Dataset Information

Quantitative prediction of integrase inhibitor resistance from genotype through consensus linear regression modeling.

ABSTRACT:

Background

Integrase inhibitors (INI) form a new drug class in the treatment of HIV-1 patients. We developed a linear regression modeling approach to make a quantitative raltegravir (RAL) resistance phenotype prediction, as Fold Change in IC50 against a wild type virus, from mutations in the integrase genotype.

Methods

We developed a clonal genotype-phenotype database with 991 clones from 153 clinical isolates of INI naïve and RAL treated patients, and 28 site-directed mutants.We did the development of the RAL linear regression model in two stages, employing a genetic algorithm (GA) to select integrase mutations by consensus. First, we ran multiple GAs to generate first order linear regression models (GA models) that were stochastically optimized to reach a goal R2 accuracy, and consisted of a fixed-length subset of integrase mutations to estimate INI resistance. Secondly, we derived a consensus linear regression model in a forward stepwise regression procedure, considering integrase mutations or mutation pairs by descending prevalence in the GA models.

Results

The most frequently occurring mutations in the GA models were 92Q, 97A, 143R and 155H (all 100%), 143G (90%), 148H/R (89%), 148K (88%), 151I (81%), 121Y (75%), 143C (72%), and 74M (69%). The RAL second order model contained 30 single mutations and five mutation pairs (p?ConclusionsWe describe a systematic approach to derive a model for predicting INI resistance from a limited amount of clonal samples. Our RAL second order model is made available as an Additional file for calculating a resistance phenotype as the sum of integrase mutations and mutation pairs.

SUBMITTER: Van der Borght K

PROVIDER: S-EPMC3551713 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Publications

Quantitative prediction of integrase inhibitor resistance from genotype through consensus linear regression modeling.

Van der Borght Koen K Verheyen Ann A Feyaerts Maxim M Van Wesenbeeck Liesbeth L Verlinden Yvan Y Van Craenenbroeck Elke E van Vlijmen Herman H

Virology journal 20130103

<h4>Background</h4>Integrase inhibitors (INI) form a new drug class in the treatment of HIV-1 patients. We developed a linear regression modeling approach to make a quantitative raltegravir (RAL) resistance phenotype prediction, as Fold Change in IC50 against a wild type virus, from mutations in the integrase genotype.<h4>Methods</h4>We developed a clonal genotype-phenotype database with 991 clones from 153 clinical isolates of INI naïve and RAL treated patients, and 28 site-directed mutants.We ...[more]

PMID: 23282253

Similar Datasets

Project description:There has been no systematic review of the prevalence of transmitted integrase strand transfer inhibitor (INSTI) resistance. We systematically searched the English-language PubMed database and GenBank to identify studies published since 2010 reporting 50 or more INSTI-naive HIV-1-infected adults undergoing integrase genotyping. We extracted data related to country, sample year, specimen type, sequencing method, and subtype. For studies with sequences in GenBank, we determined the prevalence of three categories of INSTI-associated resistance mutations: (1) nonpolymorphic INSTI-selected drug resistance mutations (DRMs) that we refer to as surveillance DRMs; (2) rarely selected nonpolymorphic INSTI-associated DRMs; and (3) common polymorphic accessory INSTI-selected DRMs. A total of 103 studies met inclusion criteria including 75 studies in GenBank containing integrase sequences from 16,481 INSTI-naive persons. The median sample year was 2013 (interquartile range: 2008-2014). The prevalence of INSTI surveillance DRMs, rarely selected DRMs, and common polymorphic accessory INSTI-selected DRMs were 0.5%, 0.8%, and 6.2%, respectively. There was no association between the presence of nonpolymorphic surveillance DRM and region, sample year, or subtype. Two surveillance DRMs, E138K and R263K occurred in 0.15% and 0.10% of naive sequences, respectively. Several lines of evidence suggested that the 0.5% prevalence of surveillance DRMs partly reflects the cumulative natural occurrence of these mutations in the absence of selective drug pressure. There was an unexplained temporal increase in the proportion of sequences with polymorphic accessory mutations. The prevalence of INSTI-associated surveillance DRMs is low even in regions where INSTIs have been a major component of antiretroviral therapy for several years. The presence of INSTI-associated surveillance DRMs in INSTI-naive persons likely results from actual cases of transmitted INSTI resistance and from a low background level reflecting the cumulative rare natural occurrence of several nonpolymorphic mutations.

Dataset Information

Quantitative prediction of integrase inhibitor resistance from genotype through consensus linear regression modeling.

Background

Methods

Results

Publications

Quantitative prediction of integrase inhibitor resistance from genotype through consensus linear regression modeling.

Similar Datasets

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