Background: Event analysis is a promising approach to estimate the acceptance of medication alerts issued by computerized physician order entry (CPOE) systems with an integrated clinical decision support system (CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, which can be a time-consuming process, especially when performed manually. Objective: We present a new automated event analysis approach, which was applied to a large data set generated in a CPOE-CDSS with passive, noninterruptive alerts. Methods: Medication and alert data generated over 3.5 months within the CPOE-CDSS at Heidelberg University Hospital were divided into 24-hour time intervals in which the alert display was correlated with associated prescription changes. Alerts were considered “persistent” if they were displayed in every consecutive 24-hour time interval due to a respective active prescription until patient discharge and were considered “absent” if they were no longer displayed during continuous prescriptions in the subsequent interval. Results: Overall, 1670 patient cases with 11,428 alerts were analyzed. Alerts were displayed for a median of 3 (IQR 1-7) consecutive 24-hour time intervals, with the shortest alerts displayed for drug-allergy interactions and the longest alerts displayed for potentially inappropriate medication for the elderly (PIM). Among the total 11,428 alerts, 56.1% (n=6413) became absent, most commonly among alerts for drug-drug interactions (1915/2366, 80.9%) and least commonly among PIM alerts (199/499, 39.9%). Conclusions: This new approach to estimate alert acceptance based on event analysis can be flexibly adapted to the automated evaluation of passive, noninterruptive alerts. This enables large data sets of longitudinal patient cases to be processed, allows for the derivation of the ratios of persistent and absent alerts, and facilitates the comparison and prospective monitoring of these alerts.

Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia.

Individual sirolimus whole blood concentrations are highly variable, critically influenced by the concomitant use of cytochrome P450 (CYP) 3A inducers or inhibitors, and also modulated by food. Therapeutic drug monitoring is therefore recommended, especially at treatment start or in circumstances that can influence sirolimus exposure. In this case report, we highlight the challenge of achieving therapeutic sirolimus concentrations and present pragmatic solutions with regimen adaptions, pharmacokinetic enhancement (use of a drug–drug interaction), concentration monitoring, and subsequent modeling of population pharmacokinetics to support treatment decisions. In a 69‐year‐old female patient with allogeneic hematopoietic stem cell transplantation, sirolimus concentrations were stable until she developed cerebral toxoplasmosis with tonic–clonic seizures. During treatment of this acute infection, sirolimus concentrations dropped to subtherapeutic levels and remained largely unaffected by dose increases. [Correction added on 4 May 2024, after first online publication: The word “tacrolimus concentrations” has been changed to “sirolimus concentrations” in the preceding sentence.] Only the simultaneous administration of the CYP3A4 inhibitor fluconazole and a shortening of the sirolimus dosing intervals to a (non‐approved) twice‐daily administration led to successful control of the concentrations, which ultimately even made a dose reduction possible. This intervention resulted in an increase of sirolimus mean trough concentration to 5.85 ng/mL, i.e., into the desired target range. Additionally, a higher ratio of sirolimus trough levels/daily dose from 26.9 to 109 ng/mL/mg/kg/day was achieved with the initiation of fluconazole. Thus, this case report describes the use of clinical pharmacological concepts and pharmacokinetic modeling to optimize treatment strategies in an individual patient. This strategy could be generalized to other CYP inhibitors and other treatment regimens.

Introduction: Real-world effectiveness of a new treatment is relevant information for patients, healthcare professionals and payers, especially when patients encountered in routine clinical care differ significantly from those recruited in the randomised controlled trials (RCTs) that led to approval. However, obtaining effect estimates can be challenging when a new drug has only recently been marketed and real-world data (RWD) are not yet available. For new breast cancer (BC) therapies, we illustrate how RCT inferences can be transported to a target population and how a synthetic population can be generated to mimic a target population for which no RWD is yet available. Methods and analysis: In our framework, we defined the data-generating process for the RCT population and the real-world (target) population with confounders, effect-modulating covariates and survival times as outcomes. First, we conducted generalisability and transportability (G&T) analyses to transport the RCT results to the simulated target population, applying the inverse probability of sampling weighting and outcome model-based estimator approach. We then used Synthea to generate a synthetic target population based on German BC survival rates and combined both approaches into a coherent strategy. Results: Effect estimates (HRs with 95% CIs) transported from the RCT to our defined target population closely matched the expected real-world effect (RCT: 0.68 (0.65; 0.71); real-world: 0.75 (0.71; 0.79); transported from RCT: 0.76 (0.71; 0.81)). BC survival rates were very similar between observed and synthetic data (prediction error in absolute survival rates: 1.62%). Conclusion: Combining G&T with synthetic data may inform decision-making in situations where RWD are not (yet) available.

