Design and Statistical Issues Related to Testing Experimental Therapy in Multiple Sclerosis

1992 ◽  
pp. 91-122 ◽  
Author(s):  
William Weiss ◽  
Emanuel M. Stadlan
Keyword(s):  
Multiple Sclerosis ◽  
Experimental Therapy ◽  
Statistical Issues

scholarly journals Prediction of combination therapies based on topological modeling of the immune signaling network in Multiple Sclerosis

2019 ◽  
Author(s):  
Marti Bernardo-Faura ◽  
Melanie Rinas ◽  
Jakob Wirbel ◽  
Inna Pertsovskaya ◽  
Vicky Pliaka ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Combination Therapy ◽  
Ex Vivo ◽  
Complex Diseases ◽  
Cell Receptor ◽  
Signaling Network ◽  
Patient Specific ◽  
Signaling Networks ◽  
Experimental Therapy ◽  
Combination Therapies

AbstractSignal transduction deregulation is a hallmark of many complex diseases, including Multiple Sclerosis (MS). Here, we performed ex vivo multiplexed phosphoproteomic assays in PBMCs from 180 MS patients either untreated or treated with fingolimod, natalizumab, interferon-beta, glatiramer acetate or the experimental therapy epigallocatechin gallate (EGCG), and from 60 matched healthy controls. Fitting a bespoke literature-derived network of MS-related pathways using logic modeling yielded a signaling network specific for each patient. Patient models were merged to characterize healthy-, disease- and drug-specific signaling networks. We defined a co-druggability score based on the topology for each drug’s network. We used this score to identify kinase interactions whose activity could be reverted to a "healthy-like" status by combination therapy. We predicted several combinations with approved MS drugs. Specifically, TAK1 kinase, involved in TGF-B, toll-like receptor, B-cell receptor and response to inflammation pathways was found to be highly deregulated and co-druggable with four MS drugs. One of these predicted combinations, Fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Our approach based on patient-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.One sentence summaryA new approach to predict combination therapies based on modeling signaling architecture using phosphoproteomics from patients with Multiple Sclerosis characterizes deregulated signaling pathways and reveals new therapeutic targets and drug combinations.

Statistical Issues Related to the Use of MRI Data in Multiple Sclerosis

2007 ◽  
Vol 17 ◽  
pp. 56S-59S ◽  
Author(s):  
Maria Pia Sormani ◽  
Massimo Filippi
Keyword(s):  
Multiple Sclerosis ◽  
Statistical Issues

Microglia-derived macrophages in early multiple sclerosis plaques

1996 ◽  
Vol 22 (3) ◽  
pp. 207-215 ◽  
Author(s):  
H. Li ◽  
M. L. Cuzner ◽  
J. Newcombe
Keyword(s):  
Multiple Sclerosis
Sign in / Sign up

Export Citation Format

Share Document