- Marzio Pennisi ORCID:orcid.org/0000-0003-0231-765312,
- Giulia Russo ORCID:orcid.org/0000-0001-6616-785614,
- Giuseppe Sgroi ORCID:orcid.org/0000-0001-5062-469613,
- Giuseppe Alessandro Parasiliti Palumbo ORCID:orcid.org/0000-0003-4646-985113 &
- …
- Francesco Pappalardo ORCID:orcid.org/0000-0003-1668-332014
Part of the book series:Lecture Notes in Computer Science ((LNBI,volume 12313))
Included in the following conference series:
465Accesses
Abstract
We present an improved version of an agent-based model developed to reproduce the typical oscillating behavior of relapsing remitting multiple sclerosis, a demyelinating autoimmune disease of the central nervous system. The model now includes the effects of vitamin D, a possible immune-modulator that can potentially influence the disease course, as well as the mechanisms of action of daclizumab, a monoclonal antibody that was previously reported as the unique third line treatment for MS, i.e., to be used only in patients who had an inadequate response to the other therapies, but then retired from market due to the arising of severe side effects. The use of this computational approach, capable of qualitatively reproducing the main effects of daclizumab, is used to grasp some useful insights to delineate the possible causes that led to the withdrawal of the drug. Furthermore, we explore the possibility to combine vitamin D administration with a reduced dosage of daclizumab, in order to qualitatively delineate if a combined treatment can lead to similar efficacy, thus entitling a reduced risk of adverse effects.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 5719
- Price includes VAT (Japan)
- Softcover Book
- JPY 7149
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ascherio, A., et al.: Epstein-Barr virus antibodies and risk of multiple sclerosis: a prospective study. JAMA286(24), 3083–3088 (2001)
Barrat, F., et al.: In vitro generation of interleukin 10-producing regulatory CD4(+) T cells is induced by immunosuppressive drugs and inhibited by T helper type 1 (Th1)- and Th2-inducing cytokines. J. Exp. Med.195(5), 603–613 (2002)
Beccuti, M., et al.: GPU accelerated analysis of Treg-Teff cross regulation in relapsing-remitting multiple sclerosis. In: Mencagli, G., et al. (eds.) Euro-Par 2018. LNCS, vol. 11339, pp. 626–637. Springer, Cham (2019).https://doi.org/10.1007/978-3-030-10549-5_49
Bhalla, A., Amento, E., Glimcher, B.S.L.: 1,25-Dihydroxyvitamin D3 inhibits antigen-induced T cell activation. J. Immunol.133(4), 1748–1754 (1984)
Bielekova, B., Becker, B.L.: Monoclonal antibodies in MS: mechanisms of action. Neurology74(Suppl 1), 31–40 (2010)
Bielekova, B., et al.: Regulatory CD56(bright) natural killer cells mediate immunomodulatory effects of IL-2R\(\alpha \)-targeted therapy (daclizumab) in multiple sclerosis. Proc. Natl. Acad. Sci. USA103(15), 5941–5946 (2006)
Bielekova, B., et al.: Intrathecal effects of daclizumab treatment of multiple sclerosis. Neurology77(21), 1877–1886 (2011)
Chiacchio, F., Pennisi, M., Russo, G., Motta, S., Pappalardo, F.: Agent-based modeling of the immune system: NetLogo, a promising framework. BioMed Res. Int.2014, 907171 (2014)
Chimeh, M.K., Richmond, P., Heywood, P., Pennisi, M., Pappalardo, F.: Parallelisation strategies for agent based simulation of immune systems. BMC Bioinform.20(Suppl 6), 1–14 (2019).https://doi.org/10.1186/s12859-019-3181-y
Gold, R., et al.: Daclizumab high-yield process in relapsing-remitting multiple sclerosis (SELECT): a randomised, double-blind, placebo-controlled trial. Lancet381, 2167–75 (2013)
Godin, D.: The causal cascade to multiple sclerosis: a model for MS pathogenesis. PLoS One4(2), e4565 (2009)
Gorman, S., et al.: Topically applied 1,25-Dihydroxyvitamin D3 enhances the suppressive activity of CD4+CD25+ cells in the draining lymph nodes. J. Immunol.179(9), 6273–6283 (2007)
