PubMed · 14397496
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H ZAVERSNIK. 1955. [Experience with serpasil].. https://pubmed.ncbi.nlm.nih.gov/14397496/
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In a traditional pharmacokinetics (PK), bioavailability (BA) /bioequivalence (BE) study, the same number of time points and sampling times are used for each subject. Often, an indirect inference is then made on some PK parameters such as area under the plasma concentration curve (AUC), maximum plasma concentration (C(max)), time to maximum plasma concentration (T(max)) or half-life. However, since these PK parameters are summarized from repeated measurements, a lot of information can be lost. The indirect inferences on some PK parameters are not always accurate. Taking the repeated measurements of the concentration curve into consideration, a functional linear model has been developed to compare concentration curves directly instead of the PK parameters. Considering the nature of repeated measurements, a multiple testing procedure is proposed to assess the equality of two concentration curves. A real data set is used to demonstrate the proposed procedure.
Indapamide and internal standard (5-chloro-2-methoxy-N-[2-(4-sulphamoylphenyl)ethyl]benzamide) were isolated from plasma by a single step liquid-liquid extraction in t-butyl methyl ether. The chromatographic separation was achieved on a reversed-phase C(18) monolithic column with a mobile phase consisting in a methanol/aqueous 0.1% formic acid mixture and a flow rate of 0.8 ml/min, in isocratic conditions, within 11 min. Target compounds were transferred in an ion trap analyzer via an atmospheric pressure electrospray interface (AP-ESI). The mass analyzer was used in a selected reaction monitoring (SRM) mode, in order to enhance on detection selectivity. Whole method produces quantitation limit for indapamide of 1 ng/ml. Method was successfully applied to assess bioequivalence of two sustained release marketed pharmaceutical formulations of indapamide 1.5 mg coated tablets, carried-out in a single/multiple doses, randomized design.
Standard intent-to-treat analyses of randomized clinical trials can yield biased estimates of treatment efficacy and toxicity when not all patients comply with their assigned treatment. Flexible methods have been proposed which correct for this by modelling expected contrasts between an individual's observed outcome and his/her potential outcome in the absence of exposure. Because such comparisons often require untestable assumptions, a sensitivity analysis is warranted. We show how this can be performed in a meaningful and practically useful way. Following the approach of Molenberghs, Kenward and Goetghebeur in a missing data context, we evaluate the separate contributions of structural uninformativeness and sampling variation to uncertainty about the population parameters. This leads us to consider Honestly Estimated Ignorance Regions (HEIRs) and Estimated Uncertainty RegiOns (EUROs), respectively. We use the results to estimate the causal effect of observed exposure on successful blood pressure reduction in a randomized controlled clinical trial with partial non-compliance.