TY - GEN
T1 - Estimating Daclizumab effects in Multiple Sclerosis using Stochastic Symmetric Nets
AU - Pernice, Simone
AU - Beccuti, Marco
AU - Do, Pietro
AU - Pennisi, Marzio
AU - Pappalardo, Francesco
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - Multiple Sclerosis (MS) is an immune-mediated inflammatory disease of the central nervous system which damages the myelin sheath enveloping nerve cells causing severe physical disability in patients. Relapsing Remitting Multiple Sclerosis (RRMS) is one of the most common form of MS and it is characterized by a series of attacks of new or increasing neurologic symptoms, followed by periods of remission. Recently, many treatments were proposed and studied to contrast the RRMS progression. Among these drugs Daclizumab, an antibody tailored against the interleukin -2 receptor of T cells, exhibited promising results. Unfortunately, more recent studies on Daclizumab highlight severe adverse effects, that led to its retirement from the EU marketing authorization process. Motivated by these recent studies, in this paper we describe how computational modelling can be efficiently exploited to improve our understanding on Daclizumab mechanism of action, and on how this mechanism leads towards the observed undesirable effects.
AB - Multiple Sclerosis (MS) is an immune-mediated inflammatory disease of the central nervous system which damages the myelin sheath enveloping nerve cells causing severe physical disability in patients. Relapsing Remitting Multiple Sclerosis (RRMS) is one of the most common form of MS and it is characterized by a series of attacks of new or increasing neurologic symptoms, followed by periods of remission. Recently, many treatments were proposed and studied to contrast the RRMS progression. Among these drugs Daclizumab, an antibody tailored against the interleukin -2 receptor of T cells, exhibited promising results. Unfortunately, more recent studies on Daclizumab highlight severe adverse effects, that led to its retirement from the EU marketing authorization process. Motivated by these recent studies, in this paper we describe how computational modelling can be efficiently exploited to improve our understanding on Daclizumab mechanism of action, and on how this mechanism leads towards the observed undesirable effects.
KW - Computational modelling
KW - Multiple sclerosis
KW - Sensitivity Analysis
KW - Uncertainty Analysis
UR - http://www.scopus.com/inward/record.url?scp=85062527310&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2018.8621259
DO - 10.1109/BIBM.2018.8621259
M3 - Conference contribution
AN - SCOPUS:85062527310
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 1393
EP - 1400
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -