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

> **NIH NIH U01** · OHIO STATE UNIVERSITY · 2021 · $387,617

## Abstract

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.

## Key facts

- **NIH application ID:** 10305083
- **Project number:** 1U01CA248240-01A1
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Lang Li
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $387,617
- **Award type:** 1
- **Project period:** 2021-09-24 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10305083

## Citation

> US National Institutes of Health, RePORTER application 10305083, An informatics bridge over the valley of death for cancer Phase I trials of drug-combination therapies (1U01CA248240-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10305083. Licensed CC0.

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