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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.

为癌症联合药物治疗的I期临床试验架起一座跨越死亡之谷的信息学桥梁