Abstract

BackgroundCopaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS). On an individual level, patients show variability in their response to Copaxone, with some achieving significantly higher response levels. The involvement of genes (e.g., HLA-DRB1*1501) with high inter-individual variability in Copaxone’s mechanism of action (MoA) suggests the potential contribution of genetics to treatment response. This study aimed to identify genetic variants associated with Copaxone response in patient cohorts from late-phase clinical trials.MethodsSingle nucleotide polymorphisms (SNPs) associated with high and low levels of response to Copaxone were identified using genome-wide SNP data in a discovery cohort of 580 patients from two phase III clinical trials of Copaxone. Multivariable Bayesian modeling on the resulting SNPs in an expanded discovery cohort with 1171 patients identified a multi-SNP signature of Copaxone response. This signature was examined in 941 Copaxone-treated MS patients from seven independent late-phase trials of Copaxone and assessed for specificity to Copaxone in 310 Avonex-treated and 311 placebo-treated patients, also from late-phase trials.ResultsA four-SNP signature consisting of rs80191572 (in UVRAG), rs28724893 (in HLA-DQB2), rs1789084 (in MBP), and rs139890339 (in ZAK(CDCA7)) was identified as significantly associated with Copaxone response. Copaxone-treated signature-positive patients had a greater reduction in annualized relapse rate (ARR) compared to signature-negative patients in both discovery and independent cohorts, an effect not observed in Avonex-treated patients. Additionally, signature-positive placebo-treated cohorts did not show a reduction in ARR, demonstrating the predictive as opposed to prognostic nature of the signature. A 10% subset of patients, delineated by the signature, showed marked improvements across multiple clinical parameters, including ARR, MRI measures, and higher proportion with no evidence of disease activity (NEDA).ConclusionsThis study is the largest pharmacogenetic study in MS reported to date. Gene regions underlying the four-SNP signature have been linked with pathways associated with either Copaxone’s MoA or the pathophysiology of MS. The pronounced association of the four-SNP signature with clinical improvements in a ~10% subset of the MS patient population demonstrates the complex interplay of immune mechanisms and the individualized nature of response to Copaxone.

Highlights

  • Copaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS)

  • Eleven Single nucleotide polymorphism (SNP) were associated with extreme phenotypes of Copaxone response Eleven SNPs were associated with high versus low response to Copaxone based on the initial exploratory analysis of genome-wide SNP data from the patients in both the The Glatiramer Acetate Low-Frequency Administration study (GALA) DB and FORTE DB discovery cohorts (“Methods”, Table 2). These SNPs were not associated with response in the GALA DB placebo arm

  • In step 1 of the analysis, out of 35 candidate variants tested, two SNPs in complete linkage disequilibrium (LD) tagging the HLADRB1*15:01 allele met the threshold for selection in both GALA DB and FORTE DB. rs3135391 was selected for all subsequent analyses

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Summary

Introduction

Copaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS). Fourteen disease-modifying therapies (DMTs) are currently approved for management of multiple sclerosis in the USA [10], benefiting patients by reducing relapses, delaying disability progression and reducing central nervous system lesions. These therapies vary in their mechanism of action (MoA), administration routes, and side effect profiles, with patients demonstrating substantial variability in their responses to each drug [11]. This variability, together with the plethora of treatment options, underscores the need for predictive markers of response to optimize treatment selection for individual patients of multiple sclerosis [11]

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