Elena Fountzilas , Apostolia-Maria Tsimberidou , Henry Hiep Vo , Razelle Kurzrock
{"title":"从肿瘤诊断篮子到 N-of-1 平台试验和真实世界数据:改变精准肿瘤学临床试验设计","authors":"Elena Fountzilas , Apostolia-Maria Tsimberidou , Henry Hiep Vo , Razelle Kurzrock","doi":"10.1016/j.ctrv.2024.102703","DOIUrl":null,"url":null,"abstract":"<div><p>Choosing the right drug(s) for the right patient via advanced genomic sequencing and multi-omic interrogation is the <em>sine qua non</em> of precision cancer medicine. Traditional cancer clinical trial designs follow well-defined protocols to evaluate the efficacy of new therapies in patient groups, usually identified by their histology/tissue of origin of their malignancy. In contrast, precision medicine seeks to optimize benefit in individual patients, i.e., to define who benefits rather than determine whether the overall group benefits. Since cancer is a disease driven by molecular alterations, innovative trial designs, including biomarker-defined tumor-agnostic basket trials, are driving ground-breaking regulatory approvals and deployment of gene- and immune-targeted drugs. Molecular interrogation further reveals the disruptive reality that advanced cancers are extraordinarily complex and individually distinct. Therefore, optimized treatment often requires drug combinations and N-of-1 customization, addressed by a new generation of N-of-1 trials. Real-world data and structured master registry trials are also providing massive datasets that are further fueling a transformation in oncology. Finally, machine learning is facilitating rapid discovery, and it is plausible that high-throughput computing, <em>in silico</em> modeling, and 3-dimensional printing may be exploitable in the near future to discover and design customized drugs in real time.</p></div>","PeriodicalId":9,"journal":{"name":"ACS Catalysis ","volume":null,"pages":null},"PeriodicalIF":11.3000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tumor-agnostic baskets to N-of-1 platform trials and real-world data: Transforming precision oncology clinical trial design\",\"authors\":\"Elena Fountzilas , Apostolia-Maria Tsimberidou , Henry Hiep Vo , Razelle Kurzrock\",\"doi\":\"10.1016/j.ctrv.2024.102703\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Choosing the right drug(s) for the right patient via advanced genomic sequencing and multi-omic interrogation is the <em>sine qua non</em> of precision cancer medicine. Traditional cancer clinical trial designs follow well-defined protocols to evaluate the efficacy of new therapies in patient groups, usually identified by their histology/tissue of origin of their malignancy. In contrast, precision medicine seeks to optimize benefit in individual patients, i.e., to define who benefits rather than determine whether the overall group benefits. Since cancer is a disease driven by molecular alterations, innovative trial designs, including biomarker-defined tumor-agnostic basket trials, are driving ground-breaking regulatory approvals and deployment of gene- and immune-targeted drugs. Molecular interrogation further reveals the disruptive reality that advanced cancers are extraordinarily complex and individually distinct. Therefore, optimized treatment often requires drug combinations and N-of-1 customization, addressed by a new generation of N-of-1 trials. Real-world data and structured master registry trials are also providing massive datasets that are further fueling a transformation in oncology. Finally, machine learning is facilitating rapid discovery, and it is plausible that high-throughput computing, <em>in silico</em> modeling, and 3-dimensional printing may be exploitable in the near future to discover and design customized drugs in real time.</p></div>\",\"PeriodicalId\":9,\"journal\":{\"name\":\"ACS Catalysis \",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.3000,\"publicationDate\":\"2024-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Catalysis \",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0305737224000215\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Catalysis ","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305737224000215","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Tumor-agnostic baskets to N-of-1 platform trials and real-world data: Transforming precision oncology clinical trial design
Choosing the right drug(s) for the right patient via advanced genomic sequencing and multi-omic interrogation is the sine qua non of precision cancer medicine. Traditional cancer clinical trial designs follow well-defined protocols to evaluate the efficacy of new therapies in patient groups, usually identified by their histology/tissue of origin of their malignancy. In contrast, precision medicine seeks to optimize benefit in individual patients, i.e., to define who benefits rather than determine whether the overall group benefits. Since cancer is a disease driven by molecular alterations, innovative trial designs, including biomarker-defined tumor-agnostic basket trials, are driving ground-breaking regulatory approvals and deployment of gene- and immune-targeted drugs. Molecular interrogation further reveals the disruptive reality that advanced cancers are extraordinarily complex and individually distinct. Therefore, optimized treatment often requires drug combinations and N-of-1 customization, addressed by a new generation of N-of-1 trials. Real-world data and structured master registry trials are also providing massive datasets that are further fueling a transformation in oncology. Finally, machine learning is facilitating rapid discovery, and it is plausible that high-throughput computing, in silico modeling, and 3-dimensional printing may be exploitable in the near future to discover and design customized drugs in real time.
期刊介绍:
ACS Catalysis is an esteemed journal that publishes original research in the fields of heterogeneous catalysis, molecular catalysis, and biocatalysis. It offers broad coverage across diverse areas such as life sciences, organometallics and synthesis, photochemistry and electrochemistry, drug discovery and synthesis, materials science, environmental protection, polymer discovery and synthesis, and energy and fuels.
The scope of the journal is to showcase innovative work in various aspects of catalysis. This includes new reactions and novel synthetic approaches utilizing known catalysts, the discovery or modification of new catalysts, elucidation of catalytic mechanisms through cutting-edge investigations, practical enhancements of existing processes, as well as conceptual advances in the field. Contributions to ACS Catalysis can encompass both experimental and theoretical research focused on catalytic molecules, macromolecules, and materials that exhibit catalytic turnover.