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Travelli C, Colombo G, Aliotta M, Fagiani F, Fava N, De Sanctis R, Grolla AA, Garcia JGN, Clemente N, Portararo P, Costanza M, Condorelli F, Colombo MP, Sangaletti S, Genazzani AA. Extracellular nicotinamide phosphoribosyltransferase (eNAMPT) neutralization counteracts T cell immune evasion in breast cancer. J Immunother Cancer 2023; 11:e007010. [PMID: 37880182 PMCID: PMC10603332 DOI: 10.1136/jitc-2023-007010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Nicotinamide phosphoribosyltransferase (NAMPT) is a key intracellular enzyme that participates in nicotinamide adenine dinucleotide (NAD) homeostasis as well as a released cytokine (eNAMPT) that is elevated in inflammatory conditions and in cancer. In patients with breast cancer, circulating eNAMPT is elevated and its plasma levels correlate with prognosis and staging. In light of this, we investigated the contribution of eNAMPT in triple negative mammary carcinoma progression by investigating the effect of its neutralization via a specific neutralizing monoclonal antibody (C269). METHODS We used female BALB/c mice injected with 4T1 clone 5 cells and female C57BL6 injected with EO771 cells, evaluating tumoral size, spleen weight and number of metastases. We injected two times a week the anti-eNAMPT neutralizing antibody and we sacrificed the mice after 28 days. Harvested tumors were analyzed by histopathology, flow cytometry, western blot, immunohistochemistry, immunofluorescence and RNA sequencing to define tumor characteristics (isolating tumor infiltrating lymphocytes and tumoral cells) and to investigate the molecular mechanisms behind the observed phenotype. Moreover, we dissected the functional relationship between T cells and tumoral cells using three-dimensional (3D) co-cultures. RESULTS The neutralization of eNAMPT with C269 led to decreased tumor size and reduced number of lung metastases. RNA sequencing and functional assays showed that eNAMPT controlled T-cell response via the programmed death-ligand 1/programmed cell death protein 1 (PD-L1/PD-1) axis and its neutralization led to a restoration of antitumoral immune responses. In particular, eNAMPT neutralization was able to activate CD8+IFNγ+GrzB+ T cells, reducing the immunosuppressive phenotype of T regulatory cells. CONCLUSIONS These studies indicate for the first time eNAMPT as a novel immunotherapeutic target for triple negative breast cancer.
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Affiliation(s)
- Cristina Travelli
- Department of Drug Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Giorgia Colombo
- Department of Pharmaceutical Science, University of Eastern Piedmont, Novara, Italy
| | - Martina Aliotta
- Department of Drug Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Francesca Fagiani
- Department of Drug Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Natalia Fava
- Department of Drug Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Rita De Sanctis
- Department of Medical Oncology and Hematology Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Ambra A Grolla
- Department of Pharmaceutical Science, University of Eastern Piedmont, Novara, Italy
| | - Joe G N Garcia
- Department of Medicine, University of Arizona Health Sciences, Tucson, Arizona, USA
| | - Nausicaa Clemente
- Dipartimento di Scienze della Salute, Interdisciplinary Research Center of Autoimmune Diseases-IRCAD, Università del Piemonte Orientale, Novara, Italy
| | - Paola Portararo
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Massimo Costanza
- Department of Clinical Neuroscience, Istituto Nazionale Neurologico Carlo Besta, Milan, Italy
| | - Fabrizio Condorelli
- Department of Pharmaceutical Science, University of Eastern Piedmont, Novara, Italy
| | - Mario Paolo Colombo
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Sabina Sangaletti
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Armando A Genazzani
- Department of Pharmaceutical Science, University of Eastern Piedmont, Novara, Italy
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Revell AD, Wang D, Perez-Elias MJ, Wood R, Cogill D, Tempelman H, Hamers RL, Reiss P, van Sighem A, Rehm CA, Agan B, Alvarez-Uria G, Montaner JSG, Lane HC, Larder BA. 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. J Antimicrob Chemother 2021; 76:1898-1906. [PMID: 33792714 DOI: 10.1093/jac/dkab078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 02/23/2021] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Andrew D Revell
- The HIV Resistance Response Database Initiative (RDI), London, UK
| | - Dechao Wang
- The HIV Resistance Response Database Initiative (RDI), London, UK
| | | | - Robin Wood
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
| | - Dolphina Cogill
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
| | | | - Raph L Hamers
- Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
| | - Peter Reiss
- Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands.,Stichting HIV Monitoring, Amsterdam, The Netherlands
| | | | - Catherine A Rehm
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Brian Agan
- Uniformed Services University of the Health Sciences and Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | | | | | - H Clifford Lane
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Brendan A Larder
- The HIV Resistance Response Database Initiative (RDI), London, UK
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Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response. J Acquir Immune Defic Syndr 2020; 81:207-215. [PMID: 30865186 DOI: 10.1097/qai.0000000000001989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
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.
