Identification of Drug Compounds for Capsular Contracture Based on Text Mining and Deep Learning

Abstract

Background: Capsular contracture is a common and unpredictable complication after breast implant placement. Currently, the pathogenesis of capsular contracture is unclear, and the effectiveness of nonsurgical treatment is still doubtful. The authors’ study aimed to investigate new drug therapies for capsular contracture by using computational methods. Methods: Genes related to capsular contracture were identified by text mining and GeneCodis. Then, the candidate key genes were selected through protein-protein interaction analysis in Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. Drugs targeting the candidate genes with relation to capsular contracture were screened out in Pharmaprojects. Based on the drug-target interaction analysis by DeepPurpose, candidate drugs with highest predicted binding affinity were obtained eventually. Results: The authors’ study identified 55 genes related to capsular contracture. Gene set enrichment analysis and protein-protein interaction analysis generated eight candidate genes. One hundred drugs targeting the candidate genes were selected. The seven candidate drugs with the highest predicted binding affinity were determined by DeepPurpose, including tumor necrosis factor alpha antagonist, estrogen receptor agonist, insulin-like growth factor 1 receptor, tyrosine kinase inhibitor, and matrix metallopeptidase 1 inhibitor. Conclusion: Text mining and DeepPurpose can be used as a promising tool for drug discovery in exploring nonsurgical treatment to capsular contracture. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, V.