Personalized HIV therapy to control drug resistance

Drug Discovery Today: Technologies - Tập 11 - Trang 57-64 - 2014
Thomas Lengauer1, Nico Pfeifer1, Rolf Kaiser2
1Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany
2Institute of Virology, University of Cologne, Germany

Tài liệu tham khảo

Lengauer, 2012, Bioinformatical assistance of selecting anti-HIV therapies: where do we stand?, Intervirology, 55, 108, 10.1159/000332000 Walter, 1999, Rapid, phenotypic HIV-1 drug sensitivity assay for protease and reverse transcriptase inhibitors, J Clin Virol, 13, 71, 10.1016/S1386-6532(99)00010-4 Lengauer, 2006, Bioinformatics-assisted anti-HIV therapy, Nat Rev Microbiol, 4, 790, 10.1038/nrmicro1477 Johnson, 2013, Update of the drug resistance mutations in HIV-1: March 2013, Top Antivir Med, 21, 6 Durant, 1999, Drug-resistance genotyping in HIV-1 therapy: the VIRADAPT randomised controlled trial, Lancet, 353, 2195, 10.1016/S0140-6736(98)12291-2 Rhee, 2003, Human immunodeficiency virus reverse transcriptase and protease sequence database, Nucleic Acids Res, 31, 298, 10.1093/nar/gkg100 Van Laethem, 2002, A genotypic drug resistance interpretation algorithm that significantly predicts therapy response in HIV-1-infected patients, Antivir Ther, 7, 123, 10.1177/135965350200700206 Rousseau, 2001, Patterns of resistance mutations to antiretroviral drugs in extensively treated HIV-1-infected patients with failure of highly active antiretroviral therapy, J Acquir Immune Defic Syndr, 26, 36, 10.1097/00126334-200101010-00005 Beerenwinkel, 2002, Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype, Proc Natl Acad Sci U S A, 99, 8271, 10.1073/pnas.112177799 Altmann, 2009, Keeping models that predict response to antiretroviral therapy up-to-date: fusion of pure data-driven approaches with rules-based methods, Rev Antiviral Ther, 1, A92 Obermeier, 2012, HIV-GRADE: a publicly available, rules-based drug resistance interpretation algorithm integrating bioinformatic knowledge, Intervirology, 55, 102, 10.1159/000331999 Tang, 2012, The HIVdb system for HIV-1 genotypic resistance interpretation, Intervirology, 55, 98, 10.1159/000331998 Vercauteren, 2013, Clinical evaluation of Rega 8: an updated genotypic interpretation system that significantly predicts HIV-therapy response, PLoS ONE, 8, pe61436, 10.1371/journal.pone.0061436 Eberle, 2012, The evolution of drug resistance interpretation algorithms: ANRS, REGA and extension of resistance analysis to HIV-1 group O and HIV-2, Intervirology, 55, 128, 10.1159/000332009 Gibb, 2002, Evolution of antiretroviral phenotypic and genotypic drug resistance in antiretroviral-naive HIV-1-infected children treated with abacavir/lamivudine, zidovudine/lamivudine or abacavir/zidovudine, with or without nelfinavir (the PENTA 5 trial), Antivir Ther, 7, 293, 10.1177/135965350200700410 Dam, 2009, Gag mutations strongly contribute to HIV-1 resistance to protease inhibitors in highly drug-experienced patients besides compensating for fitness loss, PLoS Pathog, 5, pe1000345, 10.1371/journal.ppat.1000345 Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents; 2013. Available from: http://www.aidsinfo.nih.gov/contentfiles/lvguidelines/adultandadolescentgl.pdf Vandamme, 2011, European recommendations for the clinical use of HIV drug resistance testing: 2011 update, AIDS Rev, 13, 77 Sing, 2007, Predicting HIV coreceptor usage on the basis of genetic and clinical covariates, Antivir Ther, 12, 1097, 10.1177/135965350701200709 Däumer, 2011, Genotypic tropism testing by massively parallel sequencing: qualitative and quantitative analysis, BMC Med Inform Decis Making, 11, 30, 10.1186/1472-6947-11-30 Thielen, 2012, Geno2pheno[454]: a web server for the prediction of HIV-1 coreceptor usage from next-generation sequencing data, Intervirology, 55, 113, 10.1159/000332002 Pfeifer, 2012, Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data, Bioinformatics, 28, pi589, 10.1093/bioinformatics/bts373 Vandekerckhove, 2011, European guidelines on the clinical management of HIV-1 tropism testing, Lancet Infect Dis, 11, 394, 10.1016/S1473-3099(10)70319-4 Wirden, 2011, Historical HIV-RNA resistance test results are more informative than proviral DNA genotyping in cases of suppressed or residual viraemia, J Antimicrob Chemother, 66, 709, 10.1093/jac/dkq544 Kabamba-Mukadi, 2010, HIV-1 proviral resistance mutations: usefulness in clinical practice, HIV Med, 11, 483 De Luca, 2003, Variable prediction of antiretroviral treatment outcome by different systems for interpreting genotypic human immunodeficiency virus type 1 drug resistance, J Infect Dis, 187, 1934, 10.1086/375355 Rosenbloom, 2012, Antiretroviral dynamics determines HIV evolution and predicts therapy outcome, Nat Med, 18, 1378, 10.1038/nm.2892 Beerenwinkel, 2013, The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients, PLoS Comput Biol, 9, e1003203, 10.1371/journal.pcbi.1003203 Heider, 2013, Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction, Bioinformatics, 29, 1946, 10.1093/bioinformatics/btt331 Altmann, 2009, Predicting the