Background:Bleeding complications are a major contributor to adverse drug events among older inpatients, particularly in those treated with antithrombotic agents. Timely and accurate detection of bleeding events is essential for improving drug safety surveillance and clinical risk management. Objective:The study aimed to develop and validate automated algorithms for detecting major bleeding (MB) and clinically relevant nonmajor bleeding (CRNMB) events from electronic medical records (EMRs) by combining structured data-based rule models and a natural language processing (NLP) approach, and to evaluate their performance and generalizability against a manually reviewed gold standard and an external dataset. Methods:We conducted a multicenter retrospective study using routinely collected EMR data from 3 Swiss university hospitals. Patients 65 years or older who received at least one antithrombotic agent and were hospitalized between January 2015 and December 2016 were included. To detect MB and CRNMB events, rule-based algorithms were developed using structured data (International Statistical Classification of Diseases, 10th Revision, German Modification [ICD-10-GM] codes, laboratory values, transfusion records, and antihemorrhagic prescriptions), with variables and cutoff values defined according to adapted International Society on Thrombosis and Haemostasis definitions and expert consensus. In parallel, a supervised NLP model was applied to discharge summaries from one hospital. A manual review of 754 EMRs served as the reference standard for internal validation, and the algorithm performance of the structured data algorithms (SDA), NLP, and their combination (SDA+NLP) was evaluated against this manually reviewed gold standard using standard performance metrics. External validation was performed on an independent dataset from the Lausanne University Hospital to assess model robustness and generalizability. Results:Among 36,039 inpatient stays, SDA identified 8.26% (n=2979) as MB and 15.04% (n=5419) as CRNMB cases. ICD-10-GM codes alone detected 28.5% (n=849) of MB and 31.48% (n=1706) of CRNMB cases, while laboratory data contributed most to event detection (n=1994, 66.94% for MB and n=3663, 67.60% for CRNMB). Integrating SDA with NLP improved detection, identifying 12.2% (920/7513) of MB and 27.4% (2062/7513) of CRNMB cases at 1 hospital. The combined model achieved the best performance (sensitivity 0.84, positive predictive value 0.51, F1-score 0.64). External validation on Lausanne University Hospital 2021-2022 data (n=24,054 stays) confirmed the algorithms' reproducibility; the prevalence of MB decreased while CRNMB increased, reflecting evolving clinical practices and antithrombotic use patterns. Conclusions:Our integrated approach, combining SDA with NLP, enhances the detection of hemorrhagic events in older hospitalized patients treated with antithrombotic agents, suggesting its potential usefulness for drug safety monitoring and clinical risk management.

Pharmacogenetic testing represents a promising approach for the individualization of drug therapies. Nevertheless, its integration within the Swiss healthcare system remains limited to date. This study aimed to explore the knowledge, attitudes, and perceptions of physicians, pharmacists, and the general public toward PGx testing conducted in community pharmacy settings in Switzerland. A mixed-methods design was employed. Structured questionnaires were used to assess participants’ knowledge, perceived clinical utility, and anticipated barriers to implementation, while semi-structured interviews allowed for a more in-depth exploration of individual perspectives. Quantitative data were analyzed using descriptive statistics, consistent with the exploratory scope of the study, and qualitative data were processed through thematic analysis. A total of 332 participants completed the questionnaire, including 100 physicians, 122 pharmacists, and 110 members of the general public, and 26 took part in the interviews. PGx testing was perceived as clinically relevant across all stakeholder groups. The main barriers identified included insufficient specialized training, the absence of standardized guidelines, financial constraints, and ethical concerns. The need for strengthened interprofessional collaboration was also highlighted. This study sheds light on the key challenges associated with the integration of PGx testing in community pharmacy practice in Switzerland, spanning educational, organizational, and ethical dimensions. The findings should be interpreted within a descriptive framework, as the study design does not support causal or inferential conclusions. To enable successful implementation, it appears essential to develop targeted training programs, structure interprofessional collaboration, and establish robust policies for the protection of genetic data.

