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.

No abstract

Caffeine is by far the most ubiquitous psychostimulant worldwide found in tea, coffee, cocoa, energy drinks, and many other beverages. Caffeine is almost completely metabolized in the liver by the cytochrome P-450 enzyme system mainly to 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 147 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 (i) the alteration of caffeine pharmacokinetics with smoking and oral contraceptive use; (ii) drug-drug interactions with caffeine as possible confounding factors of caffeine pharmacokinetics or source of adverse effects; (iii) alteration of caffeine pharmacokinetics in disease; and (iv) 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.

Hypercholesterolemia is a multifaceted plasma lipid disorder with heterogeneous causes including lifestyle and genetic factors. A key feature of hypercholesterolemia is elevated plasma levels of low-density lipoprotein cholesterol (LDL-C). Several genetic variants have been reported to be associated with hypercholesterolemia, known as familial hypercholesterolemia (FH). Important variants affect the LDL receptor (LDLR), which mediates the uptake of LDL-C from the plasma, apoliporotein B (APOB), which is involved in the binding of LDL-C to the LDLR, and proprotein convertase subtilisin/kexin type 9 (PCSK9), which modulates the degradation of the LDLR. A typical treatment for hypercholesterolemia is statin medication, with simvastatin being one of the most commonly prescribed statins. In this work, the LDL-C lowering therapy with simvastatin in hypercholesterolemia was investigated using a computational modeling approach. A physiologically based pharmacokinetic model of simvastatin integrated with a pharmacodynamic model of plasma LDL-C (PBPK/PD) was developed based on extensive data curation. A key component of the model is LDL-C turnover by the liver, consisting of: hepatic cholesterol synthesis with the key enzymes HMG-CoA reductase and HMG-CoA synthase; cholesterol export from the liver as VLDL-C; de novo synthesis of LDLR; transport of LDLR to the membrane; binding of LDL-C by LDLR via APOB; endocytosis of the LDLR-LDL-C complex; recycling of LDLR from the complex. The model was applied to study the effects of simvastatin therapy in hypercholesterolemia due to different causes in the LDLR pathway corresponding to different subtypes of hypercholesterolemia. Model predictions of LDL-C lowering therapy were validated with independent clinical data sets. Key findings are: (i) hepatic LDLR turnover is highly heterogeneous among FH classes; (ii) despite this heterogeneity, simvastatin therapy results in a consistent reduction in plasma LDL-C regardless of class; and (iii) simvastatin therapy shows a dose-dependent reduction in LDL-C. Our model suggests that the underlying cause of hypercholesterolemia does not influence simvastatin therapy. Furthermore, our model supports the treatment strategy of stepwise dose adjustment to achieve target LDL-C levels. Both the model and the database are freely available for reuse.