Understanding individual variability in opioid responses: A call to arms

Abstract

Dr. Kelly Dunn and colleagues have published an intriguing exploration of heterogeneity and individual differences in the affective and side effects of opioids.¹ Their analysis is based on a rich psychopharmacological dataset in which 100 healthy volunteers received four doses of hydromorphone and placebo over five test days. It is vital and urgent that we understand how these pain management medications affect individuals to identify risks.

The debate on how opioids affect the experience of pleasure and pain has roots at least as far back as 1925, when Lorenz Kolb shared his observation that opioids rarely caused pleasure (as opposed to relief) in ‘any one except the emotionally unstable, the psychopath or the neurotic’ (p. 699).² After the development of randomized controlled trials, a seminal 1955 study measured the effects of double-blinded doses of morphine and heroin, among other drug classes, in 20 healthy male college students. The average response to opioids was not pleasure, but dysphoria.³

This early conclusion was not supported in a more nuanced investigation, however. Based on qualitative interviews with heroin-addicted individuals, observations of college students trying heroin for the first time, and a reanalysis of the 1955 RCT data, MacAuliffe⁴ argued that a significant minority of participants did report positive effects from opioids. He also provided evidence that negative initial responses to heroin were frequent in addicted populations, but that social support within groups of addicted individuals promoted perseverance (through several injections of heroin) until the opioid's desirable effects outweighed the initial nausea and vomiting it caused.

In the recent paper, Dunn and colleagues draw inspiration from the alcohol literature, where people with minimal responses to early drinking have been reported to have a higher risk of developing problematic alcohol use. They also draw on findings related to variation in the mu-opioid receptor gene, specifically the A118G polymorphism. Despite initial high expectations and early evidence linking the polymorphism to differential alcohol and nicotine responses,⁵ relatively few studies have examined the impact of A118G on acute opioid effects.⁶ The scarce evidence likely relates to increasing recognition that the explanatory value of individual genetic polymorphisms is typically insufficient for an average-sized drug study.

Inspired by Dunn et al.’s findings, we briefly revisited our own unpublished drug effects data relating to this polymorphism. In an early study on how opioids affect reward behaviours, we selected a high proportion of G allele carriers of this specific polymorphism.^{7, 8} A total of 49 healthy young men (27 AA and 22 G carriers) attended three sessions, where they received either 50 mg of the non-selective opioid antagonist naltrexone, an inert placebo or a morphine pill. Our morphine dose of 10 mg is an analgesic dose, comparable to the 2 and 4 mg doses of hydromorphone used in Dr. Dunn's study.

Consistent with Dunn et al.’s findings of higher stimulating and pleasant drug effects in G-carriers compared with AA-carrying participants, our group of G-carriers showed the highest morphine liking ratings (Figure 1A). Drug liking was significantly higher for morphine compared with placebo in G-carriers. In contrast, the AA group's average ratings were comparable between drugs. Unsurprisingly, given our limited number of participants for a candidate gene analysis, the interaction between genotype and drug type was not significant, nor did we find other convincing evidence linking the A116G polymorphism to addiction-relevant measures in our dataset. Exploration of genotype effects on other pleasure-related ratings revealed significant group * drug interactions that we show here for potential hypothesis generation: morphine-enhanced ratings of predicted pleasure for non-social rewards in AA carriers and for social rewards in G carriers (see Figure 1B,C). To measure drug effects on levels of predicted pleasure, we created a state visual analogue scale (VAS 0–100) version⁷ of the Snaith–Hamilton Pleasure Scale and analysed changes from pre-drug baseline.

The planned examination of the polymorphism-by-medication interaction onresponses to different reward modalities was eventually halted by our lab due to the realization that either very large samples or very precise measures of high relevance to the polymorphism in question are necessary to gain robust and reproducible findings. We were therefore excited and encouraged by Dr. Dunn et al.’s rationale and findings and would like to take this opportunity to issue a call to the larger field: let us pool our resources and datasets! As a start, we have made the data reported here openly available (osf.io/ny3jw/). Understanding how opioids affect individuals, both acutely and over time, is key to both pain management (a high proportion of patients terminate treatment after mere days due to intolerable side effects) and to understand the contexts and individual factors associated with opioids' abuse potential.

In addition to the rich, detailed and precise measurements routinely collected in the handful of outstanding psychopharmacology laboratories, such as those of Dr. Dunn, Dr. Harriet De Wit and Dr. Sandra Comer, relevant opioid effects data are available from studies by several medical specialities.⁹ Opioids remain vital to the management of acute, and for many, also chronic pain. It is high time that the opioid field take a leaf from the book of fields where researchers have harmonized data into large-scale databases. In addition to genetics, which has moved from candidate gene approaches to samples reaching hundreds of thousands, we can also look to neuroimaging and experimental psychology approaches such as the Consortium of Placebo Effects.¹⁰ Many important new insights were gained from this consortium due to the pooling of rich datasets into a database of individual brain and behavioural responses to placebo treatments for pain. A database resource on individual responses to opioids would ensure sufficient statistical power to both explore and replicate findings on how these vital medications act in different individuals and at different times.

Dr. Kelly Dunn and colleagues have made an impressive start to the important emerging field of individual differences in opioid effects.

The authors report no conflicts of interest.