Imatinib therapeutic drug monitoring (TDM) contributes at optimizing exposure, yet its implementation is limited by logistical constraints, including strict sampling time requirements. Model-Informed Precision Dosing (MIPD) offers a promising approach to dosage individualization by leveraging population pharmacokinetic (popPK) models, thereby mitigating the constraints of sample collection time. Integrating MIPD and Point-of-Care (POC) analytical methods in ambulatory settings could further improve TDM feasibility and accessibility. This study serves as a proof of concept for MIPD integration in the workflow of imatinib TDM. To identify optimal sampling time for predicting imatinib steady-state trough concentrations (Cmin, SS) using a popPK model for an MIPD application. A popPK model developed from data of 146 patients (244 concentrations) was used to simulate individual concentration-time profiles of 1000 patients under standard dosing (400 mg once or twice daily) from treatment initiation to steady-state (reached after 11 days of treatment). Empirical Bayes estimates were generated from single or paired sampling time points and used to predict Cmin, SS. Predictive performance was evaluated using mean prediction error, coefficient of determination, root mean square prediction error (RMSPE), and successful prediction rates based on recommended therapeutic ranges. Sampling 5–24 h and 1–12 h post-dose for once and twice daily administrations, respectively, yielded an RMSPE < 41

Therapeutic Drug Monitoring (TDM) supports individualized pharmacotherapy, yet its implementation in routine care remains limited by operational constraints, data quality issues, and the need for expert interpretation. Model-Informed Precision Dosing (MIPD) addresses several of these limitations through population pharmacokinetic modeling and Bayesian forecasting, but its effective deployment in clinical practice still requires human TDM experts. This paper presents Tucuxi‑CDSS, a configurable and open-source Clinical Decision Support System (CDSS) designed to enable safe, standardized, and automated MIPD-guided dose individualization, with a particular focus on safeguarding against pre-analytical and data-entry errors. We detail Tucuxi-CDSS system architecture, data validation workflow, decision logic, and configuration mechanisms, with a particular focus on safeguarding against pre-analytical and data-entry errors. The system is built around extensive configurability through XML-based drug files, language files, and reporting templates. Verification was performed through unit testing and system-level tests, while validation was conducted by comparing CDSS outputs against those of a human TDM expert in an in-silico study. The resulting system supports end-to-end MIPD workflows, from data import and automated error detection through dosage adaptation and clinical report generation. A pilot deployment for rifampicin dosing optimization in routine tuberculosis care is currently underway in Tanzania, and an illustrative use case with imatinib demonstrates the system's covariate handling and reporting capabilities. We also briefly present the results of the validation test where we compare its effectiveness against a human TDM expert in an in-silico study. Tucuxi‑CDSS provides a modular, extensible, and open-source platform capable of delivering expert-level TDM oversight in settings where trained pharmacologists are scarce. Its performance evaluation against a TDM expert is presented in a companion paper and supports future prospective studies on CDSS adoption in clinical practice.

INTRODUCTION:Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer. The pro-drug 6-mercaptopurine (6-MP), essential during maintenance, is converted into active 6-thioguanine (6-TGN) and toxic 6-methylmercaptopurine (6-MMP) metabolites, resulting in marked variability in efficacy and toxicity. 6-MP therapy is further limited by poor adherence, variable absorption, and complex metabolism. Allopurinol is sometimes used to correct skewed metabolism, though its precise clinical role remains unclear. METHODS:This study aimed to develop population pharmacokinetic (popPK)-based strategies to optimize 6-MP dosing in children and improve therapeutic outcomes. A popPK model was developed using 6-MMP and 6-TGN concentrations from the pediatric oncology cohort. Model-based simulations in 1000 virtual patients were performed to explore optimized dosing strategies, with and without allopurinol, aiming to reach the therapeutic target (6-MMP <5700 pmol/8 × 108 RBC and 6-TGN between 230 and 450 pmol/8 × 108 RBC). RESULTS:The popPK model revealed a linear correlation between 6-MP dose and metabolite concentrations. Allopurinol co-administration substantially shifted metabolites' distribution from 80% 6-MMP/20% 6-TGN to 21% 6-MMP/79% 6-TGN. Simulations identified optimal 6-MP doses of 40-75 mg/m2 without allopurinol, and only 10-15 mg/m2 when co-administered. CONCLUSION:Allopurinol co-treatment reduces toxicity while maintaining therapeutic efficacy at lower 6-MP doses. The proposed model warrants prospective evaluation for clinical relevance confirmation.

