Investigating the Anti-glioblastoma Potential of Alkaloids from the Solanaceae Family: A Multi-Targeted Approach Using Network Pharmacology and Molecular Modeling Approaches

Glioblastoma is a malignant and invasive brain cancer that is difficult to treat and has a short survival rate. This work aims to identify the efficiency of alkaloids from Solanaceae family as anti-glioblastoma drugs employing network pharmacology, molecular docking and molecular dynamics studies. Initially, key alkaloids were identified and screened for drug-likeness, oral bioavailability and molecular weight, resulting in the selection of seven promising compounds: Scopolamine, Atropine, Norhyoscyamine, Solanidine, Vindoline, Duboisine and Homatropine. By analyzing the microarray data, DEGs were obtained related to glioblastoma, and combined with the identified alkaloid targets to establish the compound–target–pathway network. Gene ontology and KEGG pathway analyses identified important biological processes and signaling pathways that contribute to glioblastoma development. Molecular docking analysis indicated the potential of the identified alkaloids to bind effectively with receptors including EGFR, MDM2 and AURKA which are fundamental in glioblastoma development. Molecular dynamics simulations at 100 ns supported the stabilities and interactions of these protein–ligand complexes as well. This thorough review indicates that Solanaceae alkaloids could serve as multifunctional drug candidates for glioblastoma treatment and provide a new direction for the pharmacotherapy of glioblastoma based on natural resources.

Authors

Institutions

Publication Details

Journal
Journal of Computational Biophysics and Chemistry
Published
2025-04-09
DOI
https://doi.org/10.1142/s2737416525500425
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Investigating the Anti-glioblastoma Potential of Alkaloids from the Solanaceae Family: A Multi-Targeted Approach Using Network Pharmacology and Molecular Modeling Approaches

Nasimudeen R. Jabir, M. S. Khan, Khadeejah Alsolami, Shams Tabrez et al.
1 citations
Journal of Computational Biophysics and Chemistry
Computational Drug Discovery Methods
1.50
article

Investigating the Anti-glioblastoma Potential of Alkaloids from the Solanaceae Family: A Multi-Targeted Approach Using Network Pharmacology and Molecular Modeling Approaches

Nasimudeen R. Jabir, M. S. Khan, Khadeejah Alsolami, Shams Tabrez, Chelapram K. Firoz, Abdulaziz Alamri, Mohammad Hassan Alhashmi
article en
1 citations

Abstract

Glioblastoma is a malignant and invasive brain cancer that is difficult to treat and has a short survival rate. This work aims to identify the efficiency of alkaloids from Solanaceae family as anti-glioblastoma drugs employing network pharmacology, molecular docking and molecular dynamics studies. Initially, key alkaloids were identified and screened for drug-likeness, oral bioavailability and molecular weight, resulting in the selection of seven promising compounds: Scopolamine, Atropine, Norhyoscyamine, Solanidine, Vindoline, Duboisine and Homatropine. By analyzing the microarray data, DEGs were obtained related to glioblastoma, and combined with the identified alkaloid targets to establish the compound–target–pathway network. Gene ontology and KEGG pathway analyses identified important biological processes and signaling pathways that contribute to glioblastoma development. Molecular docking analysis indicated the potential of the identified alkaloids to bind effectively with receptors including EGFR, MDM2 and AURKA which are fundamental in glioblastoma development. Molecular dynamics simulations at 100 ns supported the stabilities and interactions of these protein–ligand complexes as well. This thorough review indicates that Solanaceae alkaloids could serve as multifunctional drug candidates for glioblastoma treatment and provide a new direction for the pharmacotherapy of glioblastoma based on natural resources.

Journal of Computational Biophysics and ChemistryVol. 25(02)
PRIST University (IN), Taif University (SA), King Abdulaziz University (SA), King Saud University (SA), Meenakshi Academy of Higher Education and Research (IN)
Openalex Percentile: Top 18%
Computational Drug Discovery Methods
1.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.