Application of a mechanistic, systems model of lipoprotein metabolism and kinetics to target selection and biomarker identification in the reverse cholesterol transport (RCT) pathway
James Lu(1), Katrin HŁbner(2), M. Nazeem Nanjee(3), Eliot A. Brinton(4), Norman Mazer(1)
(1) Clinical Pharmacology, F. Hoffman-La Roche Ltd., Basel, Switzerland; (2) Department of Modeling Biological Processes, BioQuant, University of Heidelberg, Heidelberg, Germany; (3) Division of Cardiovascular Genetics, University of Utah, Salt Lake City, UT, USA; (4) Atherometabolic Research, Utah Foundation for Biomedical Research, Salt Lake City, UT, USA
Objectives: The inverse association between the levels of high density lipoprotein cholesterol (HDL-C) with cardio-vascular (CV) risk has led to the "HDL-cholesterol hypothesis" whereby interventions raising HDL-C are expected to decrease CV risk . However, the recent failures of HDL-C raising compounds (e.g., CETP inhibitors/modulators ) to reduce CV risk have prompted a revision of this hypothesis. The "HDL flux hypothesis" has been proposed : interventions should aim to promote cholesterol efflux into the reverse cholesterol transport (RCT) pathway, leading to plague regression. This new conceptual framework calls for a re-evaluation of targets and biomarkers.
Methods: In contrast to the stochastic, particle-based model previously presented , the current work utilizes a coarse-grained model that describes the dynamics of cholesterol and apoA-I pools by a system of ODEs. Importantly, the cyclic process of HDL particle maturation and re-generation is described, employing geometrical concepts . HDL re-generation results in a feedback loop linking the clearance rate of HDL-C back to the RCT input rate. The 17 parameters in the model are estimated using the maximum a posteriori approach: prior estimates of key parameters are taken from published flux values in normal subjects, which are subsequently informed by the calibration data. "Virtual populations" are created by sampling model parameters from a multivariate normal distribution around the mean to understand epidemiological relationships. Using correlation and principal component analyses of simulation outputs, biomarkers are examined and selected based on further mathematical analysis.
Results: Our model predicts that CETP inhibitors raise HDL-C due to a reduction in its clearance rate, but do not increase the RCT input rate. We believe this provides an explanation for their lack of CV benefit. In contrast, we identify targets that increase both HDL-C and RCT: e.g., ABCA1. Using the model, we further predict that the ratio of lipoprotein parameters pre-b/apoA-I is a biomarker of therapeutic response under ABCA1 upregulation.
Conclusions: A challenge in understanding the effects of perturbations to the RCT pathway is the presence of a feedback loop due to the cyclic nature of HDL metabolism. To meet this challenge, a systems model has been built to help select targets and identify biomarkers. The model shows that only some targets which increase HDL-C are associated with increases in RCT.
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