Often referred to as “medication assisted treatment” (MAT), pharmacologic relapse prevention treatments are commonly used for the treatment of substance use disorders. They rewire the brain so that people have more control over their decisions around the substance, and they block cravings and reduce the amount people use when they relapse. People will typically take these medications for months or years after they stop or significantly cut back their use to keep them from sliding back into old habits.

There are many pathways to developing an addiction. Furthermore, existing medication treatment options are very different from one another; they all block addiction behavior but work via different mechanisms. It’s no wonder that not all treatments work for all people, even within a drug class.

What Medications Are We Talking About When We Say “MAT”?

For opioids, the three most effective and commonly prescribed relapse prevention agents are naltrexone, methadone, and buprenorphine. These medications work well, but there are limitations to them too.

For illicit stimulants (methamphetamine, cocaine), there is a long list of viable options, but unfortunately, none are highly effective (... yet; this is why we need to learn more about how to predict who needs which medication). These include topiramate, a combination of bupropion and naltrexone, a combination of topiramate and prescription stimulants, prescription stimulants alone, disulfiram, and some antidepressants such as bupropion, among others.

For nicotine, bupropion, nicotine replacement, and varenicline are the most standard, commonly-prescribed relapse prevention agents, with some growing evidence for cysteine.

As has been done with alcohol use disorder  researchers have been working to identify ways of subgrouping, or subtyping, within these substance use disorder subgroups that are both valid and clinically useful. A major goal of this research is to improve the efficiency and efficacy of treatment: the ideal subgrouping methods would tell us something about what the individual in front of us needs most.

Stimulant Use Disorders

In stimulant use disorder research, a reasonable amount of progress in sub-typing and treatment-matching research has been made. For example, the presence of a certain genetic marker that relates directly to the proposed mechanism of action of the medication disulfiram—namely, the enzyme that metabolizes dopamine to norepinephrine—may predict whether someone will be able to reduce their cocaine use by taking it. Disulfiram inhibits the enzyme which converts dopamine to norepinephrine, and therefore increases dopamine levels; cocaine raises dopamine levels too.

People with higher impulsivity were more likely to reduce or stop their cocaine use with topiramate compared to those with low impulsivity. Along with another study in alcohol use disorder, this study hints that one of topiramate’s mechanisms of action in addictions is to improve impulse control in people who are deficient in it.

Also, studies show that someone with comorbid attention deficit hyperactivity disorder (ADHD) will be more likely to do well with a prescription stimulant in terms of cocaine use reduction. These findings make sense since prescription stimulants are a treatment for ADHD, and improving impulse control and attention in people that are deficient in this area will make it easier for them to stay abstinent. People who don’t have ADHD don’t have as much room for improvement in that particular aspect of their brain function, so this treatment doesn’t add much.

Other studies have been done showing that people with less severe methamphetamine use disorder respond to bupropion better than more severe ones, individuals with comorbid opioid use disorder do better in reducing their use with atomoxetine compared to those without, and those with comorbid alcohol use disorder and of older age respond better to a selective serotonin reuptake inhibitor in terms of their cocaine use than younger folks and people without AUD.

Nicotine Use Disorders

Studies on smokers have also identified treatment-matching effects. For example, a recent study found that topiramate (which, incidentally, is not commonly used to treat nicotine use disorder) reduced smoking much more effectively in people sober from alcohol for over a year in those who had Type B alcoholism than those with Type A.

A growing subfield of nicotine use disorder study, called metabolism-informed care, proposes to guide medication choice based on a patient’s speed of nicotine metabolism. In one study, “normal metabolizers,” as measured by a blood draw, received varenicline; slow metabolizers received the nicotine patch. Their approach improved treatment matching, and the authors concluded that this method holds promise for improving overall treatment outcomes (e.g. quit rates).

Other

There are ongoing studies on people who have problems with opioids, cannabis, and club drugs, in terms of finding useful and valid subtypes, but, so far, little of this research has been extended further to defining treatment choices. There is even a growing literature on gambling disorder subtypes.

Conclusion

Most of the clinically-relevant progress in the illicit substance and nicotine use disorder realm in terms of subtyping and treatment matching is in the area of stimulant use disorder. Namely, comorbid depression, comorbid AUD, older age, and comorbid ADHD might alert the clinician to consider prioritizing one medication over another for people with illicit stimulant problems.

Although less clinically relevant, because of the cost of testing, but still with potential for utility, genetic markers have shown promise for stimulants too. In nicotine use disorder, Babor typology in people in recovery from AUD trying to quit smoking and nicotine metabolism rates are characteristics that hold promise for helping clinicians choose between medication options. It will be interesting to see where research takes us next!