Tackling the Opioid Crisis: Genetic testing to identify addiction risk
Blog Oct 03, 2017 | By Anna MacDonald, Editor for Technology Networks
Deaths from opioid overdoses are rapidly rising, more than doubling between 2005 and 2015, to reach over 30,000 in the USA, with prescription opioids being involved in nearly half of these deaths. Historically, opioids were prescribed to cancer patients and those recovering from surgery, but more recently have been used as a treatment for those suffering from other forms of chronic pain. This has led to an explosion in the amount of prescription opioids, with dispensing rates nearly quadrupling from 1999 to 2015 in the U.S. This opioid crisis has highlighted the urgent need to improve opioid prescribing practices, to minimise the chances of addiction, whilst ensuring treatments are still available to patients that need them.
One way of reaching this goal could be genetic testing, to identify patients most at genetic risk of addiction, before opioids are prescribed. This was the focus of the poster that won the Industry Division award at the recent American Association for Clinical Chemistry (AACC) meeting in San Diego. Dr Keri Donaldson, CEO and founder of predictive health intelligence company Prescient Medicine, and lead author of the poster, “Risk assessment of opioid addiction with a multi-variant genetic panel involved in the dopamine pathway” tells us more about the work and its implications for patients.
Can you give us a little background of the opioid crisis and its origins?
The origin of the opioid crisis is multifactorial. Contributing factors include: a confluence of better clinical understanding of how to manage a patient’s pain; better access to pain management treatments like opioids; as well as the marketing of these treatments to both patients and physicians. But, how we got here is far less important than the scope of what we now face. As recently as 2016, the opioid crisis is estimated to have claimed more than 60,000 lives – that’s the same number of deaths we saw in 20 years of the Vietnam War, and is equivalent to one or two 9/11’s per month, every year. Opioid deaths are now the #1 killer of adults under the age of 50 in this country. To say that this crisis is a mass casual event is an understatement.
What is thought to be responsible for the variation in addiction risk between individuals?
Associating genetics with risk of drug or alcohol addiction risk or dependency is not new. Genetic predisposition to addiction is well-established and can be traced back to the 1950’s or 1960’s. What is new, however, is the technology we now have where we can use machine learning or automated intelligence (AI) to identify how these genes may contribute to addiction risk. Our study used 16 single nucleotide polymorphisms (SNPs), or genetic mutations in the brain reward pathways to generate a risk score. In a second study, we evaluated the ability of this test to predict addiction risk, and found that the test – with 97% specificity – was able to identify patients not at increased genetic risk of opioid addiction.
How can this information be harnessed to identify those most at risk of opioid addiction?
LifeKit® Predict is specifically designed to give doctors – and patients – the information they need to make informed decisions before prescribing or taking an opioid. If, for instance, the test discovers an individual has a high genetic risk of addiction, they may want to talk to their doctor about effective, non-opioid alternatives for their pain.
What difference can genetic testing make to patients?
Well we’ve seen what role genetic testing or personalized medicine can play in treating insidious diseases like cancer. Now we’re applying those same principles to the disease of addiction. Every day we’re learning more about how our genes affect things like addiction risk, and how we might respond to certain medications. As a result, it’s becoming increasingly important that we have clear genetic information to help us make informed treatment decisions.
Dr Donaldson was speaking to Anna MacDonald, Editor for Technology Networks.