Rivaroxaban is a popular direct factor Xa inhibitor used for anticoagulation therapy in patients with nonvalvular atrial fibrillation (NVAF). The aim of this study was to establish a population pharmacokinetic (PPK) model for rivaroxaban in elderly Chinese patients with nonvalvular atrial fibrillation, evaluate precision dosing regimens, and analyze hemorrhagic risk after rivaroxaban treatment. A 1-compartment population PK model with estimated glomerular filtration rate (eGFR), total bilirubin (TBIL), and ABCB1 rs1045642 as major covariates for apparent clearance was developed using the nonlinear mixed-effects model (NONMEM). A Monte Carlo simulation was performed to evaluate various dosing schemes and different levels of covariates for the target range of therapeutic drug-monitoring concentrations (C max,ss and C min,ss ). The exposure to rivaroxaban was simulated and assessed through hemorrhagic risk evaluation. The results showed that the average probability of target attainment (PTA) for optimal dosing regimens with different covariate levels for the targeted C max,ss and C min,ss were 29.35% to 31.3% and 64.91% to 65.8%, respectively. A dosage of 10 mg of rivaroxaban in elderly Chinese patients with normal renal and liver function was appropriate. The area under the concentration-time curve estimated over 24 hours with precision dosing at steady state (AUC 24,ss ) was statistically significantly associated with an increased risk of bleeding events (OR 1.0006, 95%CI 1.0003 to 1.001, P < .0001), and the bleeding risk increased by 1.82-fold for every 1000 μg*h/L increase in AUC 24,ss . A lower dose is recommended for elderly patients with renal impairment to avoid overexposure and bleeding events. The PPK model could inform individualized dosing for elderly Chinese patients with nonvalvular atrial fibrillation receiving rivaroxaban anticoagulation therapy.

Gastric cancer (GC) is one of the most common malignant tumors in the world. GPx4, as the core regulator of ferroptosis, has become a potential molecular target for developing anticancer agents. In the present study, we found that GPx4 was overexpressed and negatively correlated with poor prognosis in GC, while it was associated with the GC development. Molecular docking and structure-based virtual screening assays were used to screen potential GPx4 inhibitors, and we identified a novel GPx4 inhibitor, polyphyllin B (PB), which can induce ferroptosis by down-regulating GPx4 expression in GC cells. It has also been shown to inhibit cell proliferation, suppress invasion and migration, induce apoptosis, and block the cell cycle progression in GC cells in vitro. Then, immunofluorescence and western blotting assay confirmed that PB can regulate the expression of LC3B, TFR1, NOCA4 and FTH1 in vitro, which suggested that suggest that PB may increase the level of Fe 2+ by transporting Fe 3+ into the cell by TFR1 and promoting NCOA4-dependent iron autophagy. In addition, PB can also suppresses tumor growth in an orthotopic mouse model of GC via regulating the expression of GPx4, TFR1, NOCA4 and FTH1 in vivo. In summary, we confirmed that GPx4 may be a potential target for GC treatment, PB may be a novel and promising drug for the treatment of GC, which shows good antitumor efficacy without causing significant host toxicity via inducing ferroptosis in both gastric cancer cells and mouse models.

Background: Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy (NCT) is essential for personalized treatment of locally advanced gastric cancer (LAGC). The aim of this study was to identify radio-clinical signatures from pretreatment oversampled computed tomography (CT) images to predict the response to NCT and prognosis of LAGC patients. Methods: LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. An SE-ResNet50-based chemotherapy response prediction system was developed from pretreatment CT images preprocessed with an imaging oversampling method (i.e. DeepSMOTE). Then, the deep learning (DL) signature and clinic-based features were fed into the deep learning radio-clinical signature (DLCS). The predictive performance of the model was evaluated based on discrimination, calibration, and clinical usefulness. An additional model was built to predict overall survival (OS) and explore the survival benefit of the proposed DL signature and clinicopathological characteristics. Results: A total of 1060 LAGC patients were recruited from six hospitals; the training cohort (TC) and internal validation cohort (IVC) patients were randomly selected from center I. An external validation cohort (EVC) of 265 patients from five other centers was also included. The DLCS exhibited excellent performance in predicting the response to NCT in the IVC [area under the curve (AUC), 0.86] and EVC (AUC, 0.82), with good calibration in all cohorts ( P >0.05). Moreover, the DLCS model outperformed the clinical model ( P <0.05). Additionally, we found that the DL signature could serve as an independent factor for prognosis [hazard ratio (HR), 0.828, P =0.004]. The concordance index (C-index), integrated area under the time-dependent ROC curve (iAUC), and integrated Brier score (IBS) for the OS model were 0.64, 1.24, and 0.71 in the test set. Conclusion: The authors proposed a DLCS model that combined imaging features with clinical risk factors to accurately predict tumor response and identify the risk of OS in LAGC patients prior to NCT, which can then be used to guide personalized treatment plans with the help of computerized tumor-level characterization.

