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Leveraging Artificial Intelligence and Multi-Omics Data to Predict Opioid Addiction

NCT06540105 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2024-09-20

No results posted yet for this study

Summary

The primary goal of this proposal is to validate a novel genomic and microbiome predictive model that may be used to assess a person's risk of developing opioid use disorder (OUD). The following will be tested: (1) MODUS (Measuring risk for Opioid use Disorder Using SNPs), which is a genomic panel consisting of a set number of proven single nucleotide polymorphisms (SNP) that utilizes machine learning to determine an individual's risk; and (2) MICROUD (MICRObiome for Opioid Use Disorder), which will be a novel microbiome prediction panel for OUD risk. MODUS and MICROUD will be developed using existing public datasets with genomic and microbiome data (e.g., All of Us, Human Microbiome Project). During development of these predictive models, in parallel, an external prospective validation cohort will be recruited consisting of subjects from the University of California, San Diego, Veteran Affairs of San Diego, and Veteran Affairs of Palo Alto (each site with separate IRB). The hypothesis is that MODUS and MICROUD will have high predictive potential for identifying high risk patients for OUD.

Conditions

Sponsors & Collaborators

Principal Investigators

  • Rodney A Gabriel, MD · University of California, San Diego

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-08-26
Primary Completion
2027-06-29
Completion
2027-09-30

Countries

  • United States

Study Locations

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Entities

Diseases
Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06540105 on ClinicalTrials.gov