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Abstract Solid transplant recipients are at increased risk for invasive aspergillosis. Tacrolimus and Voriconazole is one of the most frequently utilized treatments for those recipients with invasive fungal infections. However, it is difficult to use them properly due to the interaction between them. This study aimed to investigate the potential drug-drug interaction between Tacrolimus and Voriconazole by multiple methods, including in vitro liver microsome method and the PBPK(Physiologically Based Pharmacokinetic) model. Midazolam and testosterone were used as probe substrates to evaluate individual differences in CYP3A4/5 metabolic activity. A comprehensive interaction analysis was also conducted based on the STITCH database and the DD-Inter system. Furthermore, a PBPK model was constructed by the data from the literature to simulate the real metabolic process in vivo. The research employed multiple methodologies to demonstrate that the co-administration of Voriconazole significantly enhances Tacrolimus concentrations, considering genotypes and the activity of CYP3A4/5 genotypes. The findings indicated a decrease in the relative percentages of midazolam and testosterone metabolites with increasing Voriconazole concentration. Moreover, the results for residual Tacrolimus in the 30-minute incubation group revealed that Voriconazole exerts a mild inhibitory effect on the in vitro metabolism of Tacrolimus. The STITCH database and DD-Inter system analysis also suggested that Tacrolimus and Voriconazole share a strong association in liver metabolism, most likely interacting with CYP3A4/5 and CYP2C19. Furthermore, the result of PBPK analysis indicated that Tacrolimus AUC increases with Voriconazole co-therapy. Moreover, the AUC of Tacrolimus in intermediate CYP2C19 metabolizers (IM) was the highest at 10.1 µmol·min/L, followed by poor metabolizers (PM) at 8.13 µmol·min/L, and extensive metabolizers (EM) at 2.18 µmol·min/L. And the genotype of CYP3A5 poor metabolizer (PM) had AUC of Tacrolimus at 3.13µmol·min/L and extensive metabolizer (EM) at 2.18µmol·min/L. Microsomal studies, PBPK models, and multiple other analyses have comprehensively elucidated the impact of Voriconazole on Tacrolimus concentrations. These findings can serve as a valuable point of reference for concurrently administering these two medications. These findings also indicate that the genotypes of CYP2C19 play an important role in the development of DDI during concurrent Tacrolimus and Voriconazole treatment, which may have some guidance for clinical medication.

Abstract Background The rise of antibiotic resistance has become a critical global health concern, resulting in numerous fatalities annually. Among resistant pathogens, methicillin-resistant Staphylococcus aureus (MRSA) poses a particularly severe and expanding threat. However, there is a lack of comprehensive studies on the prevalence, patterns, and contributing factors of MRSA resistance within mainland China. Methods In this study, the data of MRSA antimicrobial resistance in 31 provinces from 2014 to 2021 were obtained from the China Antimicrobial Resistance Surveillance System (CARSS). Environment and medical care data were obtained either from the National Bureau of Statistics (NBS) database or the National Ministry of Ecology and Environment (NMEE). Multiple linear regression and restricted cubic spline (RCS) was used to explore the potential influencing factors of MRSA antibiotic resistance rate (AMR). Results Significant positive associations were identified between MRSA AMR and various factors, including cadmium pollution, soybean and poultry production, and oil consumption. Conversely, an inverse J-shaped relationship was observed between tap water coverage and MRSA resistance. Other noteworthy correlations included the effects of corn and pork production, as well as antibiotic use intensity. Our predictive model explained 52.7% of the variability in MRSA AMR, demonstrating strong predictive capability. Conclusions This study highlights the significant role of environmental, agricultural, and healthcare factors in influencing MRSA AMR in China. The findings emphasize the need for integrated strategies addressing regional environmental and healthcare quality in combating antibiotic resistance. Extending this research globally could enhance understanding of MRSA resistance dynamics on a broader scale. Graphical Abstract

