# Antibiotic Model-Informed Precision Dosing in Critical Illness

> **NIH NIH R35** · CINCINNATI CHILDRENS HOSP MED CTR · 2022 · $397,500

## Abstract

PROJECT SUMMARY
 Pathophysiologic changes during critical illness can affect drug pharmacokinetics (PK, how the body
affects the drug) and pharmacodynamics (PD, how the drug affects the body), but the current paradigm of drug
dosing is a standard “one-size-fits-all” approach, rather than a personalized approach. Emerging data in adults
demonstrate increased risk of morbidity and mortality with standard doses of antibiotics in critically ill patients
due to lack of PD target attainment. β-lactam antibiotics are a prime example of drugs demonstrating high PK
and PD variability in critically ill patients. Thus, critically ill patients are at risk of having low antibiotic exposures
leading to ineffective bactericidal activity or high antibiotic exposures resulting in toxicity. By using real-time
drug concentrations, individual patient and disease factors, and population PK models, model-informed
precision dosing (MIPD) can ensure adequate antibiotic exposure while avoiding toxicity.
 My proposed research program will address three critical knowledge gaps that are necessary to fill prior to
implementation of antibiotic MIPD. First, the appropriate patient populations who will most benefit from
precision dosing remain unknown. Implementation of MIPD for every patient admitted to the intensive care unit
may be resource-intensive and costly. However, simply increasing antibiotic doses or frequency of
administration without data-driven management can be dangerous as it carries risks for antibiotic-associated
toxicity, including nephrotoxicity and neurotoxicity. Therefore, it is critical to optimize the benefit-to-risk ratio of
therapeutic interventions for individual patients, a fundamental concept of precision medicine. Second, there
remains a knowledge gap on the association of precision dosing of antibiotics and clinical outcomes. Studies
examining antibiotic exposure and outcomes in adults have had mixed results; some show improved outcomes
with PD target attainment and some show no difference in outcomes with regards to target attainment. Third,
many of the antibiotic population PK models needed for MIPD have not been prospectively validated in
critically ill patients, so it is unknown which models should be used for precision dosing. To address these
knowledge gaps, Project 1 will utilize innovative modeling and simulation to identify patient and disease
factors associated with antibiotic under-exposure (risk of ineffective antibacterial activity) or over-exposure (risk
of toxicity) and investigate mechanisms underlying toxicities. Project 2 will investigate the effect of precision
dosing on clinical outcomes by evaluating the association between PD target attainment and clinical outcomes
at the individual level. Project 3 will prospectively validate our models and previously published models to
ensure accurate predictive ability in critically ill patients. With these prospectively validated models, we will lay
the foundation to build MIPD decision ...

## Key facts

- **NIH application ID:** 10496845
- **Project number:** 1R35GM146701-01
- **Recipient organization:** CINCINNATI CHILDRENS HOSP MED CTR
- **Principal Investigator:** Sonya C Tang Girdwood
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $397,500
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10496845

## Citation

> US National Institutes of Health, RePORTER application 10496845, Antibiotic Model-Informed Precision Dosing in Critical Illness (1R35GM146701-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10496845. Licensed CC0.

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