Unknown

Dataset Information

0

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?

SUBMITTER: Van der Borght K 

PROVIDER: S-EPMC3551713 | BioStudies | 2013-01-01T00:00:00Z

REPOSITORIES: biostudies

Similar Datasets

1000-01-01 | S-EPMC3987104 | BioStudies
2010-01-01 | S-EPMC3006360 | BioStudies
2012-01-01 | S-EPMC3399858 | BioStudies
2019-01-01 | S-EPMC6933261 | BioStudies
2011-01-01 | S-EPMC3134476 | BioStudies
2012-01-01 | S-EPMC3547692 | BioStudies
2010-01-01 | S-EPMC2996813 | BioStudies
1000-01-01 | S-EPMC4613743 | BioStudies
1000-01-01 | S-EPMC2546438 | BioStudies
2012-01-01 | S-EPMC3370736 | BioStudies