title: 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 < 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.
authors.affiliation: Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com
authors.forename: Koen
authors.initials: K
authors.lastname: Van der Borght
authors.forename: Ann
authors.initials: A
authors.lastname: Verheyen
authors.forename: Maxim
authors.initials: M
authors.lastname: Feyaerts
authors.forename: Liesbeth
authors.initials: L
authors.lastname: Van Wesenbeeck
authors.forename: Yvan
authors.initials: Y
authors.lastname: Verlinden
authors.forename: Elke
authors.initials: E
authors.lastname: Van Craenenbroeck
authors.forename: Herman
authors.initials: H
authors.lastname: van Vlijmen
mesh_terms: D016384:Consensus Sequence; D024882:Drug Resistance, Viral; D005838:Genotype; D019427:HIV Integrase / Q000235:genetics*; D019428:HIV Integrase Inhibitors / Q000494:pharmacology*; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics; D006801:Humans; D020128:Inhibitory Concentration 50; D016014:Linear Models; D008826:Microbial Sensitivity Tests / Q000379:methods; D011760:Pyrrolidinones / Q000494:pharmacology; D000068898:Raltegravir Potassium
chemical_list: D019428:HIV Integrase Inhibitors; D011760:Pyrrolidinones; D000068898:Raltegravir Potassium; D019427:HIV Integrase; C514801:p31 integrase protein, Human immunodeficiency virus 1