Aim: The study aimed to establish a population pharmacokinetic (PPK) model for Chinese patients with nephrotic syndrome using the patient genotype and Wuzhi capsule dose as the main test factors. Methods: Adult patients with nephrotic syndrome receiving tacrolimus treatment were enrolled. A nonlinear mixed effects model was used to determine the influencing factors of inter-individual tacrolimus metabolism variation and establish a PPK model. To optimize the tacrolimus dosage, 10000 Monte Carlo simulations were performed. Results: The typical population tacrolimus clearance (CL/F) value was 16.9 L/h. Increased Wuzhi capsule and albumin doses both decreased the tacrolimus CL/F. In CYP3A5 homozygous mutation carriers; the CL/F was 39% lower than that of carriers of the wild-type and heterozygous mutation. The tacrolimus CL/F in patients who were co-administered glucocorticoids was 1.23-fold higher than that in the control. According to the patient genotype and combined use of glucocorticoids, 26 combinations of Wuzhi capsule and tacrolimus doses were matched. The Monte Carlo simulation identified the most suitable combination scheme. Conclusion: An improved tacrolimus PPK model for patients with nephrotic syndrome was established and the most suitable combination of Wuzhi capsule and tacrolimus doses was identified, thus facilitating the selection of a more economical and safe administration regimen.

Background: Methotrexate is widely recommended as a first-line treatment for the intensive systemic and consolidation phases of childhood acute lymphoblastic leukemia. However, methotrexate-induced nephrotoxicity is a severe adverse reaction, of which the mechanism remains unclear. Methods: An untargeted metabolomics analysis of serum from childhood acute lymphoblastic leukemia with delayed methotrexate excretion with or without acute kidney injury was performed to identify altered metabolites and metabolic pathways. An independent external validation cohort and in vitro assays further confirmed the candidate metabolites and the mechanisms underlying the nephrotoxicity of methotrexate. Results: Four metabolites showed significant differences between normal excretion and delayed excretion, seven metabolites reflected the differences between groups with or without acute kidney injury, and six pathways were finally enriched. In particular, oxidized glutathione is confirmed as an candidate metabolite involved in the toxicity of methotrexate. Based on the depletion of glutathione mediated cell death, it was found that methotrexate overload significantly reduced cell viability, triggered reactive oxygen species and intracellular Fe 2+ accumulation, and altered the expression of ferroptosis-related proteins in HK-2 cells. These methotrexate-induced changes were alleviated or reversed by the administration of a ferroptosis inhibitor, further suggesting that ferroptosis promoted methotrexate-induced cytotoxicity in HK-2 cells. Conclusions: Our findings revealed complex metabolomic profiles and provided novel insights into the mechanism by which ferroptosis contributes to the nephrotoxic effects of methotrexate.

Several systematic reviews have been published on tacrolimus (TAC) population pharmacokinetic (PPK) modeling; however, most of them have focused on the transplant patient population. This study investigated TAC population pharmacokinetic characteristics in non-transplant patients through a systematic review of TAC population pharmacokinetic studies carried out in this patient population, with the aim of clarifying factors affecting TAC pharmacokinetic behavior and promoting individualized TAC-based treatment in non-transplant patients. The Cochrane Library, PubMed, and Embase databases, as well as Chinese databases (SinoMed, Wanfang, and China National Knowledge Infrastructure) and related references, were searched using a non-linear mixed-effects modeling approach, from the time of inception of the databases to July 2022, to identify TAC population pharmacokinetic studies modeled in non-transplant patients. Eighteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, 56% and 28% were carried out in pediatric and adult patients, respectively. Over half of the studies (56%) were conducted in patients with nephrotic syndrome. Combined medications, body weight, genetic polymorphisms, and physiological function were the most common covariates affecting TAC clearance, and variability in the apparent volume of distribution was largely explained by body weight. In addition, only 2 studies assessed the developed models through external evaluation. In non-transplanted patients, factors that affect TAC pharmacokinetics include combined medications, body weight, genetic polymorphisms, and physiological function. Recent investigations have focused mainly on Asian populations, and expanded trials that will use external evaluations for relevant model assessment are required to investigate generalizability to other ethnic populations.