针对胶质母细胞瘤的端到端硅内和体外药物再利用管道。

Ko-Hong Lin, Jay-Jiguang Zhu, Judith A Smith, Yejin Kim, Xiaoqian Jiang
{"title":"针对胶质母细胞瘤的端到端硅内和体外药物再利用管道。","authors":"Ko-Hong Lin, Jay-Jiguang Zhu, Judith A Smith, Yejin Kim, Xiaoqian Jiang","doi":"10.1109/ichi57859.2023.00135","DOIUrl":null,"url":null,"abstract":"<p><p>Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our <i>in-silico</i> analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted <i>in-vitro</i> experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between <i>in-silico</i> analysis and <i>in-vitro</i> validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma.</p>","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10956733/pdf/","citationCount":"0","resultStr":"{\"title\":\"An End-to-end <i>In-Silico</i> and <i>In-Vitro</i> Drug Repurposing Pipeline for Glioblastoma.\",\"authors\":\"Ko-Hong Lin, Jay-Jiguang Zhu, Judith A Smith, Yejin Kim, Xiaoqian Jiang\",\"doi\":\"10.1109/ichi57859.2023.00135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our <i>in-silico</i> analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted <i>in-vitro</i> experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between <i>in-silico</i> analysis and <i>in-vitro</i> validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma.</p>\",\"PeriodicalId\":73284,\"journal\":{\"name\":\"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10956733/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ichi57859.2023.00135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ichi57859.2023.00135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/11 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

胶质母细胞瘤是一种侵袭性极强、预后极差的脑癌,我们的研究旨在应对胶质母细胞瘤药物开发中的挑战。我们提出了一个计算框架,利用基于机器学习的倾向得分匹配来估计反事实治疗效果,并预测药物组合的协同效应。通过内嵌分析,我们确定了有希望的候选药物和值得进一步研究的药物组合。为了验证这些计算结果,我们在 U87 和 T98G 两种 GBM 细胞系上进行了体外实验。实验结果表明,一些确定的药物和药物组合确实对 GBM 细胞的生长有很强的抑制作用。我们的端到端管道展示了将计算模型与生物实验相结合以加快药物再利用和发现工作的可行性。通过弥合体内分析和体外验证之间的差距,我们证明了这种方法在加速开发胶质母细胞瘤新型有效疗法方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma.

Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our in-silico analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted in-vitro experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between in-silico analysis and in-vitro validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信