Five genes identified as prognostic markers for colorectal cancer through the integration of genome-wide association study and expression quantitative trait loci data.

Background: Colorectal cancer (CRC) is a prominent form of cancer globally, ranking second in terms of prevalence and serving as a leading cause of cancer-related deaths, but the underlying biological interpretation remains largely unknown. Methods: We used the summary data-based Mendelian randomization method to integrate CRC genome-wide association studies (ncase = 7062; ncontrol = 195,745) and expression quantitative trait loci summary data in peripheral whole blood (Consortium for Architecture of Gene Expression: n = 2765; Genotype-Tissue Expression [v8]: n = 755) and colon tissue (colon-transverse: n = 406; colon-sigmoid: n = 373) and identified related genes. Results: Genes ABTB1, CYP21A2, NLRP1, PHKG1 and PIP5K1C have emerged as significant prognostic markers for CRC patient survival. Functional analysis revealed their involvement in cancer cell migration and invasion mechanisms, providing valuable insights for the development of future anti-CRC drugs. Conclusion: We successfully identified five CRC risk genes, providing new insights and research directions for the effective mechanisms of CRC.

Individual dose recommendations for drug interaction between tacrolimus and voriconazole in adult liver transplant recipients: A semiphysiologically based population pharmacokinetic modeling approach.

The magnitude of drug-drug interaction between tacrolimus and voriconazole is highly variable, and individually tailoring the tacrolimus dose when concomitantly administered with voriconazole remains difficult. This study aimed to develop a semiphysiologically based population pharmacokinetic (semi-PBPK) model and a web-based dashboard to identify the dynamic inhibition of tacrolimus metabolism caused by voriconazole and provide individual tacrolimus regimens for Chinese adult liver transplant recipients. A total of 264 tacrolimus concentrations and 146 voriconazole concentrations were prospectively collected from 32 transplant recipients. A semi-PBPK model with physiological compartments including the gut wall, portal vein, and liver was developed using the nonlinear mixed-effects modeling software NONMEM (version 7.4). A web-based dashboard was established in R software (version 3.6.1) to recommend the individual tacrolimus regimens when concomitantly administered with voriconazole. The reversible inhibition of tacrolimus metabolism caused by voriconazole was investigated in both the liver and the gut wall. Moreover, voriconazole could highly inhibit the CYP3A activity in the gut wall more than in the liver. BMI and postoperative days were identified as significant covariates on intrinsic intestinal and hepatic clearance of tacrolimus, respectively. Age and postoperative days were identified as significant covariates on the volume of distribution of voriconazole. The individual tacrolimus regimens when concomitantly administered with voriconazole could be recommended in the dashboard (https://tac-vor-ddi.shinyapps.io/shinyapp3/). In conclusion, the semi-PBPK model successfully described the dynamic inhibition process between tacrolimus and voriconazole, and the web-based dashboard could provide individual tacrolimus regimens when concomitantly administered with voriconazole.

Donor and recipient polymorphisms of MAPK signaling pathway genes influence post-transplant liver function in Chinese liver transplant patients taking tacrolimus.

Tacrolimus (TAC) is an immunosuppressive drug that is widely used for patients who underwent liver transplantation. In addition to inhibiting the action of calcineurin, TAC also exerts its immunosuppressive effects by interfering with mitogen activated protein kinase (MAPK) pathway. In this study, we investigated the impact of both recipient and donor genetic polymorphisms of MAPK kinase kinase (MAP3K) genes on clinical events in Han Chinese liver transplantation recipients taking TAC. Fifty-seven tag SNPs of 11 genes (MEKK1, MEKK2, MEKK4, MLK1, MLK3, ASK1, TAO1, TAO2, Tpl2, TAK1 and ZAK1) in the MAPK pathway were detected by MALDI-TOF MS assay in 175 TAC-treated liver transplant recipients. The associations of SNPs with incidence of acute rejection, TAC-induced acute nephrotoxicity, and post-transplantation liver and kidney function were explored using Kaplan-Meier survival analysis, Cox-proportional hazard model and linear mixed model, respectively. For the sites significantly associated with clinical events, the dual-luciferase reporter gene system was used to perform preliminary function verification. The results showed that (1) Donor-recipient combinational (D-R) MEKK1 rs62355944 and D-R MLK1 rs8006424 genotypes were significant influence factors of post-transplantation γ-glutamyl transpeptidase (GGT) level (P < 0.0001); (2) D-R MLK1 rs8006424 genotypes were found to significantly affect the alkaline phosphatase (ALP) level after transplantation (P < 0.0001). The results of the dual luciferase reporter gene system demonstrated that the luciferase activity of the pGL3-rs8006424A was significantly higher than that of pGL3-rs8006424G (3.47 ± 0.10 vs 2.97 ± 0.08, P = 0.002). Therefore, MEKK1 rs62355944 and MLK1 rs8006424 might serve as biomarkers to predict post-transplant liver function in liver transplant patients.

