A genetic test developed by Prescient Medicine can predict individual risk of opioid addiction, the company said, based on studies by the company and a collaboration partner whose results were disclosed today.
In one study, Sherman Chang, Ph.D., vp of research and development at the company’s collaboration partner AutoGenomics, and colleagues, researched the scientific literature to describe genetic variations between opioid- addicted and non-addicted populations, then developed a predictive algorithm to determine opioid addiction risk.
The algorithm produced an addiction risk score based on 16 mutations, or single nucleotide polymorphisms (SNPs), in the brain reward pathways. Dr. Chang and colleagues developed an addiction panel targeting the 16 SNPs: 5-HTR2A (rs7997012), 5-HTTLPR (rS25531), COMT (rs4680), DRD2 (rs1800497), DRD1 (rs4532), DRD4 (rs3758653), DAT1 (rS6347), DBH (rs1611115), MTHFR (rs1801133), OPRK1 (rs1051660), GABA (rs211014), OPRM1 (rs1799971), MUOR (rs9479757), GAL (rs948854), DOR (rs2236861) and ATP-BCT (rs1045642).
To assess the panel, the researchers carried out a pair of independent clinical studies. In the first, Prescient Medicine researchers led by founder and CEO Keri Donaldson, M.D., compared the frequency of the identified mutations in 37 patients with prescription opioid or heroin addiction to 30 age- and gender-matched patients with no history of addiction, in order to validate that the genes had predictive value.
In the second clinical study, Dr. Chang and colleagues assessed the efficacy of the panel by using it to genotype samples for the 16 SNPs from 70 patients diagnosed with prescription opioid/heroin addiction, and 68 healthy control patients, using a multiplexed film-based microarray technology.
The risk model designed by Dr. Chang and colleagues computes a score from one to 100, with any score over 52 representing an elevated risk of addiction. Fifty-three of the 70 addicts showed an addiction risk score greater than 52, versus 49 of the 68 controls. Both the positive and negative predictive values of this model were determined to be 74%.
Prescient Medicine’s LifeKit® Predict test showed that it could identify with 97% certainty whether an individual has a low likelihood of developing an addiction to opioids—and an 88% likelihood of predicting if an individual may be at increased risk for opioid addiction.
“These findings confirmed what previously published data have shown—that there is a strong genetic component to opioid addiction, and with the right tools, an individual's risk of opioid dependency can be predicted,” he concluded,” Dr. Donaldson said in a statement.
Physicians are expected to use those findings to pursue alternative non-opioid therapies for individuals with a high risk of opioid dependency, with the goal of preventing addiction before it starts, according to Prescient Medicine.
The findings were reported in “Multi-variant Genetic Panel for Genetic Risk of Opioid Addiction,” published in the current August issue of Annals of Clinical & Laboratory Science.
In addition, Prescient Medicine presented data in a poster at the 69th American Association for Clinical Chemistry (AACC) Annual Scientific Meeting & Clinical Lab Expo, held in San Diego. At the Expo, a poster based on the risk model “Risk assessment of opioid addiction with a multi-variant genetic panel involved in the dopamine pathway,” was selected among 72 competing abstracts to receive the 2017 Industry Division Poster Award.