Therapeutic drug monitoring (TDM) of vancomycin, ideally based on the ratio of the daily area under the curve to the minimal inhibitory concentration at steady state (AUC24,SS/MIC), is hindered by sampling limitations in neonates, so trough concentration (Ctrough) is often used as a surrogate. This virtual TDM study aimed to evaluate the performance of Bayesian model-informed precision dosing (MIPD) for vancomycin in neonates. Reference pharmacokinetic (PK) parameters and drug concentrations were simulated in NONMEM for 1000 virtual neonates using a published population PK model. Four TDM strategies were compared based on the percentage of patients achieving an AUC24,SS/MIC between 360 and 540 h. Strategy 1 maintained the initial regimen unchanged; strategy 2 adjusted doses using a standard rule of three to target a Ctrough of 10–15 mg/L; strategies 3 and 4 used the MIPD software Tucuxi with a steady-state Ctrough and two concentrations after the first dose, respectively, for Bayesian dosage optimization. Individual dosages were adjusted following each strategy, recalculating AUC24,SS to determine the percentage of target attainment. Iterative adjustments were performed by resampling Ctrough. A sub-study evaluated optimal sampling time by comparing Tucuxi-estimated AUC24,SS with the reference value. Less than 49

Background: Therapeutic Drug Monitoring (TDM) aims to optimize clinical outcomes but is hindered by logistical delays and susceptibility to data errors. Integrating Model-Informed Precision Dosing (MIPD) with Point-of-Care testing, fortified by rule-based validation and alerts, forms a transformative Clinical Decision Support System (CDSS) essential for securing this complex workflow. Methods: This in silico study rigorously evaluated a rule-based CDSS for imatinib against a human TDM expert. We simulated a set of 60 virtual patients using a previously developed population pharmacokinetic model, and deliberately enriched with data quality errors (e.g., timing inaccuracies, reporting errors) to stress-test system resilience. We compared CDSS and TDM expert performances based on error detection capabilities and clinical impact on therapeutic target attainment and safety. Results: The CDSS and the expert demonstrated comparable error detection capabilities. Although the CDSS showed numerically higher sensitivity (53% CI95% [35–70] vs 45% CI95% [30–61]) and lower specificity (83% CI95 [63–95] vs 100% CI95 [81–100]). Clinically, the CDSS significantly outperformed the expert in target attainment (55% vs 37%; p=0.046). However, while the expert generated no detrimental interventions, the CDSS resulted in four detrimental outcomes, where patients initially within the target range were shifted outside it. Conclusion: The CDSS demonstrated superior efficacy in optimizing imatinib exposure while serving as a robust digital gatekeeper. While active optimization drives higher efficacy, a few observed detrimental cases highlight that algorithmic safety remains contingent on data integrity. This proof-of-concept study validates the CDSS architecture as a prerequisite for future prospective clinical trials.

We aimed to assess whether Pelargonium sidoides extract (EPs®7630) reduces symptom duration or antibiotic use compared with usual care in adults with acute bronchitis. We conducted a pragmatic randomised-controlled trial across 36 primary care practices and five walk-in clinics in Switzerland. Adults ( ≥ 18 years) consulting a general practitioner for the first time for a new episode of acute bronchitis, with a cough of up to eight days' duration, were eligible for inclusion. The co-primary outcomes were (1) number of days required to achieve a 50% reduction in symptoms from the peak value, assessed using the Acute Bronchitis Severity Score (ABSS), and (2) the proportion of participants who used antibiotics. Missing data in intention-to-treat (ITT) analyses were multiply imputed. 332 participants were enrolled and randomly assigned: 155 to EPs®7630 and 177 to usual care. Neither co-primary outcomes showed a statistically significant difference between groups. No significant difference in time to 50% reduction of symptoms between the EPs®7630 and usual care groups was observed (adjusted regression coefficient 0.05 [95% CI - 0.13-0.23]; p = 0.578). Antibiotic use was 7 percentage points lower (31% relative reduction) in the EPs®7630 group (17.4%, 20 of 155) than in the usual care group (25.2%, 33 of 177), although the difference was not statistically significant (adjusted risk ratio 0.78 [95% CI 0.49-1.26]; p = 0.309). Adverse events were reported more frequently in the EPs®7630 group (32.3%, 50 of 155) than in the usual care group (21.5%, 38 of 177; hazard ratio 1.40 [95% CI 1.03-1.89]; p = 0.030); all adverse drug events were mild. EPs®7630 did not reduce symptom duration or antibiotic use significantly and was associated with more frequent events, that were all mild and previously described. Despite the absence of statistical significance, the observed reduction in antibiotic use warrants further investigation in larger trials to clarify its potential role within antimicrobial stewardship strategies.

