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

一座跨越死亡之谷的信息学桥梁，用于药物联合癌症治疗的I期试验 总结 I期研究通常侧重于药物毒性和药代动力学，大多数（58%）预期用于癌症的药物 这些疗法在最初的试验中失败了。因此，I期研究代表了药物治疗过程中最大的死亡谷。 发展与单药I期研究的设计不同，联合用药研究的设计需要 预先知道任何一种药物是否会改变另一种药物的暴露，药物是否具有相同的毒性， 药物具有确定的最大耐受剂量。虽然大量的毒性和PK数据已公开 域来源，数据没有整合，没有一个单一的数据库整合有关毒性和 PK.此外，关于单药和药物组合MTD和DLT的数据存在于文献中，但 没有任何数据库。我们有信心在第一阶段的死亡谷上建造一座桥梁， 癌症多药研究和开发利用信息学和药物计量学方法， 单药毒性和PK数据丰富的优势。在这一补助金中，我们提出了一个 整合毒性和PK数据的翻译药物相互作用知识库（TDCKB）。目标1将发展 新的主动学习方法，以挖掘关于药物相互作用的毒性和PK证据， 文学主动学习方法将采用几项创新，包括随机负抽样， 基于PubMed查询的预筛选分层主动学习，以及嵌入式深度学习。最终 通过彻底集成这些创新组件，优化了主动学习方法。Aim 2将开发 用于癌症研究的药物相互作用知识库（TDCKB）。TDCKB将整合毒性 以及来自各种数据来源的单药和联合用药的PK证据。DDI的证据将 分类为毒性或PK，并注释证据的强度。综合证据， 如重叠毒性和两种药物之间的预期药物相互作用，将有助于I期药物 组合试验设计在数据整理和TDCKB期间，将仔细进行质量控制 软件开发计划让TDCKB用户和ITCR社区参与进来。

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.