Literature DB >> 26343337

Agent based modeling of the effects of potential treatments over the blood-brain barrier in multiple sclerosis.

Marzio Pennisi1, Giulia Russo2, Santo Motta1, Francesco Pappalardo2.   

Abstract

Multiple sclerosis is a disease of the central nervous system that involves the destruction of the insulating sheath of axons, causing severe disabilities. Since the etiology of the disease is not yet fully understood, the use of novel techniques that may help to understand the disease, to suggest potential therapies and to test the effects of candidate treatments is highly advisable. To this end we developed an agent based model that demonstrated its ability to reproduce the typical oscillatory behavior observed in the most common form of multiple sclerosis, relapsing-remitting multiple sclerosis. The model has then been used to test the potential beneficial effects of vitamin D over the disease. Many scientific studies underlined the importance of the blood-brain barrier and of the mechanisms that influence its permeability on the development of the disease. In the present paper we further extend our previously developed model with a mechanism that mimics the blood-brain barrier behavior. The goal of our work is to suggest the best strategies to follow for developing new potential treatments that intervene in the blood-brain barrier. Results suggest that the best treatments should potentially prevent the opening of the blood-brain barrier, as treatments that help in recovering the blood-brain barrier functionality could be less effective.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  ABM; BBB; Modeling; Multiple sclerosis

Mesh:

Substances:

Year:  2015        PMID: 26343337     DOI: 10.1016/j.jim.2015.08.014

Source DB:  PubMed          Journal:  J Immunol Methods        ISSN: 0022-1759            Impact factor:   2.303


  5 in total

1.  Model verification tools: a computational framework for verification assessment of mechanistic agent-based models.

Authors:  Giulia Russo; Giuseppe Alessandro Parasiliti Palumbo; Marzio Pennisi; Francesco Pappalardo
Journal:  BMC Bioinformatics       Date:  2022-05-19       Impact factor: 3.307

2.  The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis.

Authors:  Francesco Pappalardo; Giulia Russo; Marzio Pennisi; Giuseppe Alessandro Parasiliti Palumbo; Giuseppe Sgroi; Santo Motta; Davide Maimone
Journal:  Cells       Date:  2020-03-01       Impact factor: 6.600

3.  In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products.

Authors:  Marco Viceconti; Francesco Pappalardo; Blanca Rodriguez; Marc Horner; Jeff Bischoff; Flora Musuamba Tshinanu
Journal:  Methods       Date:  2020-01-25       Impact factor: 3.608

4.  Predicting the artificial immunity induced by RUTI® vaccine against tuberculosis using universal immune system simulator (UISS).

Authors:  Marzio Pennisi; Giulia Russo; Giuseppe Sgroi; Angela Bonaccorso; Giuseppe Alessandro Parasiliti Palumbo; Epifanio Fichera; Dipendra Kumar Mitra; Kenneth B Walker; Pere-Joan Cardona; Merce Amat; Marco Viceconti; Francesco Pappalardo
Journal:  BMC Bioinformatics       Date:  2019-12-10       Impact factor: 3.169

5.  Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination.

Authors:  Dimitrios Kiagias; Giulia Russo; Giuseppe Sgroi; Francesco Pappalardo; Miguel A Juárez
Journal:  Front Med Technol       Date:  2021-10-22
  5 in total

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