Encorafenib and binimetinib pharmacokinetic (PK) studies in real-world cancer patients remain scarce. We aimed to characterize their population pharmacokinetics (popPK) in routine clinical care and compare real-life exposure metrics with clinical trials data. We conducted population pharmacokinetic (popPK) analyses in real-world patients with BRAFV600E/K-mutant metastatic melanoma to characterize interindividual variability (IIV) and identify significant covariates in MonolixSuite™. Final model-based Bayesian individual PK metrics (i.e., trough concentration, Cmin, maximal concentration, Cmax, unbound average concentration, Caverage, and area under the curve, AUCτ) were compared with data from clinical trials and potency-anchored metrics (reported half-maximal inhibitory concentration, IC50). Data from 66 encorafenib and 69 binimetinib patients (310 and 311 samples) were analyzed. For both drugs, a two-compartment model with first-order absorption and linear elimination best described the data. None of the studied covariates were significantly associated with model parameters. Model-predicted PK metrics (geometric mean and CV(

Background: Optimal dosing of cefepime in infants 1–2 months remains undefined. Objectives: We aimed to quantify the risk of potentially neurotoxic exposure with high-dose cefepime (50 mg/kg/8 h) in infants 1–2 months of age, as compared to adjacent age groups (neonates, infants 2–12 months) and lower dose treatment (50 mg/kg/12 h). Methods: Pharmacometric simulations were performed using two published population pharmacokinetic models combined with demographic data, including serum creatinine, for neonates and infants ≤ 12 months. Adult-derived safety thresholds for potential neurotoxicity were defined as steady-state trough concentration (Ctrough) > 20 or > 35 mg/L, respectively. The corresponding probability of target attainment (PTA) was calculated as free concentration, 50% of the time during the dosing interval above the minimal inhibitory concentration (MIC) breakpoint of 8 mg/L (Pseudomonas spp.) (50% fT>MIC8mg/L). Results: The predicted risk of Ctrough > 20 (>35) mg/L under high-dose cefepime was 40–54% (12–22%) in infants 1–2 months while providing high PTA (100%). It was predicted to be 1.3–1.7 fold higher in neonates (model 1), and reduced 1.8–2.4 fold in infants 2–12 months (model 1), or to be similar (model 2), respectively. Both models predicted approximately 2–4 fold reduced risk using lower dose treatments while maintaining high PTA (≥97%). Conclusions: The risk of potential neurotoxic concentrations in infants > 1 month treated with cefepime 50 mg/kg/8 h is high if defined by adult safety thresholds. Lower dose cefepime in infants 1–2 months could be a safe option without compromising PTA, if defined as 50% fT>MIC8mg/L. Achievement of 100% fT>MIC8mg/L may require prolonged infusion time even under high-dose treatment. Future research is required to evaluate potentially age-dependent safety thresholds.

P-glycoprotein is a critical efflux transporter that may significantly affect the pharmacokinetics of various drugs by influencing their absorption, distribution and elimination. While European and American regulatory guidelines provide lists of P-glycoprotein modulators, they lack specificity concerning in vivo studies and clear guidance on inducers, creating uncertainty in their clinical relevance. A systematic search on in vivo clinical studies involving healthy volunteers using fexofenadine, dabigatran and digoxin as P-glycoprotein substrates has been performed in accordance with the PRISMA guidelines. A total of 151 studies assessing the impact of P-glycoprotein modulators on the concentration–time profile of P-glycoprotein substrates were retrieved. Additionally, data on the P-glycoprotein modulators’ effect on cytochrome P450 3A4 induction or inhibition were also collected. P-gp modulators were classified as potent, moderate, weak or non-interactors for P-glycoprotein, with or without cytochrome P450 3A4 impact, on the basis of the area under the concentration–time curve ratio. This classification was adapted from the Food and Drug Administration criteria for cytochrome interactions. This systematic review identified 49 area under the plasma concentration–time curve ratio values corresponding to P-glycoprotein inhibitors, 23 to P-glycoprotein inducers and 131 to non-interactors. Of these, only 32.5

