An Industry Survey With Focus On Cardiovascular Safety Pharmacology Study Design And Data Interpretation

INTERNATIONAL JOURNAL OF TOXICOLOGY(2020)

引用 7|浏览50
暂无评分
摘要
Introduction:The Safety Pharmacology Society (SPS) conducted a membership survey to examine industry practices related mainly to cardiovascular (CV) safety pharmacology (SP).Methods:Questions addressed nonclinical study design, data analysis methods, drug-induced effects, and conventional and novel CV assays.Results:The most frequent therapeutic area targeted by drugs developed by the companies/institutions that employ survey responders was oncology. The most frequently observed drug-mediated effects included an increased heart rate, increased arterial blood pressure, hERG (I-Kr) block, decreased arterial blood pressure, decreased heart rate, QTc prolongation, and changes in body temperature. Broadly implemented study practices included Latin square crossover study design with n = 4 for nonrodent CV studies, statistical analysis of data (eg, analysis of variance), use of arrhythmia detection software, and the inclusion of data from all study animals when integrating SP studies into toxicology studies. Most responders frequently used individual animal housing conditions. Responders commonly evaluated drug effects on multiple ion channels, but in silico modeling methods were used much less frequently. Most responders rarely measured the J-T-peak interval in CV studies. Uncertainties relative to Standard for Exchange of Nonclinical Data applications for data derived from CV SP studies were common. Although available, the use of human induced pluripotent stem cell cardiomyocytes remains rare. The respiratory SP study was rarely involved with identifying drug-induced functional issues. Responders indicated that the study-derived no observed effect level was more frequently determined than the no observed adverse effect level in CV SP studies; however, a large proportion of survey responders used neither.
更多
查看译文
关键词
cardiovascular safety, arrhythmia, QT, heart disease, CIPA
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要