胶囊挛缩是乳房植入物放置后常见且难以预测的并发症。目前，胶囊挛缩的发病机理尚不清楚，非手术治疗的有效性仍存疑。我们的研究旨在使用计算方法研究胶囊挛缩的新药物治疗方案。通过文本挖掘和GeneCodis鉴定与胶囊挛缩相关的基因。然后通过STRING和Cytoscape的蛋白质相互作用分析筛选出候选关键基因。在Pharmaprojects中筛选出与胶囊挛缩有关的靶向候选基因的药物。根据DeepPurpose的药物靶标相互作用分析，最终获得了预测结合亲和力最高的候选药物。我们的研究发现与胶囊挛缩相关的基因有55个。基因集富集分析和蛋白质相互作用分析生成了8个候选基因。选择了100种靶向候选基因的药物。通过DeepPurpose确定了预测结合亲和力最高的7种候选药物，包括肿瘤坏死因子α（TNF-α）拮抗剂、雌激素受体（ESR）激动剂、胰岛素样生长因子1（IGF-1）受体酪氨酸激酶抑制剂和基质金属蛋白酶1（MMP1）抑制剂。文本挖掘和DeepPurpose可用作探索胶囊挛缩非手术治疗的有前途的药物发现工具。版权所有 ©2023美国整形外科医师协会。

Capsular contracture is a common and unpredictable complication after breast implant placement. Currently, the pathogenesis of capsular contracture is unclear and the effectiveness of non-surgical treatment is still doubtful. Our study aimed to investigate new drug therapies for capsular contracture by using computational 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 STRING 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.Our study identified 55 genes related to capsular contracture. Gene set enrichment analysis and protein-protein interaction analysis generated 8 candidate genes. 100 drugs targeting the candidate genes were selected. 7 candidate drugs with highest predicted binding affinity were determined by DeepPurpose, including tumor necrosis factor alpha (TNF-α) antagonist, estrogen receptor (ESR) agonist, insulin like growth factor 1 (IGF-1) receptor tyrosine kinase inhibitor and matrix metallopeptidase 1 (MMP1) inhibitor.Text mining and DeepPurpose can be used as a promising tool for drug discovery in exploring non-surgical treatment to capsular contracture.Copyright © 2023 by the American Society of Plastic Surgeons.