Personalized HIV therapy to control drug resistance

Drug Discovery Today: Technologies

Volumn 11, Issue 1, 2014, Pages 57-64

  Lengauer, Thomas ^a   Pfeifer, Nico ^a   Kaiser, Rolf ^b  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^b UNIVERSITY OF COLOGNE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT;

ANTIVIRAL RESISTANCE; ANTIVIRAL THERAPY; ARTICLE; BIOINFORMATICS; COMPUTER ASSISTED THERAPY; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTED PATIENT; PERSONALIZED MEDICINE; HIV INFECTIONS;

ANTI-HIV AGENTS; DRUG RESISTANCE, VIRAL; HIV INFECTIONS; HUMANS; INDIVIDUALIZED MEDICINE;

EID: 84899951451     PISSN: None     EISSN: 17406749     Source Type: Journal    
DOI: 10.1016/j.ddtec.2014.02.004     Document Type: Review

References (63)

1. T. Lengauer Bioinformatical assistance of selecting anti-HIV therapies: where do we stand? Intervirology 55 2012 108 112
2. H. Walter, B. Schmidt, K. Korn, A.M. Vandamme, T. Harrer, and K. Überla Rapid, phenotypic HIV-1 drug sensitivity assay for protease and reverse transcriptase inhibitors J Clin Virol 13 1-2 1999 71 80
3. T. Lengauer, and T. Sing Bioinformatics-assisted anti-HIV therapy Nat Rev Microbiol 4 10 2006 790 797
4. V.A. Johnson, V. Calvez, H.F. Gunthard, R. Paredes, D. Pillay, and R.W. Shafer et al. Update of the drug resistance mutations in HIV-1: March 2013 Top Antivir Med 21 1 2013 6 14
5. J. Durant, P. Clevenbergh, P. Halfon, P. Delgiudice, S. Porsin, and P. Simonet et al. Drug-resistance genotyping in HIV-1 therapy: the VIRADAPT randomised controlled trial Lancet 353 9171 1999 2195 2199
6. S.Y. Rhee, S.Y. Rhee, M.J. Gonzales, R. Kantor, B.J. Betts, J. Ravela, and R.W. Shafer Human immunodeficiency virus reverse transcriptase and protease sequence database Nucleic Acids Res 31 1 2003 298 303
7. K. Van Laethem, A. De Luca, A. Antinori, A. Cingolani, C.F. Perno, and A.M. Vandamme A genotypic drug resistance interpretation algorithm that significantly predicts therapy response in HIV-1-infected patients Antivir Ther 7 2 2002 123 129
8. M.N. Rousseau, L. Vergne, B. Montes, M. Peeters, J. Reynes, and E. Delaporte et al. 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 1 2001 36 43
9. N. Beerenwinkel, B. Schmidt, H. Walter, R. Kaiser, T. Lengauer, and D. Hoffmann et al. Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype Proc Natl Acad Sci U S A 99 12 2002 8271 8276
10. A. Altmann, A. Thielen, D. Frentz, K.V. Laethem, L. Bracciale, and F. Incardona et al. 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 2009 A92
11. M. Obermeier, A. Pironti, T. Berg, P. Braun, M. Däumer, and J. Eberle et al. HIV-GRADE: a publicly available, rules-based drug resistance interpretation algorithm integrating bioinformatic knowledge Intervirology 55 2 2012 102 107
12. M.W. Tang, T.F. Liu, and R.W. Shafer The HIVdb system for HIV-1 genotypic resistance interpretation Intervirology 55 2 2012 98 101
13. J. Vercauteren, G. Beheydt, M. Prosperi, P. Libin, S. Imbrechts, and R. Camacho et al. Clinical evaluation of Rega 8: an updated genotypic interpretation system that significantly predicts HIV-therapy response PLoS ONE 8 4 2013 pe61436
14. J. Eberle, and L. Gürtler The evolution of drug resistance interpretation algorithms: ANRS, REGA and extension of resistance analysis to HIV-1 group O and HIV-2 Intervirology 55 2 2012 128 133
15. D.M. Gibb, A.S. Walker, S. Kaye, A. De Rossi, M. Ait-Khaled, and D. Pillay et al. 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 4 2002 293 303
16. E. Dam, R. Quercia, B. Glass, D. Descamps, O. Launay, and X. Duval et al. Gag mutations strongly contribute to HIV-1 resistance to protease inhibitors in highly drug-experienced patients besides compensating for fitness loss PLoS Pathog 5 3 2009 pe1000345
17. 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
18. A.M. Vandamme, R.J. Camacho, F. Ceccherini-Silberstein, A. De Luca, L. Palmisano, and D. Paraskevis et al. European recommendations for the clinical use of HIV drug resistance testing: 2011 update AIDS Rev 13 2 2011 77 108
19. T. Sing, A.J. Low, N. Beerenwinkel, O. Sander, P.K. Cheung, and F.S. Domingues et al. Predicting HIV coreceptor usage on the basis of genetic and clinical covariates Antivir Ther 12 7 2007 1097 1106
