# Precision coordination of therapeutic and prophylactic antibiotics to reduce infection, toxicity, and emergence of resistance following acute abdominal surgery

> **NIH AHRQ R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $466,165

## Abstract

Acute appendicitis is the most common abdominal surgical emergency in the world, with a lifetime risk of 8.6%
in males and 6.9% in females. In the U.S., acute appendicitis affects > 280,000 individuals and contributes to 1
million patient days of admission each year. Surgical site infection (SSI) rates following appendectomy for
uncomplicated, non-perforated appendicitis remain unacceptably high. A recent review reported pelvic abscess
rates of 9.4% following appendectomy. The highest risks for microbial contamination of the peritoneum occur
when the appendix is transected. Regardless of surgical technique, the retained appendiceal stump ALWAYS
exposes appendiceal mucosa and luminal bacteria to the normally sterile peritoneal cavity. It is thus critical that
optimal antibiotic tissue exposure occurs during this operative risk window.
Antibiotic intervention for acute appendicitis is complex. Therapeutic antibiotics are initiated empirically in the
emergency department, often hours before the appendectomy (usually a 4-18 hour wait). Thus, at the time of
surgery, there are already therapeutic antibiotics on board, and the role of prophylactic antibiotics is unclear.
To date, there has been no consensus on the blended use of therapeutic and prophylactic antibiotics for acute
surgeries, and in many cases, prophylaxis is not administered because therapeutic antibiotics have already
been administered. Unfortunately, in these instances antibiotic tissue levels are likely below the minimum
inhibitory concentration at the time of appendiceal transection due to the short half-life and lack of protocoled
dose timing relative to surgical incision. Consequently, the appendix, a known microbiome reservoir for the
colon, has its lumen open to the peritoneum during appendectomy, guaranteeing some level of tissue and
peritoneal microbial contamination during the surgery, increasing risk for SSI and abscess formation. A more
personalized method of dosing antibiotics in patients with acute appendicitis could reduce SSI risk, limit
unnecessary antibiotic use, reduce overdosing risks, and curb the development of antibiotic resistance.
We hypothesize that personalized antibiotic dosing based on time to surgical appendectomy can
optimize tissue antibiotic exposure at the surgical site, avoid use of unnecessary antibiotics (selective
antibiotic resistance pressure), and reduce toxicity. Our Specific Aims are therefore to 1) characterize the
plasma, tissue and surgical site tissue concentration of therapeutic antibiotics in patients undergoing
appendectomy; 2) design a precision dosing nomogram for therapeutic beta-lactam antibiotics using
quantitative tissue PK-PD modeling and simulation that factors antimicrobial susceptibility distributions, patient
demographics and morphotypes as well as timing to achieve optimal tissue exposure at appendectomy; and
3) pilot and evaluate the effectiveness of a precision blended antibiotic treatment and prophylaxis nomogram
for append...

## Key facts

- **NIH application ID:** 10458602
- **Project number:** 5R01HS027788-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Manjunath P Pai
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2022
- **Award amount:** $466,165
- **Award type:** 5
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458602

## Citation

> US National Institutes of Health, RePORTER application 10458602, Precision coordination of therapeutic and prophylactic antibiotics to reduce infection, toxicity, and emergence of resistance following acute abdominal surgery (5R01HS027788-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10458602. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*