New Study Confirms Promise of Personalized Medicine to Predict Opioid Addiction Risk

HUMMELSTOWN, Pa., Aug. 30, 2017 /PRNewswire/ -- As the opioid epidemic continues to spiral out of control, new data confirm genetic testing can predict individual risk of addiction to opioids. The study, "Multi-variant Genetic Panel for Genetic Risk of Opioid Addiction," was published in the current issue of Annals of Clinical & Laboratory Science. The study findings suggest that there is now a validated tool to help prevent opioid abuse that has the potential to positively impact the nation's growing opioid epidemic.

The test, called LifeKit^® Predict, was developed by Prescient Medicine, a predictive health intelligence company, and is based on published data that suggest genetic factors play a significant role in prescription opioid addiction. The study set out to first describe genetic variations between opioid- addicted and non-addicted populations, with the goal of developing a predictive algorithm to determine opioid addiction risk. The algorithm produced an addiction risk score that is based on 16 single nucleotide polymorphisms, or genetic mutations, in the brain reward pathways, and was developed using 37 patients with prescription opioid or heroin addiction and 30 age- and gender-matched non-addicted patients. This became the basis for the predictive model.

"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," said Keri Donaldson, MD, lead author and founder and CEO of Prescient Medicine. "Using those data, we identified candidate genes to act as markers and then validated that those genes had predictive value in a 67-patient study. 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.

Using the outcome of the initial study, researchers then conducted a second study where they evaluated 138 patient samples to assess the efficacy of the panel. These data showed that LifeKit^® Predict can identify—with 97% certainty—that an individual has a low likelihood of becoming addicted to opioids. The test also showed an 88% likelihood of predicting that an individual has an increased risk for opioid addiction. Armed with that predictive insight, physicians can then opt for alternative non-opioid therapies for individuals with a high risk of opioid dependency, potentially preventing addiction before it starts.

"There's no doubt that the huge surge in prescription opioid use in this country has spawned the deadliest drug overdose crisis in U.S. history. But to date, much of the focus has been on reactive solutions that have been largely ineffective, versus working to prevent addiction before it even starts," said Joe Garbely, DO, Medical Director and Vice President of Medical Services at Caron Treatment Centers, a nationally renowned addiction and behavioral healthcare treatment facility. "While the use of predictive, personalized medicine to determine opioid addiction risk is still a relatively new science, I am incredibly encouraged by the data and the potential of genetic tools like LifeKit^® Predict to help prevent opioid abuse and ultimately save lives," he said.

Industry Recognition

Recently, Prescient Medicine presented related data in a poster at the 69th American Association for Clinical Chemistry (AACC) Annual Scientific Meeting & Clinical Lab Expo held in San Diego. Find AACC's press release on the data here.

As a part of the meeting, the poster, entitled "Risk assessment of opioid addiction with a multi-variant genetic panel involved in the dopamine pathway," was selected among 72 competing abstracts as the recipient of the 2017 Industry Division Poster Award. The award is given to acknowledge work that makes a significant contribution to the in vitro diagnostics (IVD) industry in the areas of management, regulatory, or improved patient care through a new or improved medical device (i.e., diagnostic method, reagent system, or other). 

Study Design

The authors genotyped 16 single nucleotide polymorphisms involved in the brain reward pathways in patients with and without opioid addiction. Then, 37 patients diagnosed with prescription opioid or heroin addiction and 30 age- and gender-matched controls were used to derive the predictive score. External generalizability of the model was tested on an additional 138 samples not included in the original learning set. 

About the Opioid Epidemic

The numbers tell the story. The U.S. opioid crisis is the largest public health crisis in history, with more than 59,000 opioid-related drug overdoses in 2016 alone, making opioids the spawn of the deadliest drug overdose crisis in U.S. history. Accordingly, the economic impact of opioid abuse is no less daunting.

Nationwide, opioid-related hospitalizations cost about $20 billion annually. Societal costs for opioids—including for social services, public safety, and criminal justice—will be about $83 billion in 2017. Nearly one-fourth of that economic burden is paid by public, tax-funded sources.

And it's only getting worse. The U.S. is the world leader in opioid prescriptions and it's estimated that as many as 650,000 people will die over the next 10 years from opioid overdoses—more than the entire city of Baltimore. That means that the U.S. risks losing the equivalent of a whole American city in just one decade.

About Prescient Medicine

Prescient Medicine is focused on providing tools that advance the precision healthcare movement. Our powerful tests and analytic solutions offer deeper predictive insights so doctors and patients have the data they need to make better, more informed clinical decisions, leading to the best possible patient outcomes. Our lead solution, LifeKit^® Predict, is a validated genetic test designed to predict individual risk of opioid addiction. Prescient has locations in Pennsylvania, Illinois, Kentucky, and Missouri. Visit us at http://www.prescientmedicine.com/.

SOURCE Prescient Medicine

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