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Review
. 2022 Feb 25:12:752826.
doi: 10.3389/fphar.2021.752826. eCollection 2021.

Pharmacokinetics of Caffeine: A Systematic Analysis of Reported Data for Application in Metabolic Phenotyping and Liver Function Testing

Jan Grzegorzewski  1 , Florian Bartsch  1 , Adrian Köller  1 , Matthias König  1
Affiliations
Review

Pharmacokinetics of Caffeine: A Systematic Analysis of Reported Data for Application in Metabolic Phenotyping and Liver Function Testing

Jan Grzegorzewski et al. Front Pharmacol. .

Abstract

Caffeine is by far the most ubiquitous psychostimulant worldwide found in tea, coffee, cocoa, energy drinks, and many other beverages and food. 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. Besides its stimulating properties, two important applications of caffeine are metabolic phenotyping of cytochrome P450 1A2 (CYP1A2) and liver function testing. An open challenge in this context is to identify underlying causes of the large inter-individual variability in caffeine pharmacokinetics. 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. 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. 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). 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. 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. In conclusion, our data set and analyses provide important resources which could enable more accurate caffeine-based metabolic phenotyping and liver function testing.

Keywords: CYP1A2; caffeine; drug-disease interactions; drug-drug interactions; liver function test; oral contraceptives; pharmacokinetics; smoking.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
PRISMA flow diagram. (A) Overview of search strategy and inclusion/exclusion criteria applied in the systematic analysis of pharmacokinetics of caffeine. The applied workflow resulted in n = 141 included studies. (B) Subsets of the included studies were used for the various analyses. For details see the Section 2.
FIGURE 2
FIGURE 2
Overview of studies in the caffeine pharmacokinetics data set. The data set consists of 141 studies containing 500 groups, 4,714 individuals, 387 interventions, 24 ,571 outputs, and 846 time-courses. The circular plot is structured in stripes and rings. Each stripe represents a different study, each ring the amount of different data types for the respective study. The dots represent the respective amount of data with the dot size corresponding to the number of entries per dot. The rings contain the following information for the respective study (A) name of the study; (B) number of outputs (pharmacokinetics parameters and other measurements). 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. Purple dots represent participants with individual data, green dots represent collectively reported participants; (E) number of interventions applied to the participants in the study. For additional information see Table 1.
FIGURE 3
FIGURE 3
Dose-dependent effect of smoking and oral contraceptive use on caffeine pharmacokinetics. A stratified meta-analysis of caffeine clearance (A) and half-life (B) depending on reported smoking and oral contraceptive use and dose was performed. Black: Control subjects are non-smokers and not taking oral contraceptives; Orange: Oral contraceptive users independent of smoking status (smokers and non-smokers). Blue: Smoking are smokers not consuming oral contraceptives. Grey: Unknown data correspond to subjects with unreported smoking and oral contraceptive status. Marker shape, and size describe the datatype and group size, respectively. Data representing smokers or oral contraceptive consumers is labeled by the respective study name. 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. The color intensity of each bin represents the number of subjects falling in a given hexagonal bin area. Data selection criteria and visualization are described in the Section 2.
FIGURE 4
FIGURE 4
Effect of type of assay on pharmacokinetic parameters. A stratified meta-analysis of caffeine clearance (A) and half-life (B) depending on reported assay type and dose. Orange: Measurements performed with mass spectrometry (LC MS, LC MS/MS, HPLC MS/MS, HPLC-ESI-MS/MS, ES MS/MS). Blue: Measurements performed with immunoassay (RIA, EMIT). Black: Separation performed with chromatography but quantification assay was not reported (HPLC, RP-HPLC, GLC, CGC, CC, GC). Marker shape, and size describe the datatype and group size, respectively. Data with reported quantification method are labeled by the respective study name. The hexagonal bin plots in the lower panel (C, D) correspond to the subset of data for the mass spectrometry quantification, immunoassay quantification and chromatography. The color intensity of each bin represents the number of subjects falling in a given hexagonal bin area. Data selection criteria and visualization are described in the Section 2.
FIGURE 5
FIGURE 5
Effects of caffeine-drug and caffeine-disease interactions. (A) Caffeine-drug interactions based on the caffeine area under the concentration curve (AUC). Data is stratified based on co-administration of drugs with caffeine and dose. Violet: caffeine administrated as part of a drug cocktail. Common co-administrations are dextrometorphan, metoprolol, midazolam, omeprazole, and warfarin.; Black: single caffeine administration (no co-administration); Brown: co-administration with an inducing effect on the elimination of caffeine; Green: co-administration with an inhibiting effect on the elimination of caffeine; Blue: co-administrations with no effect on the pharmacokinetics of caffeine; Orange co-administration of oral contraceptives. (B) Caffeine-disease interactions based on caffeine clearance and dose. Data was stratified based on the health status and reported diseases, with black data points corresponding to healthy subjects. (C) Effect sizes of caffeine-drug interaction for studies with a controlled study design, mostly randomized control trials (RCT). The effect size is based on the log AUC ratio between caffeine application alone and caffeine with co-administration of the respective drug. The drugs were characterized as having either a strong, moderate, weak or no effect. Strong, moderate and weak inhibitors increase the AUC ≥5 -fold, ≥2 to <5 -fold, ≥1.25 to <2 -fold, respectively. (D) Effect size of caffeine-disease interactions for studies with a controlled study design, mostly case-controlled studies. The effect size is based on the log clearance ratio between subjects with and without a specific condition/disease. The diseases were characterized as having either a strong, moderate, weak or no effect. Strong, moderate and weak effect decreased the clearance by ≥80 percent, ≥50 to <80 percent and ≥20 to <50 percent, respectively. Data selection criteria and visualization are described in the Section 2.
FIGURE 6
FIGURE 6
Meta-analysis of caffeine and paraxanthine concentrations in plasma, serum and saliva. (A) Caffeine concentrations in saliva versus caffeine concentrations in plasma or serum. Individual data points come from a single investigation taken at identical times after caffeine dosing. Marker shape encodes the different study. Markers are color-coded by caffeine dose in mg. Empty markers correspond to data in which dosage was reported per body weight but no information on the subject weight was available. (B) Paraxanthine concentrations in saliva versus paraxanthine concentrations in plasma or serum analogue to (A). (C) Caffeine clearance calculated from caffeine concentrations in saliva versus plasma or serum clearance. The panels A, B and C are in linear scale with a log-log inlet showing the same data. The dashed line in A, B and C represents a linear regression (y = A ⋅ x) with wide shaded area being 95% confidence interval of the sample variability and narrow shaded area the 95% confidence interval of the fitted mean of the scaling factor A. (D) Time dependency of the metabolic ratio paraxanthine/caffeine. Metabolic ratios are measured in plasma, serum, or saliva. Data points belonging to a single time course from a study are connected via a line. The dashed line corresponds to the linear regression log(y)= B ⋅ log(t)+ A. Jitter was applied on the time axes for better visibility of overlapping points. Data selection criteria and visualization are described in the Section 2.
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