Studying Sex Differences in Rodent Models of Addictive Behavior

Abstract

Animal models of addictive behaviors are useful for uncovering neural mechanisms involved in the development of dependence and for identifying risk factors for drug abuse. One such risk factor is biological sex, which strongly moderates drug self-administration behavior in rodents. Female rodents are more likely to acquire drug self-administration behaviors, consume higher amounts of drug, and reinstate drug-seeking behavior more readily. Despite this female vulnerability, preclinical addiction research has largely been done in male animals. The study of sex differences in rodent models of addictive behavior is increasing, however, as more investigators are choosing to include both male and female animals in experiments. This commentary is meant to serve as an introductory guide for preclinical investigators new to the study of sex differences in addiction. We provide an overview of self-administration models, a broad view of female versus male self-administration behaviors, and suggestions for study design and implementation. Inclusion of female subjects in preclinical addiction research is timely, as problem drug and alcohol use in women is increasing. With proper attention, design, and analysis, the study of sex differences in addiction has the potential to uncover novel neural mechanisms and lead to greater translational success for addiction research. © 2021 Wiley Periodicals LLC.

Keywords: addiction; alcohol; drug abuse; rodent; self-administration; sex differences.

Figure 1

Considerations for studying sex differences in drug self-administration. Studies of rodent drug self-administration should generally include animals of both sexes, with equal numbers of male and female animals run concurrently. It is not necessary to increase the planned sample size of the study unless preliminary analyses reveal trends or statistically significant results that suggest an effect of sex on the measure of interest. When sex differences are not revealed, data from males and females can be combined for final analysis and visualization. When sex differences are present, additional animals should be tested to attain the statistical power to detect an effect of sex, and all data analyses and visualization should include sex as a variable. All analyses involving sex (including those that were not statistically significant) should be clearly reported in the main body of any published reports on the dataset.