Statistical power analysis of cardiovascular safety pharmacology studies in conscious rats
Siddhartha Bhatt1, Dingzhou Li2, Declan Flynn1
1Global Safety Pharmacology, Pfizer Worldwide Research and Development, Groton, CT, USA.
Unlabelled:
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.
Methods:
Data 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 60s (from 1h predose to 24h post-dose) and reduced to 15min 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 100mg/kg are reported as a representative example of data analysis methods.
Results:
Phentolamine 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 4-5mmHg. For parallel studies (n=8), detectable changes at 80% power were 6-7mmHg. Detectable heart rate changes for both study designs were 20-22bpm.
Discussion:
Based 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.
Insights
This study demonstrates that cardiovascular safety pharmacology studies in rats are effective for early detection of drug-induced cardiovascular risks. Understanding statistical power helps in selecting appropriate models and study designs for drug development.
Area of Science:
- Pharmacology
- Toxicology
- Drug Safety
Background:
- Cardiovascular toxicity is a significant challenge in novel therapeutic development.
- Early identification of cardiovascular liability is crucial for effective risk mitigation.
- Current understanding of data analysis and statistical power in rat cardiovascular safety studies is limited.
Purpose of the Study:
- To analyze the statistical power of cardiovascular safety pharmacology studies in rats.
- To establish relationships between detectable cardiovascular changes and statistical power.
- To inform the selection of appropriate models and study designs for early-stage cardiovascular risk assessment.
Main Methods:
- Utilized data from 24 crossover and 12 parallel design cardiovascular telemetry rat studies.
- Analyzed telemetry parameters (heart rate, blood pressure, body temperature, activity) using repeated measure ANOVA and ANCOVA.
- Performed statistical power analysis to generate power curves and determine detectable cardiovascular changes.
Main Results:
- Phentolamine administration revealed dose-dependent decreases in blood pressure and reflex tachycardia, characteristic of alpha-adrenergic antagonism.
- Detectable blood pressure changes at 80% power were 4-5mmHg for crossover and 6-7mmHg for parallel studies.
- Detectable heart rate changes at 80% power were 20-22bpm for both study designs.
Conclusions:
- The conscious rat cardiovascular model is a sensitive tool for detecting and mitigating cardiovascular risk in early safety studies.
- These findings enable informed selection of models and study designs for early-stage cardiovascular assessments.
- Improved understanding of statistical power enhances decision-making in drug development safety evaluations.
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