Computer- Aided optimization of combined anti-retroviral therapy for HIV: New drugs, new drug targets and drug resistance

Current HIV Research

Volumn 14, Issue 2, 2016, Pages 101-109

  Zazzi, Maurizio ^a   Cozzi Lepri, Alessandro ^b   Prosperi, Mattia C F ^c,d  

^a UNIVERSITY OF SIENA   (Italy)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

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

^d UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Anti retroviral therapy; Drug resistance; Expert system; HIV; Statistical learning

Indexed keywords

ADEFOVIR; DOLUTEGRAVIR; EMTRICITABINE; ENFUVIRTIDE; ENTECAVIR; ETRAVIRINE; FOSAMPRENAVIR; HUMAN IMMUNODEFICIENCY VIRUS FUSION INHIBITOR; INTEGRASE INHIBITOR; LAMIVUDINE; MARAVIROC; NONNUCLEOSIDE REVERSE TRANSCRIPTASE INHIBITOR; PROTEINASE INHIBITOR; RALTEGRAVIR; RILPIVIRINE; TENOFOVIR; TIPRANAVIR; ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT; ANTIRETROVIRUS AGENT; RNA DIRECTED DNA POLYMERASE INHIBITOR;

ALGORITHM; ARTICLE; CLINICAL TRIAL (TOPIC); COMPUTER ANALYSIS; COMPUTER PROGRAM; DRUG SENSITIVITY; GENETIC VARIABILITY; GENOTYPE; HEALTH CARE PLANNING; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; NONHUMAN; NUCLEOTIDE SEQUENCE; PREVALENCE; SENSITIVITY AND SPECIFICITY; TREATMENT RESPONSE; VIRUS MUTATION; ANTIVIRAL RESISTANCE; DRUG EFFECTS; EXPERT SYSTEM; GENETICS; HIV INFECTIONS; HUMAN IMMUNODEFICIENCY VIRUS 1; COMPUTER ASSISTED DRUG THERAPY; FOLLOW UP; GENE MUTATION; GENETIC TRANSCRIPTION; TREATMENT OUTCOME;

ALGORITHMS; ANTI-HIV AGENTS; ANTI-RETROVIRAL AGENTS; DRUG RESISTANCE, VIRAL; EXPERT SYSTEMS; GENOTYPE; HIV INFECTIONS; HIV-1; HUMANS;

EID: 84958951455     PISSN: 1570162X     EISSN: 18734251     Source Type: Journal    
DOI: 10.2174/1570162X13666151029102254     Document Type: Article

References (57)

