Current Pharmaceutical Design - Volume 30, Issue 39, 2024

Sirolimus, one of the immunosuppressive drugs administered to renal transplant recipients, is metabolized by cytochrome P450 (CYP) 3A5. Accordingly, CYP3A5 polymorphism is a genetic factor affecting sirolimus pharmacokinetics (PK). Therefore, we conducted a systematic review and meta-analysis on the association between sirolimus PK and CYP3A5*3 polymorphism.

We searched for studies published up to 13 June 2024 from PubMed, Embase, Cochrane Library, and Web of Science. We reviewed studies on the relationship between CYP3A5*3 polymorphism and weight-adjusted trough concentration/dose (C0 /D) ratio and dosage of sirolimus in renal transplant recipients, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We evaluated mean differences (MDs) and 95% confidence intervals (CIs).

A total of seven studies were included. The weight-adjusted C0 /D ratio of sirolimus was significantly higher in patients with the CYP3A5*3/*3 rather than CYP3A5*1/*1 or CYP3A5*1/*3 genotype (MD 95.27 ng/mL per mg/kg; 95% CI: 58.06, 132.47; I2 = 74%; p < 0.00001). Also, the weight-adjusted dosage of sirolimus was significantly lower in patients with the CYP3A5*3/*3 rather than CYP3A5*1/*1 or CYP3A5*1/*3 genotype (MD -2.60 × 10^-3 mg/kg; 95% CI: -4.52, -0.69; I2 = 44%; p = 0.008).

Our meta-analysis showed a significant effect for the CYP3A5*3 genotype on weight-adjusted C0 /D ratio and dosage of sirolimus in adult renal transplant recipients.

CRD42022354330.

NvZhen ErXian HeJi (NZEXHJ) is used to treat perimenopausal syndrome (PS), but its effect on perimenopausal coronary heart disease is unclear. Furthermore, the aim of this research is to study the effect of NZEXHJ on perimenopausal coronary heart disease (PMCHD) in a rat model based on a network pharmacology approach.

Based on network pharmacological analysis combined with molecular docking, we predicted the potential therapeutic target and pharmacological mechanism of NZEXHJ in the treatment of PMCHD. We used an ovariectomized rat (OVR) model to understand the effect of NZEXHJ on myocardial injury and further verified the target of NZEXHJ in the intervention of PMCHD.

We selected 52 active components of NZEXHJ against PMCHD and an intersection of their targets on network pharmacology, to which SCN5A, SER1, AR, and PGR were significantly correlated. The protein-protein interaction network revealed CASP3, CXCL8, IL6, MAPK1, TNF, TP53, and VEGFA in the treatment of PMCHD with NZEXHJ. Kaempferol, luteolin, and mistletoe presented good affinity towards the aforementioned targets by Molecular docking NZEXHJ exerted protecting cardiomyocytes for OVR. The mechanism was related to a reduction in the expression levels of the CXCL8, TNF, and regulating PI3K-Akt signaling pathways.

This study reveals the potential multi-component, multi-target, and multi-pathway pharmacological effects of NZEXHJ and predicts its protection against myocardial infarction in ovariectomized rats through the PI3K Akt pathway, providing a theoretical basis for the treatment of PMCHD.

The Transforming Growth Factor-Beta (TGF-β) signaling pathway plays a crucial role in the pathogenesis of diseases. This study aimed to identify differentially expressed TGF-β-related genes in liver cancer patients and to correlate these findings with clinical features and immune signatures.

The TCGA-STAD and LIRI-JP cohorts were utilized for a comprehensive analysis of TGF-β-related genes. Differential gene expression, functional enrichment, survival analysis, and machine learning techniques were employed to develop a prognostic model based on a TGF-β-related gene signature (TGFBRS).

We developed a prognostic model for liver cancer based on the expression levels of nine TGF-β-related genes. The model indicates that higher TGFBRS values are associated with poorer prognosis, higher tumor grades, more advanced pathological stages, and resistance to chemotherapy. Additionally, the TGFBRS-High subtype was characterized by elevated levels of immune-suppressive cells and increased expression of immune checkpoint molecules. Using a Gradient Boosting Decision Tree (GBDT) machine learning approach, the FKBP1A gene was identified as playing a significant role in liver cancer. Notably, knocking down FKBP1A significantly inhibited the proliferation and metastatic capabilities of liver cancer cells both in vitro and in vivo.

Our study highlights the potential of TGFBRS in predicting chemotherapy responses and in shaping the tumor immune microenvironment in liver cancer. The results identify FKBP1A as a promising molecular target for developing preventive and therapeutic strategies against liver cancer. Our findings could potentially guide personalized treatment strategies to improve the prognosis of liver cancer patients.