Repurposing Cofilin-Targeting Compounds for Ischemic Stroke Through Cheminformatics and Network Pharmacology

<b>Background/Objectives</b>: Cofilin, a key regulator of actin cytoskeleton dynamics, contributes to neuroinflammation, synaptic damage, and blood-brain barrier disruption in ischemic stroke. Despite its established role in stroke pathology, cofilin remains largely untargeted by existing therapeutics. This study aimed to identify potential cofilin-binding molecules by repurposing <i>LIMK1</i> inhibitors through an integrated computational strategy. <b>Methods</b>: A cheminformatics pipeline combined QSAR modeling with four molecular fingerprint sets and multiple machine learning algorithms. The best-performing QSAR model (substructure-Random Forest) achieved R<sup>2</sup>_train = 0.8747 and R<sup>2</sup>_test = 0.8078, supporting the reliability of compound prioritization. Feature importance was assessed through SHAP analysis. Top candidates were subjected to molecular docking against cofilin, followed by 300 ns molecular dynamics simulations, MM-GBSA binding energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Network pharmacology identified overlapping targets between selected compounds and stroke-related genes. <b>Results</b>: Three compounds, CHEMBL3613624, ZINC000653853876, and Gandotinib, were prioritized based on QSAR performance, binding affinity (-6.68, -6.25, and -5.61 Kcal/mol, respectively), and structural relevance. Docking studies confirmed key interactions with Asp98 and His133 on cofilin. Molecular dynamics simulations supported the stability of these interactions, with Gandotinib showing the highest conformational stability, and ZINC000653853876 exhibiting the most favorable energetic profile. Network pharmacology analysis revealed eight intersecting targets, including <i>MAPK1</i>, <i>PRKCB</i>, <i>HDAC1</i>, and serotonin receptors, associated with neuroinflammatory and vascular pathways in strokes. <b>Conclusions</b>: This study presents a rational, integrative repurposing framework for identifying cofilin-targeting compounds with potential therapeutic relevance in ischemic stroke. The selected candidates warrant further experimental validation.

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Journal
Pharmaceuticals
Published
2025-09-04
DOI
https://doi.org/10.3390/ph18091323
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Repurposing Cofilin-Targeting Compounds for Ischemic Stroke Through Cheminformatics and Network Pharmacology

Hayet Ben Khaled, Abida Khan, Saleh I. Alaqel, Naira Nayeem et al.
Pharmaceuticals
Computational Drug Discovery Methods
article

Repurposing Cofilin-Targeting Compounds for Ischemic Stroke Through Cheminformatics and Network Pharmacology

Hayet Ben Khaled, Abida Khan, Saleh I. Alaqel, Naira Nayeem, Mohammed M. Alanazi, Mohd Imran
article en

Abstract

<b>Background/Objectives</b>: Cofilin, a key regulator of actin cytoskeleton dynamics, contributes to neuroinflammation, synaptic damage, and blood-brain barrier disruption in ischemic stroke. Despite its established role in stroke pathology, cofilin remains largely untargeted by existing therapeutics. This study aimed to identify potential cofilin-binding molecules by repurposing <i>LIMK1</i> inhibitors through an integrated computational strategy. <b>Methods</b>: A cheminformatics pipeline combined QSAR modeling with four molecular fingerprint sets and multiple machine learning algorithms. The best-performing QSAR model (substructure-Random Forest) achieved R<sup>2</sup>_train = 0.8747 and R<sup>2</sup>_test = 0.8078, supporting the reliability of compound prioritization. Feature importance was assessed through SHAP analysis. Top candidates were subjected to molecular docking against cofilin, followed by 300 ns molecular dynamics simulations, MM-GBSA binding energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Network pharmacology identified overlapping targets between selected compounds and stroke-related genes. <b>Results</b>: Three compounds, CHEMBL3613624, ZINC000653853876, and Gandotinib, were prioritized based on QSAR performance, binding affinity (-6.68, -6.25, and -5.61 Kcal/mol, respectively), and structural relevance. Docking studies confirmed key interactions with Asp98 and His133 on cofilin. Molecular dynamics simulations supported the stability of these interactions, with Gandotinib showing the highest conformational stability, and ZINC000653853876 exhibiting the most favorable energetic profile. Network pharmacology analysis revealed eight intersecting targets, including <i>MAPK1</i>, <i>PRKCB</i>, <i>HDAC1</i>, and serotonin receptors, associated with neuroinflammatory and vascular pathways in strokes. <b>Conclusions</b>: This study presents a rational, integrative repurposing framework for identifying cofilin-targeting compounds with potential therapeutic relevance in ischemic stroke. The selected candidates warrant further experimental validation.

PharmaceuticalsVol. 18(9)
Northern Border University (SA), King Salman Center for Disability Research (SA)
King Salman Center for Disability Research
Openalex Percentile: Top 75%
Computational Drug Discovery Methods
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