Statistical power analysis of cardiovascular safety pharmacology studies in conscious rats

Cardiovascular (CV) toxicity and related attrition are a major challenge for novel therapeutic entities and identifying CV liability early is critical for effective derisking. CV safety pharmacology studies in rats are a valuable tool for early investigation of CV risk. Thorough understanding of data analysis techniques and statistical power of these studies is currently lacking and is imperative for enabling sound decision-making.MethodsData from 24 crossover and 12 parallel design CV telemetry rat studies were used for statistical power calculations. Average values of telemetry parameters (heart rate, blood pressure, body temperature, and activity) were logged every 60 s (from 1 h predose to 24 h post-dose) and reduced to 15 min mean values. These data were subsequently binned into super intervals for statistical analysis. A repeated measure analysis of variance was used for statistical analysis of crossover studies and a repeated measure analysis of covariance was used for parallel studies. Statistical power analysis was performed to generate power curves and establish relationships between detectable CV (blood pressure and heart rate) changes and statistical power. Additionally, data from a crossover CV study with phentolamine at 4, 20 and 100 mg/kg are reported as a representative example of data analysis methods.ResultsPhentolamine produced a CV profile characteristic of alpha adrenergic receptor antagonism, evidenced by a dose-dependent decrease in blood pressure and reflex tachycardia. Detectable blood pressure changes at 80% statistical power for crossover studies (n = 8) were 4-5 mm Hg. For parallel studies (n = 8), detectable changes at 80% power were 6-7 mm Hg. Detectable heart rate changes for both study designs were 20-22 bpm.DiscussionBased on our results, the conscious rat CV model is a sensitive tool to detect and mitigate CV risk in early safety studies. Furthermore, these results will enable informed selection of appropriate models and study design for early stage CV studies.