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Mu Y, Kodidela S, Wang Y, Kumar S, Cory TJ. The dawn of precision medicine in HIV: state of the art of pharmacotherapy. Expert Opin Pharmacother 2018; 19:1581-1595. [PMID: 30234392 DOI: 10.1080/14656566.2018.1515916] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION Combination antiretroviral therapy (ART) reduces viral load to under the limit of detection, successfully decreasing HIV-related morbidity and mortality. Due to viral mutations, complex drug combinations and different patient response, there is an increasing demand for individualized treatment options for patients. AREAS COVERED This review first summarizes the pharmacokinetic and pharmacodynamic profile of clinical first-line drugs, which serves as guidance for antiretroviral precision medicine. Factors which have influential effects on drug efficacy and thus precision medicine are discussed: patients' pharmacogenetic information, virus mutations, comorbidities, and immune recovery. Furthermore, strategies to improve the application of precision medicine are discussed. EXPERT OPINION Precision medicine for ART requires comprehensive information on the drug, virus, and clinical data from the patients. The clinically available genetic tests are a good starting point. To better apply precision medicine, deeper knowledge of drug concentrations, HIV reservoirs, and efficacy associated genes, such as polymorphisms of drug transporters and metabolizing enzymes, are required. With advanced computer-based prediction systems which integrate more comprehensive information on pharmacokinetics, pharmacodynamics, pharmacogenomics, and the clinically relevant information of the patients, precision medicine will lead to better treatment choices and improved disease outcomes.
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Affiliation(s)
- Ying Mu
- a Department of Clinical Pharmacy and Translational Science , University of Tennessee Health Science Center College of Pharmacy , Memphis , USA
| | - Sunitha Kodidela
- b Department of Pharmaceutical Science , University of Tennessee Health Science Center College of Pharmacy , Memphis , USA
| | - Yujie Wang
- b Department of Pharmaceutical Science , University of Tennessee Health Science Center College of Pharmacy , Memphis , USA
| | - Santosh Kumar
- b Department of Pharmaceutical Science , University of Tennessee Health Science Center College of Pharmacy , Memphis , USA
| | - Theodore J Cory
- a Department of Clinical Pharmacy and Translational Science , University of Tennessee Health Science Center College of Pharmacy , Memphis , USA
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Revell AD, Wang D, Perez-Elias MJ, Wood R, Cogill D, Tempelman H, Hamers RL, Reiss P, van Sighem AI, Rehm CA, Pozniak A, Montaner JSG, Lane HC, Larder BA. 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. J Antimicrob Chemother 2018; 73:2186-2196. [PMID: 29889249 PMCID: PMC6054173 DOI: 10.1093/jac/dky179] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 04/10/2018] [Accepted: 04/17/2018] [Indexed: 11/14/2022] Open
Abstract
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.
