{"title":"PARP 抑制剂治疗晚期卵巢癌的有效性和安全性：随机对照试验的系统回顾和网络 Meta 分析》。","authors":"Juying Chen, Xiaozhe Wu, Hongzhe Wang, Xiaoshan Lian, Bing Li, Xiangbo Zhan","doi":"10.2174/1573409920666230907093331","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>This study aims to evaluate the efficacy and safety of PARP inhibitor therapy in advanced ovarian cancer and identify the optimal treatment for the survival of patients.</p><p><strong>Background: </strong>The diversity of PARP inhibitors makes clinicians confused about the optimal strategy and the most effective BRCAm mutation-based regimen for the survival of patients with advanced ovarian cancer.</p><p><strong>Objectives: </strong>The objective of this study is to compare the effects of various PARP inhibitors alone or in combination with other agents in advanced ovarian cancer.</p><p><strong>Methods: </strong>PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant studies on PARP inhibitors for ovarian cancer. Bayesian network meta-analysis was performed using Stata 15.0 and R 4.0.4. The primary outcome was the overall PFS, and the secondary outcomes included OS, AE3, DISAE, and TFST.</p><p><strong>Results: </strong>Fifteen studies involving 5,788 participants were included. The Bayesian network metaanalysis results showed that olaparibANDAI was the most beneficial in prolonging overall PFS and non-BRCAm PFS, followed by niraparibANDAI. However, for BRCAm patients, olaparibTR might be the most effective, followed by niraparibANDAI. Olaparib was the most effective for the OS of BRCAm patients. AI, olaparibANDAI, and veliparibTR were more likely to induce grade 3 or higher adverse events. AI and olaparibANDAI were more likely to cause DISAE.</p><p><strong>Conclusion: </strong>PARP inhibitors are beneficial to the survival of patients with advanced ovarian cancer. The olaparibTR is the most effective for BRCAm patients, whereas olaparibANDAI and niraparibANDAI are preferable for non-BRCAm patients. Other: More high-quality studies are desired to investigate the efficacy and safety of PARP inhibitors in patients with other genetic performances.</p>","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficacy and Safety of PARP Inhibitor Therapy in Advanced Ovarian Cancer: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials.\",\"authors\":\"Juying Chen, Xiaozhe Wu, Hongzhe Wang, Xiaoshan Lian, Bing Li, Xiangbo Zhan\",\"doi\":\"10.2174/1573409920666230907093331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>This study aims to evaluate the efficacy and safety of PARP inhibitor therapy in advanced ovarian cancer and identify the optimal treatment for the survival of patients.</p><p><strong>Background: </strong>The diversity of PARP inhibitors makes clinicians confused about the optimal strategy and the most effective BRCAm mutation-based regimen for the survival of patients with advanced ovarian cancer.</p><p><strong>Objectives: </strong>The objective of this study is to compare the effects of various PARP inhibitors alone or in combination with other agents in advanced ovarian cancer.</p><p><strong>Methods: </strong>PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant studies on PARP inhibitors for ovarian cancer. Bayesian network meta-analysis was performed using Stata 15.0 and R 4.0.4. The primary outcome was the overall PFS, and the secondary outcomes included OS, AE3, DISAE, and TFST.</p><p><strong>Results: </strong>Fifteen studies involving 5,788 participants were included. The Bayesian network metaanalysis results showed that olaparibANDAI was the most beneficial in prolonging overall PFS and non-BRCAm PFS, followed by niraparibANDAI. However, for BRCAm patients, olaparibTR might be the most effective, followed by niraparibANDAI. Olaparib was the most effective for the OS of BRCAm patients. AI, olaparibANDAI, and veliparibTR were more likely to induce grade 3 or higher adverse events. AI and olaparibANDAI were more likely to cause DISAE.</p><p><strong>Conclusion: </strong>PARP inhibitors are beneficial to the survival of patients with advanced ovarian cancer. The olaparibTR is the most effective for BRCAm patients, whereas olaparibANDAI and niraparibANDAI are preferable for non-BRCAm patients. Other: More high-quality studies are desired to investigate the efficacy and safety of PARP inhibitors in patients with other genetic performances.</p>\",\"PeriodicalId\":10886,\"journal\":{\"name\":\"Current computer-aided drug design\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current computer-aided drug design\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/1573409920666230907093331\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current computer-aided drug design","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/1573409920666230907093331","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
Efficacy and Safety of PARP Inhibitor Therapy in Advanced Ovarian Cancer: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials.
Aims: This study aims to evaluate the efficacy and safety of PARP inhibitor therapy in advanced ovarian cancer and identify the optimal treatment for the survival of patients.
Background: The diversity of PARP inhibitors makes clinicians confused about the optimal strategy and the most effective BRCAm mutation-based regimen for the survival of patients with advanced ovarian cancer.
Objectives: The objective of this study is to compare the effects of various PARP inhibitors alone or in combination with other agents in advanced ovarian cancer.
Methods: PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant studies on PARP inhibitors for ovarian cancer. Bayesian network meta-analysis was performed using Stata 15.0 and R 4.0.4. The primary outcome was the overall PFS, and the secondary outcomes included OS, AE3, DISAE, and TFST.
Results: Fifteen studies involving 5,788 participants were included. The Bayesian network metaanalysis results showed that olaparibANDAI was the most beneficial in prolonging overall PFS and non-BRCAm PFS, followed by niraparibANDAI. However, for BRCAm patients, olaparibTR might be the most effective, followed by niraparibANDAI. Olaparib was the most effective for the OS of BRCAm patients. AI, olaparibANDAI, and veliparibTR were more likely to induce grade 3 or higher adverse events. AI and olaparibANDAI were more likely to cause DISAE.
Conclusion: PARP inhibitors are beneficial to the survival of patients with advanced ovarian cancer. The olaparibTR is the most effective for BRCAm patients, whereas olaparibANDAI and niraparibANDAI are preferable for non-BRCAm patients. Other: More high-quality studies are desired to investigate the efficacy and safety of PARP inhibitors in patients with other genetic performances.
期刊介绍：
Aims & Scope
Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design.
Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.
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