1. Samji H, Cescon A, Hogg RS, et al. Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada. PLoS One 2013, 8: e81355.
2. De Luca A, Dunn D, Zazzi M, et al. Declining prevalence of HIV-1 drug resistance in antiretroviral treatment-exposed individuals in Western Europe. J Infect Dis 2013; 207: 1216-20.
3. Crouzat F, Benoit A, Kovacs C, et al. Time to virologic failure for patients taking their first antiretroviral regimen and the subsequent resistance profiles. J Int AIDS Soc 2014; 17: 19757.
4. Moniz P, Alcada F, Peres S, et al. Durability of first antiretroviral treatment in HIV chronically infected patients: why change and what are the outcomes? J Int AIDS Soc 2014; 17: 19797.
5. De La Torre-Lima J, Aguilar A, et al. Durability of the first antiretroviral treatment regimen and reasons for change in patients with HIV infection. HIV Clin Trials 2014; 15: 27-35.
6. Baxter J, Dunn D, White E, et al. Global HIV-1 transmitted drug resistance in the INSIGHT Strategic Timing of AntiRetroviral Treatment (START) trial. HIV Med 2015; 16 Suppl 1: 77-87.
7. Obermeier M, Ehret R, Wienbreyer A, Walter H, Berg T, Baumgarten A. Resistance remains a problem in treatment failure. J Int AIDS Soc 2014; 17: 19756.
8. Hamers RL, Sigaloff KC, Kityo C, Mugyenyi P, de Wit TF. Emerging HIV-1 drug resistance after roll-out of antiretroviral therapy in sub-Saharan Africa. Curr Opin HIV AIDS 2013; 8: 19- 26.
9. Zazzi M, Incardona F, Rosen-Zvi M, et al. Predicting response to antiretroviral treatment by machine learning: the EuResist project. Intervirology 2012; 55: 123-7.
10. Mocroft A, Ledergerber B, Katlama C, et al. Decline in the AIDS and death rates in the EuroSIDA study: an observational study. Lancet 2003; 362: 22-9.
11. Tang MW, Liu TF, Shafer RW. The HIVdb system for HIV-1 genotypic resistance interpretation. Intervirology 2012; 55: 98-101.
12. Vondrasek J, Wlodawer A. HIVdb: a database of the structures of human immunodeficiency virus protease. Proteins 2002; 49: 429- 31.
13. Kuiken C, Korber B, Shafer RW. HIV sequence databases. AIDS Rev 2003; 5: 52-61.
14. Prosperi MC, De Luca A. Computational models for prediction of response to antiretroviral therapies. AIDS Rev 2012; 14: 145-53.
15. Llibre JM, Pulido F, Garcia F, Garcia-Deltoro M, Blanco JL, Delgado R. Genetic barrier to resistance for dolutegravir. AIDS Rev 2015; 17: 56-64.
16. Fourati S, Charpentier C, Amiel C, et al. Cross-resistance to elvitegravir and dolutegravir in 502 patients failing on raltegravir: A French national study of raltegravir-experienced HIV-1-infected patients. J Antimicrob Chemother 2015; 70(5): 1507-12.
17. Bourgeois A, Womack S, Newsom D, Caldwell D. A review of rilpivirine and elvitegravir resistance features and implications for treatment sequencing. HIV Clin 2012; 24: 12-5.
18. Torres-Cornejo A, Benmarzouk-Hidalgo OJ, Gutierrez-Valencia A, Ruiz-Valderas R, Viciana P, Lopez-Cortes LF. Low concordance and resistance mutation emergence in the HIV protease gene among circulating and cell-associated viruses at viral replication episodes during darunavir/ritonavir monotherapy. HIV Med 2015; 16: 57-61.
19. Bethell R, Scherer J, Witvrouw M, Paquet A, Coakley E, Hall D. Short communication: Phenotypic protease inhibitor resistance and cross-resistance in the clinic from 2006 to 2008 and mutational prevalences in HIV from patients with discordant tipranavir and darunavir susceptibility phenotypes. AIDS Res Hum Retroviruses 2012; 28: 1019-24.
20. Wang Y, Liu Z, Brunzelle JS, et al. The higher barrier of darunavir and tipranavir resistance for HIV-1 protease. Biochem Biophys Res Commun 2011; 412: 737-42.
21. Picchio GR, Rimsky LT, Van Eygen V, Haddad M, Napolitano LA, Vingerhoets J. Prevalence in the USA of rilpivirine resistanceassociated mutations in clinical samples and effects on phenotypic susceptibility to rilpivirine and etravirine. Antivir Ther 2014; 19: 819-23.
22. Lambert-Niclot S, Charpentier C, Storto A, et al. Rilpivirine, emtricitabine and tenofovir resistance in HIV-1-infected rilpivirinenaive patients failing antiretroviral therapy. J Antimicrob Chemother 2014; 69: 1086-9.
23. Wensing AM, Calvez V, Gunthard HF, et al. 2014 Update of the drug resistance mutations in HIV-1. Top Antivir Med 2014; 22: 642-50.
24. Whitcomb JM, Huang W, Fransen S, 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 2007; 51: 566-75.
25. 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.
26. Charpentier C, Camacho R, Ruelle J, et al. HIV-2EU: supporting standardized HIV-2 drug resistance interpretation in Europe. Clin Infect Dis 2013; 56: 1654-8.
