This article is available online at http://www.jlr.org these cholesterol modeling studies present models that focus on LDL cholesterol (LDL-C) metabolism in plasma ( 4-6 ) or on cellular cholesterol metabolism ( 7 ). These models do not represent all relevant components of whole body cholesterol homeostasis because they lack reactions such as cholesterol absorption and biosynthesis in organs, which are the reactions targeted by important cholesterol-lowering drugs, such as statins. This implies that the models cannot fully explain how relevant plasma cholesterol-associated biomarkers are infl uenced by these drug interventions.We have, therefore, developed an in silico physiologically based kinetic (PBK) model for plasma cholesterol in the mouse that includes all relevant reactions and that correctly predicts the plasma cholesterol levels of a large variety of mouse strains with gene knockouts related to cholesterol metabolism ( 11 ). The model, of which the structure is given in Fig. 1 , is able to predict HDL cholesterol (HDL-C), non-HDL cholesterol (non-HDL-C), and total plasma cholesterol (TC) concentrations ( 12 ) as well as the intra-organ pools representing hepatic free cholesterol (Liv-FC), peripheral cholesterol (Per-C), intestinal cholesterol ester (Int-CE), hepatic cholesterol ester (Liv-CE), and intestinal free cholesterol (Int-FC) in the mouse. A similar model for humans will be of considerable value in predicting effects of drugs and genetic variations on plasma cholesterol concentrations. Therefore, the aim of this work is to adapt our model to a human version.Turnover of cholesterol in humans is quantitatively and qualitatively different from that in mice ( 13 ). Qualitative difference resides mainly in the protein cholesteryl ester In silico modeling has proven to be a useful tool in biology because it allows the study of interspecies variation and regulation of homeostasis and allows the integration of information from various sources ( 1-3 ). There are several modeling efforts on cholesterol ( 4-7 ), an important biomarker for the risk for cardiovascular events ( 8-10 ). Most of Manuscript received 4 September 2012 and in revised form 28 September 2012. Published, JLR Papers in Press, September 29, 2012 DOI 10.1194 A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans Abbreviations: C, cholesterol (sum of FC and CE); CE, cholesterol ester; CETP, cholesterol ester transfer protein; FC, free cholesterol; FH, familial hypercholesterolemia; GWAS, genome-wide association study; PBK, physiologically based kinetic; SC, sensitivity coeffi cient; SLOS, Smith-Lemli-Opitz syndrome; TC, total plasma cholesterol (sum of HDL-C and non-HDL-C).

Total cholesterol concentration in plasma has long been known to correlate with cardiovascular disease risk. Subsequent investigations have distinguished more specifi c fractions of plasma cholesterol to attribute this risk to. First, LDL cholesterol was identifi ed as a risk factor and later the size distribution within that fraction was found to be of importance [for a historical review, see Ref.( 1 )]. Based on these fi ndings, an 'atherogenic lipoprotein phenotype' has been defi ned, which takes into account a particle size profi le within the LDL class ( 2 ). In addition to the cholesterol-based risk factors, apolipoprotein (Apo) measurements, such as ApoB or the ApoB/ApoA-I ratio, have been found to indicate atherosclerosis risk (3)(4)(5).Further improvements in risk assessment will primarily result from a more detailed understanding of lipoprotein physiology. To increase quantitative insight, various multi-compartmental models have been developed to analyze experiments with radioactive or stable isotope labeled lipoprotein constituents. The fi rst models describe the fl uxes of ApoB between lipoprotein fractions ( 6-8 ) with subsequent refi nements allowing better data interpretation ( 9-15 ). Other models describe the fl uxes of triglycerides through the lipoprotein fractions (16)(17)(18)(19)(20) Abstract Increased plasma cholesterol is a known risk factor for cardiovascular disease. Lipoprotein particles transport both cholesterol and triglycerides through the blood. It is thought that the size distribution of these particles codetermines cardiovascular disease risk. New types of measurements can determine the concentration of many lipoprotein size-classes but exactly how each small class relates to disease risk is diffi cult to clear up. Because relating physiological process status to disease risk seems promising, we propose investigating how lipoprotein production, lipolysis, and uptake processes depend on particle size. To do this, we introduced a novel model framework (Particle Profi ler) and evaluated its feasibility. The framework was tested using existing stable isotope fl ux data. The model framework implementation we present here reproduced the fl ux data and derived lipoprotein size pattern changes that corresponded to measured changes. It also sensitively indicated changes in lipoprotein metabolism between patient groups that are biologically plausible. Finally, the model was able to reproduce the cholesterol and triglyceride phenotype of known genetic diseases like familial hypercholesterolemia and familial hyperchylomicronemia. In the future, Particle Profi ler can be applied for analyzing detailed lipoprotein size profi le data and deriving rates of various lipolysis and uptake processes if an independent production estimate is given.

No abstract