Objectives: In China, primary healthcare (PHC) facilities have high antibiotic prescribing rates for upper respiratory tract infections (URTIs), which are primarily viral and self-limited. This study aimed to identify the main factors influencing PHC physicians’ antibiotic decisions for URITs based on the theory of planned behavior. Methods: A convergent mixed-methods study was conducted at 30 PHC facilities across Shaanxi Province, China. A total of 108 PHC physicians completed a five-point Likert Scale questionnaire focused on behavioral components of antibiotic prescribing, including attitudes, subjective norms, perceived behavioral control, belief in past experiences, and prescribing intentions. Twenty-two physicians participated in semi-structured interviews. Results: Respondents had a good awareness of AMR (Mean = 4.49) and a weak belief regarding the benefit of antibiotics (Mean = 2.34). The mean score for subjective norms was 3.36, and respondents had good control over their prescribing behavior (Mean = 4.00). A reliance on past prescribing experiences was observed (Mean = 3.34), and physicians’ antibiotic prescribing intention was 3.40 on average. Multiple linear regression revealed that physicians showing a more favorable attitude towards antibiotics (p = 0.042) and relying more on their past experiences (p = 0.039) had a higher antibiotic prescribing intention. Qualitative interviews indicated that most physicians would consider prescribing antibiotics when facing diagnostic uncertainty. Low utilization of diagnostic tests, limited effectiveness of training programs, inadequate knowledge of guidelines, and lack of feedback on antibiotic prescriptions all contributed to antibiotic overprescribing. Conclusions: PHC physicians in China demonstrated strong intentions to prescribe antibiotics for URTIs when facing diagnostic uncertainty. Beliefs about antibiotics and previous prescribing behavior were significantly linked to prescribing intentions. Multifaceted interventions that focus on facilitating diagnostic tests, improving the quality of training, effectively implementing clinical guidelines, and providing practical feedback on antibiotic prescriptions may help reduce antibiotic overprescribing in China’s PHC facilities.

Background: In the last several years, “physiologically based pharmacokinetic (PBPK) modeling” has gathered significant emphasis in the modeling of drug absorption, disposition, and metabolism. This research study aims to elaborate the plasma/serum concentration–time profiles and pharmacokinetics (PK) of ofloxacin by establishing a PBPK model in healthy subjects and those suffering from renal impairment (RI). Methods: A comprehensive literature analysis was conducted to screen out all the systemic PK profiles and parameters specific to ofloxacin, followed by their implementation in PK-Sim^® version 12 software. This model-driven approach begins by developing the model in healthy populations using both intravenous (IV) and per-oral (PO) routes and then extrapolating it to the diseased population. The model evaluation was then strengthened for different PK variables such as the maximal plasma/serum concentration (C_max), the area under the curve from 0 to t (AUC_0–t), and plasma/serum clearance (CL) by employing various metrics such as predicted/observed ratios (R_pre/obs), visual predictive checks, the average fold error (AFE), root mean squared error (RMSE), and mean absolute error (MAE). Results: The AFE, RSME, and MAE for C_max in RI were 1.10, 0.22, and 0.16, respectively, which fell within the acceptable simulated error range. Furthermore, dosage adjustments for individuals with mild, moderate, and severe RI were presented by box-whisker plots to compare their systemic exposure with that of the healthy population. Conclusions: These model predictions have confirmed the PK variations in ofloxacin, which may assist the clinicians in optimizing dosage schedules in healthy and various categories of RI populations.

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic tool used to predict how a drug moves through the body by incorporating real human physiology, including organ sizes, blood flows, tissue compositions, and enzyme activities. It has been widely employed to estimate drug exposure in different populations with organ impairment, genotype variabilities, and physiological variations. Metoclopramide is an antiemetic and prokinetic agent that is subject to CYP2D6 polymorphism. The study aims to develop PBPK models for several CYP2D6 variants to predict changes in the pharmacokinetic (PK) behavior of metoclopramide. Methods: To conduct this study, a literature review was conducted, and the retrieved physicochemical, biochemical, and PK data were integrated into PK-Sim to develop a PBPK model. Initially, a non-genotype-specific model was developed and extrapolated to genotype-based models. The models were verified using a Visual Predicted Check (VPC), mean predicted-to-observed ratio (R_pre/obs) values, and mean relative deviation (MRD). Results: The simulated profiles were aligned with the reported data, and all the predicted and observed PK parameters were comparable, as the R_pre/obs values were within the 0.5–2 range and MRD values were <2. Moreover, an increasing trend in AUC_0–∞ was observed across CYP2D6*wt/*wt, CYP2D6*wt/*10, CYP2D6*10/*10, and CYP2D6*5/*10, with approximately 1.63-, 2.64-, and 2.88-fold increases compared with the CYP2D6*wt/*wt genotype. Conclusions: The models have adequately estimated the PK behavior of metoclopramide across different CYP2D6 variants. These models might be helpful for populations with diverse CYP2D6 genotypes in dose optimization.