BackgroundThe concurrent administration of tacrolimus and voriconazole in kidney transplant recipients can lead to drug interactions, potentially resulting in severe adverse reactions. This study aimed to establish a robust population pharmacokinetic model to explore the interaction between tacrolimus and voriconazole in greater depth.MethodsTacrolimus blood samples and laboratory data were prospectively collected from eligible patients enrolled between April 2023 and April 2024, following predefined inclusion and exclusion criteria. Using Phoenix (version 8.1), a pharmacokinetic prediction model was developed. Model performance was assessed using model fitting plots, bootstrap analysis, and visual predictive checks (VPC).ResultsThis study ultimately included 51 eligible patients, with a total of 281 blood samples collected. Analysis revealed a significant negative correlation between voriconazole concentration (Cvrc) and tacrolimus volume of clearance rate (CL), a significant positive correlation between platelets (PLT) and tacrolimus clearance (CL), and a significant negative correlation between blood cells (RBC) and tacrolimus clearance (CL).ConclusionThis study successfully established a population pharmacokinetic model for renal transplant patients concurrently receiving tacrolimus and voriconazole. The model demonstrated good predictive performance and offers valuable insights to clinicians for optimizing tacrolimus dosing in this patient population.

Abstract Background Bacterial drug resistance represents a significant global concern, with vancomycin-resistant Enterococcus faecalis posing a particularly grave threat to contemporary healthcare systems. This study aims to reveal the reasons for the prevalence of VRE in China. Methods This study collected data from the China Antimicrobial Resistance Detection System, China Statistical Yearbook, and China Meteorological Network. The collected data are meticulously organized and subjected to both single-factor and multi-factor analyses. An accurate multiple linear regression model was developed by utilizing this comprehensive dataset. Results Single-factor analysis revealed significant regional variations in the resistance rate of vancomycin-resistant Enterococcus faecalis (P = 0.003). Specifically, there were noteworthy disparities observed between regions experiencing temperate and monsoon climates (P = 0.029; P = 0.005). Furthermore, multi-factor regression analysis demonstrated a negative correlation between the drug resistance rate and both rainfall and rGDP, while a positive correlation was observed with nPI. Conclusion We successfully established a prediction model for the VRE and found that the resistance rate was low in areas with high rainfall and high per capita economic income, but high in areas with many specialized public health institutions. This is critical for public health strategies and helps policymakers and healthcare practitioners tailor antibiotic resistance approaches to local geography, meteorology, economic conditions.

ObjectivesSignificant increase in tacrolimus exposure was observed during co-administration with voriconazole, and no population pharmacokinetic model exists for tacrolimus in renal transplant recipients receiving voriconazole. To achieve target tacrolimus concentrations, an optimal dosage regimen is required. This study aims to develop individualized dosing parameters through population pharmacokinetic analysis and simulate tacrolimus concentrations under different dosage regimens.MethodsWe conducted a retrospective study of renal transplant recipients who were hospitalized at the Second Xiangya Hospital of Central South University between January 2016 and March 2021. Subsequently, pharmacokinetic analysis and Monte Carlo simulation were employed for further analysis.ResultsNineteen eligible patients receiving tacrolimus and voriconazole co-therapy were included in the study. We collected 167 blood samples and developed a one-compartment model with first-order absorption and elimination to describe the pharmacokinetic properties of tacrolimus. The final typical values for tacrolimus elimination rate constant (Ka), apparent volume of distribution (V/F), and apparent oral clearance (CL/F) were 8.39 h−1, 2690 L, and 42.87 L/h, respectively. Key covariates in the final model included voriconazole concentration and serum creatinine. Patients with higher voriconazole concentration had lower tacrolimus CL/F and V/F. In addition, higher serum creatinine levels were associated with lower tacrolimus CL/F.ConclusionOur findings suggest that clinicians can predict tacrolimus concentration and estimate optimal tacrolimus dosage based on voriconazole concentration and serum creatinine. The effect of voriconazole concentration on tacrolimus concentration was more significant than serum creatinine. These findings may inform clinical decision-making in the management of tacrolimus and voriconazole therapy in solid organ transplant recipients.

