Constructing muscle-relaxation rabbit models by continuous infusion of propofolin in the internal carotid artery：pharmacokinetic variations / 中国组织工程研究

ABSTRACT

BACKGROUND:Intracerebral administration of selective drugsviathe carotid artery is currently one of the effective methods to enhance the drug concentration in the brain and reduce the influence of drugs on other system functions. OBJECTIVE:To establish the muscle-relaxation rabbit models by infusing propofol continuously in the internal carotid artery and analyze the variations of propofol concentration. METHODS: The muscle-relaxation rabbit models were established by continuously infusing propofol at a constant speedviacatheterization in the internal carotid artery. The pharmacokinetic characteristics could be analyzed by the methods of obtaining arterial and venous blood on both sides of neck and samples of brain tissue on both sides in different points, detecting drug concentration using high pressure liquid assay, and then mathematicaly conversing the resulting data for fitting processing and statistical regression. RESULTS AND CONCLUSION:The method of determining the concentration of propofol using high pressure liquid assay is feasible, stable and reliable. Through investigating the concentration of propofol infused via the carotid artery at different time points, we discovered that the growth rate distribution of propofol concentration and data distribution are in log-normal distribution profile which belong to non-exponential kinetics model,i.e., modified log-normal distribution model,??)(ln x μ 2 1 fx ()=e 2σ2 , whereσ is the range of drug concentration growth indicating stability xk 2πσ of concentration changes, which is an integrated variable related to various factors, such as brain tissue uptake of drugs and brain circulation. The pharmacokinetic model of continuously infusing propofol in the internal carotid artery belongs to log-normal distribution function, i.e., a non-exponential function kinetics model. The brain concentration variations on both sides changing over time folow log-normal distribution function law.