$50M PROGRAM

Untangling Addiction

Every 4 minutes, someone in the world dies from drug or alcohol addiction. Most people with addictions try to quit — and can’t.

Untangling Addiction is a new $50M program focused on breakthroughs in substance abuse prevention and treatment. The program aims to create predictive models to assess an individual’s vulnerability to addiction, forecast progression, and quantitatively measure restoration of key brain circuit functions through treatment and recovery.

What if we treated addiction
like we treat cancer —
as a curable, biological illness?

Globally, an estimated 108 million people are afflicted by alcohol addiction, and about 40 million battle illicit drug addiction. In 2019, Alcohol Use Disorder (AUD) caused 168,000 deaths worldwide and was a risk factor in roughly 2.44 million more. Between 2009 and 2019, the number of individuals with Substance Use Disorders (SUDs) increased by 45%, from 27.3 million to 39.5 million. In 2019, SUDs resulted in over 128,000 deaths globally. And the numbers are getting worse.

Worldwide, AUD and SUD are costly as well as deadly — responsible for a global productivity loss equivalent to 131 million Years Lived with Disability (YLDs), incurring an annual cost exceeding $740 billion due to healthcare expenses, lost work productivity, and crime-related costs. No country or region is immune.

“Drug Addiction is a chronic, relapsing brain disorder characterized by compulsive drug seeking and use despite harmful consequences.”

– National Institute on Drug Abuse

In 2021 in the US, fewer than 4 million out of over 50 million individuals with an Alcohol Use Disorder (AUD) or Substance Use Disorder (SUD) received treatment, constituting less than 7% of Americans diagnosed with AUD or SUD.

Among those receiving treatment, the relapse statistics are equally alarming. Studies indicate that for alcohol addiction, the relapse rate is an estimated 50% in the first three months following the completion of intensive inpatient programs. For opiate addiction, a particular study revealed a 91% relapse rate, noting that 59% of individuals relapsed within the first week and 80% within a month.

This calls for a fundamental reassessment of our existing strategies, and advocates for novel approaches that quantify and delineate the neural and physiological elements disrupted by addiction. Additionally, it emphasizes the need to develop a framework that integrates this knowledge into the treatment and tracking of recovery.

“It's time for a radical shift in our battle against addiction. We need to adopt strategies that are guided by a deepened understanding of the complex neural and physiological mechanisms of drug abuse.”

– Regina E. Dugan, President & CEO of Wellcome Leap.

Untangling Addiction has three goals: (1) develop predictive tools to assess addiction risk in individuals with a minimum precision of 85%, so as to reduce the transition from prescribed opioid use to addiction by 50%; (2) devise methods that enable quantitative tracking of treatment progression and the monitoring of brain function restoration throughout the recovery process so as to double the success rate of 90-day sobriety; and (3) identify new treatments and interventions.

The program aims to unite clinicians, neurobiologists, addiction researchers, physical scientists, engineers, and computer scientists from universities, nonprofits, and the private sector, all focused on achieving the program goals and a new understanding of the neurobiological mechanisms underlying addiction.

Program Director.

Kevin Jones, PhD is a practicing professor with expertise in neurophysiology and neuropharmacology of mental health disorders, including schizophrenia, post-traumatic stress disorder and depression. He uses a variety of technical approaches to identify cellular and molecular deficits that can be targeted with novel therapeutics. He earned his PhD in Neuropharmacology from Duke University.

Further details.

To learn more about the program history, performer teams, and process, please visit the Program Details Page.