Toxicokinetics of U-47700, tramadol, and their main metabolites in pigs following intravenous administration: is a multiple species allometric scaling approach useful for the extrapolation of toxicokinetic parameters to humans?

New synthetic opioids (NSOs) pose a public health concern since their emergence on the illicit drug market and are gaining increasing importance in forensic toxicology. Like many other new psychoactive substances, NSOs are consumed without any preclinical safety data or any knowledge on toxicokinetic (TK) data. Due to ethical reasons, controlled human TK studies cannot be performed for the assessment of these relevant data. As an alternative animal experimental approach, six pigs per drug received a single intravenous dose of 100 µg/kg body weight (BW) of U-47700 or 1000 µg/kg BW of tramadol to evaluate whether this species is suitable to assess the TK of NSOs. The drugs were determined in serum and whole blood using a fully validated method based on solid-phase extraction and LC-MS/MS. The concentration-time profiles and a population (pop) TK analysis revealed that a three-compartment model best described the TK data of both opioids. Central volumes of distribution were 0.94 L/kg for U-47700 and 1.25 L/kg for tramadol and central (metabolic) clearances were estimated at 1.57 L/h/kg and 1.85 L/h/kg for U-47700 and tramadol, respectively. The final popTK model parameters for pigs were upscaled via allometric scaling techniques. In comparison to published human data, concentration-time profiles for tramadol could successfully be predicted with single species allometric scaling. Furthermore, possible profiles for U-47700 in humans were simulated. The findings of this study indicate that unlike a multiple species scaling approach, pigs in conjunction with TK modeling are a suitable tool for the assessment of TK data of NSOs and the prediction of human TK data.

Influence of the skeletal muscle index on pharmacokinetics and toxicity of fluorouracil.

BACKGROUND: The body composition of patients has been associated with tolerability and effectiveness of anticancer therapy. This study aimed to assess the influence of the skeletal muscle index (SMI) on the pharmacokinetics and toxicity of fluorouracil. METHODS: Patients treated in an oncological practice with fluorouracil-based chemotherapy and undergoing therapeutic drug monitoring were retrospectively investigated. Computed tomography images were analyzed to measure abdominal skeletal muscle areas in Hounsfield units for the psoas major muscle, back and total skeletal muscle to determine the SMI. For the latter, an automated segmentation method was used additionally. SMI measures were tested as covariates on fluorouracil clearance in a population pharmacokinetic model. Furthermore, regression analyses were performed to analyze the influence of SMI measures on the probability of clinically relevant adverse events (CTCAE grades ≥ 2). RESULTS: Fluorouracil plasma concentrations of 111 patients were available. Covariate analyses showed significant improvements of the model fit by all SMI measures. However, interindividual variability of fluorouracil clearance was only slightly reduced, whereas the SMI of the back muscle showed the largest reduction (-1.1 percentage points). Lower SMI values of the back muscle increased the probability for polyneuropathy and lower SMI of the psoas increased the probability for fatigue. CONCLUSIONS: Our results suggest that pharmacokinetics and toxicity of fluorouracil may be associated with specific SMI measures which deserve further investigation.

Model-Based Analysis of SARS-CoV-2 Infections, Hospitalization and Outcome in Germany, the Federal States and Districts.

The coronavirus disease 2019 (COVID-19) pandemic challenged many national health care systems, with hospitals reaching capacity limits of intensive care units (ICU). Thus, the estimation of acute local burden of ICUs is critical for appropriate management of health care resources. In this work, we applied non-linear mixed effects modeling to develop an epidemiological SARS-CoV-2 infection model for Germany, with its 16 federal states and 400 districts, that describes infections as well as COVID-19 inpatients, ICU patients with and without mechanical ventilation, recoveries, and fatalities during the first two waves of the pandemic until April 2021. Based on model analyses, covariates influencing the relation between infections and outcomes were explored. Non-pharmaceutical interventions imposed by governments were found to have a major impact on the spreading of SARS-CoV-2. Patient age and sex, the spread of variant B.1.1.7, and the testing strategy (number of tests performed weekly, rate of positive tests) affected the severity and outcome of recorded cases and could reduce the observed unexplained variability between the states. Modeling could reasonably link the discrepancies between fine-grained model simulations of the 400 German districts and the reported number of available ICU beds to coarse-grained COVID-19 patient distribution patterns within German regions.