High mobility group box 1 (HMGB1) is a nuclear protein that can bind to DNA and act as a co-factor for gene transcription. When released into extracellular fluid, it plays a proinflammatory role by acting as a damage-associated molecular pattern molecule (DAMP) (also known as an alarmin) to initiate innate immune responses by activating multiple cell surface receptors such as the receptor for advanced glycation end-products (RAGE) and toll-like receptors (TLRs), TLR2, TLR4 or TLR9. This proinflammatory role is now considered to be important in the pathogenesis of a wide range of kidney diseases whether they result from hemodynamic changes, renal tubular epithelial cell apoptosis, kidney tissue fibrosis or inflammation. This review summarizes our current understanding of the role of HMGB1 in kidney diseases and how the HMGB1-mediated signaling pathway may constitute a new strategy for the treatment of kidney diseases.

Objective Tacrolimus is used clinically for the long-term treatment of antirejection of transplanted organs in liver and kidney transplant recipients, although dose optimization is poorly managed. The aim of this study was to examine the association between tacrolimus pharmacokinetic variability and CYP3A4 and CYP3A5 genotypes by a population pharmacokinetic analysis based on routine drug monitoring data in adult renal transplant recipients. Materials and methods Trough tacrolimus concentrations were obtained from 161 adult kidney transplant recipients after transplantation. The population pharmacokinetic analysis was carried out using the nonlinear mixed-effect modeling software NONMEM version 7.2. The CYP3A4*1G and CYP3A5*3 genetic polymorphisms from the patients studied were determined by direct sequencing using a validated automated genetic analyzer. Results A one-compartment model with first-order absorption and elimination adequately described the pharmacokinetics of tacrolimus. Covariates including CYP3A5*3 and CYP3A4*1G alleles and hematocrit were retained in the final model. The apparent clearance of tacrolimus was about two-fold higher in kidney transplant patients with higher enzymatic activity of CYP3A5*1 and CYP3A4*1G (with the CYP3A5*1/*1 or *1/*3 and CYP3A4*1/*1G or CYP3A4*1G/*1G) compared with those with lower enzymatic activity (CYP3A5*3/*3 and CYP3A4*1/*1). Conclusion This is the first study to extensively determine the effect of CYP3A4*1G and CYP3A5*3 genetic polymorphisms and hematocrit value on tacrolimus pharmacokinetics in Chinese renal transplant recipients. The findings suggest that CYP3A5*3 and CYP3A4*1G polymorphisms and hematocrit are determinant factors in the apparent clearance of tacrolimus. The initial dose design is mainly based on CYP3A5 and CYP3A4 genotypes as well as hematocrit. This result may also be useful for maintenance tacrolimus dose optimization and may help to avoid fluctuating tacrolimus levels and improve the efficacy and tolerability of tacrolimus in kidney transplant recipients.

Background Several models have been developed for prediction of contrast‐induced nephropathy (CIN); however, they only contain patients receiving intra‐arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure‐related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. Methods and Results A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5‐fold cross‐validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver‐operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. Conclusions The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN.

Aims: To explore the population pharmacokinetics of colistin sulfate and to optimize the dosing strategy for critically ill patients.Methods: The study enrolled critically ill adult patients who received colistin sulfate intravenously for more than 72 h with at least one measurement of plasma concentration. Colistin concentrations in plasma or urine samples were measured by ultraperformance liquid chromatography tandem mass spectrometry (LC-MS/MS). The population pharmacokinetics (PPK) model for colistin sulfate was developed using the Phoenix NLME program. Monte Carlo simulation was conducted to evaluate the probability of target attainment (PTA) for optimizing dosing regimens.Results: A total of 98 plasma concentrations from 20 patients were recorded for PPK modeling. The data were adequately described by a two-compartment model with linear elimination. During modeling, creatinine clearance (CrCL) and alanine aminotransferase (ALT) were identified as covariates of the clearance (CL) and volume of peripheral compartment distribution (V2), respectively. In addition, colistin sulfate was predominantly cleared by the nonrenal pathway with a median urinary recovery of 10.05% with large inter-individual variability. Monte Carlo simulations revealed a greater creatinine clearance associated with a higher risk of sub-therapeutic exposure to colistin sulfate. The target PTA (≥90%) of dosage regimens recommended by the label sheet was achievable only in patients infected by pathogens with MIC ≤0.5 mg/L or with renal impairments.Conclusion: Our study showed that the dose of intravenous colistin sulfate was best adjusted by CrCL and ALT. Importantly, the recommended dosing regimen of 1.0–1.5 million units daily was insufficient for patients with normal renal functions (CrCL ≥80 ml/min) or those infected by pathogens with MIC ≥1.0 mg/L. The dosage of colistin sulfate should be adjusted according to renal function and drug exposure.