Labetalol is a drug that exhibits both alpha and beta-adrenergic receptor-blocking properties. The American Heart Association/American Stroke Association (AHA/ASA) has recommended labetalol as an initial treatment option for the management of severe hypertension. The physiologically based pharmacokinetic (PBPK) model is an in silico approach to determining the pharmacokinetics (PK) of a drug by incorporating blood flow and tissue composition of the organs. This study was conducted to evaluate the primary reasons for the difference in PK after intravenous (IV) and oral administration in healthy and diseased (renal and hepatic) populations. A comprehensive literature search was done using two databases, PubMed and Google Scholar. Various PK parameters were screened for the development of the PBPK model utilizing a population-based PK-Sim simulator. Simulations were performed after creating building blocks firstly in healthy individuals and then in diseased patients after IV and oral administration. The disposition of labetalol after IV and oral administration occurring in patients with the hepatic and renal disease was predicted. The model was evaluated by calculating the R_obs/_pred ratio and average fold error (AFE), which was in the two-fold error range. Moreover, Box-whisker plots were made to compare the overall concentration of the drug in the body at various stages of disease severity. The presented model provides useful quantitative estimates of drug dosing in patients fighting against severe chronic diseases.

Hydroxychloroquine (HCQ), a congener of chloroquine, is widely used in prophylaxis and the treatment of malaria, and also as a cure for rheumatoid arthritis, systemic lupus erythematosus, and various other diseases. Physiologically based pharmacokinetic modeling (PBPK) has attracted great interest in the past few years in predicting drug pharmacokinetics (PK). This study focuses on predicting the PK of HCQ in the healthy population and extrapolating it to the diseased populations, i.e., liver cirrhosis and chronic kidney disease (CKD), utilizing a systematically built whole-body PBPK model. The time vs. concentration profiles and drug-related parameters were obtained from the literature after a laborious search and in turn were integrated into PK-Sim software for designing healthy intravenous, oral, and diseased models. The model’s evaluation was performed using observed-to-predicted ratios (Robs/Rpre) and visual predictive checks within a 2-fold error range. The healthy model was then extrapolated to liver cirrhosis and CKD populations after incorporating various disease-specific pathophysiological changes. Box–whisker plots showed an increase in AUC_0-t in liver cirrhosis, whereas a decrease in AUC_0-t was seen in the CKD population. These model predictions may assist clinicians in adjusting the administered HCQ doses in patients with different degrees of hepatic and renal impairment.

Background/Objectives: Fexofenadine hydrochloride is a widely prescribed drug for treating histamine-mediated allergic reactions. This review systematically collates existing research on the clinical pharmacokinetics (PK) of fexofenadine, with a copious emphasis on examining the impact of stereoisomerism, disease states, and drug interactions. Methods: The search engines PubMed, Science Direct, Google Scholar, and Cochrane were scanned systematically for articles concerning the clinical PK of fexofenadine in humans. The extensive literature search yielded 85 articles meeting the inclusion standards. Results: The PK parameters of fexofenadine showed a linear correlation between increasing doses and proportional elevations in PK parameters such as area under the curve from time 0 to infinity (AUC_0–∞) and maximum plasma concentration (C_max). Under fed conditions, its bioavailability was reduced by approximately 50%. Findings from patients with end-stage renal disease (ESRD) displayed a 63% decline in oral clearance (CL/F) of fexofenadine. A drug–food interaction study has displayed that grapefruit juice decreased C_max (201 ng/mL vs. 128 ng/mL), accompanied by a 30% reduction in the bioavailability of fexofenadine. Furthermore, a drug–herb interaction study with St John’s Wort (SJW) has reported a reduction in CL/F by 10% after a single dose, but long-term administration reversed this effect, resulting in elevated CL/F by 17% of fexofenadine. Conclusions: Since no prior systematic review on the PK of this drug exists, this review amalgamates all pertinent PK parameters in humans by pooling up-to-date data from published studies. This detailed literature review can be advantageous for researchers who want to develop and assess PK models.

