{"title":"Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data.","authors":"Weijie Zhang, R Stephanie Huang","doi":"10.1080/17460441.2024.2365370","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.</p><p><strong>Areas covered: </strong>Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.</p><p><strong>Expert opinion: </strong>Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.</p>","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537242/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Opinion on Drug Discovery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/17460441.2024.2365370","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.
Areas covered: Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.
Expert opinion: Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.
简介前列腺癌(PC)是最常见的恶性肿瘤,在男性癌症死亡人数中占很大比例。虽然被确诊为局部性前列腺癌的患者在初期治疗中往往能取得成功,但许多患者最终会出现疾病复发和转移。如果没有有效的治疗方法,侵袭性 PC 患者的生存率非常低。为了遏制目前的高死亡率,许多研究机构都在寻找有效的治疗方法。与全新的药物设计相比,计算方法已被广泛应用,以快速、经济的方式提供可行的药物预测。特别是在PC患者的下一代测序分子图谱越来越多的情况下,计算机辅助方法可用于筛选候选药物:在本文中,作者回顾了利用 PC 患者分子图谱发现药物的计算方法的最新进展。鉴于 PC 治疗需求的独特性,他们详细讨论了这些研究的药物发现目标,并强调了这些研究对具有临床影响的药物提名的转化价值:不断发展的分子剖析技术可为计算机辅助方法提供新的视角,为不同的肿瘤微环境提供候选药物。随着将新化合物纳入大规模高通量筛选的努力不断进行,作者预计候选药物库将继续扩大。
期刊介绍:
Expert Opinion on Drug Discovery (ISSN 1746-0441 [print], 1746-045X [electronic]) is a MEDLINE-indexed, peer-reviewed, international journal publishing review articles on novel technologies involved in the drug discovery process, leading to new leads and reduced attrition rates. Each article is structured to incorporate the author’s own expert opinion on the scope for future development.
The Editors welcome:
Reviews covering chemoinformatics; bioinformatics; assay development; novel screening technologies; in vitro/in vivo models; structure-based drug design; systems biology
Drug Case Histories examining the steps involved in the preclinical and clinical development of a particular drug
The audience consists of scientists and managers in the healthcare and pharmaceutical industry, academic pharmaceutical scientists and other closely related professionals looking to enhance the success of their drug candidates through optimisation at the preclinical level.