Gregori, S., Casorati, M., Amuchastegui, S., Smiroldo, S., Davalli, A., Adorini, L.: Regulatory T cells induced by 1 alpha, 25-Dihydroxyvitamin D3 and mycophenolate mofetil treatment mediate transplantation tolerance. J. Immunol.167(4), 1945–1953 (2001)
Lim, J.A., et al.: New feasible treatment for refractory autoimmune encephalitis: low-dose interleukin-2. J. Neuroimmunol.299, 107–111 (2016)
Luessi, F., Engel, S., Spreer, A., Bittner, S., Zipp, F.: GFAP\(\alpha \) IgG-associated encephalitis upon daclizumab treatment of MS. Neurol. Neuroimmunol. Neuroinflamm.5(5), e481 (2018)
Martin, R., McFarland, H.F., McFarlin, D.E.: Immunological aspects of demyelinating diseases. Ann. Rev. Immunol.10, 153–187 (2003)
Pappalardo, F., Pennisi, M., Rajput, A.M., Chiacchio, F., Motta, S.: Relapsing-remitting multiple scleroris and the role of vitamin D : an agent based model. In: ACM-BCB, pp. 744–748 (2014)
Penna, G., et al.: Expression of the inhibitory receptor ILT3 on dendritic cells is dispensable for induction of CD4+Foxp3+ regulatory t cells by 1,25-dihydroxyvitamin D3. Blood106(10), 3490–3497 (2005)
Pennisi, M., Rajput, A.M., Toldo, L., Pappalardo, F.: Agent based modeling of Treg-Teff cross regulation in relapsing-remitting multiple sclerosis. BMC Bioinf.14(Suppl 16), S9 (2013)
Pennisi, M., Russo, G., Motta, S., Pappalardo, F.: Agent based modeling of the effects of potential treatments over the blood-brain barrier in multiple sclerosis. J. Immunol. Methods427, 6–12 (2015)
Ponsonby, A., et al.: Exposure to infant siblings during early life and risk of multiple sclerosis. JAMA293(4), 463–469 (2005)
Sospedra, M., Martin, R.: Immunology of multiple sclerosis. Annu. Rev. Immunol.23, 683–747 (2005)
Sundström, P., et al.: An altered immune response to Epstein-Barr virus in multiple sclerosis: a prospective study. Neurology62, 2277–2282 (2004)
Vélez de Mendizábal, N., et al.: Modeling the effector - regulatory T cell cross-regulation reveals the intrinsic character of relapses in multiple sclerosis. BMC Syst. Biol.5(1), 1–15 (2011)
Wilensky, U.: NetLogo, Center for connected learning and computer-based modeling, Northwestern University, Evanston, IL (1999).http://ccl.northwestern.edu/netlogo/
Wuest, S.C., et al.: A role for interleukin-2 transpresentation in dendritic cellmediated T cell activation in humans, as revealed by daclizumab therapy. Nat. Med.17(5), 604–609 (2011)
Author information
Authors and Affiliations
Computer Science Institute, DiSIT, University of Eastern Piedmont, Alessandria, Italy
Marzio Pennisi
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Giuseppe Sgroi & Giuseppe Alessandro Parasiliti Palumbo
Department of Drug Science, University of Catania, Catania, Italy
Giulia Russo & Francesco Pappalardo
- Marzio Pennisi
You can also search for this author inPubMed Google Scholar
- Giulia Russo
You can also search for this author inPubMed Google Scholar
- Giuseppe Sgroi
You can also search for this author inPubMed Google Scholar
- Giuseppe Alessandro Parasiliti Palumbo
You can also search for this author inPubMed Google Scholar
- Francesco Pappalardo
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toMarzio Pennisi.
Editor information
Editors and Affiliations
University of Bergamo, Bergamo, Italy
Paolo Cazzaniga
University of Milano-Bicocca, Milan, Italy
Daniela Besozzi
National Research Council, Segrate, Italy
Ivan Merelli
Università degli Studi di Trieste, Trieste, Italy
Luca Manzoni
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pennisi, M., Russo, G., Sgroi, G., Palumbo, G.A.P., Pappalardo, F. (2020). In Silico Evaluation of Daclizumab and Vitamin D Effects in Multiple Sclerosis Using Agent Based Models. In: Cazzaniga, P., Besozzi, D., Merelli, I., Manzoni, L. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Lecture Notes in Computer Science(), vol 12313. Springer, Cham. https://doi.org/10.1007/978-3-030-63061-4_25
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-030-63060-7
Online ISBN:978-3-030-63061-4
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative