In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7. (May 2021)
- Record Type:
- Journal Article
- Title:
- In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7. (May 2021)
- Main Title:
- In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7
- Authors:
- Golubovic, Mladjan
Kostic, Tomislav
Djordjevic, Miodrag
Peric, Velimir
Lazarevic, Milan
Milic, Dragan J.
Marjanovic, Vesna
Veselinović, Aleksandar M. - Abstract:
- Abstract: The voltage-gated sodium channel Nav1.7 can be considered as a promising target for the treatment of pain. This research presents conformational-independent and 3D field-based QSAR modeling for a series of aryl sulfonamide acting as Nav1.7 inhibitors. As descriptors used for building conformation-independent QSAR models, SMILES notation and local invariants of the molecular graph were used with the Monte Carlo optimization method as a model developer. Different statistical methods, including the index of ideality of correlation, were used to test the quality of the developed models, robustness and predictability and obtained results were good. Obtained results indicate that there is a very good correlation between 3D QSAR and conformation-independent models. Molecular fragments that account for the increase/decrease of a studied activity were defined and used for the computer-aided design of new compounds as potential analgesics. The final evaluation of the developed QSAR models and designed inhibitors were carried out using molecular docking studies, bringing to light an excellent correlation with the QSAR modeling results. Graphical abstract: Image 1 Highlights: QSAR models for voltage-gated sodium channel 1.7 inhibition were developed. Monte Carlo method with SMILES notation and molecular graph descriptors was used. Different methods were applied for the determination of the model goodness. Molecular fragments with influence on inhibitory action were determined.Abstract: The voltage-gated sodium channel Nav1.7 can be considered as a promising target for the treatment of pain. This research presents conformational-independent and 3D field-based QSAR modeling for a series of aryl sulfonamide acting as Nav1.7 inhibitors. As descriptors used for building conformation-independent QSAR models, SMILES notation and local invariants of the molecular graph were used with the Monte Carlo optimization method as a model developer. Different statistical methods, including the index of ideality of correlation, were used to test the quality of the developed models, robustness and predictability and obtained results were good. Obtained results indicate that there is a very good correlation between 3D QSAR and conformation-independent models. Molecular fragments that account for the increase/decrease of a studied activity were defined and used for the computer-aided design of new compounds as potential analgesics. The final evaluation of the developed QSAR models and designed inhibitors were carried out using molecular docking studies, bringing to light an excellent correlation with the QSAR modeling results. Graphical abstract: Image 1 Highlights: QSAR models for voltage-gated sodium channel 1.7 inhibition were developed. Monte Carlo method with SMILES notation and molecular graph descriptors was used. Different methods were applied for the determination of the model goodness. Molecular fragments with influence on inhibitory action were determined. Presented study can be useful in the search for novel analgesics. … (more)
- Is Part Of:
- Computers in biology and medicine. Volume 132(2021)
- Journal:
- Computers in biology and medicine
- Issue:
- Volume 132(2021)
- Issue Display:
- Volume 132, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 132
- Issue:
- 2021
- Issue Sort Value:
- 2021-0132-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-05
- Subjects:
- Nav1.7 inhibitors -- Pain -- QSAR -- Analgesics -- Molecular modeling -- Drug design
Medicine -- Data processing -- Periodicals
Biology -- Data processing -- Periodicals
610.285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00104825/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compbiomed.2021.104346 ↗
- Languages:
- English
- ISSNs:
- 0010-4825
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.880000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 22879.xml