3D printing offers the possibility to prepare personalized tablets on demand, making it an intriguing technology for hospital pharmacies. For the implementation of 3D-printed tablets into the digital Closed Loop Medication Management system, the required tablet formulation and development of the manufacturing process as well as the pharmaceutical validation were conducted. The goal of the formulation development was to enable an optimal printing process and rapid dissolution of the printed tablets for the selected model drugs Levodopa/Carbidopa. The 3D printed tablets were prepared by direct powder extrusion. Printability, thermal properties, disintegration, dissolution, physical properties and storage stability were investigated by employing analytical methods such as HPLC-UV, DSC and TGA. The developed formulation shows a high dose accuracy and an immediate drug release for Levodopa. In addition, the tablets exhibit high crushing strength and very low friability. Unfortunately, Carbidopa did not tolerate the printing process. This is the first study to develop an immediate release excipient composition via direct powder extrusion in a hospital pharmacy setting. The developed process is suitable for the implementation in Closed-Loop Medication Management systems in hospital pharmacies and could therefore contribute to medication safety.

The negative effects of iron overload caused by repetitive blood transfusions and iron release during cytotoxic chemotherapy might be ameliorated by early treatment with an iron chelator. However, in the setting of allogeneic hematopoietic stem cell transplantation (HSCT), chelation therapy is often postponed until the late post-transplantation period because of potential drug interactions. Therefore we systematically investigated the influence of iron chelation with deferasirox on the pharmacokinetics of intravenous busulfan in adult patients in the context of routine therapeutic drug monitoring (TDM) before HSCT. We conducted a single-center, prospective, observational study in 25 adult patients with planned allogeneic HSCT after myeloablative, busulfan-based, TDM-guided conditioning chemotherapy. Busulfan was administered intravenously over 3 hours with an initial dose of 3.2 mg/kg once daily (based on adjusted ideal body weight [AIBW] in overweight patients). Four consecutive dosages were planned to achieve a cumulative area under the curve (AUC) of 80 mg · h/L. Patients received deferasirox for transfusional iron overload as per approval from the start of conditioning until day 3 after transplantation. Model-based calculation of the busulfan AUC was carried out by means of Bayesian prediction based on a population pharmacokinetic model after the first or second dose of busulfan, and dose adjustments were performed accordingly. Calculated median cumulative AUC before dose adjustment was 93.7 mg · h/L (65.1-151.4 mg · h/L), which was considerably above the target AUC of 80 mg · h/L ± 10%. Median dose adjustment was -17.1% (-50.0% to 18.2%), and patients ultimately received busulfan with a median cumulative dose of 10.60 mg/kg (6.38 - 15.62) mg/kg. A busulfan dose reduction was necessary in 19 patients (76%) whereas a dose increase was only needed in 1 patient. After dose adjustment the median AUC was 79.7 mg/L · h (62.5 - 84.2 mg/L · h). Median busulfan clearance was 0.134 L/h/kg (0.084 - 0.203 L/h/kg), which is significantly lower than the average clearance of 0.2 L/h/kg reported in the literature, whereas volume of distribution was not altered. We were able to demonstrate, that TDM is the key point to facilitate a safe co-administration of both medications, because the intake of deferasirox leads to a considerable increase in the busulfan AUC of about 35% to 40%. The reason for the increase in busulfan AUC is a reduction in busulfan clearance by about one third; therefore a lower initial dose of busulfan followed by TDM could be considered in patients with comedication with deferasirox.

