Literature DB >> 17622942

Pharmacogenetics of glatiramer acetate therapy for multiple sclerosis reveals drug-response markers.

Iris Grossman1, Nili Avidan, Clara Singer, Dan Goldstaub, Liat Hayardeny, Eli Eyal, Edna Ben-Asher, Tamar Paperna, Itsik Pe'er, Doron Lancet, Jacques S Beckmann, Ariel Miller.   

Abstract

Genetic-based optimization of treatment prescription is becoming a central research focus in the management of chronic diseases, such as multiple sclerosis, which incur a prolonged drug-regimen adjustment. This study was aimed to identify genetic markers that can predict response to glatiramer acetate (Copaxone) immunotherapy for relapsing multiple sclerosis. For this purpose, we genotyped fractional cohorts of two glatiramer acetate clinical trials for HLA-DRB1*1501 and 61 single nucleotide polymorphisms within a total of 27 candidate genes. Statistical analyses included single nucleotide polymorphism-by-single nucleotide polymorphism and haplotype tests of drug-by-genotype effects in drug-treated versus placebo-treated groups. We report the detection of a statistically significant association between glatiramer acetate response and a single nucleotide polymorphism in a T-cell receptor beta (TRB@) variant replicated in the two independent cohorts (odds ratio=6.85). Findings in the Cathepsin S (CTSS) gene (P=0.049 corrected for all single nucleotide polymorphisms and definitions tested, odds ratio=11.59) in one of the cohorts indicate a possible association that needs to be further investigated. Additionally, we recorded nominally significant associations of response with five other genes, MBP, CD86, FAS, IL1R1 and IL12RB2, which are likely to be involved in glatiramer acetate's mode-of-action, both directly and indirectly. Each of these association signals in and of itself is consistent with the no-association null-hypothesis, but the number of detected associations is surprising vis-à-vis chance expectation. Moreover, the restriction of these associations to the glatiramer acetate-treated group, rather than the placebo group, clearly demonstrates drug-specific genetic effects. These findings provide additional progress toward development of pharmacogenetics-based personalized treatment for multiple sclerosis.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17622942     DOI: 10.1097/FPC.0b013e3281299169

Source DB:  PubMed          Journal:  Pharmacogenet Genomics        ISSN: 1744-6872            Impact factor:   2.089


  15 in total

Review 1.  From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions.

Authors:  Daniel W Nebert; Ge Zhang; Elliot S Vesell
Journal:  Drug Metab Rev       Date:  2008       Impact factor: 4.518

2.  Switching therapies in multiple sclerosis.

Authors:  Patricia K Coyle
Journal:  CNS Drugs       Date:  2013-04       Impact factor: 5.749

Review 3.  Current developments in pharmacogenomics of multiple sclerosis.

Authors:  Rebecca J Carlson; J Ronald Doucette; Adil J Nazarali
Journal:  Cell Mol Neurobiol       Date:  2014-08-15       Impact factor: 5.046

Review 4.  Pharmacogenomics and multiple sclerosis: moving toward individualized medicine.

Authors:  Manuel Comabella; Koen Vandenbroeck
Journal:  Curr Neurol Neurosci Rep       Date:  2011-10       Impact factor: 5.081

Review 5.  An integrated approach to design novel therapeutic interventions for demyelinating disorders.

Authors:  Oscar G Vidaurre; Jia Liu; Jeffery Haines; Juan Sandoval; Richard Nowakowski; Patrizia Casaccia
Journal:  Eur J Neurosci       Date:  2012-06       Impact factor: 3.386

Review 6.  Predicting responders to therapies for multiple sclerosis.

Authors:  Jordi Río; Manuel Comabella; Xavier Montalban
Journal:  Nat Rev Neurol       Date:  2009-10       Impact factor: 42.937

7.  A systems medicine approach reveals disordered immune system and lipid metabolism in multiple sclerosis patients.

Authors:  M Pazhouhandeh; M-A Sahraian; S D Siadat; A Fateh; F Vaziri; F Tabrizi; F Ajorloo; A K Arshadi; E Fatemi; S Piri Gavgani; F Mahboudi; F Rahimi Jamnani
Journal:  Clin Exp Immunol       Date:  2018-01-25       Impact factor: 4.330

Review 8.  The emerging agenda of stratified medicine in neurology.

Authors:  Paul M Matthews; Paul Edison; Olivia C Geraghty; Michael R Johnson
Journal:  Nat Rev Neurol       Date:  2013-12-10       Impact factor: 42.937

9.  Transcriptomic Analysis of Peripheral Monocytes upon Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients.

Authors:  G Sferruzza; F Clarelli; E Mascia; L Ferrè; L Ottoboni; M Sorosina; S Santoro; L Moiola; V Martinelli; G Comi; F Martinelli Boneschi; M Filippi; P Provero; Federica Esposito
Journal:  Mol Neurobiol       Date:  2021-06-28       Impact factor: 5.590

10.  Alignment and classification of time series gene expression in clinical studies.

Authors:  Tien-ho Lin; Naftali Kaminski; Ziv Bar-Joseph
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.