Population Pharmacokinetic Modeling Combined With Machine Learning Approach Improved Tacrolimus Trough Concentration Prediction in Chinese Adult Liver Transplant Recipients.

This study aimed to develop and evaluate a population pharmacokinetic (PPK) combined machine learning approach to predict tacrolimus trough concentrations for Chinese adult liver transplant recipients in the early posttransplant period. Tacrolimus trough concentrations were retrospectively collected from routine monitoring records of liver transplant recipients and divided into the training data set (1287 concentrations in 145 recipients) and the test data set (296 concentrations in 36 recipients). A PPK model was first established using NONMEM. Then a machine learning model of Xgboost was adapted to fit the estimated individual pharmacokinetic parameters obtained from the PPK model with Bayesian forecasting. The performance of the final PPK model and Xgboost model was compared in the test data set. In the final PPK model, tacrolimus daily dose, postoperative days, hematocrit, aspartate aminotransferase, and concomitant voriconazole, were identified to significantly influence the clearance. The postoperative days along with hematocrit significantly influence the volume of distribution. In the Xgboost model, the first 5 predictors for predicting the clearance were concomitant with voriconazole, sex, single nucleotide polymorphisms of CYP3A4*1G and CYP3A5*3 in recipients, and tacrolimus daily dose, for the volume of distribution were postoperative days, age, weight, total bilirubin and graft : recipient weight ratio. In the test data set, the Xgboost model showed the minimum median prediction error of tacrolimus concentrations, less than the PPK model with or without Bayesian forecasting. In conclusion, a PPK combined machine learning approach could improve the prediction of tacrolimus concentrations for Chinese adult liver transplant recipients in the early posttransplant period.

TLR10 genotypes affect long-term graft function in tacrolimus-treated solid organ transplant recipients.

The present study was conducted to investigate the relationship between single nucleotide polymorphisms (SNPs) in TLR10 and the clinical outcomes of renal transplant patients who took tacrolimus (TAC) as an immunosuppressant, and further confirmed the results in liver transplant patients. A total of 172 renal transplant patients and 145 pairs of liver transplant recipients and donors were included. Nineteen SNPs of TLR10 gene were detected by matrix-assisted laser desorption ionization-time-of-flight-mass spectrometry (MALDI-TOF-MS). The associations of recipient SNPs with TAC-related clinical outcomes were explored in renal transplant recipients. The relationship between recipient and donor SNPs and the clinical outcomes of liver transplant patients were investigated to confirm the results. Three SNPs (rs28393318, rs11466655 and rs11096957) in renal transplant recipients were found to influence the graft function after transplantation (P = 0.00003, 0.001 and 0.000003, respectively). The recipient rs11096957 was also found to affect the TBil, and DBil levels in liver transplant recipients (P = 0.001 and 0.002). In this study, we identified significant association signals from TLR10 polymorphisms with clinical outcomes in TAC-treated transplant patients in a Chinese Han-based sample. We provide some evidence for the effect between rs11096957 in TLR10 gene on the graft functions in both renal and liver transplantation.

LMX and employee turnover intention: A social identity approach.

This study explored how leader-member exchange (LMX) affects employee turnover intention through the mediation of relational and organizational identification, and through the moderating role of leader competence in the relationship between LMX and relational identification. Study 1 explores the indirect effect of LMX on employee turnover intention through relational and organizational identification by analyzing data collected from 210 employees in China at two points in time. Study 2 explores the moderating role of leader competence in the relationship between LMX and relational identification by analyzing data collected from 872 employees in China. The results show that LMX affected employee turnover intention through the serial mediation of relational and organizational identification, and that leader competence moderated the relationship between LMX and relational identification. Our findings provide implications for research on employee turnover intention and identification. Limitations and future research directions are also discussed.

Effect of albumin and CYP2B6 polymorphisms on exposure of efavirenz: A population pharmacokinetic analysis in Chinese HIV-infected adults.

