Literature DB >> 10839585

The role of abacavir (ABC, 1592) in antiretroviral therapy-experienced patients: results from a randomized, double-blind, trial. CNA3002 European Study Team.

C Katlama1, B Clotet, A Plettenberg, J Jost, K Arasteh, E Bernasconi, V Jeantils, A Cutrell, C Stone, M Ait-Khaled, S Purdon.   

Abstract

OBJECTIVE: To compare the antiviral activity of abacavir (ABC) with stable background therapy (SBG) and SBG alone in antiretroviral therapy-experienced subjects as demonstrated by the proportion of subjects with plasma HIV-1 RNA < or = 400 copies/ml, plasma HIV-1 RNA and CD4 cell count profiles, and safety and tolerance of the two regimens over 16 weeks.
DESIGN: One-hundred and eighty-five HIV-1 infected adults, with CD4 cell counts > or = 100 x 10(6)/l and plasma HIV-1 RNA of 400-50,000 copies/ml and who had received SBG therapy for at least 12 weeks, were randomized to receive ABC (300 mg twice daily) or placebo in a double blind, multi-centre study.
METHODS: Antiretroviral activity was assessed by measuring changes in plasma HIV-1 RNA levels and CD4 cell counts. Genotypic and phenotypic resistance was determined at baseline and week 16. Evaluation of safety and tolerance was based on clinical adverse events and laboratory analyses.
RESULTS: At week 16 significantly more subjects receiving ABC + SBG had plasma HIV-1 RNA < or = 400 copies/ml (36/92, 39%) than subjects receiving SBG alone (7/93, 8%; P < 0.001). A similar response was observed in both the lamivudine naive and lamivudine-experienced subjects. The presence of the M184V mutation did not preclude an antiviral response to ABC; 73% of subjects with the M184V mutation alone experienced a > or = 1.0 log10 copies/ml reduction in plasma HIV-1 RNA or had a value of < or = 400 copies/ml by week 16.
CONCLUSIONS: ABC was generally well tolerated and exerted significant antiviral effect when added to combination antiretroviral therapy over 16 weeks.

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Year:  2000        PMID: 10839585     DOI: 10.1097/00002030-200005050-00003

Source DB:  PubMed          Journal:  AIDS        ISSN: 0269-9370            Impact factor:   4.177


  10 in total

Review 1.  Clinical management of treatment-experienced, HIV/AIDS patients in the combination antiretroviral therapy era.

Authors:  Mark A Boyd; Andrew M Hill
Journal:  Pharmacoeconomics       Date:  2010       Impact factor: 4.981

2.  The Genetic Basis of HIV-1 Resistance to Reverse Transcriptase and Protease Inhibitors.

Authors:  Robert W Shafer; Rami Kantor; Matthew J Gonzales
Journal:  AIDS Rev       Date:  2000       Impact factor: 2.500

3.  Prediction of abacavir resistance from genotypic data: impact of zidovudine and lamivudine resistance in vitro and in vivo.

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Journal:  Antimicrob Agents Chemother       Date:  2002-01       Impact factor: 5.191

4.  HIV-1 genetic diversity and drug resistance among Senegalese patients in the public health system.

Authors:  Moussa Thiam; Halimatou Diop-Ndiaye; Aminata Diaw Diouf; Nicole Vidal; Ousseynou Ndiaye; Ibrahima Ndiaye; Ndeye Fatou Ngom-Gueye; Sada Diallo; Oumy Diop Diongue; Makhtar Camara; Abdoulaye Seck; Souleymane Mboup; Coumba Toure-Kane
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Review 5.  Genotypic testing for human immunodeficiency virus type 1 drug resistance.

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6.  Web resources for HIV type 1 genotypic-resistance test interpretation.

Authors:  Tommy F Liu; Robert W Shafer
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7.  Zidovudine, lamivudine, and abacavir have different effects on resting cells infected with human immunodeficiency virus in vitro.

Authors:  Jesús Saavedra-Lozano; Cynthia C McCoig; Yanying Cao; Ellen S Vitetta; Octavio Ramilo
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Review 8.  Triple nucleoside reverse transcriptase inhibitor therapy in children.

Authors:  Jennifer Handforth; Mike Sharland
Journal:  Paediatr Drugs       Date:  2004       Impact factor: 3.022

9.  Human immunodeficiency virus type 1 reverse transcriptase mutation selection during in vitro exposure to tenofovir alone or combined with abacavir or lamivudine.

Authors:  Chris Stone; Mounir Ait-Khaled; Charles Craig; Philip Griffin; Margaret Tisdale
Journal:  Antimicrob Agents Chemother       Date:  2004-04       Impact factor: 5.191

10.  Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance.

Authors:  Yashik Singh
Journal:  Healthc Inform Res       Date:  2017-10-31
  10 in total

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