Timely Ordering of Pharmacogenetic Testing

Timely Ordering of Pharmacogenetic Testing in Pediatric Oncology

The goal of this trial is to learn if a machine learning (ML) model can help optimize drug therapy in the pediatric population. The main question[s] it aims to answer are whether a machine learning model predicting receipt of a targeted medication within the next three months:

• Increases the offering of pharmacogenetic testing prior to receipt of a targeted medication
• Increases the number of patients with pharmacogenetic results prior to receipt of a targeted medication
• Increases the number of patients who have alteration in medication choice or dose based on pharmacogenetic results

This trial only focuses on the prediction and provision of participants with a high-risk of receiving a medication with a pharmacogenetic indication in the next three months.

Study Overview

• Status: Recruiting
• Conditions: Machine Learning; Pediatrics; Prediction Models; Precision Medicine
• Intervention / Treatment: Other: ML-based intervention

Detailed Description

This study aims to evaluate the effectiveness of a ML model in predicting patients at high risk of requiring a "targeted medication" within the next three months. A machine learning model will predict, the morning following admission to any inpatient service, whether there will be receipt of a targeted medication within the next three months. The research team will be notified regarding eligible patients each morning, and the research team or pharmacogenomics team will approach the patient's primary care team as applicable. By leveraging ML, this study seeks to enhance the identification of patients who would benefit from such medications in a timely and resource-efficient manner.

The study team identified specific medications as indications for pharmacogenetic testing based on prevalence and level of evidence for modifying prescribing practices. These pre-selected medications are referred to as "targeted medications" and are as follows: azathioprine, brivaracetam, clobazam, clopidogrel, flecainide, phenytoin, tacrolimus, voriconazole and warfarin. Only systemically administered (oral, subcutaneous, intramuscular or intravenous) medications or prescriptions (e.g. not topical, intrathecal or intravitreal) are included. Phenytoin was only considered if given orally (to exclude emergency administration without a plan for ongoing treatment).

Pharmacogenetic testing will be offered to participants and conducted as addressed in an associated pharmacogenetic testing protocol (REB# 1000053445 PI: Iris Cohn).

• Study Type: Interventional
• Enrollment (Estimated): 275
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Lillian Sung, MD, PhD; Phone Number: 4168135287; Email: lillian.sung@sickkids.ca
• Study Contact Backup: Name: Agata Wolochacz, BMSc; Phone Number: 309976 4168137654; Email: lillian.sung@sickkids.ca

Study Locations

• Canada
  • Ontario
    • Toronto, Ontario, Canada, M5G1X8
      • Recruiting
      • The Hospital for Sick Children
      • Principal Investigator: Lillian Sung, MD, PhD
      • Contact: Lillian Sung, MD, PhD; Phone Number: 4168135287; Email: lillian.sung@sickkids.ca

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Inpatient at The Hospital for Sick Children
• Between 6 months to 18 years old

Exclusion Criteria:

• Prior pharmacogenetic testing and/or prior receipt of a targeted medication
• Current Intensive Care Unit (ICU) admission
• Expected hospital discharge is prior to midnight on the day of admission

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Supportive Care
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: ML model; Participants predicted by an ML model to receive a "targeted medication" within three months following admission.	Other: ML-based intervention; A ML-based model will predict and identify participants that are at high-risk of receiving a targeted medication within three months after their hospital admission date.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Proportion of Patients with Pharmacogenetic Testing	The primary outcome will be the proportion of patients with pharmacogenetic testing offered among those who receive a medication with a pharmacogenetic indication within three months of prediction time. Testing must be offered prior to receipt of the first targeted medication.	Day 1 to 3 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of patients with pharmacogenetic results available prior to receipt of targeted medication	Measured via chart review	Day 1
Number of patients who have alteration in medication choice or dose based on pharmacogenetic results	Measured via chart review	Day 1

Collaborators and Investigators

• Sponsor: The Hospital for Sick Children
• Investigators: Principal Investigator: Lillian Sung, MD, PhD, The Hospital for Sick Children

Study record dates

Study Major Dates

• Study Start (Actual): June 10, 2025
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 10, 2027

Study Registration Dates

• First Submitted: March 13, 2025
• First Submitted That Met QC Criteria: March 27, 2025
• First Posted (Actual): March 30, 2025

Study Record Updates

• Last Update Posted (Actual): March 5, 2026
• Last Update Submitted That Met QC Criteria: March 3, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: pediatrics; pharmacogenetics; machine learning; precision medicine; prediction models
• Other Study ID Numbers: 3423

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No