20. M. Däumer, R. Kaiser, R. Klein, T. Lengauer, B. Thiele, and A. Thielen Genotypic tropism testing by massively parallel sequencing: qualitative and quantitative analysis BMC Med Inform Decis Making 11 1 2011 30
21. A. Thielen, and T. Lengauer Geno2pheno[454]: a web server for the prediction of HIV-1 coreceptor usage from next-generation sequencing data Intervirology 55 2 2012 113 117
22. N. Pfeifer, and T. Lengauer Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data Bioinformatics 28 18 2012 pi589 pi595
23. L.P.R. Vandekerckhove, A.M.J. Wensing, R. Kaiser, F. Brun-Vézinet, B. Clotet, and A. De Luca et al. European guidelines on the clinical management of HIV-1 tropism testing Lancet Infect Dis 11 5 2011 394 407
24. M. Wirden, C. Soulie, M-A. Valantin, S. Fourati, A. Simon, and S. Lambert-Niclot et al. Historical HIV-RNA resistance test results are more informative than proviral DNA genotyping in cases of suppressed or residual viraemia J Antimicrob Chemother 66 4 2011 709 712
25. B. Kabamba-Mukadi, A. Duquenne, P. Henrivaux, F. Musuamba, J. Ruelle, and J-C. Yombi et al. HIV-1 proviral resistance mutations: usefulness in clinical practice HIV Med 11 8 2010 483 492
26. A. De Luca, A. Cingolani, S. Di Giambenedetto, M.P. Trotta, F. Baldini, and M.G. Rizzo et al. Variable prediction of antiretroviral treatment outcome by different systems for interpreting genotypic human immunodeficiency virus type 1 drug resistance J Infect Dis 187 12 2003 1934 1943
27. D.I.S. Rosenbloom, A.L. Hill, S.A. Rabi, R.F. Siliciano, and M.A. Nowak Antiretroviral dynamics determines HIV evolution and predicts therapy outcome Nat Med 18 9 2012 1378 1385
28. N. Beerenwinkel, H. Montazeri, H. Schuhmacher, P. Knupfer, V. Von Wyl, and H. Furrer et al. The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients PLoS Comput Biol 9 8 2013 e1003203
29. D. Heider, R. Senge, W. Cheng, and E. Hüllermeier Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction Bioinformatics 29 16 2013 1946 1952
30. A. Altmann, M. Däumer, N. Beerenwinkel, Y. Peres, E. Schülter, and J. Büch et al. Predicting the response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database J Infect Dis 199 7 2009 999 1006
31. M. Rosen-Zvi, A. Altmann, M. Prosperi, E. Aharoni, H. Neuvirth, and A. Sönnerborg et al. Selecting anti-HIV therapies based on a variety of genomic and clinical factors Bioinformatics 24 13 2008 pi399 pi406
32. A.D. Revell, D. Wang, M.A. Boyd, S. Emery, A.L. Pozniak, and F. De Wolf et al. The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool AIDS 25 15 2011 1855 1863
33. J. Bogojeska, D. Stöckel, M. Zazzi, R. Kaiser, F. Incardona, and M. Rosen-Zvi et al. History-alignment models for bias-aware prediction of virological response to HIV combination therapy J Machine Learn Res 22 2012 118 126
34. H. Saigo, A. Altmann, J. Bogojeska, F. Müller, S. Nowozin, and T. Lengauer Learning from past treatments and their outcome improves prediction of in vivo response to anti-HIV therapy Stat Appl Genet Mol Biol 10 1 2011 pArticle6
35. J. Bogojeska History distribution matching method for predicting effectiveness of HIV combination therapies J. Shawe-Taylor, Advances in neural information processing systems 2011 424 432
36. C.B. Moore, M. John, I.R. James, F.T. Christiansen, C.S. Witt, and S.A. Mallal Evidence of HIV-1 adaptation to HLA-restricted immune responses at a population level Science 296 5572 2002 1439 1443
37. S. Ranasinghe, S. Cutler, I. Davis, R. Lu, D.Z. Soghoian, and Y. Qi et al. Association of HLA-DRB1-restricted CD4(+) T cell responses with HIV immune control Nat Med 19 7 2013 930 933
38. M. Däumer, S. Awerkiew, S.S. Aragón, V. Kartashev, T. Poplavskaja, and R. Klein et al. 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 3 2010 275 278
39. A.D. Revell, D. Wang, R. Wood, C. Morrow, H. Tempelman, and R.L. Hamers et al. 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 6 2013 1406 1414
40. M.C. Prosperi, M. Rosen-Zvi, A. Altmann, M. Zazzi, S. Di Giambenedetto, and R. Kaiser et al. Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models PLoS ONE 5 10 2010 e13753
