![]() ![]() (A) Caffeine-drug interactions based on the caffeine area under the concentration curve (AUC). Data selection criteria and visualization are described in the Section 2.Įffects of caffeine-drug and caffeine-disease interactions. The color intensity of each bin represents the number of subjects falling in a given hexagonal bin area. The hexagonal bin plots in the lower panel (C, D) correspond to the subset of data for the control, smoking, and oral contraceptive consuming subjects. Data representing smokers or oral contraceptive consumers is labeled by the respective study name. Marker shape, and size describe the datatype and group size, respectively. Grey: Unknown data correspond to subjects with unreported smoking and oral contraceptive status. Blue: Smoking are smokers not consuming oral contraceptives. Black: Control subjects are non-smokers and not taking oral contraceptives Orange: Oral contraceptive users independent of smoking status (smokers and non-smokers). A stratified meta-analysis of caffeine clearance (A) and half-life (B) depending on reported smoking and oral contraceptive use and dose was performed. ![]() For additional information see Table 1.ĭose-dependent effect of smoking and oral contraceptive use on caffeine pharmacokinetics. Purple dots represent participants with individual data, green dots represent collectively reported participants (E) number of interventions applied to the participants in the study. Red dots represent reported data, blue dots data calculated from time-courses reported in the study (C) number of time-courses (D) number of participants. The rings contain the following information for the respective study (A) name of the study (B) number of outputs (pharmacokinetics parameters and other measurements). The dots represent the respective amount of data with the dot size corresponding to the number of entries per dot. Each stripe represents a different study, each ring the amount of different data types for the respective study. The circular plot is structured in stripes and rings. The data set consists of 141 studies containing 500 groups, 4,714 individuals, 387 interventions, 24 ,571 outputs, and 846 time-courses. Overview of studies in the caffeine pharmacokinetics data set. In conclusion, our data set and analyses provide important resources which could enable more accurate caffeine-based metabolic phenotyping and liver function testing.ĬYP1A2 caffeine drug-disease interactions drug-drug interactions liver function test oral contraceptives pharmacokinetics smoking.Ĭopyright © 2022 Grzegorzewski, Bartsch , Köller and König. Specifically, we analyzed 1) the alteration of caffeine pharmacokinetics with smoking and use of oral contraceptives 2) drug-drug interactions with caffeine as possible confounding factors of caffeine pharmacokinetics or source of adverse effects 3) alteration of caffeine pharmacokinetics in disease and 4) the applicability of caffeine as a salivary test substance by comparison of plasma and saliva data. We demonstrate via multiple applications how the data set can be used to solidify existing knowledge and gain new insights relevant for metabolic phenotyping and liver function testing based on caffeine. The data set is enriched by meta-data on the characteristics of studied patient cohorts and subjects (e.g., age, body weight, smoking status, health status), the applied interventions (e.g., dosing, substance, route of application), measured pharmacokinetic time-courses, and pharmacokinetic parameters (e.g., clearance, half-life, area under the curve). Here we report the first integrative and systematic analysis of data on caffeine pharmacokinetics from 141 publications and provide a comprehensive high-quality data set on the pharmacokinetics of caffeine, caffeine metabolites, and their metabolic ratios in human adults. Data is urgently needed to understand and quantify confounding factors such as lifestyle (e.g., smoking), the effects of drug-caffeine interactions (e.g., medication metabolized via CYP1A2), and the effect of disease. ![]() An open challenge in this context is to identify underlying causes of the large inter-individual variability in caffeine pharmacokinetics. Besides its stimulating properties, two important applications of caffeine are metabolic phenotyping of cytochrome P450 1A2 (CYP1A2) and liver function testing. Caffeine is almost exclusively metabolized in the liver by the cytochrome P-450 enzyme system to the main product paraxanthine and the additional products theobromine and theophylline. Caffeine is by far the most ubiquitous psychostimulant worldwide found in tea, coffee, cocoa, energy drinks, and many other beverages and food.
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