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Affiliation(s)
- Andrew D Revell
- The HIV Resistance Response Database Initiative (RDI), London, UK
| | - Dechao Wang
- The HIV Resistance Response Database Initiative (RDI), London, UK
| | | | - Robin Wood
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
| | - Dolphina Cogill
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
| | | | - Raph L Hamers
- Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
| | - Peter Reiss
- Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
- Stichting HIV Monitoring, Amsterdam, The Netherlands
| | | | - Catherine A Rehm
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | | | | | - H Clifford Lane
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Brendan A Larder
- The HIV Resistance Response Database Initiative (RDI), London, UK
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Kebede M, Zegeye DT, Zeleke BM. Predicting CD4 count changes among patients on antiretroviral treatment: Application of data mining techniques. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 152:149-157. [PMID: 29054255 DOI: 10.1016/j.cmpb.2017.09.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 09/06/2017] [Accepted: 09/14/2017] [Indexed: 05/06/2023]
Abstract
BACKGROUND AND OBJECTIVES To monitor the progress of therapy and disease progression, periodic CD4 counts are required throughout the course of HIV/AIDS care and support. The demand for CD4 count measurement is increasing as ART programs expand over the last decade. This study aimed to predict CD4 count changes and to identify the predictors of CD4 count changes among patients on ART. METHODS A cross-sectional study was conducted at the University of Gondar Hospital from 3,104 adult patients on ART with CD4 counts measured at least twice (baseline and most recent). Data were retrieved from the HIV care clinic electronic database and patients` charts. Descriptive data were analyzed by SPSS version 20. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was followed to undertake the study. WEKA version 3.8 was used to conduct a predictive data mining. Before building the predictive data mining models, information gain values and correlation-based Feature Selection methods were used for attribute selection. Variables were ranked according to their relevance based on their information gain values. J48, Neural Network, and Random Forest algorithms were experimented to assess model accuracies. RESULT The median duration of ART was 191.5 weeks. The mean CD4 count change was 243 (SD 191.14) cells per microliter. Overall, 2427 (78.2%) patients had their CD4 counts increased by at least 100 cells per microliter, while 4% had a decline from the baseline CD4 value. Baseline variables including age, educational status, CD8 count, ART regimen, and hemoglobin levels predicted CD4 count changes with predictive accuracies of J48, Neural Network, and Random Forest being 87.1%, 83.5%, and 99.8%, respectively. Random Forest algorithm had a superior performance accuracy level than both J48 and Artificial Neural Network. The precision, sensitivity and recall values of Random Forest were also more than 99%. CONCLUSIONS Nearly accurate prediction results were obtained using Random Forest algorithm. This algorithm could be used in a low-resource setting to build a web-based prediction model for CD4 count changes.
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Affiliation(s)
- Mihiretu Kebede
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, Bremen, Germany; University of Gondar, Institute of Public Health, Department of Health Informatics, Gondar, Ethiopia.
| | | | - Berihun Megabiaw Zeleke
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia; Department of Epidemiology and Biostatistics, Institute of Public Health, University of Gondar, Gondar, Ethiopia
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Paredes R, Tzou PL, van Zyl G, Barrow G, Camacho R, Carmona S, Grant PM, Gupta RK, Hamers RL, Harrigan PR, Jordan MR, Kantor R, Katzenstein DA, Kuritzkes DR, Maldarelli F, Otelea D, Wallis CL, Schapiro JM, Shafer RW. Collaborative update of a rule-based expert system for HIV-1 genotypic resistance test interpretation. PLoS One 2017; 12:e0181357. [PMID: 28753637 PMCID: PMC5533429 DOI: 10.1371/journal.pone.0181357] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2017] [Accepted: 06/27/2017] [Indexed: 12/21/2022] Open
Abstract
INTRODUCTION HIV-1 genotypic resistance test (GRT) interpretation systems (IS) require updates as new studies on HIV-1 drug resistance are published and as treatment guidelines evolve. METHODS An expert panel was created to provide recommendations for the update of the Stanford HIV Drug Resistance Database (HIVDB) GRT-IS. The panel was polled on the ARVs to be included in a GRT report, and the drug-resistance interpretations associated with 160 drug-resistance mutation (DRM) pattern-ARV combinations. The DRM pattern-ARV combinations included 52 nucleoside RT inhibitor (NRTI) DRM pattern-ARV combinations (13 patterns x 4 NRTIs), 27 nonnucleoside RT inhibitor (NNRTI) DRM pattern-ARV combinations (9 patterns x 3 NNRTIs), 39 protease inhibitor (PI) DRM pattern-ARV combinations (13 patterns x 3 PIs) and 42 integrase strand transfer inhibitor (INSTI) DRM pattern-ARV combinations (14 patterns x 3 INSTIs). RESULTS There was universal agreement that a GRT report should include the NRTIs lamivudine, abacavir, zidovudine, emtricitabine, and tenofovir disoproxil fumarate; the NNRTIs efavirenz, etravirine, nevirapine, and rilpivirine; the PIs atazanavir/r, darunavir/r, and lopinavir/r (with "/r" indicating pharmacological boosting with ritonavir or cobicistat); and the INSTIs dolutegravir, elvitegravir, and raltegravir. There was a range of opinion as to whether the NRTIs stavudine and didanosine and the PIs nelfinavir, indinavir/r, saquinavir/r, fosamprenavir/r, and tipranavir/r should be included. The expert panel members provided highly concordant DRM pattern-ARV interpretations with only 6% of NRTI, 6% of NNRTI, 5% of PI, and 3% of INSTI individual expert interpretations differing from the expert panel median by more than one resistance level. The expert panel median differed from the HIVDB 7.0 GRT-IS for 20 (12.5%) of the 160 DRM pattern-ARV combinations including 12 NRTI, two NNRTI, and six INSTI pattern-ARV combinations. Eighteen of these differences were updated in HIVDB 8.1 GRT-IS to reflect the expert panel median. Additionally, HIVDB users are now provided with the option to exclude those ARVs not considered to be universally required. CONCLUSIONS The HIVDB GRT-IS was updated through a collaborative process to reflect changes in HIV drug resistance knowledge, treatment guidelines, and expert opinion. Such a process broadens consensus among experts and identifies areas requiring further study.
