Levetiracetam (LEV) is an anti‐epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically‐based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole‐body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh‐A (CP‐A), Child Pugh‐B (CP‐B), and Child Pugh‐C (CP‐C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Background: Propylthiouracil (PTU) treats hyperthyroidism and thyroid crisis in all age groups. A variety of serious adverse effects can occur during clinical use and require attention to its pharmacokinetic and pharmacodynamic characteristics in various populations.Objective: To provide information for individualized dosing and clinical evaluation of PTU in the clinical setting by developing a physiologically based pharmacokinetic (PBPK) model, predicting ADME characteristics, and extrapolating to elderly and pediatric populations.Methods: Relevant databases and literature were retrieved to collect PTU’s pharmacochemical properties and ADME parameters, etc. A PBPK model for adults was developed using PK-Sim® software to predict tissue distribution and extrapolated to elderly and pediatric populations. The mean fold error (MFE) method was used to compare the differences between predicted and observed values to assess the accuracy of the PBPK model. The model was validated using PTU pharmacokinetic data in healthy adult populations.Result: The MFE ratios of predicted to observed values of AUC_0-t, C_max, and T_max were mainly within 0.5 and 2. PTU concentrations in various tissues are lower than venous plasma concentrations. Compared to healthy adults, the pediatric population requires quantitative adjustment to the appropriate dose to achieve the same plasma exposure levels, while the elderly do not require dose adjustments.Conclusion: The PBPK model of PTU was successfully developed, externally validated, and applied to tissue distribution prediction and special population extrapolation, which provides a reference for clinical individualized drug administration and evaluation.

Background: Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease and is currently the leading cause of sudden death in adolescent athletes. Schisandrin is a quality marker of the traditional Chinese medicine Schisandra chinen-sis, which has an excellent therapeutic effect on HCM, but its pharmacological mecha-nism remains unclear. Objective: This study aimed to explore the potential and provide scientific evidence for schisandrin as a lead compound against hypertrophic cardiomyopathy. Methods: The drug-like properties of schisandrin were predicted using the Swis-sADME website. Then, the PharmMapper database was used to predict potential drug targets and match gene names in the Uniprot database. HCM targets were collected from NCBI, OMIM, and Genecards databases and intersected with drug targets. The intersection targets were imported into the STRING database for PPI analysis, and core targets were identified. KEGG and GO enrichment analysis was performed on the core targets through the DAVID database, and all network maps were imported into Cyto-scape software for visualization optimization. HCM-related datasets were downloaded from the GEO database to analyze core targets and screen differentially expressed target genes for molecular docking. Results: After the PPI network analysis of the intersection targets of drugs and diseases, 12 core targets were screened out. The KEGG analysis results showed that they were mainly involved in Rap1, TNF, FoxO, PI3K-Akt, and other signaling pathways. After differential analysis, PPARG, EGFR, and MMP3 targets were also screened. The molecular docking results showed that schisandrin was well bound to the protein backbone of each target. Conclusion: This study used network pharmacology combined with differential expres-sion and molecular docking to predict that schisandrin may treat HCM by acting on PPARG, EGFR, and MMP3 targets, and the regulatory process may involve signaling pathways, such as Rap1, TNF, FoxO, and PI3K-Akt, which may provide a valuable reference for subsequent studies.

Warfarin is a widely used anticoagulant, and its S‐enantiomer has higher potency compared to the R‐enantiomer. S‐warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug–drug interactions (DDIs). This study provides a whole‐body physiologically‐based pharmacokinetic/PD (PBPK/PD) model of S‐warfarin for predicting the effects of drug–drug−gene interactions on S‐warfarin PKs and PDs. The PBPK/PD model of S‐warfarin was developed in PK‐Sim and MoBi. Drug‐dependent parameters were obtained from the literature or optimized. Of the 34 S‐warfarin plasma concentration‐time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S‐warfarin plasma concentration‐time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration‐time curve to the last measurable timepoint (AUC_last) ratio within twofold of the observed values. The anticoagulant effect of S‐warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose‐adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.

Metformin is commonly used as first‐line treatment for T2DM (type2 diabetes mellitus). Owing to the high pharmacokinetic (PK) variability, several population pharmacokinetic (PPK) models have been developed for metformin to explore potential covariates that affect its pharmacokinetic variation. This comprehensive review summarized the published PPK studies of metformin, aimed to summarize PPK models of metformin. Most studies described metformin pharmacokinetics as a 2‐compartment (2‐CMT) model with 4 study describing its pharmacokinetics as 1‐compartment (1‐CMT). Studies on metformin PPK have shown that obesity, creatinine clearance (CL_Cr), gene polymorphism, degree of renal function damage, and pathological conditions all have a certain impact on the PK parameters of metformin. It is particularly important to formulate individualized dosing regimens. For future PPK studies of metformin, we believe that more attention should be paid to special populations.

Topiramate (TPM) is a broad‐spectrum antiepileptic drug (AED) commonly prescribed for approved and off‐label uses. Routine monitoring is suggested for clinical usage of TPM in special population due to its broad side effect profile. Therefore, it is crucial to further explore its pharmacokinetic characteristics. Physio‐chemical properties of TPM were initially determined from online database and further optimized while establishing the PBPK model for healthy adults using the PK‐Sim software. The model was then extrapolated to patients with renal impairment and patients who were hepatically impaired. A drug–drug interaction (DDI) model was also built to simulate plasma TPM concentrations while concomitantly used with carbamazepine (CBZ). The goodness‐of‐fit method and average fold error (AFE) method were used to compare the differences between predicted and observed values to assess the accuracy of the PBPK model. Almost all of the predicted concentration fell within twofold error range of corresponding observed concentrations. The AFE ratio of predicted to observed values of C_max and AUC_0‐inf was all within 0.5 and 2. It is recommended that the doses be reduced to 70%, 50%, and 40% of the healthy adult dose for the chronic kidney disease (CKD) stage 3, stage 4, and stage 5 patients, respectively, and reduced to ~70%, and 35% for the Child–Pugh‐B, and Child–Pugh C scored patient with hepatic impairment, respectively. If TPM is co‐administered with CBZ, increasing TPM doses to 150%–175% of the monotherapy dose is recommended according to model simulation.