Background/Objectives: Cyclosporine A (CsA) is a key immunosuppressant in post-transplantation therapy protocol characterized by large interindividual and intraindividual pharmacokinetic (PK) variability and a narrow therapeutic range necessitating therapeutic drug monitoring (TDM) to prevent graft rejection and minimize side effects. TDM data can be used for developing PK models with the objective of identification and quantification of variability factors that contribute to the differences in CsA concentrations. Methods: Retrospectively collected data from medical records of 58 patients (children and young adults) regarding CsA blood concentrations, concomitant medications, and laboratory findings of significance were used for the population PK model development in NONMEM^® (version 7.5) with first-order conditional estimation method with interaction (FOCE-I). Simulation of the concentrations and area under the curve (AUC) was performed in the web application e-campsis^®. RStudio (version 4.5.0) was used for the purpose of descriptive statistics analysis and graphs plotting. Results: A one-compartment model with first-order absorption and elimination best described the data. Value of clearance (CL/F) was estimated to be 15 L/h, and volume of distribution (V/F) was 71.1 L for a typical patient weighing 40 kg. Interindividual variability (IIV) on CL/F and V/F was 34.91% and 43.05%, respectively. Interoccasional variability (IOV) was 12.25%. Body weight (WT) was introduced allometrically on CL/F and V/F, with the estimated exponent of 0.89 for CL/F and 1 (fixed) for V/F. According to the final model, CL/F decreases with increasing haemoglobin (HGB) value. A difference of almost 22.5% in CL/F was observed among patients’ HGB values reported in the study. Conclusions: Our findings indicate that HGB levels significantly influence CsA PK, particularly minimum concentration (C_min), highlighting the importance of regular HGB levels monitoring together with CsA levels.

Therapeutic drug monitoring (TDM) of cyclosporine is indicated due to its variable pharmacokinetics. Demographic, clinical and TDM data are used to develop a population pharmacokinetic (popPK) model. These models identify factors causing variability in drug exposure parameters like maximum (Cmax), minimum concentration (Cmin) and area under the curve (AUC). Based on data from 58 patients from the University Children's Hospital, a popPK model was developed with body mass and haemoglobin as variability factors. The model was used to simulate Cmax, Cmin and AUC in the e-campsis® program (Calvagone, France) in a virtual population of patients in two different clinical scenarios: when patients have normal and reduced haemoglobin levels, and each of the two scenarios at two time points. The median values of Cmin, Cmax and AUC in the low-level haemoglobin group at the first and second time points were, respectively: 215 ng/mL, 1851 ng/mL and 6343 ng/mL, and 221 ng/mL, 1918 ng/mL and 6687 ng/mL; and in normallevel haemoglobin group 331 ng/mL, 2265 ng/mL and 7857 ngh/mL, and 318 ng/mL, 2035 ng/mL and 7210 ngh/mL. The most pronounced effect of haemoglobin is on Cmin, where the group with normal haemoglobin had 53.95% higher Cmin levels, i.e. 43.89% compared to the group with reduced levels. Application of simulations based on popPK models is a useful tool for predicting drug exposure. Thus, the balance between the efficacy and safety of the therapy is optimized, which enables precise and timely correction of the dosage in order to achieve the maximum therapeutic response.

Pharmacists, as the most accessible healthcare professionals, play a key role in supporting mental health. Recognition of the importance of their active involvement in treating patients with anxiety disorders is increasing. This study aimed to examine pharmacists' attitudes and perceptions regarding the provision of pharmaceutical care to patients with anxiety. Data were collected using a specially designed questionnaire adapted from related studies. Statistical analysis was performed in Microsoft Excel and SPSS Statistics. Survey reliability was confirmed with Cronbach's Alpha (a = 0.892). Associations between respondents' characteristics (gender, work experience, education level) and scores on individual survey items were analyzed using the Mann-Whitney U and Kruskal-Wallis test. The sample consisted of 104 women and 17 men, with a mean age of 38.16 ± 10.86 years and a mean work experience of 12.24 ± 10.72 years. Most work in community pharmacies (91.74%) and lack postgraduate education (85.12%). The majority reported regularly advising patients on the appropriate use of medication (57.02% always; 27.27% often) and on the risks of self-discontinuing therapy (43.8% always; 31.4% frequently). In addition, 56.2% always refer patients to a physician when necessary, while fewer personally contact physicians (9.92% never; 23.97% rarely). About 23% seldom conduct patient follow-up, and roughly 20% do not routinely check for contraindications. Pharmacists with 11-15 years of experience were significantly more willing to contact physicians than those with 1-5 years of experience, while no difference was observed in the other groups (6-11, 16-20, >20 years). Results indicate that neither gender nor education level significantly influences pharmacists' professional activities. The effect of work experience observed in specific subgroups warrants further investigation to determine whether it reflects a genuine need for additional support of younger pharmacists.