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Affiliation(s)
| | - Philip L. Tzou
- Division of Infectious Diseases, Stanford University, Stanford, CA, United States of America
| | - Gert van Zyl
- Division of Medical Virology, Stellenbosch University and NHLS Tygerberg, Cape Town, South Africa
| | - Geoff Barrow
- Centre for HIV/AIDS Research, Education and Services (CHARES), Department of Medicine, University of the West Indies, Kingston Jamaica
| | - Ricardo Camacho
- Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Sergio Carmona
- Department of Molecular Medicine and Haematology, University of the Witwatersrand, Johannesburg, South Africa
| | - Philip M. Grant
- Division of Infectious Diseases, Stanford University, Stanford, CA, United States of America
| | | | - Raph L. Hamers
- Amsterdam Institute for Global Health and Development, Department of Global Health, Academic Medical Center of the University of Amsterdam, Amsterdam, The Netherlands
| | | | - Michael R. Jordan
- Tufts University School of Medicine, Boston, MA, United States of America
| | - Rami Kantor
- Division of Infectious Diseases, Alpert Medical School, Brown University, Providence, RI, United States of America
| | - David A. Katzenstein
- Division of Infectious Diseases, Stanford University, Stanford, CA, United States of America
| | - Daniel R. Kuritzkes
- Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Frank Maldarelli
- HIV Dynamics and Replication Program, CCR, National Cancer Institute, NIH, Translational Research Unit, Frederick, MD, United States of America
| | - Dan Otelea
- Molecular Diagnostics Laboratory, National Institute for Infectious Diseases, Bucharest, Romania
| | | | | | - Robert W. Shafer
- Division of Infectious Diseases, Stanford University, Stanford, CA, United States of America
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Pironti A, Pfeifer N, Walter H, Jensen BEO, Zazzi M, Gomes P, Kaiser R, Lengauer T. Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool. PLoS One 2017; 12:e0174992. [PMID: 28394945 PMCID: PMC5386274 DOI: 10.1371/journal.pone.0174992] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 03/17/2017] [Indexed: 01/31/2023] Open
Abstract
Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs.
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Affiliation(s)
- Alejandro Pironti
- Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany
- * E-mail:
| | - Nico Pfeifer
- Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany
| | - Hauke Walter
- Medizinisches Infektiologiezentrum Berlin, Berlin, Germany
- Medizinisches Labor Stendal, Stendal, Germany
| | - Björn-Erik O. Jensen
- Clinic for Gastroenterology, Hepatology, and Infectiology, University Clinic of Düsseldorf, Düsseldorf, Germany
| | - Maurizio Zazzi
- Department of Medical Biotechnology, University of Siena, Siena, Italy
| | - Perpétua Gomes
- Laboratorio de Biologia Molecular, LMCBM, SPC, HEM - Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- Centro de Investigacao Interdisciplinar Egas Moniz (CiiEM), Instituto Superior de Ciencias da Saude Sul, Caparica, Portugal
| | - Rolf Kaiser
- Institute for Virology, University Clinic of Cologne, Cologne, Germany
| | - Thomas Lengauer
- Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany
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Revell AD, Wang D, Wood R, Morrow C, Tempelman H, Hamers RL, Reiss P, van Sighem AI, Nelson M, Montaner JSG, Lane HC, Larder BA. 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. J Antimicrob Chemother 2016; 71:2928-37. [PMID: 27330070 PMCID: PMC5031919 DOI: 10.1093/jac/dkw217] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 05/06/2016] [Accepted: 05/09/2016] [Indexed: 12/27/2022] Open
Abstract
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.