41. M. Zazzi, R. Kaiser, A. Sönnerborg, D. Struck, A. Altmann, and M. Prosperi et al. Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study) HIV Med 12 4 2011 211 218
42. B.A. Larder, A. Revell, J.M. Mican, B.K. Agan, M. Harris, and C. Torti et al. 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 1 2011 29 36
43. R.A. McGovern, A. Thielen, S. Portsmouth, T. Mo, W. Dong, and C.K. Woods et al. 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 3 2012 279 286
44. L.C. Swenson, T. Mo, W.W.Y. Dong, X. Zhong, C.K. Woods, and M.A. Jensen et al. Deep sequencing to infer HIV-1 co-receptor usage: application to three clinical trials of maraviroc in treatment-experienced patients J Infect Dis 203 2 2011 237 245
45. R. Simon The use of genomics in clinical trial design Clin Cancer Res 14 19 2008 5984 5993
46. B. Freidlin, L.M. McShane, and E.L. Korn Randomized clinical trials with biomarkers: design issues J Natl Cancer Inst 102 3 2010 152 160
47. N. Mani, J. Murray, R.M. Gulick, F. Josephson, and V. Miller et al. Novel clinical trial designs for the development of new antiretroviral agents AIDS 26 8 2012 899 907
48. K.M. Chan-Tack, K.A. Struble, N. Morgensztejn, J.S. Murray, R. Gulick, and B. Cheng et al. HIV clinical trial design for antiretroviral development: moving forward AIDS 22 18 2008 2419 2427
49. F.Y. Hsieh, D.A. Bloch, and M.D. Larsen A simple method of sample size calculation for linear and logistic regression Stat Med 17 14 1998 1623 1634
50. B. Efron Size, power and false discovery rates Ann Stat 35 4 2007 1351 1377
51. B. Avidor, S. Girshengorn, N. Matus, H. Talio, S. Achsanov, and I. Zeldis et al. Evaluation of a benchtop HIV ultradeep pyrosequencing drug resistance assay in the clinical laboratory J Clin Microbiol 51 3 2013 880 886
52. B.B. Simen, J.F. Simons, K.H. Hullsiek, R.M. Novak, R.D. Macarthur, and J.D. Baxter et al. Low-abundance drug-resistant viral variants in chronically HIV-infected, antiretroviral treatment-naive patients significantly impact treatment outcomes J Infect Dis 199 5 2009 693 701
53. C. Bock, and T. Lengauer Managing drug resistance in cancer: lessons from HIV therapy Nat Rev Cancer 12 7 2012 494 501
54. A. Foeglein, and H. Walter Determination of HIV-1 coreceptor tropism in clinical practise Eur J Med Res 12 9 2007 473 482
55. J. Weber, A.C. Vazquez, D. Winner, R.M. Gibson, A.M. Rhea, and J.D. Rose et al. Sensitive cell-based assay for determination of human immunodeficiency virus type 1 coreceptor tropism J Clin Microbiol 51 5 2013 1517 1527
56. J.M. Whitcomb, W. Huang, S. Fransen, K. Limoli, J. Toma, and T. Wrin et al. Development and characterization of a novel single-cycle recombinant-virus assay to determine human immunodeficiency virus type 1 coreceptor tropism Antimicrob Agents Chemother 51 2 2007 566 575
57. Z.L. Brumme, W.W. Dong, B. Yip, B. Wynhoven, N.G. Hoffman, and R. Swanstrom et al. Clinical and immunological impact of HIV envelope V3 sequence variation after starting initial triple antiretroviral therapy AIDS 18 4 2004 F1 F9
58. T. Lengauer, O.I. Sander, S. Sierra, A. Thielen, and R. Kaiser et al. Bioinformatics prediction of HIV coreceptor usage Nat Biotechnol 25 12 2007 1407 1410
59. J.N. Dybowski, D. Heider, and D. Hoffmann Prediction of co-receptor usage of HIV-1 from genotype PLoS Comput Biol 6 4 2010 e1000743
60. K. Bozek, T. Lengauer, S. Sierra, R. Kaiser, and F.S. Domingues Analysis of physicochemical and structural properties determining HIV-1 coreceptor usage PLoS Comput Biol 9 3 2013 e1002977
61. M.C.F. Prosperi, L. Bracciale, M. Fabbiani, S. Di Giambenedetto, F. Razzolini, and G. Meini et al. Comparative determination of HIV-1 co-receptor tropism by Enhanced Sensitivity Trofile, gp120 V3-loop RNA and DNA genotyping Retrovirology 7 2010 56
62. F.J. Palella Jr., K.M. Delaney, A.C. Moorman, M.O. Loveless, J. Fuhrer, and G.A. Satten et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators N Engl J Med 338 13 1998 853 860
63. M. Neff ATS, CDC, and IDSA update recommendations on the treatment of tuberculosis Am Fam Phys 68 9 2003 1854 1857-8, 1861-2