Objective To study bystander effect and cell death by HSV TK and ACV therapy on renal carcinoma cell lines GRC 1. Methods Different mix culture cells, which contained GRC 1/TK cells 0%、25%、50% and 100%,were treated with ACV 60 ?g/ml, the morphology and quantity of tumor cells being studied and checked with FCM and electron microscope. Results After exposure to ACV, GRC 1/TK cells, even only 25%, underwent cell death and bystander effect, but there was no obvious change in the morphology and quantity of GRC 1 cells exposed to ACV. Conclusions There were bystander effect and cell death on HSV TK and ACV therapy in renal carcinoma cell lines GRC 1. The mechanism of bystander effect may have deat with apoptosis.

To investigate the relationship between the expression of c-myc gene and genesis,devel opment and prognosis of renal cancer. Methods :The synthesis of P62 protein in 42 renal cancer and mRNA tran scription of c-myc gene in 30 renal cancer were tested by using SABC immunohistochemistry and in situ hybridza tion. Results:P62 positive were noted in 66.7％ renal cancer and overexpression of c-myc mRNA noted in 55.3％ renal cancer. In addition, there was negative relation between gene expression and tumor cell＇s differentation or prognosis of patient. Conclusion:C-myc gene may participate in prognosis of renal cancer and can be used as an index for judging malignant degree of renal cancer cells and evaluate prognosis of patients.

Epidermal growth factor receptor-tyrosine kinase inhibitor （EGFR-TKI） represent a class of small-molecule targeted therapeutics for oncology treatment， and serve as first-line therapy for advanced non-small cell lung cancer （NSCLC） with EGFR- sensitive mutations， with representative agents including gefitinib， dacomitinib， and osimertinib. In clinical practice， dose adjustment of EGFR-TKI may be required for cancer patients under special circumstances such as drug combinations or hepatic/ renal impairment. Physiologically-based pharmacokinetic （PBPK） model， capable of predicting pharmacokinetic （PK） processes in humans， has emerged as a vital tool for clinical dose optimization. This article sorts the modeling methodologies， workflows， and commonly used software tools for PBPK model， and summarizes the current applications of PBPK model in EGFR-TKI precision therapy as of June 30， 2024. Findings demonstrate that PBPK modeling methods commonly employ the “bottom-up” approach and the middle-out approach. The process typically involves four steps： parameter collection， compartment selection， model validation， and model application. Commonly used software for modeling includes Simcyp， GastroPlus， and open-source software such as PK- Sim. PBPK model can be utilized for predicting drug-drug interactions of EGFR-TKI co-administered with metabolic enzyme inducers or inhibitors， acid-suppressive drugs， or traditional Chinese and Western medicines. It can also adjust dosages in conjunction with genomics， predict PK processes in special populations （such as patients with liver or kidney dysfunction， pediatric patients）， evaluate the efficacy and safety of drugs， and extrapolate PK predictions from animal models to humans.