A new study confirms Prescient Medicine’s genetic test helps discern patients most at risk for opioid addiction.
Pennsylvania-based Prescient Medicine announced that genetic testing of individuals can yield information regarding their propensity for opioid dependency. In the study "Multi-variant Genetic Panel for Genetic Risk of Opioid Addiction," published recently in the current issue of Annals of Clinical & Laboratory Science, Prescient Medicine said the study’s findings, demonstrate the power of the company’s test to help prevent opioid drug abuse and positively impact the nation’s growing abuse problem.
Called LifeKit® Predict, the test, was developed based on published data that Prescient Medicine said suggests genetic factors play a real role in whether or not a patient is more, or less susceptible to dependency and addiction. According to Prescient Medicine, the study first set out to describe genetic variations between addicted and non-addicted patients with the objective of developing an algorithm to determine addiction risk. The algorithm, said the company, produced an addiction risk score based on 16 single nucleotide polymorphisms (genetic mutations) in the brain’s reward pathways. Prescient Medicine explained the algorithm was developed from 37 patients with prescription opioid or heroin dependency and another 30 age- and gender-matched non-addicted subjects.
Keri Donaldson, Lead Study Author, Founder and CEO of Prescient Medicine, described the test’s development. "Our first step included a collaboration with AutoGenomics, Inc., who thoroughly researched the scientific literature to identify and better understand the genes associated with the brain reward pathways.” Using that data, said Donaldson, the company identified candidate genes to act as markers and then validated that those genes offered predictive value in a 67-patient study. “These findings,” he concluded, “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."