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A liquid chromatography tandem mass spectrometry method for the analysis of ten kinase inhibitors (afatinib, axitinib, bosutinib,cabozantinib, dabrafenib, lenvatinib, nilotinib, osimertinib, ruxolitinib, and trametinib) in human serum and plasma for theapplication in daily clinical routine has been developed and validated according to the US Food and Drug Administration andEuropean Medicines Agency validation guidelines for bioanalytical methods. After protein precipitation of plasma samples withacetonitrile, chromatographic separation was performed at ambient temperature using a Waters XBridge® Phenyl 3.5μm(2.1×50 mm) column. The mobile phases consisted of water-methanol (9:1, v/v) with 10 mM ammonium bicarbonate as phase A andmethanol-water (9:1, v/v) with 10 mM ammonium bicarbonate as phase B. Gradient elution was applied at a flow rate of 400μL/min. Analytes were detected and quantified using multiple reaction monitoring in electrospray ionization positive mode. Stableisotopically labeled compounds of each kinase inhibitor were used as internal standards. The acquisition time was 7.0 min perrun. All analytes and internal standards eluted within 3.0 min. The calibration curves were linear over the range of 2–500 ng/mLfor afatinib, axitinib, bosutinib, lenvatinib, ruxolitinib, and trametinib, and 6–1500 ng/mL for cabozantinib, dabrafenib, nilotinib,and osimertinib (coefficients of correlation≥0.99). Validation assays for accuracy and precision, matrix effect, recovery,carryover, and stability were appropriate according to regulatory agencies. The rapid and sensitive assay ensures high throughputand was successfully applied to monitor concentrations of kinase inhibitors in patients.
(1) Background: The aim of our study was to identify specific risk factors for fatal outcome in critically ill COVID-19 patients. (2) Methods: Our data set consisted of 840 patients enclosed in the LEOSS registry. Using lasso regression for variable selection, a multifactorial logistic regression model was fitted to the response variable survival. Specific risk factors and their odds ratios were derived. A nomogram was developed as a graphical representation of the model. (3) Results: 14 variables were identified as independent factors contributing to the risk of death for critically ill COVID-19 patients: age (OR 1.08, CI 1.06–1.10), cardiovascular disease (OR 1.64, CI 1.06–2.55), pulmonary disease (OR 1.87, CI 1.16–3.03), baseline Statin treatment (0.54, CI 0.33–0.87), oxygen saturation (unit = 1%, OR 0.94, CI 0.92–0.96), leukocytes (unit 1000/μL, OR 1.04, CI 1.01–1.07), lymphocytes (unit 100/μL, OR 0.96, CI 0.94–0.99), platelets (unit 100,000/μL, OR 0.70, CI 0.62–0.80), procalcitonin (unit ng/mL, OR 1.11, CI 1.05–1.18), kidney failure (OR 1.68, CI 1.05–2.70), congestive heart failure (OR 2.62, CI 1.11–6.21), severe liver failure (OR 4.93, CI 1.94–12.52), and a quick SOFA score of 3 (OR 1.78, CI 1.14–2.78). The nomogram graphically displays the importance of these 14 factors for mortality. (4) Conclusions: There are risk factors that are specific to the subpopulation of critically ill COVID-19 patients.
Purpose
Knowledge on Ruxolitinib exposure in patients with graft versus host disease (GvHD) is scarce. The purpose of this prospective study was to analyze Ruxolitinib concentrations of GvHD patients and to investigate effects of CYP3A4 and CYP2C9 inhibitors and other covariates as well as concentration-dependent effects.
Methods
262 blood samples of 29 patients with acute or chronic GvHD who were administered Ruxolitinib during clinical routine were analyzed. A population pharmacokinetic model obtained from myelofibrosis patients was adapted to our population and was used to identify relevant pharmacokinetic properties and covariates on drug exposure. Relationships between Ruxolitinib exposure and adverse events were assessed.
Results
Median of individual mean trough serum concentrations was 39.9 ng/mL at 10 mg twice daily (IQR 27.1 ng/mL, range 5.6-99.8 ng/mL). Applying a population pharmacokinetic model revealed that concentrations in our cohort were significantly higher compared to myelofibrosis patients receiving the same daily dose (p < 0.001). Increased Ruxolitinib exposure was caused by a significant reduction in Ruxolitinib clearance by approximately 50%. Additional comedication with at least one strong CYP3A4 or CYP2C9 inhibitor led to a further reduction by 15% (p < 0.05). No other covariate affected pharmacokinetics significantly. Mean trough concentrations of patients requiring dose reduction related to adverse events were significantly elevated (p < 0.05).
Conclusion
Ruxolitinib exposure is increased in GvHD patients in comparison to myelofibrosis patients due to reduced clearance and comedication with CYP3A4 or CYP2C9 inhibitors. Elevated Ruxolitinib trough concentrations might be a surrogate for toxicity.
Purpose
The ongoing pandemic caused by the novel severe acute respiratory coronavirus 2 (SARS-CoV-2) has stressed health systems worldwide. Patients with chronic kidney disease (CKD) seem to be more prone to a severe course of coronavirus disease (COVID-19) due to comorbidities and an altered immune system. The study’s aim was to identify factors predicting mortality among SARS-CoV-2-infected patients with CKD.
Methods
We analyzed 2817 SARS-CoV-2-infected patients enrolled in the Lean European Open Survey on SARS-CoV-2-infected patients and identified 426 patients with pre-existing CKD. Group comparisons were performed via Chi-squared test. Using univariate and multivariable logistic regression, predictive factors for mortality were identified.
Results
Comparative analyses to patients without CKD revealed a higher mortality (140/426, 32.9% versus 354/2391, 14.8%). Higher age could be confirmed as a demographic predictor for mortality in CKD patients (> 85 years compared to 15–65 years, adjusted odds ratio (aOR) 6.49, 95% CI 1.27–33.20, p = 0.025). We further identified markedly elevated lactate dehydrogenase (> 2 × upper limit of normal, aOR 23.21, 95% CI 3.66–147.11, p < 0.001), thrombocytopenia (< 120,000/µl, aOR 11.66, 95% CI 2.49–54.70, p = 0.002), anemia (Hb < 10 g/dl, aOR 3.21, 95% CI 1.17–8.82, p = 0.024), and C-reactive protein (≥ 30 mg/l, aOR 3.44, 95% CI 1.13–10.45, p = 0.029) as predictors, while renal replacement therapy was not related to mortality (aOR 1.15, 95% CI 0.68–1.93, p = 0.611).
Conclusion
The identified predictors include routinely measured and universally available parameters. Their assessment might facilitate risk stratification in this highly vulnerable cohort as early as at initial medical evaluation for SARS-CoV-2.