In 2023, 48.5 million Americans had a substance use disorder (SUD), yet only 14.6% received treatment—leaving more than 41 million without care. [1] Standard, one-size-fits-all care can’t meet that need. People with the same diagnosis differ in biology, psychology, and context, so responses to the same treatment often differ, and return-to-use remains common (40–60%, similar to other chronic conditions). [2][3]
Precision medicine addresses that gap: start with proven treatments, then use health history, co-occurring conditions, current symptoms, and—only when it would change a decision—selective tests (labs or genetics) to match care to the person, not just the diagnosis. [4]
What is precision medicine?
Precision medicine customizes care for you, not an “average” patient. Clinicians weigh your history, goals, symptoms, and context—and add tests only when they’re likely to change a decision. The aim is to reduce trial-and-error, improve outcomes, and limit side effects. [4]
What it isn’t:
Tools and technologies for personalizing addiction care
- Biomarkers are objective signals—such as certain genes, proteins, or stress-hormone patterns—that may help predict who is at higher risk or who is more likely to benefit from a treatment. They show promise, but none are yet reliable enough for everyday clinical use in addiction care. [2]
- Brain scans such as MRI or PET can show activity in circuits linked to reward, relief, or self-control. These patterns may one day help match people to the right treatments. For now, they remain research tools—not part of routine clinic care. [2]
- Pharmacogenetics (often called pharmacogenomics) looks at how your genes may affect how medicines work for you. For example, a common mu-opioid receptor gene variant has been linked in some alcohol studies to better outcomes with naltrexone. Still, findings are mixed. At present, no genetic test is reliable enough to guide routine care. [4][9]
- Epigenetics. Life experiences and substance use can change how genes are switched on or off without altering the DNA sequence. Alcohol, for example, has been linked to shifts in stress and reward genes. Clinical use is still years away. [2]
- Apps and wearables can help people track sleep, mood, cravings, and location-based triggers, and can prompt timely support. These tools are already being tested, but privacy and consent are essential. [7]
Brain scans, biomarker panels, and most genetic testing are research tools for now—not part of routine clinic care. All of these tools share one goal: making treatment decisions less about guesswork and more about matching the right care to the right person.
Precision medicine in SUD: what it looks like now
A 2025 review makes a straightforward point: no single measure is enough. Behavior, biology, and life context each add useful information, and using them together predicts outcomes better. [2] The review points to early leads—like neurofilament light chain (a protein that rises when brain cells are stressed) as a possible relapse signal—along with brain-activity patterns tied to reward, relief, and self-control, and computer tools that learn from data. The bottom line: practical combinations of measures beat one-off tests. [2]
Alcohol Use Disorder (AUD)
For AUD, matching treatment matters. Some respond well to naltrexone, others to acamprosate, and some to therapy first. While genetics and brain imaging may eventually refine choices, today, clinicians can already tailor care based on drinking patterns, cravings, mental health, and past medication response. [2][4]
Opioid Use Disorder (OUD)
For OUD, starting and staying on medication is pivotal. Buprenorphine and methadone consistently reduce overdose and all-cause mortality; extended-release naltrexone helps prevent relapse in selected patients, though mortality effects are less certain. [3]
Stimulant Use Disorders (e.g., methamphetamine, cocaine)
There are no FDA-approved medications. Personalization instead focuses on behavior and context: contingency management (small, structured rewards for meeting treatment goals), CBT tuned to attention or impulsivity, plus sleep, nutrition, and co-occurring conditions. Digital monitoring and “just-in-time” interventions are also showing promise. [6][7]
Cannabis Use Disorder
Precision tools for cannabis use disorder are still early-stage. Differences in cannabinoid-receptor genes have been explored, but results are mixed, and many genes and the environment influence overall risk. Personalization today means centering goals (cut down vs. quit), addressing triggers such as insomnia or social anxiety, and tailoring approaches like motivational enhancement or CBT. [8]
From lab to clinic: Benefits and barriers
Less trial-and-error means fewer side effects, faster stabilization, and better engagement—crucial when momentum can determine whether someone stays in care.
The challenges
- Evidence & equity. Any test or algorithm should work for people of different ages, races, and backgrounds—otherwise it can make care less fair. [2]
- Cost & workflow. Genetic tests or brain scans can run hundreds of dollars per patient and require staff training and clear step-by-step plans. [2]
- Privacy, choice & consent. Apps, wearables, and genetic tests should be opt-in with clear data rules. They shouldn’t be required by employers, health insurers, or courts, and saying no shouldn’t affect your care. [7]
- No gatekeeping. Tools like AvertD aren’t for diagnosis or stand-alone decisions. They shouldn’t be used to deny pain treatment, addiction medications, housing, or to label someone “high risk” in legal settings. [10][11]
- Context is part of precision. Start with what often drives outcomes—housing, pain, sleep, and mental health—using information clinics already collect. This improves fit and fairness. [2]
- U.S. regulation. No biomarker is FDA-approved for routine addiction treatment. The FDA’s Biomarker Qualification Program exists, but tools need replicated proof that they improve care, not just predictions, before broad use. [2][5]
Regulatory note on biomarkers
Recent regulatory moves underscore both the promise and the limits of biomarker testing in addiction care. In 2023, the FDA approved AvertD—a genetic test designed to estimate a person’s risk of opioid use disorder before prescribing opioids for acute pain. It highlights both potential and limitations, as it’s not intended for diagnosis or standalone treatment decisions. [10][11]
The near future
Expect more trials that match treatment to people’s genetics or behavioral profiles, especially in AUD. Digital health will likely play a bigger role in spotting risk early and tailoring outreach. Health systems will only adopt these tools once they show they actually improve outcomes, not just predictions. [2]
A brief history, symptom profile, a few cognitive tasks, and—when useful—low-cost labs can feed a plan that updates as life does. [7]
Practical takeaways
For clinicians (start now):
- Stratify by need. Use what you already collect—mental health, pain, sleep, housing, social support—to decide treatment intensity and type. [2]
- Start with proven tools. For OUD, focus on access and retention with medications. For stimulants, combine contingency management with CBT tailored to attention or impulsivity. [6]
- Make data visible. Have patients track cravings, sleep, and stress (paper or app). Review at each visit and adjust. [7]
- Be explicit about goals. Agree on whether the aim is reducing use, abstaining, or preventing overdose, then choose measures that match.
For patients (what to expect):
- Bring your context. Tell your clinician what’s hardest—sleep, anxiety, certain people or places, or pain. That’s the raw material for personalization.
- Track a few signals. Moods, cravings, sleep, and risky places are enough to start. Share patterns at visits. [7]
- Know the timeline. Precision medicine doesn’t guarantee results, but it improves the odds of finding a plan that fits you faster. [2]
Final thought
Precision medicine doesn’t replace the fundamentals of addiction care—it sharpens them. By matching interventions to people rather than diagnoses alone, we can shorten the path to stability and make recovery plans sturdier over time. The promise is practical: better-fitting care, fewer setbacks, and more room for life to grow around treatment. For patients and clinicians alike, the future is less trial-and-error and more care that fits.
