Background: Adequate antibiotic dosing may improve outcomes in critically ill patients but is challenging due to altered and variable pharmacokinetics. To address this challenge, AutoKinetics was developed, a decision support system for bedside, real-time, data-driven and personalised antibiotic dosing. This study evaluates the feasibility, safety and efficacy of its clinical implementation. Methods: In this two-centre randomised clinical trial, critically ill patients with sepsis or septic shock were randomised to AutoKinetics dosing or standard dosing for four antibiotics: vancomycin, ciprofloxacin, meropenem, and ceftriaxone. Adult patients with a confirmed or suspected infection and either lactate > 2 mmol/L or vasopressor requirement were eligible for inclusion. The primary outcome was pharmacokinetic target attainment in the first 24 h after randomisation. Clinical endpoints included mortality, ICU length of stay and incidence of acute kidney injury. Results: After inclusion of 252 patients, the study was stopped early due to the COVID-19 pandemic. In the ciprofloxacin intervention group, the primary outcome was obtained in 69% compared to 3% in the control group (OR 62.5, CI 11.4–1173.78, p < 0.001). Furthermore, target attainment was faster (26 h, CI 18–42 h, p < 0.001) and better (65% increase, CI 49–84%, p < 0.001). For the other antibiotics, AutoKinetics dosing did not improve target attainment. Clinical endpoints were not significantly different. Importantly, higher dosing did not lead to increased mortality or renal failure. Conclusions: In critically ill patients, personalised dosing was feasible, safe and significantly improved target attainment for ciprofloxacin. Trial registration: The trial was prospectively registered at Netherlands Trial Register (NTR), NL6501/NTR6689 on 25 August 2017 and at the European Clinical Trials Database (EudraCT), 2017-002478-37 on 6 November 2017.

The spice curcumin is supposed to have many different beneficial health effects. To understand the complete pharmacokinetics of curcumin we need an analytical method to determine curcumin and its metabolites in human plasma, urine or feces. We have developed an HPLC-MS/MS method for the simultaneous analysis of curcumin, demethoxycurcumin, bisdemethoxycurcumin, tetrahydrocurcumin and piperine in human plasma, urine or feces. Sample pretreatment involved a simple liquid-liquid extraction with tert-butyl methyl ether. Conjugated curcumin and analogs can be measured after enzymatic hydrolysis. Reversed-phase chromatography with a linear gradient of 50–95% methanol in 0.1% formic acid was used. Total run time is 15 min. The method was validated with regards to stability, specificity, sensitivity, linearity, accuracy, repeatability and reproducibility. The applicability of the method was tested using actual patients samples. The LLOQ in plasma, urine and feces for curcumin, demethoxycurcumin, bisdemethoxycurcumin, tetrahydrocurcumin and piperine ranged from 1 to 5 nM. Whereas all compounds could be quantified on a linear range between 2 and 400 nM. Plasma and feces recovery of curcumin was 97.1 ± 3.7% and 99.4 ± 16.2%, whereas urine showed a recovery of 57.1 ± 9.3%. All compounds had acceptable in-between day or between day variability in the different matrixes. A HPLC-MS/MS method was developed and validated for the simultaneous quantification of curcumin, demethoxycurcumin, bisdemethoxycurcumin, tetrahydrocurcumin and piperine in human plasma, urine or feces. This method will aid in critically verifying the pharmacokinetics of curcumin made by supplement manufacturers and help us to provide insight in the claimed bioavailability of curcumin supplements.

Busulfan is administered intravenously in conditioning regimens prior to allogeneic hematopoietic stem cell transplantation (HSCT), with model‐informed precision dosing (MIPD) targeting a 4‐day cumulative AUC of 80–100 mg*hour/L. Three pharmacokinetic (PK) models—Bognar, Langenhorst, and McCune—were evaluated using data with a minimum of 1 and a median of 2 PK sampling days from pediatric and adult patients at two tertiary hospitals in the Netherlands. Simulations with the best‐performing model evaluated dose optimization and the role of therapeutic drug monitoring (TDM). The Bognar and Langenhorst models, with overlapping datasets (25% between models, 21% with the current), accurately described busulfan concentration‐time curves in all 535 patients (0.18–72 years), especially in those > 60 years (bias and precision in clearance; −5.6%, 6.48% and 16.55%, 16.95%; in observed concentrations −1.86%, −6.52% and 9.12%, 3.58% for both models > 60 years). The McCune model underestimated clearance by 36% in children < 2 years of age. Despite accounting for age‐dependent glutathione depletion, the Langenhorst model did not outperform the simpler Bognar model. Model‐based initial dosing did not improve target attainment over SmPC‐based dosing. However, TDM‐based MIPD with a single TDM on days 2–4 significantly improved target attainment (49% vs. 87%), with a number needed to treat for TDM of 2.5. This study demonstrates the clinical efficacy of simple PK models such as the Bognar model, providing that simplicity does not compromise performance. It highlights the inadequacy of nomogram‐based dosing alone and reinforces the critical role of TDM in optimizing busulfan exposure for pediatric and adult myeloablative HSCT patients.

This study aimed to determine whether published pharmacokinetic (PK) models can adequately predict the PK profile of imatinib in a new indication, such as coronavirus disease 2019 (COVID‐19). Total (bound + unbound) and unbound imatinib plasma concentrations obtained from 134 patients with COVID‐19 participating in the CounterCovid study and from an historical dataset of 20 patients with gastrointestinal stromal tumor (GIST) and 85 patients with chronic myeloid leukemia (CML) were compared. Total imatinib area under the concentration time curve (AUC), maximum concentration (C_max) and trough concentration (C_trough) were 2.32‐fold (95% confidence interval [CI] 1.34–3.29), 2.31‐fold (95% CI 1.33–3.29), and 2.32‐fold (95% CI 1.11–3.53) lower, respectively, for patients with CML/GIST compared with patients with COVID‐19, whereas unbound concentrations were comparable among groups. Inclusion of alpha1‐acid glycoprotein (AAG) concentrations measured in patients with COVID‐19 into a previously published model developed to predict free imatinib concentrations in patients with GIST using total imatinib and plasma AAG concentration measurements (AAG‐PK‐Model) gave an estimated mean (SD) prediction error (PE) of −20% (31%) for total and −7.0% (56%) for unbound concentrations. Further covariate modeling with this combined dataset showed that in addition to AAG; age, bodyweight, albumin, CRP, and intensive care unit admission were predictive of total imatinib oral clearance. In conclusion, high total and unaltered unbound concentrations of imatinib in COVID‐19 compared to CML/GIST were a result of variability in acute phase proteins. This is a textbook example of how failure to take into account differences in plasma protein binding and the unbound fraction when interpreting PK of highly protein bound drugs, such as imatinib, could lead to selection of a dose with suboptimal efficacy in patients with COVID‐19.