What is known and objective Teicoplanin is widely used for the treatment of infections caused by drug‐resistant Gram‐positive bacteria. Since there is a good correlation between trough levels and clinical outcome, therapeutic drug monitoring (TDM) is recommended to achieve better clinical curative effects. However, TDM of teicoplanin is not routine in China. So, a programme was initiated in 2017, including both HPLC method establishment and interlaboratory quality assessment, for the measurement of teicoplanin. Methods A main centre and a quality control centre were set up in the study. An HPLC‐based method of teicoplanin determination in plasma was developed by the main centre. Analysis was performed using a Waters Symmetry C18 column (250 mm × 4.6 mm, 5 µm). The mobile phase was NaH2PO4 (0.01 mol/L) and acetonitrile (75:25 v/v; pH 3.3), with a flow rate of 1.0 mL/min and a detection wavelength of 215 nm. Piperacillin sodium was selected as an internal standard (IS). Twenty‐six additional TDM centres were then recruited to adopt this method. Then, all the centres were asked to take part in a quality control assessment evaluated by the quality control centre. Results For all TDM centres, linearity of teicoplanin concentration ranges was between 3.125 and 100 µg/mL. Intraday and interday accuracies ranged from 87.1% to 118.4%. Intraday and interday precision ranged from 0.3% to 13.8%. Therapeutic drug monitoring centres all passed inter‐room quality assessment. All samples tested met the acceptance criteria. Then, 542 samples were collected. Patients with sub‐optimal (≤10 mg/L) plasma teicoplanin concentrations constituted 42% of the total study population. What is new and conclusions For the first time, a simple, rapid and accurate HPLC method for determining teicoplanin levels was successfully applied to therapeutic drug monitoring in clinical practice for twenty‐seven TDM centres in China. The results demonstrated excellent interlaboratory agreement for teicoplanin testing and provide support for clinical laboratory quality management and results inter‐accreditation.

Tacrolimus and voriconazole are usually used simultaneously in lung transplantations. Voriconazole can increase tacrolimus concentrations by inhibiting the cytochrome P450 (CYP) enzyme, which poses a great challenge for dose adjustment. The aim of this study is to clarify the correlation between voriconazole exposure and tacrolimus trough concentrations (C0), and to establish a population pharmacokinetic model including voriconazole trough concentrations (VOZ) as a covariate for dose optimization. All data were retrospectively collected from lung transplantation patients who were subjected to therapeutic drug monitoring of tacrolimus and voriconazole. The correlation between C0 and VOZ or voriconazole daily doses was analyzed by Spearman's correlation. A total of 52 patients accounting for 351 pairs of tacrolimus and voriconazole trough concentrations were included. C0 and C0/daily dose of tacrolimus (DD) had a significant correlation with VOZ (P < .01) rather than voriconazole daily doses. A linear one‐compartment model with first‐order absorption and elimination was used as the basic model for population pharmacokinetic analysis. Body weight (WT), DD, VOZ, and hematocrit (HCT) were included as covariates in the final model. With the increase in voriconazole concentrations, the apparent total clearance (clearance/bioavailability, CL/F) of tacrolimus decreased significantly. The simulation results showed that the highest proportion of C0 within the target range can only reach <50% when the optimal initial drug regimen was given. Therefore, both tacrolimus and voriconazole concentrations need to be continuously monitored during treatments in lung transplantation patients, and the tacrolimus dose can be optimized according to VOZ based on the established pharmacokinetic model.