Interpretation of pharmacometabolomics results, aiming particularly at biomarker (sets) discovery for drug exposure, remains a major challenge. The metabotyping of drug exposure depends on resolution of specific metabolomics techniques and comprises individual metabolic phenotypes (“metabotypes”), disease-, drug- and microbiome-specific patterns, as well as conditional metabolic states (e. g. fasting). In this clinical trial with 16 healthy participants, an exploratory objective was to evaluate the untargeted urinary metabolomics of voriconazole, administered in four single doses, using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Voriconazole is a second-generation triazole and a potent inhibitor of drug-metabolizing enzymes such as cytochrome P450 (CYP) isozymes CYP3A4 and CYP2C19. Therefore, identification of metabolites reflecting acute CYP3A4 inhibition was of particular interest. On two treatment days without and with voriconazole (with background microdosed midazolam and omeprazole administration for CYP3A4 and CYP2C19 phenotyping, respectively), spot urine was collected after overnight fasting (predose) and 4 h later (postdose fasting). In the postdose versus predose fingerprints, most changes at the annotated metabolite level were attributable to fasting metabolomics or potential confounders. ¹H-NMR spectroscopy identified neither a short-term voriconazole-specific signature nor patterns or metabolites potentially reflecting acute CYP3A4 inhibition. Our study emphasizes crucial significance of strict standardization of fasting time and minimization of confounder influences by clinical trial design as well as selection of adequate baselines and high-resolution analytical techniques in pharmacometabolomics research, especially for biomarker discovery.

Evaluation of blood-brain barrier (BBB) permeability is a crucial step in drug research and development, particularly for the pharmacotherapy of targets in the central nervous system. Current preclinical permeability predictions are hindered by limited transferability or BBB model inaccessibility for analysis. In order to generate relevant pharmacological data, it is necessary to establish qualified physiological, translational, and quantitative systems with validated methods. 3D BBB organoids composed of human cerebral microvascular endothelial cells (hCMEC/D3), and primary pericytes and astrocytes were generated. The BBB barrier function was evaluated using the paracellular marker ᴅ-Tyr(Me)Orn. Confocal laser scanning microscopy was used to determine the architecture of the cell arrangements, crucial BBB hallmarks and the apoptosis rate (cleaved caspase 3). An experimental setup was established to evaluate drug uptake into the organoids using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The concentration- and time-dependent intraorganoidal drug uptake was determined for 13 drugs, and used to calculate the ratio of intra- and extraorganoidal concentrations. These organoid uptake ratios were subsequently correlated with reported in vivo brain-to-plasma ratios. Additionally, the performance of four in silico BBB permeability prediction tools was assessed with the in vivo data. Optimal analysis time point was identified as day 3 post self-assembly based on organoid architecture, endothelial coverage and apoptosis rate. Modular barrier properties were shown by a reduction of intraorganoidal ᴅ-Tyr(Me)Orn after cell connection were compromised, and potential wash out cross-validated by fluorescent Cy5 in the wash solution. Deming regression demonstrated a correlation with pseudo R ² of 0.64 between the organoid uptake ratios and reported mammalian in vivo brain-to-plasma ratios. In contrast, the in silico tools evaluated displayed no correlations with these in vivo data. We established and validated a simple and rapid experimental setup for the UPLC-MS/MS drug quantification into single 3D BBB organoids. We demonstrated that the 3D BBB organoids have translational relevance with regard to human and mammalian in vivo BBB permeability. This system therefore qualifies as New Approach Methodology (NAM) and can be used for approximate evaluation of in vivo brain uptake, while outperforming state of the art in silico models.