Pharmacometabolomics with a combination of PLS-DA and random forest algorithm analyses reveal meloxicam alters feline plasma metabolite profiles

Repeated administration of meloxicam to cats is often limited by the potential damage to multiple organ systems. Identifying molecules that predict the adverse effects of meloxicam would help to monitor and individualize its administration, maximizing meloxicam's beneficial effects. The objectives of this study were to (a) determine if the repeated administration of meloxicam to cats alters the plasma metabolome and (b) identify plasma metabolites that may serve to monitor during the administration of meloxicam in cats. Purpose bred young adult cats (n = 12) were treated with meloxicam at 0.3 mg/kg or saline subcutaneously once daily for up to 17 days. An untargeted metabolomics approach was applied to plasma samples collected prior to and at designated time points after meloxicam or saline administration. To refine the discovery of biomarkers, the machine-learning algorithms, partial least squares discriminant analysis (PLS-DA) and random forest (RF), were trained and validated using a separate unrelated group of meloxicam- and saline-treated cats (n = 8). A total of 74 metabolites were included in the statistical analysis. Metabolomic analysis shows that the repeated administration of meloxicam alters multiple substances in plasma, including nonvolatile organic acids, aromatic amino acids, monosaccharides, and inorganic compounds as early as four days following administration of meloxicam. Seventeen plasma molecules were able to distinguish meloxicam-treated from saline-treated cats. The metabolomic changes discovered in this study may help to unveil unknown mechanisms of NSAID-induced side effects. In addition, some metabolites could be valuable for individualizing the administration of meloxicam to cats to mitigate adverse effects.