Literature DB >> 25264400

Resistance evolution in HIV - modeling when to intervene.

Liliana Mabel Peinado Cortes1, Ryan Zurakowski2.   

Abstract

The treatment of HIV is complicated by the evolution of antiviral drug resistant virus and the limited availability of antigenically independent antiviral regimens. The consequences to the patient of successive virological failures is such that many strategies to minimize the occurrence of such failures are being investigated. In this paper, a Markov chain-based model of virological failure is introduced. This model considers sequential failure events, and differentiates between several modes of virological failure. This model is then used to evaluate the resistance- targeted interventions by means of testing the impact of a viral load preconditioning strategy on total treatment regimen longevity in HIV patients. It is shown that a proposed intervention targeting pre-existing resistance has the potential to increase the expected time to three sequential virological failures by an average of 3.3 years per patient. When combined with an intervention targeting patient compliance, the total potential increase in the time to three sequential virological failures is as high as 11.2 years. The impact on patient and public health is discussed.

Entities:  

Year:  2012        PMID: 25264400      PMCID: PMC4175725          DOI: 10.1109/ACC.2012.6315693

Source DB:  PubMed          Journal:  Proc Am Control Conf        ISSN: 0743-1619


  43 in total

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Authors:  R M Ribeiro; S Bonhoeffer
Journal:  Proc Natl Acad Sci U S A       Date:  2000-07-05       Impact factor: 11.205

Review 2.  The causes and consequences of HIV evolution.

Authors:  Andrew Rambaut; David Posada; Keith A Crandall; Edward C Holmes
Journal:  Nat Rev Genet       Date:  2004-01       Impact factor: 53.242

Review 3.  HIV mutagenesis and the evolution of antiretroviral drug resistance.

Authors:  Louis M Mansky
Journal:  Drug Resist Updat       Date:  2002-12       Impact factor: 18.500

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Authors:  Mark S Roberts; Kimberly A Nucifora; R Scott Braithwaite
Journal:  Med Care       Date:  2010-06       Impact factor: 2.983

5.  A phylogenetic and Markov model approach for the reconstruction of mutational pathways of drug resistance.

Authors:  Patricia Buendia; Brice Cadwallader; Victor DeGruttola
Journal:  Bioinformatics       Date:  2009-08-03       Impact factor: 6.937

6.  Management of HIV-infected Patients with Multidrug-resistant Virus.

Authors:  Julio S.G. Montaner; Marianne Harris
Journal:  Curr Infect Dis Rep       Date:  2002-06       Impact factor: 3.725

7.  Measurement error robustness of a closed-loop minimal sampling method for HIV therapy switching.

Authors:  E Fabian Cardozo; Ryan Zurakowski
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

Review 8.  Optimal timing and best antiretroviral regimen in treatment-naive HIV-infected individuals with advanced disease.

Authors:  Christian Manzardo; Mauro Zaccarelli; Fernando Agüero; Andrea Antinori; José M Miró
Journal:  J Acquir Immune Defic Syndr       Date:  2007-09       Impact factor: 3.731

9.  Trends in multidrug treatment failure and subsequent mortality among antiretroviral therapy-experienced patients with HIV infection in North America.

Authors:  Steven G Deeks; Stephen J Gange; Mari M Kitahata; Michael S Saag; Amy C Justice; Robert S Hogg; Joseph J Eron; John T Brooks; Sean B Rourke; M John Gill; Ronald J Bosch; Constance A Benson; Ann C Collier; Jeffrey N Martin; Marina B Klein; Lisa P Jacobson; Benigno Rodriguez; Timothy R Sterling; Gregory D Kirk; Sonia Napravnik; Anita R Rachlis; Liviana M Calzavara; Michael A Horberg; Michael J Silverberg; Kelly A Gebo; Margot B Kushel; James J Goedert; Rosemary G McKaig; Richard D Moore
Journal:  Clin Infect Dis       Date:  2009-11-15       Impact factor: 9.079

10.  Asymmetric division of activated latently infected cells may explain the decay kinetics of the HIV-1 latent reservoir and intermittent viral blips.

Authors:  Libin Rong; Alan S Perelson
Journal:  Math Biosci       Date:  2008-10-17       Impact factor: 2.144

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