# Perioperative Precision Medicine: Translating Science to Clinical Practice to Improve Safety and Efficacy of Opioids in Neonates, Children and Nursing Mothers

> **NIH NIH U01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $1,084,538

## Abstract

Project Summary: Perioperative opioid adverse effects--from common but less-serious post-operative
nausea and vomiting to the more serious respiratory depression and death—are current but preventable
challenges in managing surgical pain. Severe surgical pain is still poorly managed, yet clinicians must also
avoid unpredictable and life-threatening opioid adverse effects as well as long-term opioid use/misuse. This
application innovatively proposes to translate evidence into a proactive clinical practice to optimize post-
surgical pain control and decrease opioid-related adverse effects. Current evidence shows that opioid me-
tabolism, opioids’ analgesic and adverse effects are influenced by genetic variations. The FDA warns
against the use of codeine and tramadol in children (due to postoperative anoxic brain injuries and deaths)
and in nursing mothers (due to serious breathing problems and infantile death). Preoperative genotyping
and personalized analgesia are not practiced despite evidence, regulatory warnings, CPIC guidelines, cost-
effectiveness and insurance coverage for CYP2D6 testing due to translational bottlenecks, lack of infra-
structure and knowledge gaps in how to personalize opioid use and dose precisely for optimal outcomes.
Thus, there is an urgent and unmet critical need for a perioperative precision analgesia infrastructure to
overcome the translational barriers and to improve safety and effectiveness of opioids in children and nurs-
ing mothers. Personalizing analgesia based on genetic risks will reduce opioid use, adverse-effects, and
accelerate value-based care opportunities. However, these opportunities are constrained by lack of trans-
lational platforms and major gaps in our understanding of how to personalize and precisely dose opioids. In
this collaborative CTSA project, we propose to overcome the translational barriers by developing an inno-
vative perioperative precision analgesia platform (PPAP) to reduce serious adverse outcomes of opioids,
and improve safety of opioids in: 1) children undergoing painful surgery, and 2) nursing mothers and their
infants. We have robust evidence and implementation expertise on genetic risk factors including CYP2D6
and other genetic variations for opioids’ analgesic efficacy and opioid-related serious adverse effects, meth-
ods of implementation of genotypes with clinical decision support in electronic health records, genotype-
based perioperative opioid use and innovative digital tools for electronic patient reported outcomes at all
participating CTSA hubs. This application with innovative preoperative genotyping, integrated personalized
decision support aims to enhance understanding of opioid metabolism, personalized opioid selection, pre-
cision dosing, and clinical outcomes in neonates, children, and nursing mothers, and to disseminate findings
to other CTSA hubs. A unified CTSA-wide PPAP will enable genetic risk predictions and personalized in-
terventions to maximize pain relief...

## Key facts

- **NIH application ID:** 10828901
- **Project number:** 5U01TR003719-03
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Senthilkumar Sadhasivam
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,084,538
- **Award type:** 5
- **Project period:** 2022-08-03 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10828901

## Citation

> US National Institutes of Health, RePORTER application 10828901, Perioperative Precision Medicine: Translating Science to Clinical Practice to Improve Safety and Efficacy of Opioids in Neonates, Children and Nursing Mothers (5U01TR003719-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10828901. Licensed CC0.

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