Literature DB >> 24798724

Comparative pharmacogenetics of multiple sclerosis: IFN-β versus glatiramer acetate.

Olga G Kulakova1, Ekaterina Yu Tsareva, Dmitrijs Lvovs, Alexander V Favorov, Alexey N Boyko, Olga O Favorova.   

Abstract

Various diseases require the selection of preferable treatment out of available alternatives. Multiple sclerosis (MS), an autoimmune inflammatory/neurodegenerative disease of the CNS, requires long-term medication with either specific disease-modifying therapy (DMT) - IFN-β or glatiramer acetate (GA) - which remain the only first-line DMTs in all countries. A significant share of MS patients are resistant to treatment with one or the other DMT; therefore, the earliest choice of preferable DMT is of particular importance. A number of conventional pharmacogenetic studies performed up to the present day have identified the treatment-sensitive genetic biomarkers that might be specific for the particular drug; however, the suitable biomarkers for selection of one or another first-line DMT are remained to be found. Comparative pharmacogenetic analysis may allow the identification of the discriminative genetic biomarkers, which may be more informative for an a priori DMT choice than those found in conventional pharmacogenetic studies. The search for discriminative markers of preferable first-line DMT, which differ in carriage between IFN-β responders and GA responders as well as between IFN-β nonresponders and GA nonresponders, has been performed in 253 IFN-β-treated MS patients and 285 GA-treated MS patients. A bioinformatics algorithm for identification of composite biomarkers (allelic sets) was applied on a unified set of immune-response genes, which are relevant for IFN-β and/or GA modes of action, and identical clinical criteria of treatment response. We found the range of discriminative markers, which include polymorphic variants of CCR5, IFNAR1, TGFB1, DRB1 or CTLA4 genes, in different combinations. Every allelic set includes the CCR5 genetic variant, which probably suggests its crucial role in the modulation of the DMT response. Special attention should be given to the (CCR5*d+ IFNAR1*G) discriminative combination, which clearly points towards IFN-β treatment choice for carriers of this combination. As a whole the comparative approach provides an option for the identification of prognostic composite biomarkers for a preferable medication among available alternatives.

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Year:  2014        PMID: 24798724     DOI: 10.2217/pgs.14.26

Source DB:  PubMed          Journal:  Pharmacogenomics        ISSN: 1462-2416            Impact factor:   2.533


  6 in total

Review 1.  A comprehensive review on the role of chemokines in the pathogenesis of multiple sclerosis.

Authors:  Soudeh Ghafouri-Fard; Kasra Honarmand; Mohammad Taheri
Journal:  Metab Brain Dis       Date:  2021-01-06       Impact factor: 3.584

Review 2.  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

3.  An unsupervised machine learning method for discovering patient clusters based on genetic signatures.

Authors:  Christian Lopez; Scott Tucker; Tarik Salameh; Conrad Tucker
Journal:  J Biomed Inform       Date:  2018-07-29       Impact factor: 6.317

Review 4.  Pharmacogenomics of Multiple Sclerosis: A Systematic Review.

Authors:  Keli Hočevar; Smiljana Ristić; Borut Peterlin
Journal:  Front Neurol       Date:  2019-02-26       Impact factor: 4.003

5.  B-Cell Activity Predicts Response to Glatiramer Acetate and Interferon in Relapsing-Remitting Multiple Sclerosis.

Authors:  Sabine Tacke; Stefan Braune; Damiano M Rovituso; Tjalf Ziemssen; Paul V Lehmann; Heidi Dikow; Arnfin Bergmann; Stefanie Kuerten
Journal:  Neurol Neuroimmunol Neuroinflamm       Date:  2021-03-11

6.  A pharmacogenetic signature of high response to Copaxone in late-phase clinical-trial cohorts of multiple sclerosis.

Authors:  Colin J Ross; Fadi Towfic; Jyoti Shankar; Daphna Laifenfeld; Mathis Thoma; Matthew Davis; Brian Weiner; Rebecca Kusko; Ben Zeskind; Volker Knappertz; Iris Grossman; Michael R Hayden
Journal:  Genome Med       Date:  2017-05-31       Impact factor: 11.117

  6 in total

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