Abstract
Neuropathic pain occurs due to physical damage, injury, or dysfunction of neuronal fibers. The pathophysiology of neuropathic pain is too complex. Therefore, an accurate and reliable prediction of the appropriate hits/ligands for the treatment of neuropathic pain is a challenging process. However, computer-aided drug discovery approaches contributed significantly to discovering newer hits/ligands for the treatment of neuropathic pain. The computational approaches like homology modeling, induced-fit molecular docking, structure-activity relationships, metadynamics, and virtual screening were cited in the literature for the identification of potential hit molecules against neuropathic pain. These hit molecules act as inducible nitric oxide synthase inhibitors, FLAT antagonists, TRPA1 modulators, voltage-gated sodium channel binder, cannabinoid receptor-2 agonists, sigma-1 receptor antagonists, etc. Sigma-1 receptor is a distinct type of opioid receptor and several patents were obtained for sigma-1 receptor antagonists for the treatment of neuropathic pain. These molecules were found to have a profound role in the management of neuropathic pain. The present review describes the validated therapeutic targets, potential chemical scaffolds, and crucial protein-ligand interactions for the management of neuropathic pain based on the recently reported computational methodologies of the present and past decades. The study can help the researcher to discover newer drugs/drug-like molecules against neuropathic pain.
Keywords: Computer-aided drug discovery, Induced-fit docking, Molecular dynamics simulation, Neuropathic pain, TRPA1 modulators, FLAT antagonists.
Graphical Abstract
Current Topics in Medicinal Chemistry
Title:Computer-Aided Drug Discovery (CADD) Approaches for the Management of Neuropathic Pain
Volume: 21 Issue: 32
Author(s): Muthusamy Ramesh*Arunachalam Muthuraman*
Affiliation:
- Department of Pharmaceutical Analysis, Omega College of Pharmacy, Hyderabad-501 301,India
- Pharmacology Unit, Faculty of Pharmacy, AIMST University, 08100-Bedong, Kedah Darul Aman,Malaysia
Keywords: Computer-aided drug discovery, Induced-fit docking, Molecular dynamics simulation, Neuropathic pain, TRPA1 modulators, FLAT antagonists.
Abstract: Neuropathic pain occurs due to physical damage, injury, or dysfunction of neuronal fibers. The pathophysiology of neuropathic pain is too complex. Therefore, an accurate and reliable prediction of the appropriate hits/ligands for the treatment of neuropathic pain is a challenging process. However, computer-aided drug discovery approaches contributed significantly to discovering newer hits/ligands for the treatment of neuropathic pain. The computational approaches like homology modeling, induced-fit molecular docking, structure-activity relationships, metadynamics, and virtual screening were cited in the literature for the identification of potential hit molecules against neuropathic pain. These hit molecules act as inducible nitric oxide synthase inhibitors, FLAT antagonists, TRPA1 modulators, voltage-gated sodium channel binder, cannabinoid receptor-2 agonists, sigma-1 receptor antagonists, etc. Sigma-1 receptor is a distinct type of opioid receptor and several patents were obtained for sigma-1 receptor antagonists for the treatment of neuropathic pain. These molecules were found to have a profound role in the management of neuropathic pain. The present review describes the validated therapeutic targets, potential chemical scaffolds, and crucial protein-ligand interactions for the management of neuropathic pain based on the recently reported computational methodologies of the present and past decades. The study can help the researcher to discover newer drugs/drug-like molecules against neuropathic pain.
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Cite this article as:
Ramesh Muthusamy *, Muthuraman Arunachalam *, Computer-Aided Drug Discovery (CADD) Approaches for the Management of Neuropathic Pain, Current Topics in Medicinal Chemistry 2021; 21 (32) . https://dx.doi.org/10.2174/1568026621666211122161932
DOI https://dx.doi.org/10.2174/1568026621666211122161932 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
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