Denis Menshykau, Thomas Wendl, Katrin Coböken, and Thomas Eissing
Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, Germany
Objectives: We are developing a Cardiovascular (CV) Systems Pharmacology Platform, which integrates available knowledge of human CV physiology and information regarding drugs mode of action with the aim to predict whole-body hemodynamic response to pharmacological or other interventions.1 On the basis of a publically funded and currently conducted clinical study on heart failure patients with aortic stenosis (SMART study2), we exemplify the capabilities of the developed platform with modelling patient response to aortic valve replacement surgery.
Methods: The CV Systems Pharmacology Platform includes a detailed description of the relevant physiology of the human CV system.1,3 Based on obtained individual patient data before/after surgery, the CV model was individually adjusted to account for the mechanic properties of the malfunctioning/implanted aortic valve. These individualized models were parameterized to reproduce hemodynamic characteristics such as stroke volume, ejection fraction, heart rate, systolic and diastolic blood pressures in the corresponding patients with heart failure. The individualized CV models were used to identify changes in physiological parameters in patients before and after aortic valve surgery.
Results: Analysis of derived CV model parameters demonstrate that patients with aortic stenosis –in contrast to healthy individuals -have elevated peripheral resistance, reduced arterial compliance, altered end-diastolic pressure-volume relationship and increased end-systolic elastance. The pressure-volume loops inferred from patient data demonstrate that valve-replacement drastically reduces systolic pressure in the left ventricle. Furthermore, estimated arterial resistances of patients after surgery are comparable to those observed in healthy subjects and lower than before surgery. We finally discuss approaches to predict outcomes of valve replacement surgery based on clinical measurements before the surgery.
Conclusions: We developed a personalized computational model for the CV system of heart failure patients before and after a valve replacement surgery. Analysis of model parameters inferred from patient data provides additional information about the changes in the CV system induced by the surgery. Using the identified hemodynamic changes of the current patient population, the CV model enables predictions for future aortic valve stenosis patients. This, in turn, will facilitate an informed adjustment of the concomitant pharmacologic medication.
References:
[1] Coböken et al. Development of a Cardiovascular Systems Pharmacology Platform, PAGE-Meeting Poster 2015
[2] http://www.sys-med.de/en/demonstrators/smart/
[3] Eissing T. et al. A computational system biology software platform for the multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front. Physio. (2011)
Reference: PAGE 25 (2016) Abstr 5933 [www.page-meeting.org/?abstract=5933]
Poster: Drug/Disease modeling - Other topics