Biological variation is ubiquitous in nature. Despite highly standardized breeding and husbandry under controlled environmental conditions, phenotypic diversity exists in laboratory mice and rats just as it does in humans. The resulting inter-individual variability affects various characteristics of animal disease models, including the responsiveness to drugs. Thus, the common practice of averaging data within an experimental group can lead to misinterpretations in neuroscience and other research fields. In this commentary, the impact of inter-individual variation in drug responsiveness is illustrated by examples from the testing of antiseizure medications in rodent temporal lobe epilepsy models. Individual mice and rats rendered epileptic by treatment according to standardized protocols fall into groups that either do or do not respond to antiseizure medications, thus mimicking the clinical situation in patients with epilepsy. Population responses are not normally distributed, and divergent responding is concealed in averages subjected to parametric statistical tests. Genetic, epigenetic, and environmental factors are believed to contribute to inter-individual variation in drug response but the specific molecular and physiological causes are not well understood. Being aware of inter-individual variability in rodents allows an improved interpretation of both behavioral phenotypes and drug effects in a pharmacological experiment.

Bipolar disorder (BD) is a neuropsychiatric mood disorder manifested by recurrent episodes of mania and depression. More than half of BD patients are non-responsive to lithium, the first-line treatment drug, complicating BD clinical management. Given its unknown etiology, it is pertinent to understand the genetic signatures that lead to variability in lithium response. We discovered a set of differentially expressed genes (DEGs) from the lymphoblastoid cell lines (LCLs) of 10 controls and 19 BD patients belonging mainly to the immunoglobulin gene family that can be used as potential biomarkers to diagnose and treat BD. Importantly, we trained machine learning algorithms on our datasets that predicted the lithium response of BD subtypes with minimal errors, even when used on a different cohort of 24 BD patients acquired by a different laboratory. This proves the scalability of our methodology for predicting lithium response in BD and for a prompt and suitable decision on therapeutic interventions.