Decoding Atorvastatin-Induced Arthritis: A Systems Pharmacology Approach Integrating Pharmacovigilance, Bioinformatics, and Pathway Enrichment
Author(s)
Eswaran Maheswari, Sr., PhD1, Bindu M A, B Pharm1, Brunda MA, B Pharm2.
1Department of Pharmacy Practice, Faculty of Pharmacy, M S Ramaiah University of Applied Sciences, Bangalore, India, 2Department of Pharmacy Practice, Faculty of Pharmacy, M S Ramaiah University Of Applied Sciences, Bangalore, Karnataka, India.
1Department of Pharmacy Practice, Faculty of Pharmacy, M S Ramaiah University of Applied Sciences, Bangalore, India, 2Department of Pharmacy Practice, Faculty of Pharmacy, M S Ramaiah University Of Applied Sciences, Bangalore, Karnataka, India.
OBJECTIVES: Atorvastatin is administered for the treatment of hyperlipidaemia and cardiovascular complications. This study was conducted to investigate the novel signal of atorvastatin employing integrative strategies that combines network pharmacology and molecular docking.
METHODS: Open Vigil 2.1 and FDA adverse event reporting database (FAERs) was used to detect the novel signal of atorvastatin by disproportionality analysis. The signal was considered positive if number of events (n)>2, Proportional Reporting Ratio (PRR)>2 and Chi square >4. Genes were retrieved through four databases like gene cards, OMIM, NCBI and huge navigator. The atorvastatin targets were obtained by setting thresholds using Swiss Target Prediction at a likelihood score higher than 0.1. The Protein-Protein Interaction (PPI) networks of atorvastatin induced arthritis was built by STRING. The hub genes were identified using CytoHubba. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment were used to analyse the key pathways. Atorvastatin's binding affinity with targets was assessed using molecular docking simulations.
RESULTS: Analysis using Open Vigil and FAERS showed arthritis as a potential signal with n=242, PRR=2.23, and χ²=230.64. Network research identified 88 intersecting gene, including hub genes such as CASP3 and MMP9. Analysis of GO and KEGG pathways enrichment unveiled that extracellular matrix remodelling and inflammatory signalling as the common mechanisms responsible for arthritis. This was further corroborated by molecular docking which proclaimed significant binding affinities of MMP9 (-7.68 kcal/mol) and CASP3 (-6.84 kcal/mol) with atorvastatin. Two-dimensional interactions showed that atorvastatin and MMP9 had three hydrogen bonds and two π stacking with ASP182, GLH227, HIS226 and HSP230. Atorvastatin and CASP3 was stabilised by three hydrogen bonds and two π stacking with ASN140, ASP352, and ASN167.
CONCLUSIONS: The authors suggest conduct of further experimental validation and greater clinical awareness among health care practitioners to improve patient safety among patients prone to inflammatory diseases like arthritis.
METHODS: Open Vigil 2.1 and FDA adverse event reporting database (FAERs) was used to detect the novel signal of atorvastatin by disproportionality analysis. The signal was considered positive if number of events (n)>2, Proportional Reporting Ratio (PRR)>2 and Chi square >4. Genes were retrieved through four databases like gene cards, OMIM, NCBI and huge navigator. The atorvastatin targets were obtained by setting thresholds using Swiss Target Prediction at a likelihood score higher than 0.1. The Protein-Protein Interaction (PPI) networks of atorvastatin induced arthritis was built by STRING. The hub genes were identified using CytoHubba. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment were used to analyse the key pathways. Atorvastatin's binding affinity with targets was assessed using molecular docking simulations.
RESULTS: Analysis using Open Vigil and FAERS showed arthritis as a potential signal with n=242, PRR=2.23, and χ²=230.64. Network research identified 88 intersecting gene, including hub genes such as CASP3 and MMP9. Analysis of GO and KEGG pathways enrichment unveiled that extracellular matrix remodelling and inflammatory signalling as the common mechanisms responsible for arthritis. This was further corroborated by molecular docking which proclaimed significant binding affinities of MMP9 (-7.68 kcal/mol) and CASP3 (-6.84 kcal/mol) with atorvastatin. Two-dimensional interactions showed that atorvastatin and MMP9 had three hydrogen bonds and two π stacking with ASP182, GLH227, HIS226 and HSP230. Atorvastatin and CASP3 was stabilised by three hydrogen bonds and two π stacking with ASN140, ASP352, and ASN167.
CONCLUSIONS: The authors suggest conduct of further experimental validation and greater clinical awareness among health care practitioners to improve patient safety among patients prone to inflammatory diseases like arthritis.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
RWD54
Topic
Clinical Outcomes, Epidemiology & Public Health, Real World Data & Information Systems
Topic Subcategory
Distributed Data & Research Networks
Disease
Musculoskeletal Disorders (Arthritis, Bone Disorders, Osteoporosis, Other Musculoskeletal)