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：
10305083
负责人：
Lang Li
金额：
$ 38.76万
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/10305083
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 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.

I阶段死亡山谷的信息桥，用于I期药物综合癌症疗法的试验概括第一阶段的研究通常关注药物毒性和药代动力学，大多数（58％）药物旨在癌症疗法未能通过这些初步试验。这是第一阶段的研究代表了药物过程中最大的死亡山谷发展。与单药I期研究的设计不同，药物组合研究的设计需要关于任何一种药物是否改变其他药物的暴露，药物共享毒性的事先了解，每种药物药物的最大耐受剂量已确定。尽管大量毒性和PK数据可在公共场合使用域源，数据未集成，没有单个数据库整合有关毒性和 PK。此外，文献中存在有关单药和药物组合MTD和DLT的数据任何数据库都不存在。我们有信心可以在整个I阶段死亡谷建造一座桥梁癌症多药的研究和开发利用信息和药物计量学方法来采用可用于单一药物的绝对毒性和PK数据的优势。在这笔赠款中，我们建议整合毒性和PK数据的转化药物交流知识库（TDCKB）。 AIM 1将发展新型的主动学习方法，以了解来自毒性的证据和PK关于药物相互作用的证据文学。主动学习方法将采用几项创新，包括随机负抽样，通过基于PubMed查询的预筛选和嵌入深度学习对主动学习进行分层。决赛通过彻底整合这些创新组件，可以优化主动学习方法。 AIM 2将发展用于癌症研究的转化药物交流知识库（TDCKB）。 TDCKB将整合毒性和PK证据证明了来自各种数据源的单一药物和药物组合。 DDI的证据将被归类为毒性或PK，证据的强度将被注释。综合证据，例如重叠的毒性和预测两种药物之间的药物相互作用，将有助于I期药物组合试验设计。质量控制将在数据策划和TDCKB期间仔细进行软件开发。计划了TDCKB用户和ITCR社区的参与。