title: Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach.
abstract: BACKGROUND
  Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics.
  
  
  RESULTS
  We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment.
  
  
  CONCLUSIONS
  The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.
authors.affiliation: Indiana University School of Medicine; Indianapolis, Indiana, United States of America ; Indiana Clinical and Translational Sciences Institute (CTSI), Indianapolis, Indiana, United States of America.
authors.forename: Nieves
authors.initials: N
authors.lastname: Velez de Mendizabal
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Hutmacher
authors.forename: Iñaki F
authors.initials: IF
authors.lastname: Troconiz
authors.forename: Joaquín
authors.initials: J
authors.lastname: Goñi
authors.forename: Pablo
authors.initials: P
authors.lastname: Villoslada
authors.forename: Francesca
authors.initials: F
authors.lastname: Bagnato
authors.forename: Robert R
authors.initials: RR
authors.lastname: Bies
mesh_terms: D000893:Anti-Inflammatory Agents / Q000627:therapeutic use; D018450:Disease Progression; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008775:Methylprednisolone / Q000627:therapeutic use; D008954:Models, Biological; D020529:Multiple Sclerosis, Relapsing-Remitting / Q000188:drug therapy / Q000473:pathology*; D011241:Prednisone / Q000627:therapeutic use; D011336:Probability; D012984:Software; D016008:Statistical Distributions
chemical_list: D000893:Anti-Inflammatory Agents; D011241:Prednisone; D008775:Methylprednisolone
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