An integrative pharmacokinetic-cardiovascular physiology modelling approach based on in vivo dog studies including five reference compounds

M. Wallman, J. M. Borghardt, E. Martel, N. Pairet, M. Markert, M. Jirstrand. Journal of Pharmacological and Toxicological Methods, Vol. 115, May-June 2022.


Cardiovascular (CV) effects represent a major safety issue during drug development. Typically, this risk is mitigated by preclinical in vivo CV studies, based on which measured CV readouts are analyzed independently. Here, we apply a regression approach to simultaneously integrate CV readouts, i.e., heart rate (HR), mean arterial pressure (MAP) and QT from five dog telemetry studies. These CV studies comprise data on verapamil, captopril, dofetilide, pimobendan, and formoterol, and are combined with the respective dog pharmacokinetic (PK) profiles. A published PK/CV model structure for rats is extended by a semi-mechanistic parameterization of the interaction between HR and QT specific to dogs. This semi-mechanistic modelling approach allows differentiation between compound-independent system-specific parameters (e.g., HR baseline) and compound-specific parameters (e.g., EC50). Compared to previous results in rodents, estimated parameters for dogs indicate stronger dependency of stroke volume on HR, slower HR response, faster QT response and steeper concentration-response relationships. In addition, we illustrate how to practically apply the PK/CV model to derive concentration-response relationships for CV readouts. This approach allows a more detailed quantitative evaluation based on the maximum effect on CV effects (Emax), the EC50, and the steepness of this relation (Hill coefficient) especially for HR-independent effects on QT interval duration (QTc) while taking the systemic feedback into account. This approach also allows to derive plasma concentrations associated with relevant CV effects (“threshold concentration”; CTHRESH). The presented modelling analysis highlights the potential of an integrative evaluation of CV data and provides a framework for obtaining quantitative insights from safety pharmacology evaluations.

Photo credits: Nic McPhee