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Published on: December 9, 2015
Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population
Nieves Velez de Mendizabal1, Matthew M Hutmacher, Iñaki F Troconiz
1Indiana University School of Medicine; Indianapolis, Indiana, United States of America ; Indiana Clinical and Translational Sciences Institute (CTSI), Indianapolis, Indiana, United States of America.
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.
Methods And Findings:
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.
Insights
We developed a predictive model for contrast-enhancing lesions (CEL) in Multiple Sclerosis (MS) using nonlinear mixed-effects models. This model captures relapsing-remitting dynamics and quantifies treatment effects, improving clinical trial design.
Area of Science:
- Neuroscience
- Immunology
- Medical Imaging
Background:
- Relapsing-remitting dynamics are characteristic of autoimmune diseases like Multiple Sclerosis (MS).
- Clinical relapses in MS involve acute central nervous system inflammation, visible as contrast-enhancing lesions (CEL) on MRI.
- CEL dynamics exhibit significant unpredictability and variability.
Purpose of the Study:
- To apply a population approach using nonlinear mixed-effects models to analyze CEL progression.
- To develop a robust model that accurately captures the complex dynamics of CEL.
- To improve the understanding and prediction of disease activity in MS.
Main Methods:
- Analysis of CEL counts from nine MS patients over 48 months using nonlinear mixed-effects modeling (NONMEM 7.2).
- Exploration of various discrete distribution models, with a focus on the negative binomial distribution.
- Incorporation of previous months' CEL counts to enhance predictive accuracy.
- Validation of the model's predictive capacity using a second cohort of fourteen patients.
Main Results:
- The negative binomial distribution model demonstrated the best predictive ability for CEL dynamics.
- Incorporating historical CEL data significantly improved model fitting and prediction.
- The model successfully characterized relapsing-remitting CEL dynamics and quantified inter-patient variability.
- Steroid treatment was found to resolve existing CELs but not prevent new ones.
Conclusions:
- The developed model effectively characterizes MS relapsing-remitting CEL dynamics and inter-patient variability.
- The model provides insights into the effects of steroid treatment on CELs.
- This predictive model can aid in designing future longitudinal studies, clinical trials, and evaluating novel therapies for MS.
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