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Affiliation(s)
- Andrew D Revell
- The HIV Resistance Response Database Initiative (RDI), London, UK
| | - Dechao Wang
- The HIV Resistance Response Database Initiative (RDI), London, UK
| | - Robin Wood
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
| | - Carl Morrow
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
| | | | - Raph L Hamers
- Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
| | - Peter Reiss
- Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands Stichting HIV Monitoring, Amsterdam, The Netherlands
| | | | - Mark Nelson
- Chelsea and Westminster Hospital, London, UK
| | | | - H Clifford Lane
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Brendan A Larder
- The HIV Resistance Response Database Initiative (RDI), London, UK
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Revell A, Khabo P, Ledwaba L, Emery S, Wang D, Wood R, Morrow C, Tempelman H, Hamers RL, Reiss P, van Sighem A, Pozniak A, Montaner J, Lane HC, Larder B. Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa. South Afr J HIV Med 2016; 17:450. [PMID: 29568609 PMCID: PMC5843195 DOI: 10.4102/sajhivmed.v17i1.450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 04/22/2016] [Indexed: 11/16/2022] Open
Abstract
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.
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Affiliation(s)
- Andrew Revell
- The HIV Resistance Response Database Initiative (RDI), London, United Kingdom
| | - Paul Khabo
- Project PHIDISA, South African Military Health Service (SAMHS), Lyttelton, South Africa
| | - Lotty Ledwaba
- Project PHIDISA, South African National Defence Force (SANDF), Lyttelton, South Africa
| | - Sean Emery
- Kirby Institute, University of New South Wales, Sydney, Australia
| | - Dechao Wang
- The HIV Resistance Response Database Initiative (RDI), London, United Kingdom
| | - Robin Wood
- The Desmond Tutu HIV Centre, University of Cape Town, South Africa
| | - Carl Morrow
- The Desmond Tutu HIV Centre, University of Cape Town, South Africa
| | | | - Raph L. Hamers
- Academic Medical Center of the University of Amsterdam, Amsterdam, the Netherlands
| | - Peter Reiss
- Academic Medical Center of the University of Amsterdam, Amsterdam, the Netherlands
- Stichting HIV Monitoring, Amsterdam, the Netherlands
| | | | - Anton Pozniak
- Chelsea and Westminster Hospital, London, United Kingdom
| | | | - H. Clifford Lane
- National Institute of Allergy and Infectious Diseases, Bethesda, United States
| | - Brendan Larder
- The HIV Resistance Response Database Initiative (RDI), London, United Kingdom
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De Luca A, Flandre P, Dunn D, Zazzi M, Wensing A, Santoro MM, Günthard HF, Wittkop L, Kordossis T, Garcia F, Castagna A, Cozzi-Lepri A, Churchill D, De Wit S, Brockmeyer NH, Imaz A, Mussini C, Obel N, Perno CF, Roca B, Reiss P, Schülter E, Torti C, van Sighem A, Zangerle R, Descamps D. Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen. J Antimicrob Chemother 2016; 71:1352-60. [PMID: 26825119 PMCID: PMC5808835 DOI: 10.1093/jac/dkv465] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 12/03/2015] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES The objective of this study was to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. METHODS Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV-1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). RESULTS TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27% and raltegravir or maraviroc or enfuvirtide in 53%. The prediction model included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R(2) = 0.47 [average squared error (ASE) = 0.67, P < 10(-6)]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our final model outperformed models with existing interpretation systems in both training and validation sets. CONCLUSIONS A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir.
