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

Virology Journal

Herman van Vlijmen¹  Elke Van Craenenbroeck¹  Yvan Verlinden¹  Liesbeth Van Wesenbeeck¹  Maxim Feyaerts¹  Ann Verheyen¹  Koen Van der Borght¹ 

[1] Tibotec-Virco, Beerse, Belgium

关键词: Resistance;    Raltegravir;    Linear regression;    Integrase;    Genetic algorithm;    Consensus model;   

Others  :  1152456
DOI  :  10.1186/1743-422X-10-8

 received in 2012-02-13, accepted in 2012-12-28,  发布年份 2013

【 摘 要 】

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 < 0.01): 143C/R&97A, 155H&97A/151I and 74M&151I. The R² performance of this model on the clonal training data was 0.97, and 0.78 on an unseen population genotype-phenotype dataset of 171 clinical isolates from RAL treated and INI naïve patients.

Conclusions

We 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.

【 授权许可】

   
2013 Van der Borght et al.; licensee BioMed Central Ltd.

【 图 表 】

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