# Core C – Drug Metabolism, Pharmacokinetics and Toxicology (DMPK/Tox)

> **NIH NIH U19** · EMORY UNIVERSITY · 2022 · $8,116,465

## Abstract

Summary – Core C
Core C will provide AC/DC Cores and Projects with the drug metabolism, pharmacokinetic and toxicology
(DMPK/Tox) data that is required to guide lead optimization, tolerability, and pharmacodynamic profiling. Core
C brings industry seasoned expertise and a successful track record of drug development as exemplified by the
preclinical characterization of molnupiravir (under consideration for Emergency Use Authorization as a treatment
for SARS-CoV-2 infections), and EIDD-2173 (currently in Phase 2 clinical trials for hepatitis B virus infections).
All AC/DC DMPK/Tox operations have accordingly been centralized into Core C to provide the synergies realized
by generating data that is crucial and applicable to multiple projects, and to avoid redundant studies. The
solubility, stability cellular uptake and cellular metabolic profiles of validated hits and early leads will be
determined to filter those with a low probability of success and to inform iterative discovery and optimization.
Drug metabolism, pharmacokinetic and tissue distribution data will be provided to inform animal efficacy studies
and lead optimization. Finally, the tolerability of late leads will be assessed with both in vitro assays and non-
GLP dose range finding toxicokinetic studies.

## Key facts

- **NIH application ID:** 10513938
- **Project number:** 1U19AI171403-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Alexander Kolykhalov
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $8,116,465
- **Award type:** 1
- **Project period:** 2022-05-16 → 2026-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10513938

## Citation

> US National Institutes of Health, RePORTER application 10513938, Core C – Drug Metabolism, Pharmacokinetics and Toxicology (DMPK/Tox) (1U19AI171403-01). Retrieved via AI Analytics 2026-06-16 from https://api.ai-analytics.org/grant/nih/10513938. Licensed CC0.

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