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Affiliation(s)
- Andrea De Luca
- Division of Infectious Diseases, Siena University Hospital, Siena, Italy Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | | | - David Dunn
- MRC Clinical Trials Unit at University College London, London, UK
| | - Maurizio Zazzi
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | | | | | - Huldrych F Günthard
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Switzerland and Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Linda Wittkop
- Inserm U897, ISPED, Université de Bordeaux, CHU Bordeaux, France/Cohere in Eurocoord RCC, Bordeaux, France
| | | | | | | | | | - Duncan Churchill
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | | | - Norbert H Brockmeyer
- Department of Dermatology and Venereology, Center for Sexual Health and Medicine, Ruhr University Bochum, Bochum, Germany and Competence Network for HIV/AIDS, Ruhr University Bochum, Bochum, Germany
| | - Arkaitz Imaz
- Bellvitge University Hospital, Barcelona, Catalonia, Spain
| | | | - Niels Obel
- Copenhagen University Hospital, Copenhagen, Denmark
| | | | | | - Peter Reiss
- Stichting HIV Monitoring, Amsterdam, The Netherlands, and Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands/Cohere in Eurocoord RCC, Copenhagen, Denmark
| | | | | | | | - Robert Zangerle
- Universitätsklinik für Dermatologie und Venerologie, Innsbruck, Austria
| | - Diane Descamps
- AP-HP, Hôpital Bichat-Claude Bernard, Laboratoire de Virologie, IAME, UMR_1137, INSERM, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France
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12
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Grolla AA, Torretta S, Gnemmi I, Amoruso A, Orsomando G, Gatti M, Caldarelli A, Lim D, Penengo L, Brunelleschi S, Genazzani AA, Travelli C. Nicotinamide phosphoribosyltransferase (NAMPT/PBEF/visfatin) is a tumoural cytokine released from melanoma. Pigment Cell Melanoma Res 2015; 28:718-29. [DOI: 10.1111/pcmr.12420] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 09/08/2015] [Indexed: 12/20/2022]
Affiliation(s)
- Ambra A. Grolla
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Simone Torretta
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Ilaria Gnemmi
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Angela Amoruso
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Giuseppe Orsomando
- Section of Biochemistry; Department of Clinical Sciences; Polytechnic University of Marche; Ancona Italy
| | - Marco Gatti
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Antonio Caldarelli
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Dmitry Lim
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Lorenza Penengo
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
- Institute of Pharmacology and Toxicology; University of Zürich-Vetsuisse; Zürich Switzerland
| | - Sandra Brunelleschi
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Armando A. Genazzani
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
| | - Cristina Travelli
- Department of Pharmaceutical Sciences and Department of Medical Sciences; Università del Piemonte Orientale; Novara Italy
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13
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Revell AD, Boyd MA, Wang D, Emery S, Gazzard B, Reiss P, van Sighem AI, Montaner JS, Lane HC, Larder BA. A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy. HIV Med 2014; 15:442-8. [PMID: 24735474 DOI: 10.1111/hiv.12156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/24/2014] [Indexed: 01/08/2023]
Abstract
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. One set used viral load, CD4 count, genotype, plus treatment history and time to follow-up to make its predictions; the second set did not include genotype. Genotypic sensitivity scores were derived and the ranking of the alternative regimens compared with those of the models. The accuracy of the models and that of genotyping as predictors of the virological responses to second-line regimens were compared. RESULTS The rankings of alternative regimens by the two sets of models were significantly correlated in 60-69% of cases, and the rankings by the models that use a genotype and genotyping itself were significantly correlated in 60% of cases. The two sets of models identified alternative regimens that were predicted to be effective in 97% and 100% of cases, respectively. The area under the receiver-operating curve was 0.72 and 0.74 for the two sets of models, respectively, and significantly lower at 0.55 for genotyping. CONCLUSIONS The two sets of models performed comparably well and significantly outperformed genotyping as predictors of response. The models identified alternative regimens predicted to be effective in almost all cases. It is encouraging that models that do not require a genotype were able to predict responses to common second-line therapies in settings where genotyping is unavailable.
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Affiliation(s)
- A D Revell
- The HIV Resistance Response Database Initiative (RDI), London, UK
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14
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Lengauer T, Pfeifer N, Kaiser R. Personalized HIV therapy to control drug resistance. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 11:57-64. [PMID: 24847654 DOI: 10.1016/j.ddtec.2014.02.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The therapy of HIV patients is characterized by both the high genomic diversity of the virus population harbored by the patient and a substantial volume of therapy options. The virus population is unique for each patient and time point. The large number of therapy options makes it difficult to select an optimal or near optimal therapy, especially with therapy-experienced patients. In the past decade, computer-based support for therapy selection, which assesses the level of viral resistance against drugs has become a mainstay for HIV patients. We discuss the properties of available systems and the perspectives of the field.