Background: Pneumocystis jirovecii pneumonia (PJP) has been reported with ICIs but limited to case reports. The clinical features of PJP with ICIs remain mostly unknown. This study aims to investigate the association of PJP with ICIs and describe clinical features.Methods: Reports of PJP recorded in FAERS (January 2004–December 2022) were identified through the preferred term “Pneumocystis jirovecii pneumonia”. Demographic and clinical features were described, and disproportionality signals were assessed through the Reporting Odds Ratio (ROR) and Information Component (IC), using traditional chemotherapy and targeted therapy as comparators, and adjusting signals by excluding contaminant immunosuppressive drugs and pre-existing diseases. A systematic literature review was conducted to describe clinical features of published PJP reports with ICIs. Bradford Hill criteria was adopted for global assessment of the evidence.Results: We identified 677 reports of PJP associated with ICIs, in which 300 (44.3%) PJP cases with fatal outcome. Nivolumab (IC025 2.05), pembrolizumab (IC025 1.88), ipilimumab (IC025 1.43), atezolizumab (IC025 0.36), durvalumab (IC025 1.65), nivolumab plus ipilimumab (IC025 1.59) have significant signals compared to other drugs in FAERS database. After excluding pre-existing diseases and immunosuppressive agents which may increase susceptibility of PJP, the signals for PJP associated with nivolumab, pembrolizumab, durvalumab, nivolumab plus ipilimumab remained robust (IC025 > 0). When compared to other anticancer regimens, although all ICIs showed a lower disproportionate signal for PJP than chemotherapy, nivolumab (IC025 0.33, p < 0.001), pembrolizumab (IC025 0.16, p < 0.001), both PD-1 inhibitors, presented a higher signal for PJP than targeted therapy. Male gender (IC025 0.26, p < 0.001) and age >65 years (IC025 0.38, p < 0.001) were predominant in PJP cases associated with across all ICIs. In literature, 15 PJP cases associated with ICIs were reported in 10 published case reports. 12 of 15 (80.0%) of cases received PD-1 inhibitors before PJP was diagnosed.Conclusion: By the combined analysis of post-marketing data from FAERS and published case reports, we identified ICIs may be associated with PJP, especially in males aged >65years. After accounting for confounders, PD-1 inhibitors emerged with a robust disproportionality signal when compared to PD-L1/CTLA-4 inhibitors as well as targeted therapy. Further research is warranted to validate our findings.

Introduction: Antibody–drug conjugates (ADCs) produce unparalleled efficacy in refractory neoplasms but can also lead to serious toxicities. Although ADC-related sepsis has been reported, the clinical features are not well characterized in real-world studies.Objective: The aim of this study was to identify the association between ADCs and sepsis using FAERS data and uncover the clinical characteristics of ADC-related sepsis.Methods: We performed disproportionality analysis using FAERS data and compared rates of sepsis in cancer patients receiving ADCs vs. other regimens. Associations between ADCs and sepsis were assessed using reporting odds ratios (RORs) and information component (IC). For each treatment group, we detected drug interaction signals, and conducted subgroup analyses (age, gender, and regimens) and sensitivity analyses.Results: A total of 24,618 cases were reported with ADCs between Q1, 2004 and Q3, 2021. Sepsis, septic shock, multiple organ dysfunction syndrome, and other sepsis-related toxicities were significantly associated with ADCs than other drugs in this database. Sepsis and multiple organ dysfunction syndrome have the highest safety concerns with ADCs compared with other anticancer monotherapies. Gemtuzumab ozogamicin and inotuzumab ozogamicin showed increased safety risks than other ADCs. For the top nine ADC-related sepsis, males showed higher sepsis safety concern than females (p