BACKGROUND:The multi tyrosine kinase inhibitor regorafenib is active in metastatic colorectal cancer. Improvement in clinical outcome by adding regorafenib to long-course chemoradiotherapy (LcCRT) was investigated in molecularly undefined LARC. METHODS:Patients with T3-4 and/or N+ but M0 rectal cancer were included. Neoadjuvant LcRCT consisted in capecitabine (C) 825mg/m2 d1-d38 and 28 fractions of 1.8Gy (50.4Gy). Regorafenib was added d1-14 and d22-35 in 3 dose escalation (DE) cohorts (40mg/80mg/120mg). The recommended dose (RD) was used for the expansion (EXP) cohort. Primary endpoints were dose-limiting toxicity (DLT) for DE and pathological response (near-complete regression [npCR] or complete regression [pCR]) for EXP. RESULTS:Overall, 25 patients were included. Two DLTs occurred at the regorafenib dose level of 120 mg, thereby establishing the RD at 80mg daily. Among the 19 patients who were treated at the RD, 8 (42.1%; 1-sided 80% confidence interval [CI] (lower bound): 30.7%; 95% CI, 20.3%-66.5%) reached the primary endpoint (5 [26.3%] had npCR and 3 [15.8%] pCR). One additional patient received no surgery due to clinical complete response. All patients had R0 resections and clear circumferential margins. Postoperative complications occurred in 6 patients (35.3%). The most common grade ≥ 3 treatment-related adverse event in the EXP cohort was diarrhea (2 patients). CONCLUSION:Adding regorafenib 80 mg to LcCRT in LARC resulted in both primary endpoints being met and yielded an expected pathological response rate. Toxicity was manageable, and postoperative complications were as expected.

ABSTRACT Tucuxi, a Swiss‐developed Model‐Informed Precision Dosing (MIPD) software, aims to support clinical dosage decision‐making to achieve therapeutic concentration targets. This study assessed its predictive accuracy compared to NONMEM, a gold‐standard tool for Bayesian PK predictions. A panel of models was created to mimic various pharmacokinetic scenarios following oral, bolus, or intravenous administration. For each scenario, a virtual population of 4000 patients receiving doses ranging from 10 to 120 mg every 24 h was created. Sparse and rich profiles were simulated, with either one or four samples taken per patient. Tucuxi and NONMEM predicted concentrations at sampling times, trough ( C min ) and peak ( C max ) concentrations, and area under the curve (AUC 0‐24h ) were compared by calculating their relative differences, mean prediction error (MPE) and relative root mean square error (RMSE). The bioequivalence criterion was additionally applied to compare AUC 0‐24h , C min , and C max . All the outcomes predicted by Tucuxi closely matched those predicted by NONMEM. A median of 99.8% of predicted concentrations at sampling times presented relative errors smaller than 0.1%. For all outcomes predicted, MPE and relative RMSE were 0% (−0.09, 0.07) and 0.82% (0%, 18.79%) respectively. The bioequivalence criterion, calculated for AUC 0‐24h , C min , and C max , was verified for all models, with median values of 100%. This project highlights Tucuxi's excellent predictive accuracy compared to NONMEM, demonstrating its reliability and potential for adoption in clinical practice.

