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.

Fast nonadiabatic dynamics of many-body quantum systems.

Modeling many-body quantum systems with strong interactions is one of the core challenges of modern physics. A range of methods has been developed to approach this task, each with its own idiosyncrasies, approximations, and realm of applicability. However, there remain many problems that are intractable for existing methods.

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.

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.

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.

A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy.

Objectives: We compared the use of computational models developed with and without HIV genotype vs. genotyping itself to predict effective regimens for patients experiencing first-line virological failure.

Methods: Two sets of models predicted virological response for 99 three-drug regimens for patients on a failing regimen of two nucleoside/nucleotide reverse transcriptase inhibitors and one nonnucleoside reverse transcriptase inhibitor in the Second-Line study.

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.

Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.

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.

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.

Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings.

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.

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.

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.

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.

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.

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.

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.

Long-term efficacy of routine access to antiretroviral-resistance testing in HIV type 1-infected patients: results of the clinical efficacy of resistance testing trial.

The long-term efficacy of making resistance testing routinely available to clinicians has not been established. We conducted a clinical trial at 6 US military hospitals in which volunteers infected with human immunodeficiency virus type-1 were randomized to have routine access to phenotype resistance testing (PT arm), access to genotype resistance testing (GT arm), or no access to either test (VB arm). The primary outcome measure was time to persistent treatment failure despite change(s) in antiretroviral therapy (ART) regimen.