Hemorrhage events occur most frequently in anticoagulant therapy for non-valvular atrial fibrillation (NVAF). Rivaroxaban is used widely for routine anticoagulation care. Genetic polymorphisms are thought to contribute to the wide intraindividual variability seen in rivaroxaban metabolism and the anticoagulant response. The aim of this study was to evaluate the effect of drug transport related single-nucleotide polymorphisms (SNPs) on rivaroxaban metabolism and on the risk of a hemorrhage event. A total of 216 Chinese patients with NVAF were enrolled in the study.Rivaroxaban was used for anticoagulation therapy. Rivaroxaban plasma concentrations were detected using a validated ultra-performance liquid chromatographytandem mass spectrometry (UPLC-MS/MS) method. Seven SNPs in four genes were genotyped using the Sanger dideoxy DNA sequencing method. Associations between genotype variants, the incidence of hemorrhage events, and the time of bleeding were analyzed. ABCB1 2677G (rs2032582) variation was highly associated with the dose-adjusted rivaroxaban peak concentration in plasma (C max /D) (p = 0.025, FDR = 0.042). The ABCB1 G allele carriers had a higher rivaroxaban C max /D than non-carriers. Logistic regression showed that rivaroxaban C max /D and ABCB1 genotype variants were associated with a higher incidence of hemorrhage events. No statistically significant difference was found between ABCB1 genotypes and the time of bleeding after anticoagulant therapy in 30 days. These results indicated that ABCB1 2677G (rs2032582) genetic variant affects the rivaroxaban C max /Dose and the incidence of hemorrhage events significantly.

Background: The early noninvasive screening of patients suitable for neoadjuvant chemotherapy (NCT) is essential for personalized treatment in locally advanced gastric cancer (LAGC). The aim of this study was to develop and visualized a radio-clinical biomarker from pretreatment oversampled CT images to predict the response and prognosis to NCT in LAGC patients.Methods: 1060 LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. The training (TC) and internal validation cohort (IVC) were randomly selected from center I. The external validation cohort (EVC) comprised 265 patients from 5 other centers. An SE-ResNet50-based chemotherapy response predicting system (DL signature) was developed from pretreatment CT images preprocessed with imaging oversampling method (i.e. DeepSMOTE). Then, DL signature and clinic-based features were fed into the deep learning radio-clinical signature (DLCS). The predictive performance was evaluated according to discrimination, calibration and clinical usefulness. Model for OS prediction were built to further explore the survival benefit of the proposed DL signatures and clinicopathological characteristic. Result: DLCS showed perfect performance in predicting the response to NCT in the IVC (AUC, 0.86) and EVC (AUC, 0.82), with good calibration in all cohorts (p > 0.05). In addition, the performance of DLCS was better than that of the clinical model (p＜0.05). Finally, we found that the DL signature could also serve as an independent factor for prognosis (HR, 0.828, p = 0.004). The C-index, iAUC, IBS for the OS model were 0.64, 1.24 and 0.71 in the test set.Conclusion: We proposed the DLCS that links the imaging features to clinical risk factors to generate high accuracy classification of tumor response and risk identification of OS in LAGC patients prior to NCT that then can be used for guiding personalized treatment plans with the help of the visualization of computerized tumor-level characterization.

Background Erratic tacrolimus (Tac) exposure is associated with poor long-term outcomes after kidney transplantation. However, detailed data on the association in lung transplant recipients are lacking.Aim We hypothesized that Tac intra-patient variability (Tac IPV) or mean concentration was associated with chronic lung allograft dysfunction (CLAD) and survival after lung transplantation.Methods Data from 132 lung transplant recipients were analyzed retrospectively. Tac trough concentration during 0–6, 6–12 and 12–24 months after transplantation were collected. Tac IPV was calculated based on coefficient of variation. Univariate and multivariate COX analyses were performed to identify risk factors for CLAD and survival.Results Of the 132 patients analyzed, 31 (23.5%) developed CLAD and 20 (15.2%) died. Patients received significantly more intensive monitoring of Tac concentration at 0–6 months and Tac IPV was the highest (median: 33.98%) at this period, while after 6 months it lowered to a relatively steady level. A higher mean concentration at 6–12 months [Hazard Ratio (HR) 0.358 (95% CI: 0.137–0.933)] and at 12–24 months [HR 0.351 (95% CI: 0.136–0.904)] was a protective factor for CLAD incidence, while a higher Tac IPV at 6–12 months [HR 2.839 (95% CI: 1.063–7.585)] was associated with increased risk of mortality.Conclusion Our data indicates that Tac mean concentration and IPV had significant impact on CLAD incidence and mortality in lung transplant recipients. Routine monitoring of Tac concentration and IPV may help identify in patients at increased risk for inferior long-term outcomes.