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Revell AD, Wang D, Wood R, Morrow C, Tempelman H, Hamers R, Alvarez-Uria G, Streinu-Cercel A, Ene L, Wensing A, Reiss P, van Sighem AI, Nelson M, Emery S, Montaner JSG, Lane HC, Larder BA. An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes. J Antimicrob Chemother 2013; 69:1104-10. [PMID: 24275116 DOI: 10.1093/jac/dkt447] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
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.
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Affiliation(s)
- Andrew D Revell
- HIV Resistance Response Database Initiative (RDI), London, UK
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16
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Revell AD, Alvarez-Uria G, Wang D, Pozniak A, Montaner JS, Lane HC, Larder BA. 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. BIOMED RESEARCH INTERNATIONAL 2013; 2013:579741. [PMID: 24175292 PMCID: PMC3794568 DOI: 10.1155/2013/579741] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Accepted: 07/09/2013] [Indexed: 11/17/2022]
Abstract
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.
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Affiliation(s)
- Andrew D. Revell
- The HIV Resistance Response Database Initiative (RDI), 14 Union Square, London N1 7DH, UK
| | | | - Dechao Wang
- The HIV Resistance Response Database Initiative (RDI), 14 Union Square, London N1 7DH, UK
| | - Anton Pozniak
- Chelsea and Westminster Hospital, London SW10 9NH, UK
| | | | - H. Clifford Lane
- National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA
| | - Brendan A. Larder
- The HIV Resistance Response Database Initiative (RDI), 14 Union Square, London N1 7DH, UK
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17
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Su X, Lau JTF, Mak WWS, Choi KC, Chen L, Song J, Zhang Y, Zhao G, Feng T, Chen X, Liu C, Liu J, Liu D, Cheng J. Prevalence and associated factors of depression among people living with HIV in two cities in China. J Affect Disord 2013; 149:108-15. [PMID: 23452645 DOI: 10.1016/j.jad.2013.01.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2012] [Revised: 12/31/2012] [Accepted: 01/02/2013] [Indexed: 11/30/2022]
Abstract
OBJECTIVES Depression has significant effects on morbidity and mortality in people living with HIV (PLWH). Current study estimated the rate of depressive disorder and identified the correlates of depressive disorder among PLWH in China. METHODS 258 PLWH in China were recruited and interviewed with a structured questionnaire including measurements testing perceived stress, social support, perceived discrimination, and depression. Mediating effect of perceived stress between perceived discrimination and depression and moderating effect of social support on effect of perceived discrimination and perceived stress to depression were tested. Multivariate regression was used to examine the determinants of depression. RESULTS The prevalence of mild to severe depression is 71.9%. The relationship between the perceived discrimination and depression is fully mediated by perceived stress (perceived discrimination that was statistically significant (β=0.153) to depression became non-significant after adding perceived stress in the regression model). Interaction term between social support and perceived stress has negative effects (β=-0.117) and explained a significant amount of variance (R(2)=0.018) in depression. Lower income, and higher perceived stress predicted more depressive symptoms. LIMITATIONS Cross-sectional study and self-report bias are major limitations of this study. CONCLUSION Depression among PLWH is a severe problem in China. Primary health care workers need to be trained in recognition and treatment in depression. Stress management skills and social support for PLWH are warranted.
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Affiliation(s)
- Xiaoyou Su
- Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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18
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Revell AD, Wang D, Wood R, Morrow C, Tempelman H, Hamers RL, Alvarez-Uria G, Streinu-Cercel A, Ene L, Wensing AMJ, DeWolf F, Nelson M, Montaner JS, Lane HC, Larder BA. Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings. J Antimicrob Chemother 2013; 68:1406-14. [PMID: 23485767 DOI: 10.1093/jac/dkt041] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
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.
