Background: Membranous nephropathy (MN) and IgA nephropathy (IgAN) are the most common primary glomerulopathies worldwide. The systemic metabolic changes in the progression of MN and IgAN are not fully understood. Methods: A total of 87 and 70 patients with MN and IgAN, respectively, and 30 healthy controls were enrolled in this study. Untargeted metabolomics was performed to explore the differential metabolites and metabolic pathways in the early stage of MN and IgAN. To judge the diagnostic ability of biomarkers, receiver operating characteristic curve analysis (ROC) were performed. Results: Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) suggested that patients with MN and IgAN showed an obvious separation trend from the healthy controls. In addition, 155 and 148 metabolites were identified to be significantly altered in the MN and IgAN groups, respectively. Of these, 70 metabolites were markedly altered in both disease groups; six metabolites, including L-tryptophan, L-kynurenine, gamma-aminobutyric acid (GABA), indoleacetaldehyde, 5-hydroxyindoleacetylglycine, and N-alpha-acetyllysine, showed the opposite tendency. The most affected metabolic pathways included the amino acid metabolic pathways, citrate cycle, pantothenate and CoA biosynthesis, and hormone signaling pathways. Conclusions: Substantial metabolic disorders occurred during the progression of MN and IgAN. L-tryptophan, L-kynurenine, GABA, indoleacetaldehyde, 5-hydroxyindoleacetylglycine, and N-alpha-acetyllysine may show potential as biomarkers for the identification of MN and IgAN.

Nirmatrelvir is an effective component of Paxlovid, the first oral antiviral drug granted emergency use authorization by the FDA. Nirmatrelvir is prescribed extensively in older adult patients to treat the coronavirus disease 2019 (COVID-19) infection. In this study, population pharmacokinetic modeling with clinical study data was employed to explore the pharmacokinetic profile of nirmatrelvir in older adult Chinese patients with COVID-19 infection. The result suggests that the pharmacokinetic profile of nirmatrelvir can be described by a one-compartment model with first-order absorption and elimination in this study population. The calculated apparent clearance (CL/F), apparent volumes of distribution (V/F), and absorption rate constant (k_a) for the typical patient were 4.16 L/h, 39.1 L, and 0.776, respectively. The area under the curve (AUC) of nirmatrelvir in the typical Chinese older adult was approximately three-fold higher than the AUCs in Chinese and Western young adult volunteers. At the same doses, the simulated AUCs were increased by 26%, 43%, 72%, and 135% in virtual populations with creatinine clearances of 60, 45, 30, and 15 mL/min, respectively. Our research provides an instructive reference for nirmatrelvir dose selection in older Chinese adults.

Objective: This study assessed the pharmacokinetic (PK) interactions between clarithromycin (a P-glycoprotein [P-gp] inhibitor) and four direct oral anticoagulants (DOACs) (P-gp substrates) using physiologically based PK (PBPK) models to elucidate the influence of P-gp in the interaction between them. Methods: PBPK models for clarithromycin, DABE–dabigatran (DAB), rivaroxaban, apixaban, and edoxaban were constructed using GastroPlus™ (version 9.9), based on physicochemical data and PK parameters from the literature. The models were optimized and validated in healthy subjects. We evaluated the predictive performance of the established model and further assessed the impact of P-gp on the PK of the four DOACs. Successfully validated models were then used to evaluate potential drug–drug interactions (DDIs) between clarithromycin and the DOACs. Results: The established PBPK models accurately described the PK of clarithromycin, DABE–DAB, rivaroxaban, apixaban, and edoxaban. The predicted PK parameters (C_max, T_max, AUC_0-t) were within 0.5–2 times the observed values. A sensitivity analysis of P-gp parameters indicated that an increase in P-gp expression was reduced by in vivo exposure to DOACs. The models demonstrated good predictive ability for DDIs between clarithromycin and the anticoagulants, and the ratio of the predicted values to the observed values of C_max and the area under the curve (AUC) in the DDI state was within the range of 0.5–2. Conclusions: Comprehensive PBPK models for clarithromycin, DABE–DAB, rivaroxaban, apixaban, and edoxaban were developed, which can effectively predict DDIs mediated by P-gp’s function. These models provide theoretical support for clinical dose adjustments and serve as a foundation for future PBPK model development for DOACs under specific pathological conditions.

