Over the past two decades, the escalating prescription of opioid medications for pain management has culminated in a widespread opioid epidemic, significantly impacting public health, social dynamics, and economic stability. The urgent need for improved treatments for opioid addiction necessitates a deeper understanding of its biological underpinnings, with genetic variations playing a crucial role in individual susceptibility to opioid use disorder (OUD) and influencing clinical practices. In this study, we leverage the genetic diversity of four rat strains (ACI/N, BN/NHsd, WKY/N, and F344/N) to examine the contribution of genetic factors to oxycodone metabolism and addiction-like behaviors. We used the extended access to intravenous oxycodone self-administration procedure (12 h/day, 0.15 mg/kg/injection) to comprehensively characterize oxycodone-related behaviors and pharmacokinetics. We measured escalation of oxycodone self-administration, motivation for drug consumption, tolerance to the analgesic effects of oxycodone, withdrawal-induced hyperalgesia, and oxycodone-induced respiratory depression. Additionally, we examined oxycodone-seeking behavior after four weeks of withdrawal by reintroducing the animals to environmental and cue stimuli previously associated with oxycodone self-administration. The findings revealed notable strain differences in several behavioral measures, including oxycodone metabolism. Intriguingly, BN/NHsd and WKY/N strains exhibited similar drug intake and escalation patterns but displayed significant disparities in oxycodone and oxymorphone metabolism. Minimal sex differences were observed within strains, primarily relating to oxycodone metabolism. In conclusion, this study identifies strain differences in the behavioral responses and pharmacokinetics associated with oxycodone self-administration in rats, providing a robust foundation for identifying genetic and molecular variants associated with various facets of the opioid addiction process.

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.