Background: Bleeding adverse drug events (ADEs), particularly among older inpatients receiving antithrombotic therapy, represent a major safety concern in hospitals. These events are often underdetected by conventional rule-based systems relying on structured electronic medical record data, such as the ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th Revision) codes, which lack the granularity to capture nuanced clinical narratives. Objective: This study aimed to develop and evaluate a natural language processing (NLP) model to detect and categorize bleeding ADEs in discharge summaries of older adults. Specifically, the model was designed to distinguish between "clinically significant bleeding," "severe bleeding," "history of bleeding," and "no bleeding," and was compared with a rule-based algorithm using ICD-10 codes. Methods: Clinicians manually annotated 400 discharge summaries, comprising 65,706 sentences, into four categories: "no bleeding," "clinically significant bleeding," "severe bleeding," and "history of bleeding." The dataset was divided into a training set (70%, 47,100 sentences) and a test set (30%, 18,606 sentences). Two detection approaches were developed and evaluated: (1) an NLP model using binary logistic regression and support vector machine classifiers, and (2) a traditional rule-based algorithm relying exclusively on predefined ICD-10 codes. To address class imbalance, with most sentences categorized as irrelevant ("no bleeding"), a class-weighting strategy was applied in the NLP model. Model performance was assessed using accuracy, precision, recall, F-1-score, and receiver operating characteristic (ROC) curve analyses, with manual annotations as the gold standard. Results: The NLP model significantly outperformed the rule-based approach across all evaluation metrics. At the document level, the NLP model achieved macro-average scores of 0.81 for accuracy and 0.80 for F-1-score. Precision was particularly high for detecting severe (0.92) and clinically significant bleeding events (0.87), demonstrating strong classification capability despite class imbalance. ROC analyses confirmed the model's robust diagnostic performance, yielding an area under the curve (AUC) of 0.91 when distinguishing irrelevant sentences from potential bleeding events, 0.88 for identifying historical mentions of bleeding, and notably, 0.94 for differentiating clinically significant from severe bleeding. In contrast, the rule-based ICD-10 model demonstrated high precision (0.94) for clinically significant bleeding but poor recall (0.03) for severe bleeding events, reflecting frequent missed detections. This limitation arose due to its reliance on commonly used ICD-10 codes (eg, gastrointestinal hemorrhage) and inadequate capture of rare severe bleeding conditions such as shock due to hemorrhage. Conclusions: This study highlights the considerable advantage of NLP over traditional ICD-10-based methods for detecting bleeding ADEs within electronic medical records. The NLP model effectively captured nuanced clinical narratives, including severity, negations, and historical bleeding events, demonstrating substantial promise for improving patient safety surveillance and clinical decision-making. Future research should extend validation across multiple institutions, diversify annotated datasets, and further refine temporal reasoning capabilities within NLP algorithms.

Amoxicillin is commonly used to treat erythema migrans in the first stage of Lyme disease in children, with a recommended dose of 50 mg/kg/day, administered three times a day (q8h). This model-based simulation study aimed to determine whether splitting the same daily dose into two administrations (q12h) would provide comparable drug exposure. A pharmacokinetic model suitable for a pediatric population (age: 1 month to 18 years, weight: 4-80 kg) was selected through a literature review. Simulations were performed with 15,000 virtual patients receiving 16.67 mg/kg/dose q8h, 25 mg/kg/dose q12h, or other q12h dosing variations. The target therapeutic level was defined by the percentage of time that the unbound drug concentration remained above the minimum inhibitory concentration (% fT > MIC) specific to Borrelia burgdorferi, with MICs of 0.06, 0.25, 1, 2, and 4 mg/L, requiring at least 40% and 50% of time for effective treatment. Probability of target attainment (PTA) was considered acceptable if it exceeded 50%, allowing for comparison of dosing schedules. Results indicated that the 50 mg/kg/day divided q12h regimen provided similar drug exposure to the q8h regimen for MICs below 2 mg/L (PTAs >50%). For a MIC of 2 mg/L, PTA was achieved with a higher dose of 30 mg/kg/dose q12h. However, for a MIC of 4 mg/L, the PTA criterion was not met. These findings suggest that a twice-daily dosing of 25 mg/kg/dose provides comparable bactericidal activity to the thrice-daily regimen for MICs between 0.06 and 1 mg/L. This simplified regimen may improve adherence and treatment implementation in children.

The field of pharmacogenetics (PGx) has expanded significantly in recent years, with growing evidence supporting its role in enhancing medication effectiveness and reducing adverse drug events. Yet, the integration of PGx into routine clinical practice remains limited. Community pharmacies hold a key position in the healthcare system, offering expert medication advice and maintaining close patient contact due to their accessibility. This context has driven research efforts to integrate PGx testing into healthcare systems in various countries. However, evidence on optimal strategies for embedding PGx services in community pharmacy settings is still emerging. We conducted a scoping review to provide a comprehensive overview of the implementation of PGx testing in community pharmacies, focusing on both successful strategies and challenges. A systematic search of studies involving PGx testing in community pharmacies was conducted using PubMed, Embase, the Cochrane Library and Web of Science, including all publications up to February 2025. The search considered implementation outcomes: feasibility, acceptability, adoption, fidelity, appropriateness, cost, penetration and sustainability. The process and reporting followed the PRISMA recommendations for scoping reviews (PRISMA-ScR). Study findings were classified according to Proctor’s implementation outcomes. A total of 17 studies met the inclusion criteria and were included in the review. Key implementation variables were extracted from these studies. Feasibility was supported by a manageable time process and high technical success. The appropriateness of PGx was reflected in its ability to identify numerous medication-related issues. Adoption varied between patients and prescribers. While patient engagement was high, many sharing PGx results with other physicians, integration of PGx recommendations by prescribers was inconsistent. The intervention was generally well accepted, with high satisfaction among patients and pharmacists, although some physicians expressed concerns. These findings illustrate potential approaches to implementing PGx testing in community pharmacy settings. This scoping review demonstrates the potential for PGx testing to become a viable part of routine care in community pharmacies. It highlights positive patient perceptions and provider willingness to adopt testing. However, it also identifies key barriers, including the need for standardized PGx guidelines, education for providers, and reimbursement policies. The study underscores the importance of patient education, seamless integration into pharmacy workflows, and continued research to support successful implementation.