response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database, J Infect Dis, 199, 999, 10.1086/597305 Rosen-Zvi, 2008, Selecting anti-HIV therapies based on a variety of genomic and clinical factors, Bioinformatics, 24, pi399, 10.1093/bioinformatics/btn141 Revell, 2011, The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool, AIDS, 25, 1855, 10.1097/QAD.0b013e328349a9c2 Bogojeska, 2012, History-alignment models for bias-aware prediction of virological response to HIV combination therapy, J Machine Learn Res, 22, 118 Saigo, 2011, Learning from past treatments and their outcome improves prediction of in vivo response to anti-HIV therapy, Stat Appl Genet Mol Biol, 10, pArticle6, 10.2202/1544-6115.1604 Bogojeska, 2011, History distribution matching method for predicting effectiveness of HIV combination therapies, 424 Moore, 2002, Evidence of HIV-1 adaptation to HLA-restricted immune responses at a population level, Science, 296, 1439, 10.1126/science.1069660 Ranasinghe, 2013, Association of HLA-DRB1-restricted CD4(+) T cell responses with HIV immune control, Nat Med, 19, 930, 10.1038/nm.3229 Däumer, 2010, Short communication: Selection of thymidine analogue resistance mutational patterns in children infected from a common HIV type 1 subtype G source, AIDS Res Hum Retroviruses, 26, 275, 10.1089/aid.2009.0233 Revell, 2013, Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings, J Antimicrob Chemother, 68, 1406, 10.1093/jac/dkt041 Prosperi, 2010, Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models, PLoS ONE, 5, e13753, 10.1371/journal.pone.0013753 Zazzi, 2011, Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study), HIV Med, 12, 211, 10.1111/j.1468-1293.2010.00871.x Larder, 2011, Clinical evaluation of the potential utility of computational modeling as an HIV treatment selection tool by physicians with considerable HIV experience, AIDS Patient Care STDS, 25, 29, 10.1089/apc.2010.0254 McGovern, 2012, Population-based sequencing of the V3-loop can predict the virological response to maraviroc in treatment-naive patients of the MERIT trial, J Acquir Immune Defic Syndr, 61, 279, 10.1097/QAI.0b013e31826249cf Swenson, 2011, Deep sequencing to infer HIV-1 co-receptor usage: application to three clinical trials of maraviroc in treatment-experienced patients, J Infect Dis, 203, 237, 10.1093/infdis/jiq030 Simon, 2008, The use of genomics in clinical trial design, Clin Cancer Res, 14, 5984, 10.1158/1078-0432.CCR-07-4531 Freidlin, 2010, Randomized clinical trials with biomarkers: design issues, J Natl Cancer Inst, 102, 152, 10.1093/jnci/djp477 Mani, 2012, Novel clinical trial designs for the development of new antiretroviral agents, AIDS, 26, 899, 10.1097/QAD.0b013e3283519371 Chan-Tack, 2008, HIV clinical trial design for antiretroviral development: moving forward, AIDS, 22, 2419, 10.1097/QAD.0b013e32831692e6 Hsieh, 1998, A simple method of sample size calculation for linear and logistic regression, Stat Med, 17, 1623, 10.1002/(SICI)1097-0258(19980730)17:14<1623::AID-SIM871>3.0.CO;2-S Efron, 2007, Size, power and false discovery rates, Ann Stat, 35, 1351, 10.1214/009053606000001460 Avidor, 2013, Evaluation of a benchtop HIV ultradeep pyrosequencing drug resistance assay in the clinical laboratory, J Clin Microbiol, 51, 880, 10.1128/JCM.02652-12 Simen, 2009, Low-abundance drug-resistant viral variants in chronically HIV-infected, antiretroviral treatment-naive patients significantly impact treatment outcomes, J Infect Dis, 199, 693, 10.1086/596736 Bock, 2012, Managing drug resistance in cancer: lessons from HIV therapy, Nat Rev Cancer, 12, 494, 10.1038/nrc3297 Foeglein, 2007, Determination of HIV-1 coreceptor tropism in clinical practise, Eur J Med Res, 12, 473 Weber, 2013, Sensitive cell-based assay for determination of human immunodeficiency virus type 1 coreceptor tropism, J Clin Microbiol, 51, 1517, 10.1128/JCM.00092-13 Whitcomb, 2007, Development and characterization of a novel single-cycle recombinant-virus assay to determine human immunodeficiency virus type 1 coreceptor tropism, Antimicrob Agents Chemother, 51, 566, 10.1128/AAC.00853-06 Brumme, 2004, Clinical and immunological impact of HIV envelope V3 sequence variation after starting initial triple antiretroviral therapy, AIDS, 18, F1, 10.1097/00002030-200403050-00001 Lengauer, 2007, Bioinformatics prediction of HIV coreceptor usage, Nat Biotechnol, 25, 1407, 10.1038/nbt1371 Dybowski, 2010, Prediction of co-receptor usage of HIV-1 from genotype, PLoS Comput Biol, 6, e1000743, 10.1371/journal.pcbi.1000743 Bozek, 2013, Analysis of physicochemical and structural properties determining HIV-1 coreceptor usage, PLoS Comput Biol, 9, e1002977, 10.1371/journal.pcbi.1002977 Prosperi, 2010, Comparative determination of HIV-1 co-receptor tropism by Enhanced Sensitivity Trofile, gp120 V3-loop RNA and DNA genotyping, Retrovirology, 7, 56, 10.1186/1742-4690-7-56 Palella, 1998, Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators, N Engl J Med, 338, 853, 10.1056/NEJM199803263381301 Neff, 2003, ATS, CDC, and IDSA update recommendations on the treatment of tuberculosis, Am Fam Phys, 68, 1854