Pharmacokinetics of caffeine self-administered in overdose in a Japanese patient admitted to hospital

Interaction of a caffeine overdose with clinical doses of contraceptive ethinyl estradiol in a young woman

Aim

The overdose of caffeine, a cytochrome P450 1A2 probe, in young women has become a problem. The aim of this study was to evaluate possible drug interactions between intentionally overdosed caffeine (12 g) and oral contraceptive doses of ethinyl estradiol prescribed to a young woman in a suicide attempt.

Methods

The serum concentrations of caffeine in the patient and the time-dependent ethinyl estradiol inhibition of caffeine oxidation in vitro were evaluated.

Results

The serum concentration of caffeine 4 h after overdose was 136 μg/mL; from the data obtained between 4 and 28 h after overdose, the half-life was estimated to be 33 h, which is many times larger than the normal value. Prescribed ethinyl estradiol prolonged caffeine elimination in vivo and inhibited paraxanthine formation, as evidenced by the low serum concentrations. In human liver microsomes, ethinyl estradiol (50 nM) inhibited half of caffeine N 3 -demethylation. Pre-incubation of human liver microsomes with ethinyl estradiol resulted in a powerful time-dependent inhibitory effect on caffeine N 3 -demethylation in human liver microsomes.

Conclusion

These results suggest that a prescription history of contraceptives at clinical doses may have a strong effect on the pharmacokinetics of overdosed caffeine in young women, resulting in dangerous drug interactions.

Updated in Silico Prediction Methods for Fractions Absorbed and Key Input Parameters of 355 Disparate Chemicals for Physiologically Based Pharmacokinetic Models for Time-Dependent Plasma Concentrations after Virtual Oral Doses in Humans

Human metabolic profiles for substances such as toxic food-derived compounds are usually allometrically extrapolated from traditionally determined in vivo rat concentration profiles. To evaluate internal exposures in humans without any reference to experimental data, physiologically based pharmacokinetic (PBPK) modeling could be used if the model input parameters could be estimated in silico. This approach would simplify the use of PBPK models for forward dosimetry after oral doses. In this study, the in silico estimation of input parameters for PBPK models (i.e., fraction absorbed × intestinal availability, absorption rate constants, and volumes of the systemic circulation) was updated for an panel of 355 chemicals (212 previously analyzed and 143 additional substances) using a light gradient boosting machine learning algorithms (LightGBM) based on between 11 and 29 in silico-calculated chemical descriptors. Simplified human PBPK models were then used to calculate virtual maximum plasma concentrations (C_max) and areas under the concentration-time curve (AUC) based on two sets of input parameters, i.e., traditionally derived values from in vivo data and those calculated in silico using the current updated systems. Both sets of C_max and AUC data were well correlated (r = 0.87 and r = 0.73, respectively; p < 0.01, n = 355). Therefore, input parameters for human PBPK models for a diverse range of compounds could be successfully estimated using chemical descriptors and in silico tools. This approach to pharmacokinetic modeling has potential for application in computational toxicology and in the clinical setting for assessing the potential risk of general chemicals.

A successful experience using labetalol and hemodialysis to treat near-fatal caffeine poisoning: A case report with toxicodynamics

Caffeine poisoning is relatively rare, and a near-fatal caffeine overdose is highly uncommon. We present an 18-year-old male who attempted suicide with 295 mg/kg pure caffeine powder (lethal oral dose: 150-200 mg/kg) and was successfully rescued. He presented with seizures, refractory supraventricular tachycardia and hypertension for 6 h with no response to medications and cardioversion. Even with the high level of caffeine, labetalol, which is seldom administered as a treatment for caffeine poisoning-induced tachycardia, successfully relieved refractory tachycardia. Then, hemodialysis ultimately eliminated serum caffeine and completely alleviated caffeine-related central nervous system toxicity. We discuss the clinical symptoms, management and toxicodynamics based on the concentration of caffeine and its metabolites in serum and urine.

Machine Learning Prediction of the Three Main Input Parameters of a Simplified Physiologically Based Pharmacokinetic Model Subsequently Used to Generate Time-Dependent Plasma Concentration Data in Humans after Oral Doses of 212 Disparate Chemicals

Physiologically based pharmacokinetic (PBPK) modeling has the potential to play significant roles in estimating internal chemical exposures. The three major PBPK model input parameters (i.e., absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearances) were generated in silico for 212 chemicals using machine learning algorithms. These input parameters were calculated based on sets of between 17 and 65 chemical properties that were generated by in silico prediction tools before being processed by machine learning algorithms. The resulting simplified PBPK models were used to estimate plasma concentrations after virtual oral administrations in humans. The estimated absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearance values for the 212 test compounds determined traditionally (i.e., based on fitting to measured concentration profiles) and newly estimated had correlation coefficients of 0.65, 0.68, and 0.77 (p < 0.01, n = 212), respectively. When human plasma concentrations were modeled using traditionally determined input parameters and again using in silico estimated input parameters, the two sets of maximum plasma concentrations (r = 0.85, p < 0.01, n = 212) and areas under the curve (r = 0.80, p < 0.01, n = 212) were correlated. Virtual chemical exposure levels in liver and kidney were also estimated using these simplified PBPK models along with human plasma levels. These results indicate that the PBPK model input parameters for humans of a diverse set of compounds can be reliability estimated using chemical descriptors calculated using in silico tools.