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.

Urinary chemical fingerprint left behind by repeated NSAID administration: Discovery of putative biomarkers using artificial intelligence.

Prediction and early detection of kidney damage induced by nonsteroidal anti-inflammatories (NSAIDs) would provide the best chances of maximizing the anti-inflammatory effects while minimizing the risk of kidney damage. Unfortunately, biomarkers for detecting NSAID-induced kidney damage in cats remain to be discovered. To identify potential urinary biomarkers for monitoring NSAID-based treatments, we applied an untargeted metabolomics approach to urine collected from cats treated repeatedly with meloxicam or saline for up to 17 days. Applying multivariate analysis, this study identified a panel of seven metabolites that discriminate meloxicam treated from saline treated cats. Combining artificial intelligence machine learning algorithms and an independent testing urinary metabolome data set from cats with meloxicam-induced kidney damage, a panel of metabolites was identified and validated. The panel of metabolites including tryptophan, tyrosine, taurine, threonic acid, pseudouridine, xylitol and lyxitol, successfully distinguish meloxicam-treated and saline-treated cats with up to 75-100% sensitivity and specificity. This panel of urinary metabolites may prove a useful and non-invasive diagnostic tool for monitoring potential NSAID induced kidney injury in feline patients and may act as the framework for identifying urine biomarkers of NSAID induced injury in other species.

Understanding the effect of repeated administration of meloxicam on feline renal cortex and medulla: A lipidomics and metabolomics approach.

Repeated administration of meloxicam can cause kidney damage in cats by mechanisms that remain unclear. Metabolomics and lipidomics are powerful, noninvasive approaches used to investigate tissue response to drug exposure. Thus, the objective of this study was to assess the effects of meloxicam on the feline kidney using untargeted metabolomics and lipidomics approaches. Female young-adult purpose-breed cats were allocated into the control (n = 4) and meloxicam (n = 4) groups. Cats in the control and meloxicam groups were treated daily with saline and meloxicam at 0.3 mg/kg subcutaneously for 17 days, respectively. Renal cortices and medullas were collected at the end of the treatment period. Random forest and metabolic pathway analyses were used to identify metabolites that discriminate meloxicam-treated from saline-treated cats and to identify disturbed metabolic pathways in renal tissue. Our results revealed that the repeated administration of meloxicam to cats altered the kidney metabolome and lipidome and suggest that at least 40 metabolic pathways were altered in the renal cortex and medulla. These metabolic pathways included lipid, amino acid, carbohydrate, nucleotide and energy metabolisms, and metabolism of cofactors and vitamins. This is the first study using a pharmacometabonomics approach for studying the molecular effects of meloxicam on feline kidneys.

Effect of fentanyl and lidocaine on the end-tidal sevoflurane concentration preventing motor movement in dogs.

OBJECTIVE To determine effects of fentanyl, lidocaine, and a fentanyl-lidocaine combination on the minimum alveolar concentration of sevoflurane preventing motor movement (MACNM) in dogs. ANIMALS 6 adult Beagles. PROCEDURES Dogs were anesthetized with sevoflurane in oxygen 3 times (1-week intervals). Baseline MACNM (MACNM-B) was determined starting 45 minutes after induction of anesthesia. Dogs then received 1 of 3 treatments IV: fentanyl (loading dose, 15 µg/kg; constant rate infusion [CRI], 6 µg/kg/h), lidocaine (loading dose, 2 mg/kg; CRI, 6 mg/kg/h), and the fentanyl-lidocaine combination at the same doses. Determination of treatment MACNM (MACNM-T) was initiated 90 minutes after start of the CRI. Venous blood samples were collected at the time of each treatment MACNM measurement for determination of plasma concentrations of fentanyl and lidocaine. RESULTS Mean ± SEM overall MACNM-B for the 3 treatments was 2.70 ± 0.27 vol%. The MACNM decreased from MACNM-B to MACNM-T by 39%, 21%, and 55% for fentanyl, lidocaine, and the fentanyl-lidocaine combination, respectively. This decrease differed significantly among treatments. Plasma fentanyl concentration was 3.25 and 2.94 ng/mL for fentanyl and the fentanyl-lidocaine combination, respectively. Plasma lidocaine concentration was 2,570 and 2,417 ng/mL for lidocaine and the fentanyl-lidocaine combination, respectively. Plasma fentanyl and lidocaine concentrations did not differ significantly between fentanyl and the fentanyl-lidocaine combination or between lidocaine and the fentanyl-lidocaine combination. CONCLUSIONS AND CLINICAL RELEVANCE CRIs of fentanyl, lidocaine, and the fentanyl-lidocaine combination at the doses used were associated with clinically important and significant decreases in the MACNM of sevoflurane in dogs.

Comparison of alfaxalone and propofol administered as total intravenous anaesthesia for ovariohysterectomy in dogs.

OBJECTIVE: To compare the anaesthetic and cardiopulmonary effects of alfaxalone with propofol when used for total intravenous anaesthesia (TIVA) during ovariohysterectomy in dogs. STUDY DESIGN: A prospective non-blinded randomized clinical study. ANIMALS: Fourteen healthy female crossbred bitches, aged 0.5-5 years and weight 16-42 kg. METHODS: Dogs were premedicated with acepromazine 0.01 mg kg(-1) and morphine 0.4 mg kg(-1). Anaesthesia was induced and maintained with either propofol or alfaxalone to effect for tracheal intubation followed by an infusion of the same agent. Dogs breathed spontaneously via a 'circle' circuit, with oxygen supplementation. Cardiopulmonary parameters (respiratory and heart rates, end-tidal carbon dioxide, tidal volume, and invasive blood pressures) were measured continuously and recorded at intervals related to the surgical procedure. Arterial blood samples were analysed for blood gas values. Quality of induction and recovery, and recovery times were determined. Non-parametric data were tested for significant differences between groups using the Mann-Whitney U-test and repeatedly measured data (normally distributed) for significant differences between and within groups by anova. RESULTS: Both propofol and alphaxalone injection and subsequent infusions resulted in smooth, rapid induction and satisfactory maintenance of anaesthesia. Doses for induction (mean ± SD) were 5.8 ± 0.30 and 1.9 ± 0.07 mg kg(-1) and for the CRIs, 0.37 ± 0.09 and 0.11 ± 0.01 mg kg(-1) per minute for propofol and alfaxalone respectively. Median (IQR) recovery times were to sternal 45 (33-69) and 60 (46-61) and to standing 74 (69-76) and 90 (85-107) for propofol and alphaxalone respectively. Recovery quality was good. Cardiopulmonary effects did not differ between groups. Hypoventilation occurred in both groups. CONCLUSIONS AND CLINICAL RELEVANCE: Following premedication with acepromazine and morphine, both propofol and alphaxalone produce good quality anaesthesia adequate for ovariohysterectomy. Hypoventilation occurs suggesting a need for ventilatory support during prolonged infusion periods with either anaesthetic agent.