No abstract

Celecoxib, a nonsteroidal anti‐inflammatory drug, is frequently used to treat arthritis in humans with minimal gastrointestinal side effect compared to traditional NSAIDs. The primary aim of this study was to determine the pharmacokinetic profile of celecoxib—a selective cyclooxygenase‐2 (COX‐2) inhibitor in horses. Six horses were administered a single oral dose of celecoxib at 2 mg/kg (body weight). After oral dosing, the drug reached a maximum concentration (mean ± SD) in blood of 1,088 ± 324 ng/ml in 4.58 hr. The elimination half‐life was 13.60 ± 3.18 hr, and the area under the curve was 24,142 ± 1,096 ng hr ml−1. The metabolism of celecoxib in horses was via a single oxidative pathway in which the methyl group of celecoxib is oxidized to a hydroxymethyl metabolite and is further oxidized to form a carboxylic acid metabolite. Celecoxib is eliminated mainly through faeces as unchanged drug and as metabolites in urine. Therefore, instructions for a detection time following therapeutic dosing of celecoxib can be set by the racing practitioner and veterinarians to control illegal use in horse racing based on the results of this study.

Atorvastatin and ezetimibe are lipid-lowering drugs prescribed for the treatment of hypercholesterolemia. An LC-MS-MS method has been developed and validated for the simultaneous estimation of atorvastatin and ezetimibe in human plasma using pitavastatin as an internal standard. Liquid-liquid extraction was used for the purification and preconcentration of analytes from human plasma matrix. The chromatographic separation was achieved within 3.0 min by an isocratic mobile phase consisting of 0.2% formic acid in water-acetonitrile (30:70, v/v), flowing through Agilent Eclipse-plus C18, 100 × 4.6 mm, 3.5 µm analytical column, at a flow rate of 0.6 mL min(-1). Multiple reaction monitoring transitions were measured in the positive ion mode for atorvastatin and internal standard, while ezetimibe was measured in negative ion mode. A detailed validation of the method was performed as per US-FDA guidelines and the standard curves were found to be linear in the range of 0.2-30.0 ng mL(-1) with a mean correlation coefficient >0.999 for both drugs. In human plasma, atorvastatin and ezetimibe were stable for at least 36 days at -70 ± 5 °C and 6 h at ambient temperature. After extraction from plasma, the reconstituted samples of atorvastatin and ezetimibe were stable in an autosampler at ambient temperature for 6 h. Also, the cited drugs were stable in plasma samples upon subjecting to three freeze thaw cycles. The method is simple, specific, sensitive, precise, accurate and suitable for bioequivalence and pharmacokinetic studies of this combination.

A simple, rapid, and sensitive spectrofluorometric method was developed for the determination of three antihyperlipidemic drugs, namely, rosuvastatin calcium (RSV), ezetimibe (EZE), and pitavastatin calcium (PIT). The method is based on measuring the native fluorescence of the cited drugs at their optimum excitation and emission wavelengths. The fluorescence intensity was measured atλem 362 nm, 309 nm, and 373 nm upon excitation atλex 315 nm, 260 nm, and 245 nm for RSV, EZE, and PIT, respectively. The calibration graphs were linear over the concentration ranges 0.50–10.0, 0.25–4.0, and 0.10–3.00 μg mL−1for RSV, EZE, and PIT, respectively. Besides, a spectrofluorometric method for the simultaneous determination of RSV and EZE was developed. The fluorescence was measured atλem 309 nm for EZE and 432 nm for RSV upon excitation atλex 260 nm for both. The proposed methods were applied to the determination of the cited drugs either in bulk and pharmaceutical preparations.

The objective of this study was to use machine learning to identify feelings of energy and fatigue using single-task walking gait. Participants (n = 126) were recruited from a university community and completed a single protocol where current feelings of energy and fatigue were measured using the Profile of Moods Survey–Short Form approximately 2 min prior to participants completing a two-minute walk around a 6 m track wearing APDM mobility monitors. Gait parameters for upper and lower extremity, neck, lumbar and trunk movement were collected. Gradient boosting classifiers were the most accurate classifiers for both feelings of energy (74.3%) and fatigue (74.2%) and Random Forest Regressors were the most accurate regressors for both energy (0.005) and fatigue (0.007). ANCOVA analyses of gait parameters comparing individuals who were high or low energy or fatigue suggest that individuals who are low energy have significantly greater errors in walking gait compared to those who are high energy. Individuals who are high fatigue have more symmetrical gait patterns and have trouble turning when compared to their low fatigue counterparts. Furthermore, these findings support the need to assess energy and fatigue as two distinct unipolar moods as the signals used by the algorithms were unique to each mood.