Background: Physiologically based pharmacokinetic (PBPK) modeling is an established approach used in recent years for estimating drug disposition in challenging clinical scenarios where in vivo studies are difficult to perform. Metoclopramide is a benzamide derivative, widely indicated for its antiemetic and prokinetic actions. This study aims to develop a PBPK model for metoclopramide in infants to predict its systemic exposure in this population. Methods: To develop the model, a detailed literature review was conducted, and the required data related to the drug, human physiology, and published clinical studies were retrieved. After that, all information was integrated into the PK-Sim software, and the model was initially developed in adults to create a base; subsequently, it was extrapolated to infants. After successful development, these models were verified visually and numerically using visual predictive checks (VPCs), mean predicted-to-observed ratios (R_pre/obs), average fold error (AFE), and mean relative deviation (MRD). Results: All simulated profiles were consistent with observed data; computed AFE and R_pre/obs for key pharmacokinetic (PK) parameters, including area under the concentration–time curve from 0 to t (AUC_0–t), maximum plasma concentration (C_max), and clearance (CL), fell within an acceptable two-fold error range. Moreover, the MRD values for all profiles were <2, showing promising agreement between the reported and predicted datasets. Conclusions: The current model provides a robust framework to estimate the PK of metoclopramide in infants, which may help with dose individualization in this vulnerable population.

Background: Doxazosin, an α₁-adrenergic antagonist, is commonly used in the management of hypertension and benign prostatic hyperplasia (BPH). Pharmacokinetic (PK) variability across populations may affect drug exposure and clinical response. This systematic review and meta-analysis aimed to summarize PK differences and generate pooled estimates of key parameters, including area under the curve (AUC) and maximum plasma concentration (Cmax). Methods: A systematic search of Google Scholar, PubMed, ScienceDirect, and the Cochrane Library identified 25 eligible studies reporting doxazosin PK data. All extracted AUC and Cmax values were dose-normalized prior to synthesis to ensure comparability across different doses and formulations. A random-effects meta-analysis was performed using the metafor package in R to estimate pooled dose-normalized AUC and Cmax while accounting for between-study variability. Heterogeneity was assessed using the I² statistic. Sensitivity analyses—including leave-one-out diagnostics and Baujat plots—were used to identify influential studies. Publication bias and small-study effects were evaluated through funnel plots, trim-and-fill procedures, and Egger’s regression test. Meta-regression analyses examined the influence of age and body weight on PK parameters. Results: The meta-analysis produced pooled dose-normalized estimates for AUC and Cmax, with high heterogeneity across studies (I² ≈ 90%). Leave-one-out analyses demonstrated stable pooled estimates; for dose-normalized AUC, exclusion of three influential studies reduced heterogeneity to 82% with only a modest decrease in the pooled mean. Baujat plots identified a small number of studies as key contributors to heterogeneity, while most exerted minimal influence. Funnel plots showed notable asymmetry for both AUC and Cmax, and trim-and-fill analyses suggested possible small-study effects; however, adjusted pooled estimates remained consistent. Egger’s regression confirmed significant asymmetry for dose-normalized AUC (t = 4.41, p = 0.0003) and Cmax (t = 4.35, p = 0.0001). Meta-regression revealed that body weight significantly reduced Cmax, whereas age had no significant effect on either AUC or Cmax. Conclusions: This systematic review and meta-analysis provide a comprehensive evaluation of doxazosin PK across diverse populations. Despite normalization, substantial variability remained in AUC and Cmax, related in part to ethnicity, hepatic impairment, dosage formulation, and body weight. While pooled estimates offer valuable summary reference points, the high heterogeneity and evidence of small-study effects highlight the need for more standardized PK trials and patient-level analyses to better support individualized dosing strategies.