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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Effects of dipeptide insertions between codons 69 and 70 of human immunodeficiency virus type 1 reverse transcriptase on primer unblocking, deoxynucleoside triphosphate inhibition, and DNA chain elongation.

Finger insertion mutations of human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) (T69S mutations followed by various dipeptide insertions) have a multinucleoside resistance phenotype that can be explained by decreased sensitivity to deoxynucleoside triphosphate (dNTP) inhibition of the nucleotide-dependent unblocking activity of RT. We show that RTs with SG or AG (but not SS) insertions have three- to fourfold-increased unblocking activity and that all three finger insertion mutations have threefold-decreased sensitivity to dNTP inhibition. The additional presence of M41L and T215Y mutations increased unblocking activity for all three insertions, greatly reduced the sensitivity to dNTP inhibition, and resulted in defects in in vitro DNA chain elongation.

Broad nucleoside-analogue resistance implications for human immunodeficiency virus type 1 reverse-transcriptase mutations at codons 44 and 118.

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.

Extent of cross-resistance between agents used to treat human immunodeficiency virus type 1 infection in clinically derived isolates.

The phenomenon of cross-resistance to antiretroviral agents used to treat human immunodeficiency virus type 1 infection is well known but so far has been only qualitatively described. Here, we quantitate the degree of cross-resistance among all commonly prescribed antiretroviral agents in almost 5,000 clinically derived recombinant isolates collected in the United States since January 2000.

Comparative analysis of two commercial phenotypic assays for drug susceptibility testing of human immunodeficiency virus type 1.

Human immunodeficiency virus type 1 (HIV-1) isolates from 50 plasma specimens were analyzed for phenotypic susceptibility to licensed reverse transcriptase inhibitors and protease inhibitors by the Antivirogram and PhenoSense HIV assays. Twenty of these specimens were from recently seroconverted drug-naïve persons, and 30 were from patients who were the sources of occupational exposures to HIV-1; 16 of the specimens in the latter group were from drug-experienced patients. The phenotypic results of the Antivirogram and PhenoSense HIV assays were categorized as sensitive or reduced susceptibility on the basis of the cutoff values established by the manufacturers of each assay.