创新靶点挖掘策略,为药物再利用工作导航。

3区 生物学 Q2 Biochemistry, Genetics and Molecular Biology
Kamatchi Sundara Saravanan, Kshreeraja S Satish, Ganesan Rajalekshmi Saraswathy, Ushnaa Kuri, Soujanya J Vastrad, Ritesh Giri, Prizvan Lawrence Dsouza, Adusumilli Pramod Kumar, Gouri Nair
{"title":"创新靶点挖掘策略,为药物再利用工作导航。","authors":"Kamatchi Sundara Saravanan, Kshreeraja S Satish, Ganesan Rajalekshmi Saraswathy, Ushnaa Kuri, Soujanya J Vastrad, Ritesh Giri, Prizvan Lawrence Dsouza, Adusumilli Pramod Kumar, Gouri Nair","doi":"10.1016/bs.pmbts.2024.03.025","DOIUrl":null,"url":null,"abstract":"<p><p>The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.</p>","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Innovative target mining stratagems to navigate drug repurposing endeavours.\",\"authors\":\"Kamatchi Sundara Saravanan, Kshreeraja S Satish, Ganesan Rajalekshmi Saraswathy, Ushnaa Kuri, Soujanya J Vastrad, Ritesh Giri, Prizvan Lawrence Dsouza, Adusumilli Pramod Kumar, Gouri Nair\",\"doi\":\"10.1016/bs.pmbts.2024.03.025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.</p>\",\"PeriodicalId\":49280,\"journal\":{\"name\":\"Progress in Molecular Biology and Translational Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Progress in Molecular Biology and Translational Science\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/bs.pmbts.2024.03.025\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/4/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Molecular Biology and Translational Science","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/bs.pmbts.2024.03.025","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/8 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0

摘要

将单一基因与特定疾病和特定药物联系起来的传统理论导致传统药物发现的成功率越来越低。这就需要进行重大转变,重点关注当代药物设计或药物再利用,这需要将多个基因与不同的生理或病理途径和药物联系起来。最近,药物再利用,即为临床试验中的现有药物或候选药物发现新的/未标记的适应症的艺术,因其成功率而备受关注。这一策略的限制性阶段在于靶点识别,通常通过以疾病为中心和/或以药物为中心的方法来实现。以疾病为中心的方法基于对关键生物大分子的探索,如作为相关疾病病理级联基础的基因或蛋白质。对这些病理相互作用的研究有助于确定潜在的药物靶点,从而利用这些靶点进行新型治疗干预。以药物为中心的方法涉及各种策略,如探索药物不良反应的机制,从而发现潜在靶点,因为这些不良反应可能被认为是其他疾病的理想治疗措施。目前,人工智能是一种新兴的强大工具,可用于将上述错综复杂的生物网络转化为可解释的数据,以提取精确的分子靶标。整合多种方法、大数据分析和临床确证对成功挖掘靶点至关重要。本章重点介绍了指导靶点识别的现代策略和药物再利用的各种框架。这些策略通过案例研究加以说明,案例研究选自近期针对神经退行性疾病、癌症、感染、免疫和心血管疾病的药物再利用研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Innovative target mining stratagems to navigate drug repurposing endeavours.

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

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
CiteScore
5.00
自引率
0.00%
发文量
110
审稿时长
4-8 weeks
期刊介绍: Progress in Molecular Biology and Translational Science (PMBTS) provides in-depth reviews on topics of exceptional scientific importance. If today you read an Article or Letter in Nature or a Research Article or Report in Science reporting findings of exceptional importance, you likely will find comprehensive coverage of that research area in a future PMBTS volume.
×
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