Cardiovascular safety assessments in the conscious telemetered dog: utilisation of super-intervals to enhance statistical power.
BACKGROUND
ICH S7A and S7B guidelines recommend the use of conscious animals for assessment of non-clinical cardiovascular safety of new chemical entities prior to testing in humans. Protocol design and data analysis techniques can affect the quality of the data produced and can therefore ultimately influence the clinical management of cardiovascular risk. It is therefore essential to have an understanding of the magnitude of changes detectable and the clinical relevance of these changes. This paper describes the utilisation of "super-intervals" to analyse and interpret data obtained from our conscious telemetered dog cardiovascular safety protocol and reports the statistical power achieved to detect changes in various cardiovascular parameters.


METHODS
Cardiovascular data from 18 dog telemetry studies were used to calculate the statistical power to detect changes in cardiovascular parameters. Each study followed a test compound versus vehicle cross-over experimental design with 24h monitoring (n=4). 1 min mean raw data from each individual animal was compressed into 15 min mean data for each dose group for visualisation. Larger summary periods, or "super-intervals", were then selected to best represent any observed cardiovascular effects whilst taking into account the pharmacokinetic profile of the drug e.g. intervals of 1 to 6, 7 to 14 and 14 to 22h post-dose.


RESULTS
With this methodology and study design we predict, using the median percentile that our studies have 80% power to detect the following changes: HR (+/-10bpm), LV +dP/dt max (+/-375mmHg/s), MBP (+/-5mmHg) and QTc (+/-4ms).


CONCLUSIONS
Super-intervals are a simple way to handle the high degree of natural variability seen with any ambulatory cardiovascular assessment and, in our hands, result in highly statistically powered studies. The ability of this model to detect cardiovascular changes of small, but biologically relevant, magnitude enables confident decision making around the cardiovascular safety of new chemical entities.
Sivarajah
A
A
Global Safety Pharmacology, Pfizer Global Research and Development, Sandwich, Kent, CT13 9NJ, UK.
Collins
S
S
Sutton
M R
MR
Regan
N
N
West
H
H
Holbrook
M
M
Edmunds
N
N
D000818:Animals; D000890:Anti-Infective Agents / Q000008:administration & dosage / Q000097:blood / Q000494:pharmacology*; D000959:Antihypertensive Agents / Q000008:administration & dosage / Q000097:blood / Q000494:pharmacology*; D001372:Aza Compounds / Q000008:administration & dosage / Q000097:blood / Q000494:pharmacology*; D001794:Blood Pressure / Q000187:drug effects; D002319:Cardiovascular System / Q000187:drug effects*; D003243:Consciousness; D003627:Data Interpretation, Statistical; D004285:Dogs; D004305:Dose-Response Relationship, Drug; D017292:Doxazosin / Q000008:administration & dosage / Q000097:blood / Q000494:pharmacology*; D004353:Drug Evaluation, Preclinical; D004562:Electrocardiography / Q000187:drug effects*; D024841:Fluoroquinolones; D006339:Heart Rate / Q000187:drug effects; D008133:Long QT Syndrome / Q000175:diagnosis; D008297:Male; D023421:Models, Animal; D015233:Models, Statistical; D000077266:Moxifloxacin; D011804:Quinolines / Q000008:administration & dosage / Q000097:blood / Q000494:pharmacology*; D013686:Telemetry; D013997:Time Factors
D000890:Anti-Infective Agents; D000959:Antihypertensive Agents; D001372:Aza Compounds; D024841:Fluoroquinolones; D011804:Quinolines; D017292:Doxazosin; D000077266:Moxifloxacin