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Affiliation(s)
- A D Revell
- The HIV Resistance Response Database Initiative (RDI), London, UK
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Svärd J, Sönnerborg A. Optimizing background therapy in treatment-experienced HIV-1 patients by rules-based algorithms and bioinformatics. Future Virol 2012. [DOI: 10.2217/fvl.12.66] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In HIV-1-infected patients with extensive drug resistance, the optimization of background antiretroviral therapy is essential when changing drugs after treatment failure. The genotypic sensitivity score (GSS) and phenotypic sensitivity score (PSS), determined by rules-based algorithms, are employed to predict which drugs to select in a background therapy in order to receive the best treatment response when a new drug will be used, both in investigational trials of new agents and in clinical care. However, the outcome of the GSS/PSS approach for the purpose of assessing antiretroviral efficacy in patients with multiresistance has become more problematic, despite improvements such as drug potency weighting and adding information on treatment history. Bioinformatics-based methods are more recent attractive alternatives that have demonstrated equal or better precision compared with rules-based algorithms. This review aims to discuss the usefulness of GSS/PSS and bioinformatics, respectively, for the optimization of anti-HIV background therapy in heavily treatment-experienced patients.
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Affiliation(s)
- Jenny Svärd
- Unit of Infectious Diseases, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Anders Sönnerborg
- Unit of Infectious Diseases, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
- Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
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Struck D, Wallis CL, Denisov G, Lambert C, Servais JY, Viana RV, Letsoalo E, Bronze M, Aitken SC, Schuurman R, Stevens W, Schmit JC, Rinke de Wit T, Perez Bercoff D. Automated sequence analysis and editing software for HIV drug resistance testing. J Clin Virol 2012; 54:30-5. [PMID: 22425336 DOI: 10.1016/j.jcv.2012.01.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2011] [Revised: 01/20/2012] [Accepted: 01/23/2012] [Indexed: 11/25/2022]
Abstract
BACKGROUND Access to antiretroviral treatment in resource-limited-settings is inevitably paralleled by the emergence of HIV drug resistance. Monitoring treatment efficacy and HIV drugs resistance testing are therefore of increasing importance in resource-limited settings. Yet low-cost technologies and procedures suited to the particular context and constraints of such settings are still lacking. The ART-A (Affordable Resistance Testing for Africa) consortium brought together public and private partners to address this issue. OBJECTIVES To develop an automated sequence analysis and editing software to support high throughput automated sequencing. STUDY DESIGN The ART-A Software was designed to automatically process and edit ABI chromatograms or FASTA files from HIV-1 isolates. RESULTS The ART-A Software performs the basecalling, assigns quality values, aligns query sequences against a set reference, infers a consensus sequence, identifies the HIV type and subtype, translates the nucleotide sequence to amino acids and reports insertions/deletions, premature stop codons, ambiguities and mixed calls. The results can be automatically exported to Excel to identify mutations. Automated analysis was compared to manual analysis using a panel of 1624 PR-RT sequences generated in 3 different laboratories. Discrepancies between manual and automated sequence analysis were 0.69% at the nucleotide level and 0.57% at the amino acid level (668,047 AA analyzed), and discordances at major resistance mutations were recorded in 62 cases (4.83% of differences, 0.04% of all AA) for PR and 171 (6.18% of differences, 0.03% of all AA) cases for RT. CONCLUSIONS The ART-A Software is a time-sparing tool for pre-analyzing HIV and viral quasispecies sequences in high throughput laboratories and highlighting positions requiring attention.
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Revell AD, Ene L, Duiculescu D, Wang D, Youle M, Pozniak A, Montaner J, Larder BA. The use of computational models to predict response to HIV therapy for clinical cases in Romania. Germs 2012; 2:6-11. [PMID: 24432257 DOI: 10.11599/germs.2012.1007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2011] [Accepted: 01/19/2012] [Indexed: 11/23/2022]
Abstract
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.
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Affiliation(s)
| | - Luminiţa Ene
- MD, 'Dr. Victor Babeş' Hospital for Infectious and Tropical Diseases, Bucharest, Romania
| | - Dan Duiculescu
- MD, PhD, 'Dr. Victor Babeş' Hospital for Infectious and Tropical Diseases, Bucharest, Romania
| | | | - Mike Youle
- MD, HIV Training and Resource Initiative, London, UK
| | | | - Julio Montaner
- MD, BC Centre for Excellence in HIV and AIDS, Vancouver, Canada
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