BACKGROUND: Efavirenz is a vital component used to treat HIV-1 infection. Nevertheless, it shows large between-subject variability, which affects both its therapeutic response and adverse effects. OBJECTIVE: To investigate the impact of gene polymorphisms and non-genetic factors on the variability of efavirenz pharmacokinetics and to propose the optimal dose regimens. METHODS: A total of 769 plasma samples from 376 HIV-infected Han Chinese outpatients were collected to develop a population pharmacokinetic model using NONMEM software. The impact of patient demographics, laboratory tests, concomitant medication, and genetic polymorphisms of CYP2B6 and ABCB1 on efavirenz pharmacokinetics were explored. According to the final model, the model-informed dose optimization was conducted. RESULTS: The pharmacokinetics of efavirenz was characterized by a one-compartment model with first-order absorption and elimination. The typical values of the estimated apparent oral clearance, volume of distribution, and absorption rate constant in the final model were 9.44 L/h, 200 L, and 0.727 h - 1, respectively. Efavirenz clearance was significantly influenced by CYP2B6 variants, including rs2099361, rs3745274, and rs2279343, along with albumin and weight. The volume of distribution was affected by albumin and weight. Based on the CYP2B6 polymorphisms of patients, the recommended daily doses of efavirenz were 100 mg for CYP2B6 slow metabolizers, 400 or 600 mg for intermediate metabolizers, and 800 or 1000 mg for extensive metabolizers. CONCLUSIONS: Polymorphisms of CYP2B6, along with albumin and weight, resulted as the predictors of efavirenz pharmacokinetic variability, which could be used in prescribing optimal efavirenz doses.

External Evaluation of Vancomycin Population Pharmacokinetic Models at Two Clinical Centers.

Background: Numerous vancomycin population pharmacokinetic models in neonates have been published; however, their predictive performances remain unknown. This study aims to evaluate their external predictability and explore the factors that might affect model performance. Methods: Published population pharmacokinetic models in neonates were identified from the literature and evaluated using datasets from two clinical centers, including 171 neonates with a total of 319 measurements of vancomycin levels. Predictive performance was assessed by prediction- and simulation-based diagnostics and Bayesian forecasting. Furthermore, the effect of model structure and a number of identified covariates was also investigated. Results: Eighteen published pharmacokinetic models of vancomycin were identified after a systematic literature search. Using prediction-based diagnostics, no model had a median prediction error of ≤ ± 15%, a median absolute prediction error of ≤30%, and a percentage of prediction error that fell within ±30% of >50%. A simulation-based visual predictive check of most models showed there were large deviations between observations and simulations. After Bayesian forecasting with one or two prior observations, the predicted performance improved significantly. Weight, age, and serum creatinine were identified as the most important covariates. Moreover, employing a maturation model based on weight and age as well as nonlinear model to incorporate serum creatinine level significantly improved predictive performance. Conclusion: The predictability of the pharmacokinetic models for vancomycin is closely related to the approach used for modeling covariates. Bayesian forecasting can significantly improve the predictive performance of models.

Investigating the interaction between nifedipine- and ritonavir-containing antiviral regimens: A physiologically based pharmacokinetic/pharmacodynamic analysis.

AIMS: Hypertension is a common comorbidity of patients with COVID-19, SARS or HIV infection. Such patients are often concomitantly treated with antiviral and antihypertensive agents, including ritonavir and nifedipine. Since ritonavir is a strong inhibitor of CYP3A and nifedipine is mainly metabolized via CYP3A, the combination of ritonavir and nifedipine can potentially cause drug-drug interactions. This study provides guidance on nifedipine treatment during and after coadministration with ritonavir-containing regimens, using a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) analysis. METHODS: The PBPK/PD models for 3 formations of nifedipine were developed based on the Simcyp nifedipine model and the models were verified using published data. The effects of ritonavir on nifedipine exposure and systolic blood pressure (SBP) were assessed for instant-release, sustained-release and controlled-release formulations in patients. Various nifedipine regimens were investigated when coadministered with or without ritonavir. RESULTS: PBPK/PD models for 3 formulations of nifedipine were successfully established. The predicted maximum concentration (Cmax ), area under plasma concentration-time curve (AUC), maximum reduction in SBP and area under effect-time curve were all within 0.5-2.0-fold of the observed data. Model simulations showed that the inhibitory effect of ritonavir on CYP3A4 increased the Cmax of nifedipine 17.92-48.85-fold and the AUC 63.30-84.01-fold at steady state and decreased the SBP by >40 mmHg. Thus, the combination of nifedipine and ritonavir could lead to severe hypotension. CONCLUSION: Ritonavir significantly affects the pharmacokinetics and antihypertensive effect of nifedipine. It is not recommended for patients to take nifedipine- and ritonavir-containing regimens simultaneously.