H018 is an orally administered, selective, small-molecule inhibitor of Janus kinase 1 (JAK1) made for the treatment of rheumatoid arthritis. A population pharmacokinetic/pharmacodynamic analysis was conducted to characterize the pharmacokinetic and pharmacodynamic profile of H018 and its active metabolite using data from 48 healthy Chinese volunteers who received a single dose of 10-160 mg of H018 in a phase I clinical study. A two-compartment model with delayed absorption and linear elimination adequately described the pharmacokinetic data of H018. The apparent clearance of H018 was estimated to be 39.0 L/h, and triglyceride was identified as a covariate on it. Pharmacokinetic data of the active metabolite could be well described by a twocompartment model with linear elimination. The exposure-effect relationships in terms of pSTAT1 inhibition were well described by a direct response model, with exposure captured by an active moiety that consisted of H018 and its metabolite, weighted by the ratio of in vitro JAK1 inhibitory activity (1.13). The estimated EC50 value for the active moiety is 601 nM. In the simulation using the final model, the inhibitory effect appeared to have reached a plateau in the high-dose groups, with max inhibition rates of 81.42 %, 88.42 %, and 91.89 % for 80, 120, and 160 mg dose groups, respectively. Taken together, this study will provide an instructive reference of dose selection for subsequent clinical trials.

Background: In patients with atrial fibrillation receiving long-term anticoagulation therapy with dabigatran etexilate (DABE), moxifloxacin (MFLX) is frequently coadministered to treat concurrent infections; however, the potential drug–drug interaction (DDI) between these agents remains unclear. Herein, we examined the underlying mechanism by which MFLX attenuates DABE pharmacokinetics in rats; subsequently, we elucidated the DDI in humans by establishing a physiologically based pharmacokinetic (PBPK) model based on these animal data. Methods: The 3- and 14-day effects of 40 mg/kg MFLX once daily and secondary bile acid (SBA)-containing dietary intervention on the pharmacokinetic profile of DABE and its active form, dabigatran (DAB), were examined in a rat model. Ileum tissues were harvested to measure the expression of P-glycoprotein (P-gp), pregnane X receptor (PXR), and peroxisome proliferator-activated receptor alpha (PPARα). In addition, we examined the effects of secondary bile acids (SBAs) on P-gp expression and quantified P-gp-mediated DABE efflux transport activity in Caco-2 cells. A PBPK model was used to predict the risk of DAB exposure under this DDI scenario and under combined high-risk conditions, including renal impairment and advanced age. Results: Treatment with MFLX for 3 and 14 days inhibited SBA-producing gut microbiota, thereby suppressing the conversion of primary bile acids to SBAs. Concurrently, a marked reduction in intestinal P-gp expression was observed, along with a significant enhancement of the oral bioavailability of DABE. These effects were reversed by SBA-containing diets. In vitro experiments using Caco-2 cells revealed that physiologically relevant concentrations of SBA significantly upregulated P-gp expression and function, whereas MFLX incubation alone showed no direct modulatory effect on these transporters or regulators. PBPK simulation results showed that in vivo exposure of DAB would increase by 41.7%, 104%, 251%, and 115% when coadministered with MFLX alone, with coexisting mild renal impairment, moderate renal impairment, and aging, respectively. Conclusions: MFLX increases DAB exposure by reducing SBA-regulated intestinal P-gp function. PBPK simulations suggest a low risk of DDI from MFLX coadministration alone; however, caution is warranted in patients with aging or renal impairment.