Background: Inaccurate documentation of sampling and infusion times is a potential source of error in personalizing busulfan doses using therapeutic drug monitoring (TDM). Planned times rather than the actual times for sampling and infusion time are often documented. Therefore, this study aimed to evaluate the robustness of a limited sampling TDM of busulfan with regard to inaccurate documentation. Methods: A pharmacometric analysis was conducted in NONMEM® 7.4.3 and “R” by performing stochastic simulation and estimation with four, two and one sample(s) per patient on the basis of a one-compartment- (1CMT) and two-compartment (2CMT) population pharmacokinetic model. The dosing regimens consisted of i.v. busulfan (0.8 mg/kg) every 6 h (Q6H) or 3.2 mg/kg every 24 h (Q24H) with a 2 h- and 3 h infusion time, respectively. The relative prediction error (rPE) and relative root-mean-square error (rRmse) were calculated in order to determine the accuracy and precision of the individual AUC estimation. Results: A noticeable impact on the estimated AUC based on a 1CMT-model was only observed if uncertain documentation reached ± 30 min (1.60% for Q24H and 2.19% for Q6H). Calculated rPEs and rRmse for Q6H indicate a slightly lower level of accuracy and precision when compared to Q24H. Spread of rPE’s and rRmse for the 2CMT-model were wider and higher compared to estimations based on a 1CMT-model. Conclusions: The estimated AUC was not affected substantially by inaccurate documentation of sampling and infusion time. The calculated rPEs and rRmses of estimated AUC indicate robustness and reliability for TDM of busulfan, even in presence of erroneous records.

Knowledge of the glomerular filtration rate (GFR) is mandatory when dosing renally eliminated drugs such as vancomycin. In clinical practice, different biomarkers and various equations are used to estimate GFR (eGFR), resulting in varying estimates. These variations may be explained by nonrenal factors, such as muscle status or glucocorticoid administration. This study aimed to evaluate the performance of different eGFR equations in terms of accuracy and precision compared to renal vancomycin clearance, including subgroup analyses for nonrenal confounders. We retrospectively analyzed data from 121 adult allogeneic hematopoietic stem cell transplant (allo‐HSCT) patients. All patients received vancomycin treatment including trough concentration therapeutic drug monitoring. The eGFR was calculated using eight equations and compared to the renal vancomycin clearance that was calculated using a pharmacokinetic model and served as the reference. Individual muscle status was determined by computed tomography scans. Median renal vancomycin clearance was 49 mL/minute/1.73 m ² (range 24–96). All eight eGFR equations overestimated renal vancomycin clearance. The six (partially) creatinine‐based equations were significantly less accurate (bias: 24.0–62.8 mL/minute/1.73 m ² ) than both cystatin C‐based equations (bias: 6.3–9.5 mL/minute/1.73 m ² ). This decreased accuracy for creatinine‐based eGFR was more pronounced in patients with reduced muscle status or glucocorticoid medication. All CKD‐EPI equations and the Hoek equation were more precise with an IQR of the difference to renal vancomycin clearance ≤22.5 mL/minute/1.73 m ² compared to ≥35.5 mL/minute/1.73 m ² (Cockcroft‐Gault, MDRD). In conclusion, cystatin C‐based eGFR equations are preferable to creatinine‐based approaches for vancomycin dosing in allo‐HSCT patients.

During the in-hospital optimization of dopaminergic dosage for Parkinson’s disease, drug-induced dyskinesias emerge as a common side effect. Wrist-worn devices present a substantial opportunity for continuous movement recording and the supportive identification of these dyskinesias. To bridge the gap between dyskinesia assessment and machine learning-enabled detection, the recorded information requires meaningful data representations. This study evaluates and compares two distinct representations of sensor data: a task-dependent, semantically grounded approach and automatically extracted large-scale time-series features. Each representation was assessed on public datasets to identify the best-performing machine learning model and subsequently applied to our own collected dataset to assess generalizability. Data representations incorporating semantic knowledge demonstrated comparable or superior performance to reported works, with peak F₁ scores of 0.68. Generalization to our own dataset from clinical practice resulted in an observed F₁ score of 0.53 using both setups. These results highlight the potential of semantic movement data analysis for dyskinesia detection. Dimensionality reduction in accelerometer-based movement data positively impacts performance, and models trained with semantically obtained features avoid overfitting. Expanding cohorts with standardized neurological assessments labeled by medical experts is essential for further improvements.