title: Correlation of the virological response to short-term maraviroc monotherapy with standard and deep-sequencing-based genotypic tropism prediction methods.
abstract: Genotypic tropism testing methods are emerging as the first step before prescription of the CCR5 antagonist maraviroc (MVC) to HIV-infected patients in Europe. Studies validating genotypic tests have included other active drugs that could have potentially convoluted the effects of MVC. The maraviroc clinical test (MCT) is an in vivo drug sensitivity test based on the virological response to a short-term exposure to MVC monotherapy. Thus, our aim was to compare the results of genotypic tropism testing methods with the short-term virological response to MVC monotherapy. A virological response in the MCT was defined as a ≥ 1-log(10) decrease in HIV RNA or undetectability after 8 days of drug exposure. Seventy-three patients undergoing the MCT were included in this study. We used both standard genotypic methods (n = 73) and deep sequencing (n = 27) on MCT samples at baseline. For the standard methods, the most widely used genotypic algorithms for analyzing the V3 loop sequence, geno2pheno and PSSM, were used. For deep sequencing, the geno2pheno algorithm was used with a false-positive rate cutoff of 3.5. The discordance rates between the standard genotypic methods and the virological response were approximately 20% (including mostly patients without a virological response). Interestingly, these discordance rates were similar to that obtained from deep sequencing (18.5%). The discordance rates between the genotypic methods (tropism assays predictive of the use of the CCR5 coreceptor) and the MCT (in vivo MVC sensitivity assay) indicate that the algorithms used by genotypic methods are still not sufficiently optimized.
authors.affiliation: Laboratory of Immunovirology, Biomedicine Institute of Seville, Infectious Diseases Service, Virgen del Rocío University Hospital, Seville, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Gonzalez-Serna
authors.forename: R A
authors.initials: RA
authors.lastname: McGovern
authors.forename: P R
authors.initials: PR
authors.lastname: Harrigan
authors.forename: F
authors.initials: F
authors.lastname: Vidal
authors.forename: A F Y
authors.initials: AF
authors.lastname: Poon
authors.forename: S
authors.initials: S
authors.lastname: Ferrando-Martinez
authors.forename: M A
authors.initials: MA
authors.lastname: Abad
authors.forename: M
authors.initials: M
authors.lastname: Genebat
authors.forename: M
authors.initials: M
authors.lastname: Leal
authors.forename: E
authors.initials: E
authors.lastname: Ruiz-Mateos
mesh_terms: D000328:Adult; D000465:Algorithms; D065100:CCR5 Receptor Antagonists; D002851:Chromatography, High Pressure Liquid; D003510:Cyclohexanes / Q000097:blood / Q000493:pharmacokinetics*; D005260:Female; D005838:Genotype; D023581:HIV Fusion Inhibitors / Q000097:blood / Q000493:pharmacokinetics*; D015658:HIV Infections / Q000097:blood / Q000188:drug therapy* / Q000821:virology; D015497:HIV-1 / Q000187:drug effects* / Q000502:physiology; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008297:Male; D000077592:Maraviroc; D008875:Middle Aged; D058889:Molecular Typing; D012367:RNA, Viral / Q000037:antagonists & inhibitors* / Q000096:biosynthesis; D019713:Receptors, CCR5 / Q000378:metabolism; D053719:Tandem Mass Spectrometry; D016896:Treatment Outcome; D014230:Triazoles / Q000097:blood / Q000493:pharmacokinetics*; D019562:Viral Load / Q000187:drug effects / Q000235:genetics; D056189:Viral Tropism / Q000187:drug effects
chemical_list: D065100:CCR5 Receptor Antagonists; D003510:Cyclohexanes; D023581:HIV Fusion Inhibitors; D012367:RNA, Viral; D019713:Receptors, CCR5; D014230:Triazoles; D000077592:Maraviroc