AutoGenomics to Receive2017 Industry Division PosterAward at AACC Annual Scientific Meeting and ClinicalLab Expo for Opioid Addiction Panel Study
Carlsbad, CA – Jul 31st, 2017 – AutoGenomics, Inc., a molecular diagnostics company offering an innovative and proprietary technology platform for all clinical laboratories, announced that it will receive the 2017 Industry Division Poster Award at the 69th AACC Annual Scientific Meeting and Clinical Expo to be held in San Diego, CA from July 30 to August 3, 2017. The poster, entitled “Risk assessment of opioid addiction with a multi-variant genetic panel involved in the brain reward pathway”, describes a predictive algorithm included in a multi-variant genetic panel which can be used for prescription opioid addiction risk assessment.
More than 116 million people worldwide are struggling with chronic pain and most require prescription drugs. In the US, opioid prescription misuse and heroin use accounted for the majority of accidental overdose deaths in 2016. Genetic factors play a key role in opioid prescription addiction but are generally not evaluated in clinical practice. Currently, there is no objective way for practitioners to identify pain patients in medical management who are at risk to abuse, or become addicted to, prescription medication, or to identify those pain patients who will require high dosages or an unusual regimen of medication.
A team of AutoGenomics’ researchers led by Sherman Chang, Ph.D., Vice President of Research and Development, conducted a three-year search in the scientific literature of addiction studies and conducted several independent studies which led to the selection of 16 genetic variants involved in brain reward pathways that could potentially help identify patients at risk for addiction. The genetic variant panel and the predictive algorithm developed by AutoGenomics were used for a clinical study of 70 patients diagnosed with prescription drug induced opioid/heroin addiction and 68 normal control subjects. Initial results show that the predictive algorithm clearly distinguishes between the two populations of addicted versus normal subjects. The positive predictive value was determined to be 74% and the negative predictive value was 74%. The multi-variant addiction panel is for Research Use Only and not for use in diagnostic procedures.
To validate the clinical utility of this test panel, 70 chronic pain patients referred to Dr. Forest Tennant, MD., Dr.PH, at the Veract Intractable Pain Clinic in West Covina,
California, Editor in Chief of Practical Pain Management Journal were tested. “Test results showed a high incidence of genetic variants with the dopamine receptors in contrast to the opioid receptors suggesting that since the dopaminergic pathway was defective, high dose opioids were needed for some chronic pain patients. Complementing this panel with the drug metabolism markers provides valuable information to determine which medication might be effective, especially with many opioid medications being pro- drugs that are compromised in poor metabolizers” said Dr. Tennant, He further said that “this test panel should become an indispensable tool in pain management and opioid addiction risk assessment”.
“We believe that this multi-variant addiction panel and predictive algorithm may provide health care practitioners with a significant opportunity to identify and better manage the risk of opioid addiction for patients,” said Fareed Kureshy, President and Chief Executive Officer of AutoGenomics.
AutoGenomics Inc., a privately held company based in Carlsbad, CA, has developed the first automated, microarray based multiplexing diagnostic platform that can be used to assess disease signatures with novel genomic and proteomic markers in the area of genetic disorders, infectious disease, cancer and pharmacogenetics. With the discovery of genes and their link to various disease states the platform has the versatility to revolutionize the way patients are diagnosed, monitored and managed, leading to the era of personalized medicine. www.autogenomics.com