An informatics bridge over the valley of death for cancer Phase I trials of drug-combination therapies

跨越癌症死亡之谷的信息学桥梁药物组合疗法的 I 期试验

基本信息

批准号：
10494095
负责人：
Lang Li
金额：
$ 37.95万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-24 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10494095
关键词：
Active Learning Adverse event American Association of Cancer Research American Society of Clinical Oncology Antineoplastic Agents Awareness Cancer Biology Cessation of life Clinical Clinical Data Clinical Trials Combination Drug Therapy Communities Data Data Sources Databases Development Dose Dose-Limiting Drug Combinations Drug Design Drug Exposure Drug Interactions Drug Kinetics Drug toxicity Enrollment Failure Feedback Grant Informatics Knowledge Label Literature Machine Learning Malignant Neoplasms Methodology Methods Oncologist Paper Patients Performance Pharmaceutical Preparations Pharmacoepidemiology Pharmacologic Substance Pharmacotherapy Phase Phase Ib Trial Preparation PubMed Public Domains Quality Control Records Reporting Research Research Personnel Safety Sampling Source Surveys System Toxic effect anticancer research base cancer clinical trial cancer therapy cohort combination cancer therapy data curation deep learning deep learning algorithm design drug development experience improved innovation knowledge base knowledgebase learning strategy medication safety novel pharmacometrics phase 1 study phase I trial pre-clinical research and development software development symposium translational engagement translational impact trial design

项目摘要

An informatics bridge over the valley of death for Phase I trials of drug-combination cancer therapies Summary Phase I studies usually focus on drug toxicity and pharmacokinetics, and most (58%) drugs intended as cancer therapies fail these initial trials. Thus, Phase I studies represent the largest valley of death in the course of drug development. Unlike the design of a single-drug Phase I study, the design of a drug-combination study requires prior knowledge of whether either drug changes the other’s drug exposure, the drugs share toxicities, and each drug has an established maximum tolerable dose. Although abundant toxicity and PK data are available in public domain sources, the data are not integrated, and no single database integrates data regarding both toxicity and PK. In addition, data regarding single-drug and drug-combination MTD and DLT are present in the literature but absent from any database. We are confident that a bridge can be built across the Phase I valley of death for cancer multi-drug research and development utilizing an informatics and pharmacometrics approach to take advantage of the abundant toxicity and PK data available for single drugs. In this grant, we propose a translational drug-interaction knowledgebase (TDCKB) that integrates toxicity and PK data. Aim 1 will develop novel active-learning approaches to mine evidence of toxicity and PK regarding drug interactions from the literature. The active learning methodology will employ several innovations, including random negative sampling, stratified active learning by prescreening based on PubMed query, and deep learning with embedding. The final active-learning method is optimized by a thorough integration of these innovative components. Aim 2 will develop a translational drug-interaction knowledgebase (TDCKB) for cancer research. The TDCKB will integrate toxicity and PK evidence for single drugs and drug combinations from various data sources. The evidence of DDI will be classified as either toxicity or PK, and the strength of the evidence will be annotated. Synthesized evidences, such as overlapping toxicity and predicted drug interactions between two drugs, will assist in Phase I drug combination trial design. Quality control will be conducted carefully during both data curation and TDCKB software development. Engagement of TDCKB users and the ITCR community is planned.

药物组合癌症疗法 I 期试验跨越死亡之谷的信息学桥梁概括 I 期研究通常侧重于药物毒性和药代动力学，大多数（58%）药物旨在用于癌症因此，第一阶段研究代表了药物治疗过程中最大的死亡谷。与单一药物 I 期研究的设计不同，药物组合研究的设计需要。事先了解任何一种药物是否会改变另一种药物的暴露量，这些药物具有共同的毒性，并且每种药物都具有相同的毒性尽管公众可以获得大量的毒性和药代动力学数据，但该药物已确定最大耐受剂量。域来源，数据没有集成，并且没有一个数据库集成有关毒性和毒性的数据此外，文献中也有关于单药和药物组合 MTD 和 DLT 的数据，但我们有信心在第一阶段的死亡之谷上建造一座桥梁。利用信息学和药理学方法进行癌症多药研究和开发利用单一药物丰富的毒性和 PK 数据的优势，我们提出了一项研究。将开发整合毒性和 PK 数据的转化药物相互作用知识库 (TDCKB)。新颖的主动学习方法可从药物相互作用中挖掘毒性和 PK 证据主动学习方法将采用多项创新，包括随机负采样、基于 PubMed 查询的预筛选分层主动学习，以及带有嵌入的深度学习。 Aim 2 将通过彻底集成这些创新组件来优化主动学习方法。用于癌症研究的转化药物相互作用知识库 (TDCKB) TDCKB 将整合毒性。以及来自各种数据源的单一药物和药物组合的 PK 证据。被分类为毒性或 PK，并且将注释证据的强度，综合证据。例如两种药物之间的重叠毒性和预测的药物相互作用，将有助于 I 期药物组合试验设计期间将在数据整理和 TDCKB 期间仔细进行质量控制。软件开发已计划让 TDCKB 用户和 ITCR 社区参与。