Saleh I Alaqel, Abida Khan, Mashael N Alanazi, Naira Nayeem, Hayet Ben Khaled, Mohd Imran
{"title":"通过化学信息学和网络药理学重新利用cofilin靶向化合物治疗缺血性卒中。","authors":"Saleh I Alaqel, Abida Khan, Mashael N Alanazi, Naira Nayeem, Hayet Ben Khaled, Mohd Imran","doi":"10.3390/ph18091323","DOIUrl":null,"url":null,"abstract":"<p><p><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.</p>","PeriodicalId":20198,"journal":{"name":"Pharmaceuticals","volume":"18 9","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12472226/pdf/","citationCount":"0","resultStr":"{\"title\":\"Repurposing Cofilin-Targeting Compounds for Ischemic Stroke Through Cheminformatics and Network Pharmacology.\",\"authors\":\"Saleh I Alaqel, Abida Khan, Mashael N Alanazi, Naira Nayeem, Hayet Ben Khaled, Mohd Imran\",\"doi\":\"10.3390/ph18091323\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><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. 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Repurposing Cofilin-Targeting Compounds for Ischemic Stroke Through Cheminformatics and Network Pharmacology.
Background/Objectives: 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 LIMK1 inhibitors through an integrated computational strategy. Methods: 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 R2_train = 0.8747 and R2_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. Results: 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 MAPK1, PRKCB, HDAC1, and serotonin receptors, associated with neuroinflammatory and vascular pathways in strokes. Conclusions: 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.
PharmaceuticalsPharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science
CiteScore
6.10
自引率
4.30%
发文量
1332
审稿时长
6 weeks
期刊介绍:
Pharmaceuticals (ISSN 1424-8247) is an international scientific journal of medicinal chemistry and related drug sciences.Our aim is to publish updated reviews as well as research articles with comprehensive theoretical and experimental details. Short communications are also accepted; therefore, there is no restriction on the maximum length of the papers.