Cyclosporine A (CsA) is an immunosuppressive used in both children and adult transplant recipients, as well as patients with autoimmune diseases such as nephrotic syndrome. Therapeutic drug monitoring (TDM) of CsA is essential due to its significant interindividual and intraindividual pharmacokinetic variability, narrow therapeutic index and the risk of organ rejection or autoimmune disease relapse at subtherapeutic levels, as well as the potential for serious adverse effects with overexposure. In pediatric patients, CsA pharmacokinetics can be significantly influenced by developmental physiological factors, necessitating even greater attention to TDM in this vulnerable population. This paper explores the key challenges associated with TDM in children, the clinical rationale for its use, clinical settings where it is applied and future perspectives, including the potential of model-informed precision dosing (MIPD).

Breastfeeding provides optimal nutrition, immune protection, and long-term health benefits for both infants and mothers [1]. Evidence shows that the majority of cardiovascular drug classes can be used safely in lactating women, yet information about drug safety is often not communicated to the patients [2]. This can lead to unnecessary early cessation of breastfeeding, as well as mothers discontinuing essential cardiovascular therapy [2,3]. The aim of this study was to assess the use and safety of cardiovascular drugs among breastfeeding women and evaluate whether they received advice on medication safety during breastfeeding. Data were prospectively collected within the Mama Friendly Pharmacy project. Safety of drug use was evaluated using the e-lactancia database. From 1243 patients included in the study, 48 (3.86%) were taking at least one cardiovascular drug. A total of 19 different INNs were recorded, with 42 (71.19%) oral administrations and 14 (28.81%) local (rectal) administrations. Among oral drug administrations, 92.86% were safe and compatible with breastfeeding. Likely compatibility was recorded for cinchocaine (11 administrations) and polydecanol (1 administration) both intended for rectal use, and bisoprolol (1), indamapide (1), and serrapeptase (1), all aimed for oral use. Of the 25 patients (52.83%) who did not receive any advice regarding the use of drug while breastfeeding, 14 reported that they even avoided taking their medication. Ensuring that breastfeeding women receive evidence-based guidance on drug use is essential, and pharmacists represent a key person for delivering this counselling.

Background/Objectives: Adalimumab (ADM) is a recombinant, fully human monoclonal antibody that exhibits pronounced inter- and intra-individual pharmacokinetic variability attributed to several factors. This study aims to externally evaluate the published ADM population pharmacokinetic models and their potential use in clinical practice, as well as to develop novel population pharmacokinetic model. Methods: Literature search was conducted using PubMed to identify ADM population pharmacokinetic models. Data from 195 patients with Crohn’s disease treated at the University Medical Center “Zvezdara”, Serbia, were used for the external evaluation of previously published models. In addition, the development of the new population pharmacokinetic model incorporated informative priors derived from the best-performing published model. Nonlinear mixed-effects modeling was performed in NONMEM^® (versions 7.6) for both prediction- and simulation-based diagnostics of existing models, as well as for the development of a new model. Results: Eight published pharmacokinetic models of ADM were included in the external evaluation. Although none of the models satisfied both population-level and normalized prediction distribution error (NPDE) diagnostic criteria, individual-level performance was acceptable: median prediction errors (MDPEs) were within ±20% across all studies, and median absolute prediction errors (MDAPEs) were below 30% in most cases (7 of 8 studies). The best-performing model was identified and implemented as a priori information in subsequent model development. A one-compartment model using with first-order absorption and elimination best described the data. The apparent clearance (CL/F) was estimated at 0.334 L/day, while informative priors were used for V/F and the effect of anti-drug antibodies (ADAs) on CL/F. Covariate analysis on CL/F identified C-reactive protein (CRP) and dosing regimen as statistically significant predictors (p < 0.01). Conclusions: The previous pharmacokinetic models of ADM exhibited suboptimal performance in population-level metrics and simulation-based diagnostics, while individual-level metrics showed substantial improvement. The newly developed model of ADM highlights associations among immunogenicity, drug pharmacokinetics, and inflammatory burden.