Background and ObjectiveFexofenadine is commonly used as a probe substrate to assess P-glycoprotein (Pgp) activity. While its use in healthy volunteers is well documented, data in older adult and polymorbid patients are lacking. Age- and disease-related physiological changes are expected to affect the pharmacokinetics of fexofenadine. This study aims to investigate the pharmacokinetics of fexofenadine in hospitalized older adult patients as a potential marker of Pgp activity, using data from the OptimAT study (ClinicalTrials.gov identifier: NCT03477331).MethodsPopulation pharmacokinetic (popPK) modeling was conducted using data from 449 hospitalized patients with a median age of 71 years (range: 25-97) and 10 healthy volunteers (median age: 23 years, range: 20-36). Fexofenadine plasma concentrations were analyzed using a refined two-compartment model with sequential zero/first-order absorption, while investigating the impact of covariates such as age, renal function, and Pgp inhibitors on fexofenadine pharmacokinetics.ResultsAge, renal insufficiency, and Pgp inhibitors significantly influenced fexofenadine exposure. Renal function was a key factor, with AUC0-6 increasing by 79% in mild-to-moderate and by 154% in moderate-to-severe renal impairment compared with normal renal function. Co-administration of Pgp inhibitors led to a 35% increase in AUC0-6. Across chronic kidney disease (CKD) stages, age, and Pgp inhibitor status, fexofenadine AUC0-6 ratio ranged from 1.15 (stage 1, 20-30 years) to 4.59 (stage 5, 91-100 years, with Pgp inhibitors), relative to a reference subject of 20 years, normal renal function, and no Pgp inhibitors.ConclusionClinicians should consider the risk of Pgp substrate accumulation in older adults, particularly those with advanced renal impairment. We propose typical values stratified by age and renal function to assist in interpreting Pgp phenotyping using fexofenadine exposure, thereby supporting drug optimization in this population. Further studies are needed to explore underlying mechanisms, such as reduced Pgp activity or expression.

BACKGROUND: Andrographis paniculata products have gained in popularity for the management of respiratory infections since the COVID-19 pandemic. None of these products holds marketing authorisation and all are sold as herbal food supplements. Current herbal food supplement regulations generally do not impose quality assessments prior to commercialisation, such that the quality of herbal food supplements available to consumers is largely unknown. STUDY AIM: To assess the quality, purity and labelling accuracy of A. paniculata-containing products, focusing on andrographolide content (the pharmaceutically active component) and the presence of contaminants and residues. METHODS: Forty A. paniculata-containing products were purchased from 13 countries: 13 from pharmacies and 27 from online retailers readily accessible to consumers in Switzerland. Samples were analysed using ultra-high-performance liquid chromatography–ultraviolet (UHPLC-UV) and ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS) based on the European Pharmacopoeia method. Contaminants and residues were assessed using inductively coupled plasma mass spectrometry and gas chromatography–mass spectrometry, respectively. RESULTS: All samples except one contained A. paniculata. The measured daily dose of andrographolide was compared to the labelled dose. Andrographolide content ranged from 29% to 174% of the labelled dose, with only 2 products accurately labelled, while 20 were underdosed and 1 overdosed. Two products contained quercetin, which interfered with UHPLC-UV analysis. Additionally, three online-purchased products contained toxic contaminants, including a heavy metal (mercury) or pesticides (strychnine, butralin). CONCLUSION: This study reveals widespread mislabelling and underdosing in A. paniculata-containing food supplements marketed internationally, along with the presence of impurities that pose risks to consumers in products bought online. Regulatory authorities must implement stringent quality controls to ensure consumer safety and product transparency.