Effect of Protein Binding on Exposure of Unbound and Total Mycophenolic Acid: A Population Pharmacokinetic Analysis in Chinese Adult Kidney Transplant Recipients.

OBJECTIVES: The population pharmacokinetic (popPK) characteristics of total mycophenolic acid (tMPA) have been investigated in various ethnic populations. However, investigations of popPK of unbound MPA (uMPA) are few. Thus, a popPK analysis was performed to: (1) characterize the PK of uMPA and tMPA and its 7-O-mycophenolic acid glucuronide (MPAG) metabolite in kidney transplant patients cotreated with cyclosporine (CsA), and (2) identify the clinically significant covariates that explain variability in the dose-exposure relationship. METHODS: A total of 740 uMPA, 741 tMPA, and 734 total MPAG (tMPAG) concentration-time data from 58 Chinese kidney transplant patients receiving MPA in combination with CsA were analyzed using NONMEM® software with the stochastic approximation expectation maximization (SAEM) followed by the important sampling (IMP) method. The influence of covariates was tested using a stepwise procedure. RESULTS: The PK of uMPA and unbound MPAG (uMPAG) were characterized by a two- and one-compartment model with first-order elimination, respectively. A linear protein binding model was used to link uMPA and tMPA. Apparent clearance (CL/F) and central volume of distribution (VC/F) of uMPA (CLuMPA/F and VCuMPA/F, respectively) and protein binding rate constant (k B) were estimated to be 851 L/h [relative standard error (RSE), 7.1%], 718 L (18.5%) and 53.4/h (2.3%), respectively. For uMPAG, the population values (RSE) of CL/F (CLuMPAG) and VC/F (VCuMPAG/F) were 5.71 L/h (4.4%) and 29.9 L (7.7%), respectively. Between-subject variability (BSVs) on CLuMPA/F, VCuMPA/F, CLuMPAG/F, and VCuMPAG/F were 51.0, 80.0, 31.8 and 48.4%, respectively, whereas residual unexplained variability (RUVs) for uMPA, tMPA, and uMPAG were 47.0, 45.9, and 22.0%, respectively. Significant relationships were found between k B and serum albumin (ALB) and between CLuMPAG/F and glomerular filtration rate (GFR). Additionally, model-based simulation showed that changes in ALB concentrations substantially affected tMPA but not uMPA exposure. CONCLUSIONS: The established model adequately described the popPK characteristics of the uMPA, tMPA, and MPAG. The estimated CLuMPA/F and unbound fraction of MPA (FUMPA) in Chinese kidney transplant recipients cotreated with CsA were comparable to those published previously in Caucasians. We recommend monitoring uMPA instead of tMPA to optimize mycophenolate mofetil (MMF) dosing for patients with lower ALB levels.

Population pharmacokinetics and dosing regimen optimisation of lopinavir in Chinese adults infected with HIV.

Lopinavir (LPV) is a protease inhibitor (PI) for the treatment of human immunodeficiency virus (HIV) infections. Current studies on LPV are mainly focused on Caucasians, and none have investigated the population pharmacokinetics (PPK) of LPV in Chinese population. The present study aimed to develop a PPK model for oral LPV in Chinese adults who are HIV-infected. A total of 460 LPV concentrations from 174 Chinese patients who received LPV/ritonavir (LPV/r) 400/100 mg orally every 12 hours (q12h) were analysed using the non-linear mixed-effects modelling approach. Simulations of the LPV concentration profile were performed with different dosing regimens. A one-compartment model with first-order absorption and elimination process described the data. The estimated apparent clearance (CL/F) and volume of distribution (V/F) (% relative standard error [RSE]) for oral LPV were 5.9 L/h (3%) and 117 L (8%), respectively. Body-weight was identified as a covariate on CL/F. In patients who weighed between 45 and 115 kg and received the standard 400/100 mg q12h regimen, the probability of achieving target trough concentration (Ctrough ) of 1 mg/L was >98% for PI-naïve patients and the probability of achieving target Ctrough of 4 mg/L was <80% for PI-pretreated patients. This is the first population pharmacokinetic study to characterise the PK of LPV in Chinese patients with HIV infection. There were no obvious ethnic differences in the PK of LPV between the Chinese population and Caucasian population. The simulations demonstrated that the standard dosing regimen of 400/100 mg q12h (LPV/r tablets) appears to be sufficient for PI-naïve patients but suboptimal for PI-pretreated patients. Therefore, the regimen of 800/200 mg q12h was recommended for PI-pretreated patients. Further investigation of dosage recommendation could be helpful in optimising LPV therapy for HIV infections.