Optimizing immunomodulatory therapy for MS patients: an integrated management model

Clinical Parameters to Predict Future Clinical Disease Activity After Treatment Change to Higher-Dose Subcutaneous Interferon Beta-1a From Other Platform Injectables in Patients With Relapsing-Remitting Multiple Sclerosis

Objective: To identify predictors of clinical disease activity after treatment change to higher-dose interferon beta-1a in relapsing-remitting multiple sclerosis (MS).

Methods: This was a retrospective-prospective observational multicenter study. We enrolled patients with at least one relapse on platform injectable therapy who were changed to 44 μg interferon beta-1a. Our primary endpoint was the clinical disease activity-free (cDAF) status at 6, 12, 18, and 24 months. Secondary endponts included relapse-free status and disability progression-free status at different timepoints. The primary predictor of interest was the monosymptomatic vs. polysymptomatic index relapse, based on the number of affected functional systems from the Kurtzke scale during the last relapse prior to baseline. Other secondary predictors of clinical disease activity were analyzed based on different demographic and relapse characteristics. Kaplan-Meier estimates of the cumulative probability of remaining in cDAF status were performed. The time to clinical disease activity was compared between groups using univariate Kaplan-Meier analysis and multivariate Cox regression. Multivariate analyses were processed in the form of CART (Classification & Regression Trees).

Results: A total of 300 patients entered the study; 233 (77.7%) of them completed the 24-month study period and 67 patients (22.3%) terminated early. The proportion of patients in cDAF status was 84.7, 69.5, 57.5, and 54.2% at 6, 12, 18, and 24 months. After 2 years of follow-up, 55.9% of patients remained relapse-free and 87.8% of patients remained disability progression-free. At all timepoints, the polysymptomatic index relapse was the most significant predictor of clinical disease activity of all studied variables. Hazard ratio of cDAF status for patients with monosymptomatic vs. polysymptomatic index relapse was 1.94 (95% CI 1.38–2.73). CART analyses also confirmed the polysymptomatic index relapse being the strongest predictor of clinical disease activity, followed by higher number of pre-baseline relapses with the most significant effect in the monosymptomatic index relapse group. The next strongest predictors of clinical disease activity were cerebellar syndrome as the most disabled Kurtzke functional system for the monosymptomatic relapse group, and age at first MS symptom ≥ 45 for the polysymptomatic relapse group.

Conclusions: Patients with a polysymptomatic index relapse and/or higher number of relapses within 2 years prior to baseline are at high risk of clinical disease activity, despite treatment change to higher-dose interferon beta-1a from other platform injectable therapy.

Trial registration: State Institute of Drug Control (SUKL), URL: http://www.sukl.eu/modules/nps/index.php?h=study&a=detail&id=958&lang=2, registration number 1205090000.

Response to interferon-beta therapy in relapsing-remitting multiple sclerosis: a comparison of different clinical criteria

We assessed the proportion and potential predictors of response to interferon-beta (IFNβ) therapy in relapsing-remitting (RR) multiple sclerosis (MS) patients, comparing different definitions of response: a) lower relapse rate during therapy compared to the year and the two years before therapy, b) reduction of relapse rate during therapy of at least 30% compared to the two years before therapy, c) no relapse during treatment, d) no progression on the Expanded Disability Status Scale (EDSS). Among 147 RR patients treated for at least one year, 33 received IFNβ-1b subcutaneously (SC) (Betaferon), 59 IFNβ-1a intramuscularly (Avonex) and 55 IFNβ-1a SC (Rebif). Using definitions a), b) and d), 72%, 73% and 73% patients, respectively, were considered responders. Forty-four per cent of our patients were completely relapse free. In the logistic regression model, using definitions a) and b), a higher relapse rate in the two years preceding the therapy turned out to be a significant predictor of response. Considering definition c), lower baseline relapse rate was associated with a more favourable response.

An agent-based simulation model for informed shared decision making in multiple sclerosis

Shared decision making (SDM) is concerned with patient involvement into medical decisions and chronic conditions such as Multiple sclerosis (MS), with only partially effective treatments leading to potential severe side effects, conflicting evidence, and uncertain evidence on outcomes, constitute a typical condition for SDM. As treatment options increase and patients participate more intensively in decisions, the need for evidence-based information (EBI) becomes clear. Natural history (NH) studies of MS represent the basic sources for required EBI and are especially useful to contribute to the practical exercise of prognosis formulation and to enable the evaluation of effectiveness in the context of treatment. Several of these identify early clinical factors predictive of the course of MS but there is no consensus method for determining the long term progression of disability and evolution of individual patients on the basis of observations on the early stages of the disease, which constitutes a major challenge for the practicing neurologist. Aiming at delivering more reliable prognosis estimation, this study combines the distribution of patients reaching specific levels of disability within defined time periods as determined in NH studies, with disability curves and severity scores as a function of time, in terms of percentiles and deciles respectively, derived from longitudinal data analysis studies. A computer agent-based simulation model was implemented as a comprehensive and easy to utilize tool able to predict and monitor progression of disability in MS patients, and to support the neurologist discussing prognosis scenarios with the individual patient for effective SDM.

Molecular recognition theory and sense-antisense interaction: therapeutic applications in autoimmunity

Perhaps behind only the understanding of the genetic code in importance is the comprehension of protein sequence and structure in its effect on modern scientific investigation. How proteins are structured and interact dictates a considerable amount of the body's processes in maintaining homeostasis. Unfortunately, in diseases of autoimmunity, these processes are directed against the body itself and most of the current clinical responses are severely lacking. This review addresses current therapeutics involved in the treatment of various autoimmune diseases and details potential future therapeutics designed with a more targeted approach. Detailed in this manuscript is the concept of utilizing peptides possessing an inverse hydropathy to the immunogenic region of proteins to generate anti-idiotypic (anti-Id) and anti-clonotypic T cell receptor (TCR) antibodies (Abs). Theoretically, the anti-Id Abs cross react with Id Abs and negate the powerful machinery of the adaptive immune response with little to no side effects. A series of studies by a number of groups have shown this to be an exciting and intriguing concept that will likely play a role in the future treatment of autoimmune diseases.