Literature DB >> 19654022

Clinical cut-offs for HIV-1 phenotypic resistance estimates: update based on recent pivotal clinical trial data and a revised approach to viral mixtures.

Bart Winters1, Elke Van Craenenbroeck, Koen Van der Borght, Pierre Lecocq, Jorge Villacian, Lee Bacheler.   

Abstract

The clinical utility of HIV-1 resistance testing is dependent upon accurate interpretation and application of results. The development of clinical cut-offs (CCOs) for most HIV antiretroviral drugs assessed by the vircoTYPE HIV-1 resistance test has been described previously. Updated CCOs based on new methodology and new data from clinical cohorts and pivotal clinical studies are presented in this communication. Data for analysis included the original records for CCO derivation from eight clinical trials and two cohort studies plus new records from the clinical cohorts and from the TITAN, POWER, and DUET clinical studies. Drug-specific linear regression models were developed to describe the relationship between baseline characteristics (phenotypic resistance as estimated by virtualPhenotype-LM using methods revised recently for handling mixed viral sequences; viral load; and treatment history), new treatment regimen, and 8-week virologic outcome. The clinical cut-offs were defined as the estimated phenotypic resistance levels (fold change, FC) associated with a 20% and 80% loss of drug activity. The development dataset included 6550 records with an additional 2299 reserved for validation. The updated, v.4.2 CCOs were generally close to the v4.1 values, with a trend observed toward marginally higher cut-offs for the NRTIs. These results suggest that the updated CCOs provide a relevant tool for estimating the contribution to virological response of individual antiviral drugs in antiretroviral drug combinations as used currently in clinical practice.

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Year:  2009        PMID: 19654022     DOI: 10.1016/j.jviromet.2009.07.023

Source DB:  PubMed          Journal:  J Virol Methods        ISSN: 0166-0934            Impact factor:   2.014


  8 in total

1.  HIV drug resistance detected during low-level viraemia is associated with subsequent virologic failure.

Authors:  Luke C Swenson; Jeong Eun Min; Conan K Woods; Eric Cai; Jonathan Z Li; Julio S G Montaner; P Richard Harrigan; Alejandro Gonzalez-Serna
Journal:  AIDS       Date:  2014-05-15       Impact factor: 4.177

2.  Novel method for simultaneous quantification of phenotypic resistance to maturation, protease, reverse transcriptase, and integrase HIV inhibitors based on 3'Gag(p2/p7/p1/p6)/PR/RT/INT-recombinant viruses: a useful tool in the multitarget era of antiretroviral therapy.

Authors:  Jan Weber; Ana C Vazquez; Dane Winner; Justine D Rose; Doug Wylie; Ariel M Rhea; Kenneth Henry; Jennifer Pappas; Alison Wright; Nizar Mohamed; Richard Gibson; Benigno Rodriguez; Vicente Soriano; Kevin King; Eric J Arts; Paul D Olivo; Miguel E Quiñones-Mateu
Journal:  Antimicrob Agents Chemother       Date:  2011-05-31       Impact factor: 5.191

3.  A comparative analysis of HIV drug resistance interpretation based on short reverse transcriptase sequences versus full sequences.

Authors:  Kim Steegen; Michelle Bronze; Elke Van Craenenbroeck; Bart Winters; Koen Van der Borght; Carole L Wallis; Wendy Stevens; Tobias F Rinke de Wit; Lieven J Stuyver
Journal:  AIDS Res Ther       Date:  2010-10-15       Impact factor: 2.250

4.  Phenotypic characterization of drug resistance-associated mutations in HIV-1 RT connection and RNase H domains and their correlation with thymidine analogue mutations.

Authors:  Renan B Lengruber; Krista A Delviks-Frankenberry; Galina N Nikolenko; Jessica Baumann; André F Santos; Vinay K Pathak; Marcelo A Soares
Journal:  J Antimicrob Chemother       Date:  2011-01-26       Impact factor: 5.790

5.  Residual activity of two HIV antiretroviral regimens prescribed without virological monitoring.

Authors:  D T Dunn; R L Goodall; P Munderi; C Kityo; M Ranopa; L Bacheler; M Van Houtte; C Gilks; P Kaleebu; D Pillay
Journal:  Antimicrob Agents Chemother       Date:  2011-07-18       Impact factor: 5.191

6.  Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations.

Authors:  Koen Van der Borght; Elke Van Craenenbroeck; Pierre Lecocq; Margriet Van Houtte; Barbara Van Kerckhove; Lee Bacheler; Geert Verbeke; Herman van Vlijmen
Journal:  BMC Bioinformatics       Date:  2011-10-03       Impact factor: 3.169

7.  Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data.

Authors:  Alejandro Pironti; Hauke Walter; Nico Pfeifer; Elena Knops; Nadine Lübke; Joachim Büch; Simona Di Giambenedetto; Rolf Kaiser; Thomas Lengauer
Journal:  J Acquir Immune Defic Syndr       Date:  2017-04-15       Impact factor: 3.731

8.  Comparison of predicted susceptibility between genotype and virtual phenotype HIV drug resistance interpretation systems among treatment-naive HIV-infected patients in Asia: TASER-M cohort analysis.

Authors:  Awachana Jiamsakul; Rami Kantor; Patrick C K Li; Sunee Sirivichayakul; Thira Sirisanthana; Pacharee Kantipong; Christopher K C Lee; Adeeba Kamarulzaman; Winai Ratanasuwan; Rossana Ditangco; Thida Singtoroj; Somnuek Sungkanuparph
Journal:  BMC Res Notes       Date:  2012-10-24
  8 in total

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