Quantitative understanding of drug effects on the interrelationship between mean arterial blood pressure, cardiac output and total peripheral resistance
Nelleke Snelder (3), Bart Ploeger (3), Meindert Danhof (3), Donald Stanski (1), Dean Rigel (2), Randy Webb (2), David Feldman (2), Olivier Luttringer (1)
(1) Modeling and simulation department, Novartis, Basel, Switzerland
Objectives: Since persistent hypertension is a risk factor for heart failure and is a leading cause of cardiovascular disease, evaluating mechanisms that underlie blood pressure (BP) changes is crucial in drug development. BP is maintained constant by the cardiovascular system (CVS) through adapting cardiac output (CO) and/or total peripheral resistance (TPR). Gaining insight into the pharmacology of drugs with desired or undesired effects on the CVS is pivotal in drug development and can be obtained efficiently using a translational modeling approach, in which early insights on efficacy and safety are carried forward to later development stages. This research describes the development of a mechanism-based pharmacodynamic (PD) model using preclinical data to understand drug induced disturbance of BP homeostasis.
Methods: A mechanism-based (PK)-PD modeling approach was applied to describe the dynamics of blood pressure regulation by the CVS, with the aim of developing a drug independent model by distinguishing drug- and system-specific parameters. The model was identified by comparing the preclinical effects on BP, CO and TPR, of different marketed antihypertensive drugs with different mechanisms of action.
Results: The dynamics of blood pressure regulation by the CVS, including feedback between BP, CO and TPR, could be adequately described by the PD model. Drug- and system-specific parameters were not correlated and could be estimated independently with good precision.
Conclusions: It is concluded that establishment of a translational PD model to describe drug induced disturbance of BP homeostasis enables conversion of data into knowledge about the mechanism of action. Ultimately, this approach allows for anticipation of clinical response based on preclinical data and prediction of the system behavior under conditions not previously evaluated, which will contribute to an optimization of the selection and development of new compounds early in development.