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

[Xanthan Gum-based Food Thickeners Reduce Disintegration Time of Medical and OTC Loxoprofen Sodium Tablets]

Xanthan gum-based food thickeners have been reported to potentially interfere with tablet disintegration. Loxoprofen sodium (LOX) is widely used as an antipyretic analgesic and is expected to provide rapid pain relief. In this study, we aimed to investigate the impact of a xanthan gum-based food thickener on LOX tablet disintegration. We used four different brands each of medical and OTC-LOX tablets, each containing 60 mg of LOX as the sole active ingredient. Depending on the brand, tablet hardness varied between 50.1-96.6 N and was not associated with the disintegration time. Disintegration times for medical tablets not immersed in the food thickener were 536±215, 621±159, 348±22, 369±42 s and for OTC tablets, were 358±20, 336±13, 292±13, 172±27 s. Immersion in the food thickener for 15 min reduced medical tablet disintegration time to 177±46 and 233±150 s (the third and fourth brands were disintegrated during immersion), and that for OTC tablets to 77±40, 75±110, and 37±85 s (the fourth brand was disintegrated during immersion). Despite each tablet containing different pharmaceutical additives, no correlation was found between disintegration time and presence of superdisintegrants. The OTC tablet with a light anhydrous silicic acid coating exhibited the shortest disintegration time. Thus, the disintegration time of LOX tablets is accelerated when immersed in the xanthan gum-based food thickener, potentially leading to rapid pain relief for patients.

SeDeM expert system with I-optimal mixture design for oral multiparticulate drug delivery: An encapsulated floating minitablets of loxoprofen Na and its in silico physiologically based pharmacokinetic modeling

Introduction: A SeDeM expert tool-driven I-optimal mixture design has been used to develop a directly compressible multiparticulate based extended release minitablets for gastro-retentive drug delivery systems using loxoprofen sodium as a model drug. Methods: Powder blends were subjected to stress drug-excipient compatibility studies using FTIR, thermogravimetric analysis, and DSC. SeDeM diagram expert tool was utilized to assess the suitability of the drug and excipients for direct compression. The formulations were designed using an I-optimal mixture design with proportions of methocel K100M, ethocel 10P and NaHCO₃ as variables. Powder was compressed into minitablets and encapsulated. After physicochemical evaluation lag-time, floating time, and drug release were studied. Heckel analysis for yield pressure and accelerated stability studies were performed as per ICH guidelines. The in silico PBPK Advanced Compartmental and Transit model of GastroPlus™ was used for predicting in vivo pharmacokinetic parameters. Results: Drug release follows first-order kinetics with fickian diffusion as the main mechanism for most of the formulations; however, a few formulations followed anomalous transport as the mechanism of drug release. The in-silico-based pharmacokinetic revealed relative bioavailability of 97.0%. Discussion: SeDeM expert system effectively used in QbD based development of encapsulated multiparticulates for once daily administration of loxoprofen sodium having predictable in-vivo bioavailability.

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.

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

Background

Caffeine (0.1 g) is used as a central nervous system stimulant and as a nontoxic phenotyping probe for cytochrome P450 1A2. However, an increasing number of suicide attempts by caffeine overdose have been recently reported.

Case presentation

A 25-year-old woman (body weight, 43 kg) who intentionally took an overdose of 5.9 g caffeine as a suicide attempt was emergently admitted to Kyoto Medical Center. The plasma concentrations of caffeine and its primary metabolite, N-demethylated paraxanthine, in the current case were 100 and 7.3 μg/mL, 81 and 9.9 μg/mL, 63 and 12 μg/mL, and 21 and 14 μg/mL, at 12, 20, 30, and 56 h after oral overdose, respectively. The observed apparent terminal elimination half-life of caffeine during days 1 and 2 of hospitalization was 27 h, which is several times longer than the reported normal value. This finding implied nonlinearity of caffeine pharmacokinetics over such a wide dose range, which could affect the accuracy of values simulated by a simplified physiologically based pharmacokinetic model founded on a normal dose of 100 mg. Low serum potassium levels (2.9 and 3.5 mM) on days 1 and 2 may have been caused by the caffeine overdose in the current case.

Conclusions

The patient underwent infusion with bicarbonate Ringer’s solution and potassium chloride and was discharged on the third day of hospitalization despite taking a potentially lethal dose of caffeine. The virtual plasma exposures of caffeine estimated using the current simplified PBPK model were higher than the measured values. The present results based on drug monitoring data and additional pharmacokinetic predictions could serve as a useful guide in cases of caffeine overdose.