Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats - Thao Vu; Suneeta Godbole; Lieselot L. G. Carrette; Lisa Maturin; Olivier George; Laura M. Saba; Katerina Kechris (Metabolites)
Background: Oxycodone has an elevated abuse liability profile compared to other prescription opioid medications. However, many human and rodent metabolomics studies have not been specifically focused on oxycodone. Objectives: Investigating metabolomics changes associated with oxycodone exposure can provide insights into biochemical mechanisms of the addiction cycle and prognosis prediction. Methods: Plasma samples from 16 rats at pre-exposure and intoxication time points were profiled on the Metabolon platform. A total of 941 metabolites were characterized. We employed a k-Nearest Neighbor imputation to impute metabolites with low levels of missingness and binarized metabolites with moderate levels of missingness, respectively. Results: Of the 136 binarized metabolites, 6 showed differential abundance (FDR < 0.05), including 5 that were present at pre-exposure but absent at intoxication (e.g., adenine), while linoleamide (18:2n6) exhibited the opposite behavior. Among the 798 metabolites with low levels of missingness, 364 showed significant changes between pre-exposure and intoxication (FDR < 0.01), including succinate, oleamide, and sarcosine. We identified four pathways, including tryptophan metabolism, that were nominally enriched among the metabolites that change with oxycodone exposure (p < 0.05). Furthermore, we identified several metabolites that showed nominal correlations with the Addiction Index (composite of oxycodone behaviors): 17 at pre-exposure and 8 at intoxication. In addition, the changes in abundance between pre-exposure and intoxication time points of 9 metabolites were nominally correlated with the Addiction Index, including sphingomyelins, methylhistidines, and glycerols. Conclusions: In summary, not only were we able to capture oxy-induced changes in metabolic pathways using easily accessible blood samples, but we also demonstrated the potential of blood metabolomics to better understand addiction liability.