27. Zazzi M, Prosperi M, Vicenti I, et al. 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.
28. Zazzi M, Romano L, Venturi G, et al. Comparative evaluation of three computerized algorithms for prediction of antiretroviral susceptibility from HIV type 1 genotype. J Antimicrob Chemother 2004; 53: 356-60.
29. Pironti A, Pfeifer N, Kaiser R, Walter H, Lengauer T. Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences. J Int AIDS Soc 2014; 17: 19743.
30. Vercauteren J, Beheydt G, Prosperi M, et al. Clinical evaluation of Rega 8: an updated genotypic interpretation system that significantly predicts HIV-therapy response. PLoS One 2013; 8: e61436.
31. Wagner S, Kurz M, Klimkait T, the Swiss HIVCS. Algorithm evolution for drug resistance prediction: comparison of systems for HIV-1 genotyping. Antivir Ther 2015, E-pub ahead of print.
32. Jiamsakul A, Kantor R, Li PC, et al. Comparison of predicted susceptibility between genotype and virtual phenotype HIV drug resistance interpretation systems among treatment-naive HIVinfected patients in Asia: TASER-M cohort analysis. BMC Res Notes 2012; 5: 582.
33. Frentz D, Boucher CA, Assel M, et al. Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time. PLoS One 2010; 5: e11505.
34. Prosperi MC, Di Giambenedetto S, Fanti I, et al. 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.
35. Megens S, Laethem KV. HIV-1 genetic variation and drug resistance development. Expert Rev Anti Infect Ther 2013; 11: 1159-78.
36. 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.
37. Prosperi MC, Altmann A, Rosen-Zvi M, et al. 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.
38. Cozzi-Lepri A, Prosperi MC, Kjaer J, et al. Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score. PLoS One 2011; 6: e25665.
39. 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.
40. Larder B, Wang D, Revell A. Application of artificial neural networks for decision support in medicine. Methods Mol Biol 2008; 458: 123-36.
41. Bogojeska J. History distribution matching method for predicting effectiveness of HIV combination therapies. In: NIPS. Edited by Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira FCN, Weinberger KQ; 2011: 424-32.
42. Lengauer T, Sing T. Bioinformatics-assisted anti-HIV therapy. Nat Rev Microbiol 2006; 4: 790-7.
43. Vandekerckhove LP, Wensing AM, Kaiser R, et al. European guidelines on the clinical management of HIV-1 tropism testing. Lancet Infect Dis 2011; 11: 394-407.
44. 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.
45. Oette M, Schulter E, Rosen-Zvi M, et al. Efficacy of antiretroviral therapy switch in HIV-infected patients: a 10-year analysis of the EuResist Cohort. Intervirology 2012; 55: 160-6.
46. Zazzi M, Kaiser R, Sonnerborg A, et al. Prediction of response to antiretroviral therapy by human experts and by the EuResist datadriven expert system (the EVE study). HIV Med 2011; 12: 211-8.
47. Prosperi MC, Rosen-Zvi M, Altmann A, et al. Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models. PLoS One 2010; 5: e13753.
48. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning : data mining, inference, and prediction. 2nd ed. New York, NY: Springer; 2009.
49. Revell AD, Wang D, Boyd MA, et al. 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.
50. Larder BA, Revell A, Mican JM, 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.
51. Revell AD, Wang D, Wood R, 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 2013; 68: 1406-14.
52. Revell AD, Wang D, Wood R, et al. 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 2014; 69: 1104-10.
53. Revell AD, Alvarez-Uria G, Wang D, et al. 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 Res Int 2013; 2013: 579741.
54. Revell AD, Boyd MA, Wang D, et al. A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy. HIV Med 2014; 15: 442-8.
55. Vermeiren H, Van Craenenbroeck E, Alen P, et al. Prediction of HIV-1 drug susceptibility phenotype from the viral genotype using linear regression modeling. J Virol Methods 2007; 145: 47-55.
56. Van Houtte M, Picchio G, Van Der Borght K, Pattery T, Lecocq P, Bacheler LT. A comparison of HIV-1 drug susceptibility as provided by conventional phenotyping and by a phenotype prediction tool based on viral genotype. J Med Virol 2009; 81: 1702-9.
57. Theys K, Libin P, Camacho R, et al. Discordant Predictions Could Impact Dolutegravir Use Upon Raltegravir Failure. In: Conference on Retroviruses and Opportunistic Infections (CROI) Seattle, Washington; 2015.