Newswise — SAN DIEGO – Researchers have shown that a new genetic test could potentially help stem the tide of the opioid epidemic by predicting which patients are at risk of abusing prescription opioids. These findings were presented for the first time at the 69th AACC Annual Scientific Meeting & Clinical Lab Expo in San Diego.
According to a preliminary estimate, more than 59,000 drug overdose deaths occurred in the U.S. in 2016, making this the leading cause of mortality for people under age 50. As in past years, the ongoing surge in prescription and illicit opioid abuse is likely the main driver of this grim trend. In spite of the toll opioids have taken, however, there are many scenarios in which these drugs are still the most effective treatment for pain, such as after surgery or in the case of patients with debilitating chronic pain. This leaves healthcare providers with a dilemma—how can they ensure access to opioid treatment for patients who legitimately need it without contributing to escalating rates of addiction?
A team of researchers led by Sherman Chang, PhD, vice president of research and development at AutoGenomics, Inc. of Carlsbad, California, has found that 16 genetic mutations involved in brain reward pathways could potentially help identify patients at risk for addiction. Chang’s team selected these mutations after performing an extensive search of addiction studies in the scientific literature. Using a set of samples from addiction patients and non-affected individuals, the researchers then designed a predictive algorithm based off these genetic variants to distinguish between the two populations.
Two independent clinical studies were conducted by a team of researchers at Prescient Medicine, led by CEO Keri Donaldson, MD, and Chang’s team at AutoGenomics, respectively, to evaluate this genetic panel and algorithm. In the first study, Prescient Medicine compared the frequency of the identified mutations in 37 patients with prescription opioid or heroin addiction versus 30 age- and gender-matched individuals with no history of addiction. The researchers also tested 138 additional patient samples with the genetic panel and algorithm. From this, they found that the test—called the NeurR score—was highly accurate, with a sensitivity of 97% and a specificity of 87%.
“The utility lies in identifying the at-risk population, if you can, before they get exposed to opioids,” said Donaldson. “Using this type of testing in the normal workflow prior to elective surgeries—prior to patients getting that first exposure to opioids—that’s where this makes sense. You hear these horror stories of young kids getting addicted and dying after they get exposed during elective surgeries. Identifying the population at high risk for addiction and offering them alternative pain management may prevent that type of development.”
In the second clinical study, the AutoGenomics team used the Addiction Risk Assessment Panel to test 70 patients diagnosed with prescription opioid and/or heroin addiction and 68 non-affected individuals. From the results generated, they designed a risk model that computes a score from one to 100, with any score over 52 representing an elevated risk of addiction. Of the 70 addicts tested, 53 had an addiction risk score greater than 52, while 49 of the 68 healthy controls scored under 52. Both the positive and negative predictive values of this model were determined to be 74%. Once again, these results show that the genetic panel and predictive algorithm differentiate between the two populations of addicted versus non-affected individuals.
“Test results show that many of the genetic mutations identified in this test panel—namely receptors and transporters—are present in most chronic pain patients and are helpful in identifying those subjects at risk for addiction,” said Forest Tennant, MD, DrPH, a physician at the Veract Intractable Pain Clinic in West Covina, California who collaborated with AutoGenomics on this research. “This test panel should become an indispensable tool in pain management and addiction risk assessment.”
Session Information
AACC Annual Scientific Meeting registration is free for members of the media. Reporters can register online here: https://www.xpressreg.net/register/aacc0717/media/start.asp
Abstract A-150: Risk Assessment of Opioid Addiction With a Multi-Variant Genetic Panel Involved in the Dopamine Pathway will be presented during:
Scientific Poster Session
Tuesday, August 1
9:30 a.m. – 5 p.m. (presenting authors in attendance from 12:30 – 1:30 p.m.)
Abstract B-206: Multi-Variant Genetic Panel for Genetic Risk of Opioid Addiction will be presented during:
Scientific Poster Session
Wednesday, August 2
9:30 a.m. – 5 p.m. (presenting authors in attendance from 12:30 – 1:30 p.m.)
Both sessions will take place in the Sails Pavilion, Upper Level at the San Diego Convention Center in San Diego, California.
About the 69th AACC Annual Scientific Meeting & Clinical Lab Expo
The AACC Annual Scientific Meeting offers 5 days packed with opportunities to learn about exciting science from July 30–August 3. Plenary sessions feature the latest research on CRISPR and the future of genome engineering, the public health crisis of antibiotic resistance, treating substance abuse and addiction, preserving fertility in cancer patients, and new frontiers in genomic sequencing.
At the AACC Clinical Lab Expo, more than 750 exhibitors will fill the show floor of the San Diego Convention Center with displays of the latest diagnostic technology, including but not limited to mobile health, molecular diagnostics, mass spectrometry, point-of-care, and automation.
About AACC
Dedicated to achieving better health through laboratory medicine, AACC brings together more than 50,000 clinical laboratory professionals, physicians, research scientists, and business leaders from around the world focused on clinical chemistry, molecular diagnostics, mass spectrometry, translational medicine, lab management, and other areas of progressing laboratory science. Since 1948, AACC has worked to advance the common interests of the field, providing programs that advance scientific collaboration, knowledge, expertise, and innovation. For more information, visit www.aacc.org.
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