A longitudinal model linking absolute lymphocyte count (ALC) and volume of T2 lesions to expanded disability status scale (EDSS) in patients with relapsing-remitting multiple sclerosis (RRMS)
Ana M Novakovic (1), Sebastian Ueckert (1), Alain Munafo (2), Mats O Karlsson (1)
(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden (2) Merck Institute for Pharmacometrics, Lausanne, Switzerland
Objectives: Treatment of patients with MS and development of new therapies have been challenging due to the complexity of the disease, its slow progression, and the limited sensitivity of available clinical assessment outcomes. The utility and pressing need for biomarkers in MS has been extensively demonstrated, but so far no single endpoint/biomarker has fulfilled all the requirements [1]. The aim of this analysis was to establish the relationship between three clinical markers: ALC, a marker of the pharmacological effect of cladribine tablets; total volume of T2 lesions, an MRI (Magnetic Resonance Imaging) readout representing the burden of disease and EDSS, a long-term marker of disease progression.
Methods: The analysis included ALC, total T2 volume and EDSS data from two phase III trials investigating the efficacy of cladribine tablets in 1319 patients with RRMS [2, 3]. A biomarker-driven model for EDSS was developed using IRT (Item Response Theory) methodology by linking ALC and total T2 volume to the latent IRT disability of EDSS. A sequential modeling approach was applied in the analysis [4].
Results: Inclusion of total T2 volume as mediator of IRT latent disability improved the EDSS model fit in presence of the relative change from baseline ALC (DALC) (p<0.001). However, DALC remains the single most predictive variable for EDSS time-course (p<0.001), the higher the reduction in ALC, the lower the increase in EDSS. The final IRT model allowed adequate description of EDSS data on both the item and total score level.
Conclusions: Our findings confirm the well-documented lack of proof of surrogacy of total T2 volume for EDSS [5]. However, they indicate that lesions progression (as assessed by MRI) is nevertheless associated with disease progression (as measured by EDSS). The proposed model is the first dose-exposure-biomarkers-clinical endpoint model integrating the IRT methodology. It offers a platform for the quantitative understanding of the biomarker(s)/clinical endpoint relationship in RRMS.
References:
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