Voriconazole is the mainstay for the treatment of invasive fungal infections in patients who underwent a kidney transplant. Variant CYP2C19 alleles, hepatic function, and concomitant medications are directly involved in the metabolism of voriconazole. However, the drug is also associated with numerous adverse events. The purpose of this study was to identify predictors of adverse events using binary logistic regression and to measure its trough concentration using multiple linear modeling. We conducted a prospective analysis of 93 kidney recipients cotreated with voriconazole and recorded 213 trough concentrations of it. Predictors of the adverse events were voriconazole trough concentration with the odds ratios (OR) of 2.614 (P = 0.016), cytochrome P450 2C19 (CYP2C19), and hemoglobin (OR 0.181, P = 0.005). The predictive power of these three factors was 91.30%. We also found that CYP2C19 phenotypes, hemoglobin, platelet count, and concomitant use of ilaprazole had quantitative relationships with voriconazole trough concentration. The fit coefficient of this regression equation was R2 = 0.336, demonstrating that the model explained 33.60% of interindividual variability in the disposition of voriconazole. In conclusion, predictors of adverse events are CYP2C19 phenotypes, hemoglobin, and voriconazole trough concentration. Determinants of the voriconazole trough concentration were CYP2C19 phenotypes, platelet count, hemoglobin, concomitant use of ilaprazole. If we consider these factors during voriconazole use, we are likely to maximize the treatment effect and minimize adverse events.

Tacrolimus is an immunosuppressant with a narrow therapeutic window. Tacrolimus exposure increased significantly during voriconazole co-therapy. The magnitude of this interaction is highly variable, but it is hard to predict quantitatively. We conducted a study on 91 kidney transplantation recipients with voriconazole co-therapy. Furthermore, 1701 tacrolimus concentration data were collected. Standard concentration adjusted by tacrolimus daily dose (C/D) and weight-adjusted standard concentration (CDW) increased to 6 times higher during voriconazole co-therapy. C/D and CDW increased with voriconazole concentration. Patients with the genotype of CYP3A5 *3/*3 and CYP2C19 *2/*2 or *2/*3 were more variable at the same voriconazole concentration level. The final prediction model could explain 54.27% of the variation in C/D and 51.11% of the variation in CDW. In conclusion, voriconazole was the main factor causing C/D and CDW variation, and the effect intensity should be quantitative by its concentration. Kidney transplant recipients with CYP3A5 genotype of *3/*3 and CYP2C19 genotype of *2/*2 and *2/*3 should be given more attention during voriconazole co-therapy. The prediction model established in this study may help to reduce the occurrence of rejection.

Voriconazole is a triazole antifungal agent commonly used for the treatment and prevention of invasive aspergillosis (IA). However, the study of voriconazole's use in children is limited. The present study was performed to explore maintenance dose to optimize voriconazole dosage in children and the factors affecting voriconazole trough concentration. This is a non-interventional retrospective clinical study conducted from 1 January 2016 to 31 December 2020. The study finally included 94 children with 145 voriconazole trough concentrations. The probability of achieving a targeted concentration of 1.0–5.5 µg/mL with empiric dosing increased from 43 (45.3%) to 78 (53.8%) after the TDM-guided adjustment. To achieve targeted concentration, the overall target maintenance dose for the age group of less than 2, 2 to 6, 6 to 12, and 12 to 18 years old was approximately 5.71, 6.67, 5.08 and 3.31 mg·kg−1/12 h, respectively (p < 0.001). Final multivariate analysis found that weight (p = 0.019), dose before sampling (p < 0.001), direct bilirubin (p < 0.001), urea nitrogen (p = 0.038) and phenotypes of CYP2C19 were influencing factors of voriconazole trough concentration. These factors can explain 36.2% of the variability in voriconazole trough concentration. Conclusion: In pediatric patients, voriconazole maintenance doses under the target concentration tend to be lower than the drug label recommended, but this still needs to be further studied. Age, body weight, dose, direct bilirubin, urea nitrogen and phenotypes of CYP2C19 were found to be influencing factors of voriconazole concentration in Chinese children. The influence of these factors should be taken into consideration during voriconazole use.