Drug Interactions in Infectious Diseases 2011
DOI: 10.1007/978-1-61779-213-7_20
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Design and Data Analysis in Drug Interaction Studies

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Cited by 2 publications
(2 citation statements)
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“…The recommended strategy for analyzing comparative data from drug interaction studies is to adapt the confidence interval approach used in large bioequivalence studies [31], where the aim is to show that an interaction is not clinically meaningful by the similarity of exposure (AUC (0,∞) ) and C max . Confidence limits around mean ratios for within‐subject comparisons in crossover studies are constructed from the residual mean‐square error (MSE) term in anova , which is converted to a coefficient of variation, CV W .…”
Section: Methodsmentioning
confidence: 99%
“…The recommended strategy for analyzing comparative data from drug interaction studies is to adapt the confidence interval approach used in large bioequivalence studies [31], where the aim is to show that an interaction is not clinically meaningful by the similarity of exposure (AUC (0,∞) ) and C max . Confidence limits around mean ratios for within‐subject comparisons in crossover studies are constructed from the residual mean‐square error (MSE) term in anova , which is converted to a coefficient of variation, CV W .…”
Section: Methodsmentioning
confidence: 99%
“…Verapamil enhanced the Edoxaban (P‐gp substrate) distribution towards the brain observed in MDR1a/1b knockout mice (Mikkaichi et al, 2014). Dexamethasone enhanced the intestinal metabolism of Indinavir (up to 34%) may be due to the induction of P‐gp (Nix & Gallicano, 2011; Lin et al, 1999). P‐gp also enhanced the biliary excretion of Digoxin in MDR1a knockout mice (Lin, 2003; Liu & Hu, 2000).…”
Section: Role Of P‐glycoprotein On Drug Dispositionmentioning
confidence: 99%