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.

Narrative Phase I studies usually focus on drug toxicity and drug exposure, which have the largest failure rate, 58%, of cancer drug development, i.e. valley of death. Because of complexity of cancer biology, they usually require multi-drug therapies. In order to overcome the valley of death for multi-drug cancer therapies, we propose an informatics bridge, i.e. a translational drug interaction knowledge base (TDCKB), that will integrate multi-drug exposure and toxicity data from various data sources. TDCKB will accelerate the translational cancer drug combination development research to the next phase of clinical trials.