International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 6, Issue 4 (April 2019), Pages: 33-44

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 Original Research Paper

 Title: Computational approaches to identify novel drug-like immunomodulators against multiple sclerosis

 Author(s): Asif Hassan Syed *

 Affiliation(s):

 Department of Computer Science, Faculty of Computing and Information Technology at Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-7288-3098

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.04.004

 Abstract:

Sphingosine1-Phosphate Receptor1 (S1PR1) a G protein-coupled receptor is critically involved in the trafficking of peripheral T-Lymphocyte into the Central Nervous System (CNS) leading to Remitting type of Multiple Sclerosis (RSMS). In the present scenario, the long-term benefits of the current immunomodulator against RSMS that bind specifically to S1PR1preventing the upward movement of lymphocytes toward CNS are uncertain due to the undesirable side effects. Therefore, in this paper, the author aims to screen derivatives of known immunomodulators used in Multiple Sclerosis (MS) treatment that binds specifically with S1PR1 receptor with better affinity and pharmacological properties than their parental compound. In this context, two promising analogs were screened namely CID_11623444 (L7A) and CID_445354 (RTL) of mitoxantrone and fingolimod, respectively that showed better pharmacokinetic properties, immunomodulatory activity, BBB permeability and affinity for S1PR1 receptors than their corresponding parental immunomodulator compound. Moreover, both the analogs were found to be specific inhibitors of S1PR1receptor by Baell and Holloway method. Therefore, based on the results it can be proposed that chemical analogs CID_11623444 and CID_445354 are useful lead molecules which may slow the progression of Multiple Sclerosis (MS) with greater efficacy and minimum side effects. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Relapsing-remitting multiple sclerosis, S1PR1 receptor, Virtual screening, Pharmacological properties, Novel immunomodulator

 Article History: Received 7 November 2018, Received in revised form 5 February 2019, Accepted 9 February 2019

 Acknowledgement:

This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under the grant no. (G-87-830-37). The authors, therefore, acknowledge with thanks DSR for technical and financial support.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

  Syed AH (2019). Computational approaches to identify novel drug-like immunomodulators against multiple sclerosis. International Journal of Advanced and Applied Sciences, 6(4): 33-44

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 

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