Computational models for prediction of response to antiretroviral therapies

AIDS Reviews

Volumn 14, Issue 2, 2012, Pages 145-153

  Prosperi, Mattia C F ^a,b   De Luca, Andrea ^b,c  

^a UNIVERSITY OF FLORIDA COLLEGE OF MEDICINE   (United States)

^b CATHOLIC UNIVERSITY   (Italy)

^c UNIVERSITY HOSPITAL OF SIENA   (Italy)

Author keywords

Antiretroviral therapy; Drug resistance; Expert system; Genotyping; HIV; Machine learning; Statistics

Indexed keywords

ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT; CHEMOKINE RECEPTOR CCR5; MARAVIROC; TIPRANAVIR; VIRUS RNA;

ANTIVIRAL SUSCEPTIBILITY; CD4 LYMPHOCYTE COUNT; CD8+ T LYMPHOCYTE; GENOTYPE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS 1; HUMAN IMMUNODEFICIENCY VIRUS INFECTED PATIENT; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; MACHINE LEARNING; PHENOTYPE; PREDICTION; REVIEW; TREATMENT RESPONSE; VALIDATION PROCESS; VIRAL TROPISM; VIRUS LOAD; VIRUS REPLICATION;

ANTI-HIV AGENTS; COMPUTER SIMULATION; DRUG RESISTANCE, VIRAL; DRUG THERAPY, COMBINATION; FEMALE; GENOTYPE; HIV SEROPOSITIVITY; HUMANS; MALE; PHENOTYPE; RNA, VIRAL; VIRAL LOAD;

EID: 84862576498     PISSN: 11396121     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (88)

1. Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. Department of Health and Human Services. 1-239. Available at: http://www.aidsinfo.nih.gov/contentfiles/adultandadolescentgl.pdf.
2. Mocroft A, Vella S, Benfield T, et al. Changing patterns of mortality across Europe in patients infected with HIV-1. Lancet. 1998;352:1725-30.
3. Richman D. HIV chemotherapy. Nature. 2001;410:995-1001.
4. Geeraert L, Kraus G, Pomerantz R. Hide-and-seek: the challenge of viral persistence in HIV-1 infection. Annu Rev Med. 2008;59:487-501.
5. Di Giambenedetto S, Bracciale L, Colafigli M, et al. Declining prevalence of HIV-1 drug resistance in treatment-failing patients: a clinical cohort study. Antivir Ther. 2007;12:835-9.
6. Wensing A, van de Vijver D, Angarano G, et al. SPREAD Programme. Prevalence of drug-resistant HIV-1 variants in untreated individuals in Europe: implications for clinical management. J Infect Dis. 2005;192:958-66.
7. UK Collaborative Group on HIV Drug Resistance; UK CHIC Study Group. Long-term probability of detecting drug-resistant HIV in treatment-naive patients initiating combination antiretroviral therapy. Clin Infect Dis. 2010;50:1275-85.
8. Torti C, Quiros-Roldan E, Keulen W, et al. GenPherex Study Group of the MaSTeR Cohort. Comparison between rules-based human immunodeficiency virus type 1 genotype interpretations and real or virtual phenotype: concordance analysis and correlation with clinical outcome in heavily treated patients. J Infect Dis. 2003;188:194-201.
9. Winters B, Van Craenenbroeck E, Van der Borght K, Lecocq P, Villacian J, Bacheler L. Clinical cut-offs for HIV-1 phenotypic resistance estimates: update based on recent pivotal clinical trial data and a revised approach to viral mixtures. J Virol Methods. 2009;162:101-8.
10. Rhee S, Taylor J, Wadhera G, Ben-Hur A, Brutlag D, Shafer R. Genotypic predictors of human immunodeficiency virus type 1 drug resistance. Proc Natl Acad Sci USA. 2006;103:17355-60.
11. Beerenwinkel N, Schmidt B, Walter H, et al. Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype. Proc Natl Acad Sci USA. 2002;99:8271-6.
12. Beerenwinkel N, Däumer M, Oette M, et al. Geno2pheno: estimating phenotypic drug resistance from HIV-1 genotypes. Nucleic Acids Res. 2003;31:3850-5.
13. Pasomsub E, Sukasem C, Sungkanuparph S, Kijsirikul B, Chantratita W. The application of artificial neural networks for phenotypic drug resistance prediction: evaluation and comparison with other interpretation systems. Jpn J Infect Dis. 2010;63:87-94.
14. Heider D, Verheyen J, Hoffmann D. Predicting bevirimat resistance of HIV-1 from genotype. BMC Bioinformatics. 2010;11:37.
15. Stanford HIV drug resistance data base. Available at: http://hivdb.stanford.edu/index.html.
16. De Jong J, De Ronde A, Keulen W, Tersmette M, Goudsmit J. Minimal requirements for the human immunodeficiency virus type 1 V3 domain to support the syncytium-inducing phenotype: analysis by single amino acid substitution. J Virol. 1992;66:6777-80.
17. Jensen M, van't Wout A. Predicting HIV-1 coreceptor usage with sequence analysis. AIDS Rev. 2003;5:104-12.
18. Sander O, Sing T, Sommer I, et al. Structural descriptors of gp120 V3 loop for the prediction of HIV-1 coreceptor usage. PLoS Comput Biol. 2007;3:e58.
19. Lamers S, Salemi M, McGrath M, Fogel G. Prediction of R5, X4, and R5X4 HIV-1 coreceptor usage with evolved neural networks. IEEE/ACM Trans Comput Biol Bioinform. 2008;5:291-300.
20. Boisvert S, Marchand M, Laviolette F, Corbeil J. HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels. Retrovirology. 2008;5:110.
21. Prosperi M, Fanti I, Ulivi G, Micarelli A, De Luca A, Zazzi M. Robust supervised and unsupervised statistical learning for HIV type 1 coreceptor usage analysis. AIDS Res Hum Retroviruses. 2009;25:305-14.
22. Thielen A, Sichtig N, Kaiser R, Lam J, Harrigan P, Lengauer T. Improved prediction of HIV-1 coreceptor usage with sequence information from the second hypervariable loop of gp120. J Infect Dis. 2010;202:1435-43.
23. Dybowski J, Heider D, Hoffmann D. Prediction of co-receptor usage of HIV-1 from genotype. PLoS Comput Biol. 2010;6:e1000743.
24. Sánchez V, Masiá M, Robledano C, Padilla S, Ramos J, Gutiérrez F. Performance of genotypic algorithms for predicting HIV-1 tropism measured against the enhanced-sensitivity Trofile coreceptor tropism assay. J Clin Microbiol. 2010;48:4135-9.
25. Cabral G, Ferreira J, Coelho L, et al. Concordance of HIV type 1 tropism phenotype to predictions using web-based analysis of V3 sequences: composite algorithms may be needed to properly assess viral tropism. AIDS Res Hum Retroviruses. 2011. [Epub ahead of print].
26. Trabaud M, Icard V, Scholtes C, et al. Discordance in HIV-1 co-receptor use prediction by different genotypic algorithms and phenotype assay: intermediate profile in relation to concordant predictions. J Med Virol. 2012;84:402-13.
27. Symons J, Vandekerckhove L, Paredes R, et al. Impact of triplicate testing on genotypic HIV-1 tropism prediction in routine clinical practice. Clin Microbiol Infect. 2011. [Epub ahead of print].
28. Thielen A, Lengauer T. Geno2pheno[454]: a Web server for the prediction of HIV-1 coreceptor usage from next-generation sequencing data. Intervirology. 2012;55:113-7.
29. Swenson L, Mo T, Dong W, et al. Deep V3 sequencing for HIV type 1 tropism in treatment-naive patients: a reanalysis of the MERIT trial of maraviroc. Clin Infect Dis. 2011;53:732-42.
30. Swenson L, Mo T, Dong W, et al. Deep sequencing to infer HIV-1 coreceptor usage: application to three clinical trials of maraviroc in treatment-experienced patients. J Infect Dis. 2011;203:237-45.
31. Soriano V, Perno C, Kaiser R, et al. When and how to use maraviroc in HIV-infected patients. AIDS. 2009;23:2377-85.
32. Vandekerckhove L, Wensing A, Kaiser R, et al. European Consensus Group on clinical management of tropism testing. European guidelines on the clinical management of HIV-1 tropism testing. Lancet Infect Dis. 2011;11:394-407.
33. Poveda E, Alcamí J, Paredes R, et al. Genotypic determination of HIV tropism - clinical and methodological recommendations to guide the therapeutic use of CCR5 antagonists. AIDS Rev. 2010;12:135-48.
34. Geno2Pheno. Available at: http://www.geno2pheno.org/.
35. WebPSSM. Available at: http://indra.mullins.microbiol.washington.edu/ webpssm/ and http://fortinbras.us/cgi-bin/fssm/fssm.pl.
36. Trophix (prediction of HIV-1 tropism). Available at: http://sourceforge. net/projects/trophix/.
37. McGovern R, Thielen A, Mo T, et al. Population-based V3 genotypic tropism assay: a retrospective analysis using screening samples from the A4001029 and MOTIVATE studies. AIDS. 2010;24:2517-25.
38. The IAS/USA HIV-1 drug resistance mutation list. Available at: http://www.iasusa.org/resistance-mutations/mutations-figures.pdf.
39. Liu T, Shafer R. Web Resources for HIV type 1 Genotypic-Resistance Test Interpretation. Clin Infect Dis. 2006;42:1608-18.
40. ANRS Genotypic Interpretation System. Available at: http://www. hivfrenchresistance.org/.
41. HIV-Genotypic Resistance Algorithm DEutschland (HIV-GRADE). Available at: http://www.hiv-grade.de/cms/grade/.
42. Obermeier M, Pironti A, Berg T, et al. HIV-GRADE: a publicly available, rules-based drug resistance interpretation algorithm integrating bioinformatic knowledge. Intervirology. 2012;55:102-7.
43. de Mendoza C, Anta L, García F, et al. HIV-1 genotypic drug resistance interpretation rules - 2009 Spanish guidelines. AIDS Rev. 2009;11:39-51.
44. Van Laethem K, De Luca A, Antinori A, Cingolani A, Perno CF, Vandamme A. A genotypic drug resistance interpretation algorithm that significantly predicts therapy response in HIV-1-infected patients. Antivir Ther. 2002;7:123-9.
45. Rega Genotypic Interpretation System. Available at: http://jose.med. kuleuven.be/lab/index.php?id=30.
46. ARCA AntiRetroScan. Available at: http://www.hivarca.net/hiv-resistance. asp.
47. Vingerhoets J, Tambuyzer L, Azijn H, et al. Resistance profile of etravirine: combined analysis of baseline genotypic and phenotypic data from the randomized, controlled phase III clinical studies. AIDS. 2010;24:503-14.
48. Marcelin A, Flandre P, Descamps D, et al. ANRS AC11 Resistance Study Group. Factors associated with virological response to etravirine in nonnucleoside reverse transcriptase inhibitor-experienced HIV-1-infected patients. Antimicrob Agents Chemother. 2010;54:72-7.
49. Poveda E, Anta L, Blanco J, et al. Resistance Platform of the Spanish AIDS Research Network (ResRIS). Drug resistance mutations in HIV-infected patients in the Spanish drug resistance database failing tipranavir and darunavir therapy. Antimicrob Agents Chemother. 2010;54:3018-20.
50. Cozzi-Lepri A. Initiatives for developing and comparing genotype interpretation systems: external validation of existing rule-based interpretation systems for abacavir against virological response. HIV Med. 2008;9:27-40.
51. Brun-Vézinet F, Costagliola D, Khaled M, et al. Clinically validated genotype analysis: guiding principles and statistical concerns. Antivir Ther. 2004;9:465-78.
52. Stürmer M, Doerr H, Staszewski S, Preiser W. Comparison of nine resistance interpretation systems for HIV-1 genotyping. Antivir Ther. 2003;8:239-44.
53. Ravela J, Betts B, Brun-Vézinet F, et al. HIV-1 protease and reverse transcriptase mutation patterns responsible for discordances between genotypic drug resistance interpretation algorithms. J Acquir Immune Defic Syndr. 2003;33:8-14.
54. Snoeck J, Kantor R, Shafer R, et al. Discordances between interpretation algorithms for genotypic resistance to protease and reverse transcriptase inhibitors of human immunodeficiency virus are subtype dependent. Antimicrob Agents Chemother. 2006;50:694-701.
55. Hales G, Birch C, Crowe S, et al. CREST investigators. A randomised trial comparing genotypic and virtual phenotypic interpretation of HIV drug resistance: the CREST study. PLoS Clin Trials. 2006;1:e18.
56. Gianotti N, Mondino V, Rossi M, et al. Mutations and Salvage (MuSa) Study Group. Comparison of a rule-based algorithm with a phenotype-based algorithm for the interpretation of HIV genotypes in guiding salvage regimens in HIV-infected patients by a randomized clinical trial: the mutations and salvage study. Clin Infect Dis. 2006;42:1470-80.
57. Recordon-Pinson P, Soulié C, Flandre P, et al. ANRS AC11 Resistance Study Group. Evaluation of the genotypic prediction of HIV-1 coreceptor use versus a phenotypic assay and correlation with the virological response to maraviroc: the ANRS GenoTropism study. Antimicrob Agents Chemother. 2010;54:3335-40.
58. Reuter S, Braken P, Jensen B, et al. Maraviroc in treatment-experienced patients with HIV-1 infection - experience from routine clinical practice. Eur J Med Res. 2010;15:231-7.
59. The EuResist foundation GEIE. Available at: www.euresist.org/.
60. Copenhagen HIV Programme. Available at: http://www.cphiv.dk/.
61. UK HIV resistance data base. Available at: www.hivrdb.org.uk/.
62. De Luca A, Vendittelli M, Baldini F, et al. Construction, training and clinical validation of an interpretation system for genotypic HIV-1 drug resistance based on fuzzy rules revised by virological outcomes. Antivir Ther. 2004;9:583-93.
63. Prosperi M, Ulivi G. Evolutionary fuzzy modelling for drug resistant HIV-1 treatment optimisation. In Engineering evolutionary intelligent systems: studies in computational intelligence. Abraham A, Grosan C, Pedrycz W (Eds). Springer Berlin, Heidelberg; 2008:251-87.
64. Larder B, Wang D, Revell A, et al. The development of artificial neural networks to predict virological response to combination HIV therapy. Antivir Ther. 2007;12:15-24.
65. Wang D, Larder B, Revell A, et al. A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy. Artif Intell Med. 2009;47:63-74.
66. Altmann A, Beerenwinkel N, Sing T, et al. Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance. Antivir Ther. 2007;12:169-78.
67. Altmann A, Däumer M, Beerenwinkel N, et al. Predicting the response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database. J Infect Dis. 2009;199:999-1006.
68. Deforche K, Camacho R, Van Laethem K, et al. Estimation of an in vivo fitness landscape experienced by HIV-1 under drug selective pressure useful for prediction of drug resistance evolution during treatment. Bioinformatics. 2008;24:34-41.
69. Deforche K, Cozzi-Lepri A, Theys K, et al. EuroSIDA Study Group. Modelled in vivo HIV fitness under drug selective pressure and estimated genetic barrier towards resistance are predictive for virological response. Antivir Ther. 2008;13:399-407.
70. Revell A, Wang D, Boyd M, et al. The RDI Study Group. The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool. AIDS. 2011;25:1855-63.
71. Prosperi M, Altmann A, Rosen-Zvi M, et al. EuResist and Virolab study groups. Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment. Antivir Ther. 2009;14:433-42.
72. Rosen-Zvi M, Altmann A, Prosperi M, et al. Selecting anti- HIV therapies based on a variety of genomic and clinical factors. Bioinformatics. 2008;24:i399-406.
73. Altmann A, Rosen-Zvi M, Prosperi M, et al. Comparison of classifier fusion methods for predicting response to anti HIV-1 therapy. PLoS One. 2008;3):e3470.
74. Bogojeska J, Bickel S, Altmann A, Lengauer T. Dealing with sparse data in predicting outcomes of HIV combination therapies. Bioinformatics. 2010;26:2085-92.
75. Altmann A, Sing T, Vermeiren H, et al. Advantages of predicted phenotypes and statistical learning models in inferring virological response to antiretroviral therapy from HIV genotype. Antivir Ther. 2009;14:273-83.
76. Weisser H, Altmann A, Sierra S, et al. Only slight impact of predicted replicative capacity for therapy response prediction. PLoS One. 2010;5:e9044.
77. RDI HIV-TRePS. Available at: http://www.hivrdi.org/treps/login.php.
78. The EuResist engine. Available at: http://engine.euresist.org.
79. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition, Springer, 2009.
80. Robbins G, Johnson K, Chang Y, et al. Predicting virologic failure in an HIV clinic. Clin Infect Dis. 2010;50:779-86.
81. Revell A, Wang D, Harrigan R, et al. Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings. J Antimicrob Chemother. 2010;65:605-7.
82. Prosperi M, Rosen-Zvi M, Altmann A, et al. The EuResist and Virolab study groups. Antiretroviral Therapy Optimisation without Genotype Resistance Testing: a Perspective on Treatment History Based Models. PLoS ONE. 2010;5:e13753.
83. Rhee S, Fessel W, Liu T, et al. Predictive value of HIV-1 genotypic resistance test interpretation algorithms. J Infect Dis. 2009;200:453-63.
84. Frentz D, Boucher C, Assel M, et al. Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time. PLoS One. 2010;5:e11505.
85. Zazzi M, Prosperi M, Vicenti I, et al. ARCA Collaborative Group. Rules-based HIV-1 genotypic resistance interpretation systems predict 8 week and 24 week virological antiretroviral treatment outcome and benefit from drug potency weighting. J Antimicrob Chemother. 2009;64:616-24.
86. Larder B, Revell A, Mican J, 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. 2011;25:29-36.
87. Zazzi M, Kaiser R, Sönnerborg A, et al. Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study). HIV Med. 2011;12:211-18.
88. Prosperi M, Di Giambenedetto S, Fanti I, et al. ARCA cohort. A prognostic model for estimating the time to virologic failure in HIV-1 infected patients undergoing a new combination antiretroviral therapy regimen. BMC Med Inform Decis Mak. 2011;11:40.