates is not required for progression in treated individuals. No clear temporal relationship was found between the development of partial resistance and progression.

The reverse transcriptase (RT) of HIV is an important target for chemotherapy as demonstrated by the effective treatment of AIDS patients with zidovudine, a potent inhibitor of RT. Structural studies of HIV RT were therefore undertaken with a view to designing more effective inhibitors. To obtain sufficient quantities of enzyme for these studies the reverse transcriptase gene of HIV was cloned into a high level expression plasmid yielding reverse transcriptase at a level of 10% of the total Escherichia coli proteins. Monoclonal antibodies to RT were raised in mice and have been used to purify the enzyme by immunoaffinity chromatography. Crystallization of the enzyme has been achieved and studies are underway to determine its three-dimensional structure. In addition, carboxy-terminal truncated mutants were prepared by inserting stop codons into the gene at appropriate sites. The proteins expressed were analysed for RT and RNase H activity and used for mapping RT epitopes. This, together with previous data on site-directed mutagenesis of conserved regions of HIV RT has helped to map some of the structural and functional regions of the enzyme.