What is known and objective: Limited sampling strategies (LSS), using few sampling times after dosing, have been used to reliably predict the isoniazid area under the 24hour concentration-time curve (AUC). Experience with isoniazid is very limited, and no LSS has been developed in south-Mediterranean populations. Hence, we aimed to develop an accurate and convenient LSS for predicting isoniazid AUC in Tunisian patients with extrapulmonary tuberculosis.
Methods: Pharmacokinetic profiles consisting of six blood samples each, collected during the 24-hour dosing interval, were obtained from 25 (6 men and 19 women) Tunisian patients with extrapulmonary tuberculosis. The AUC was calculated according to the linear trapezoidal rule. The isoniazid concentrations at each sampling time were correlated by a linear regression analysis with the measured AUC. We analysed all the developed models for their ability to estimate the isoniazid AUC. Error indices including the percentage of Mean Absolute Prediction Error (%MAE) and the percentage of Root Mean Squared Prediction Error (%RMSE) were used to evaluate the predictive performance. The agreement between predicted and measured AUCs was investigated using Bland and Altman and mountain plot analyses.Results and discussion: Among the 1-time-point estimations, the C 3 -predicted AUC showed the highest correlation with the measured one (r 2 = .906, %MAE = 10.45% and %RMSE = 2.69%). For the 2-time-point estimations, the model including the C 2 and C 6 provided the highest correlation between predicted and measured isoniazid AUC (r 2 = .960, %MAE = 8.02% and %RMSE = 1.75%). The C 0 /C 3 LSS model provided satisfactory correlation and agreement (r 2 = .930, %MAE = 10.19% and %RMSE = 2.32%).The best multilinear regression model for predicting the full isoniazid AUC was found to be the combination of 3 time points: C 0 , C 1 and C 6 (r 2 = .992, %MAE = 4.06% and %RMSE = 0.80%). The use of a 2-time-point LSS to predict AUC in our population could be sufficient. C 2 /C 6 combination has shown the best correlation but the
Background and Aim: Natural products continue to be a primary resource in biomedicine and biotechnology. The marine environment is highly reserve for novel pharmaceutical and medical compounds. The aim of this work was to identify bioactive components from the brown seaweed Padina pavonica with specific pharmacological potential. Methods: In the present study, we investigated the efficacy of polyphenol fraction from Padina pavonica for in vivo anti-inflammatory activity using the carrageen an-induced paw edema model in rats and there in vitro antioxidant activity using two methods: DPPH radical scavenging assay and ferric reducing antioxidant power (FRAP) [8]. Results: The polyphenol-rich fraction from the brown seaweed Padina pavonica exhibits a significant anti-inflammatory activity at the dose of 100 and 200 mg/kg and the maximum reduction of the edema was observed at the third hour with 53.49% and 58.6% of inhibition, respectively. Along with, we were interested in the investigation of the antioxidant activity. The DPPH radical-scavenging assay shows that the polyphenol fractions have an interesting cavenging activity at a low concentration (0.25 mg/mL). In addition, the Ferric reducing antioxidant power (FRAP) method reveals an antioxidant activity with IC50 = 0.4 mg/mL. Conclusions: These findings indicate that the polyphenol fraction of Padina pavonica is a promising bio source of compounds with anti-inflammatory and antioxidant potential; this may be useful as a candidate for the developing of potential therapeutic products. Keywords: Brown Seaweeds; Padina pavonica; Anti-Inflammatory Activity; Antioxidant Activity
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