Background/Objectives: Glimepiride (GLM), a commonly used sulphonylurea drug for the management of type 2 diabetes mellitus (T2DM), has been the subject of numerous studies exploring its kinetic behaviors. However, a comprehensive evaluation that synthesizes all available pharmacokinetic (PK) data across diverse populations remains limited. This systematic review aims to provide detailed knowledge about the pharmacokinetics (PK), the associated pharmacodynamics (PD), and the drug interactions of GLM, which can be used to assess key parameters and identify factors influencing variability across diverse populations and clinical settings. Methods: A systematic search of the peer-reviewed literature was combined using major databases—Google Scholar, PubMed, Cochrane, and ScienceDirect, to identify studies reporting the PK of GLM. Following the data extraction, a meta-analysis using a random effect (RE) model was performed, where feasible, to quantitatively assess the variability of key PK parameters across different studies to create a more robust PK parameter estimate. Results: The final screening has yielded 40 articles. The area under the curve (AUC_0-∞) and the peak concentration (C_max) rise proportionately with increasing doses, depicting the linear kinetics of GLM. The subjects with genotype CYP2C9 *1/*3 depicted a 4-fold higher (AUC_0-∞) as compared to that of the CYP2C9 *1/*1 population. Preliminary meta-analysis results indicated significant variability in (AUC_0-∞) and C_max values among different studies. Heterogeneity across studies was high, warranting the use of RE models. Conclusions: The findings of this review would be helpful in the development and evaluation of PK models that may aid in suggesting individualized dosing.

Physiologically based pharmacokinetic (PBPK) modeling is of great importance in the field of medicine. This study aims to construct a PBPK model, which can provide reliable drug pharmacokinetic (PK) predictions in both healthy and chronic kidney disease (CKD) subjects. To do so, firstly a review of the literature was thoroughly conducted and the PK information of vildagliptin was collected. PBPK modeling software, PK-Sim^®, was then used to build and assess the IV, oral, and drug-specific models. Next, the average fold error, visual predictive checks, and predicted/observed ratios were used for the assessment of the robustness of the model for all the essential PK parameters. This evaluation demonstrated that all PK parameters were within an acceptable limit of error, i.e., 2 fold. Also to display the influence of CKD on the total and unbound AUC (the area under the plasma concentration–time curve) and to make modifications in dose, the analysis results of the model on this aspect were further examined. This PBPK model has successfully depicted the variations of PK of vildagliptin in healthy subjects and patients with CKD, which can be useful for medical practitioners in dosage optimization in renal disease patients.

Background: Diphenhydramine is an anti-tussive used periodically to treat seasonal colds, contact dermatitis, and anaphylactic reactions. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of diphenhydramine in predicting its systemic exposure among healthy pediatrics (children and adolescents) by leveraging data files from adults (young and elderly). Methods: The data profiles comprising serum/plasma concentration over time and parameters related to diphenhydramine were scrutinized via exhaustive literature analysis and consolidated in the PK-Sim software version 11.1. This modeling methodology commences with developing an adult model and then translating it to the pediatrics which compares the predicted concentration–time datasets with the reported values. Results: The accuracy of model anticipations was then assessed for each pharmacokinetics (PK) variable, i.e., the area under the curve from 0 to infinity (AUC_0-∞), maximal serum/plasma concentration (C_max), and clearance of the diphenhydramine in plasma (CL) by employing the predicted/observed ratios (R_pre/obs), and average fold error (AFE), which fell within the pre-defined benchmark of 2-fold. The predicted and observed C_max values for pediatrics were 3-fold greater in comparison to the young adults following a 25 mg dose depicting a need to monitor dosage schedules among children closely. Conclusions: These model-based anticipations confirmed the authenticity of the developed pediatric model and enhanced the comprehension of developmental variations on PK of diphenhydramine. This may assist healthcare professionals in ensuring the significance of lifespan applicability in personalized dose regimens, promoting therapeutic efficacy and minimizing side effects in chronic conditions among children.