Literature DB >> 18572753

Modelled in vivo HIV fitness under drug selective pressure and estimated genetic barrier towards resistance are predictive for virological response.

Koen Deforche1, Alessandro Cozzi-Lepri, Kristof Theys, Bonaventura Clotet, Ricardo J Camacho, Jesper Kjaer, Kristel Van Laethem, Andrew Phillips, Yves Moreau, Jens D Lundgren, Anne-Mieke Vandamme.   

Abstract

BACKGROUND: A method has been developed to estimate a fitness landscape experienced by HIV-1 under treatment selective pressure as a function of the genotypic sequence thereby also estimating the genetic barrier to resistance.
METHODS: We evaluated the performance of two estimated fitness landscapes (nelfinavir [NFV] and zidovudine [AZT] plus lamivudine [3TC]) to predict week 12 viral load (VL) change for 176 treatment change episodes (TCEs) and probability of week 48 virological failure for 90 TCEs, in treatment experienced patients starting these drugs in combination.
RESULTS: A higher genetic barrier for AZT plus 3TC, (quantified per additional mutation required to develop resistance against these drugs) was associated with a 0.54 (95% confidence interval [CI] 0.30-0.77) larger log10 VL reduction at 12 weeks (P < 0.0001) and a 0.39 (95%/ CI 0.23-0.66) lower odds of virological failure at 48 weeks (P = 0.0005), in analyses adjusting for the pre-TCE VL and the exact time-lag between the TCE and the date of determining response VL. The strength of these associations was comparable with those seen with expert interpretation systems (Rega, ANRS and HIVDB). A higher genetic barrier to NFV resistance was the only genotypic predictor that tended to be associated with a 0.19 (95% CI 0-0.39) higher log10 VL reduction at 12 weeks (P = 0.05) and a 0.63 (95% CI 0.36-1.09) lower odds of virological failure at 48 weeks ( P = 0.10) per additional mutation.
CONCLUSIONS: These results suggest that an estimated genetic barrier derived from fitness landscapes may contribute to an improvement of predicted treatment outcome for NFV and this approach should be explored for other drugs.

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Year:  2008        PMID: 18572753

Source DB:  PubMed          Journal:  Antivir Ther        ISSN: 1359-6535


  7 in total

1.  An Evolutionary Model-Based Approach To Quantify the Genetic Barrier to Drug Resistance in Fast-Evolving Viruses and Its Application to HIV-1 Subtypes and Integrase Inhibitors.

Authors:  Kristof Theys; Pieter J K Libin; Kristel Van Laethem; Ana B Abecasis
Journal:  Antimicrob Agents Chemother       Date:  2019-07-25       Impact factor: 5.191

2.  Estimating the individualized HIV-1 genetic barrier to resistance using a nelfinavir fitness landscape.

Authors:  Kristof Theys; Koen Deforche; Gertjan Beheydt; Yves Moreau; Kristel van Laethem; Philippe Lemey; Ricardo J Camacho; Soo-Yon Rhee; Robert W Shafer; Eric Van Wijngaerden; Anne-Mieke Vandamme
Journal:  BMC Bioinformatics       Date:  2010-08-03       Impact factor: 3.169

3.  A prognostic model for estimating the time to virologic failure in HIV-1 infected patients undergoing a new combination antiretroviral therapy regimen.

Authors:  Mattia C F Prosperi; Simona Di Giambenedetto; Iuri Fanti; Genny Meini; Bianca Bruzzone; Annapaola Callegaro; Giovanni Penco; Patrizia Bagnarelli; Valeria Micheli; Elisabetta Paolini; Antonio Di Biagio; Valeria Ghisetti; Massimo Di Pietro; Maurizio Zazzi; Andrea De Luca
Journal:  BMC Med Inform Decis Mak       Date:  2011-06-14       Impact factor: 2.796

4.  Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score.

Authors:  Alessandro Cozzi-Lepri; Mattia C F Prosperi; Jesper Kjær; David Dunn; Roger Paredes; Caroline A Sabin; Jens D Lundgren; Andrew N Phillips; Deenan Pillay
Journal:  PLoS One       Date:  2011-11-16       Impact factor: 3.240

5.  The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients.

Authors:  Niko Beerenwinkel; Hesam Montazeri; Heike Schuhmacher; Patrick Knupfer; Viktor von Wyl; Hansjakob Furrer; Manuel Battegay; Bernard Hirschel; Matthias Cavassini; Pietro Vernazza; Enos Bernasconi; Sabine Yerly; Jürg Böni; Thomas Klimkait; Cristina Cellerai; Huldrych F Günthard
Journal:  PLoS Comput Biol       Date:  2013-08-29       Impact factor: 4.475

6.  Treatment-associated polymorphisms in protease are significantly associated with higher viral load and lower CD4 count in newly diagnosed drug-naive HIV-1 infected patients.

Authors:  Kristof Theys; Koen Deforche; Jurgen Vercauteren; Pieter Libin; David Amc van de Vijver; Jan Albert; Birgitta Asjö; Claudia Balotta; Marie Bruckova; Ricardo J Camacho; Bonaventura Clotet; Suzie Coughlan; Zehava Grossman; Osamah Hamouda; Andrzei Horban; Klaus Korn; Leondios G Kostrikis; Claudia Kücherer; Claus Nielsen; Dimitrios Paraskevis; Mario Poljak; Elisabeth Puchhammer-Stockl; Chiara Riva; Lidia Ruiz; Kirsi Liitsola; Jean-Claude Schmit; Rob Schuurman; Anders Sönnerborg; Danica Stanekova; Maja Stanojevic; Daniel Struck; Kristel Van Laethem; Annemarie Mj Wensing; Charles Ab Boucher; Anne-Mieke Vandamme
Journal:  Retrovirology       Date:  2012-10-03       Impact factor: 4.602

7.  Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease.

Authors:  Tian-Hao Zhang; Lei Dai; John P Barton; Yushen Du; Yuxiang Tan; Wenwen Pang; Arup K Chakraborty; James O Lloyd-Smith; Ren Sun
Journal:  PLoS Genet       Date:  2020-10-21       Impact factor: 5.917

  7 in total

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