A physiologically-based kinetic model for the prediction of plasma cholesterol concentrations in the mouse

Pas, N.C.A. van de; Woutersen, Ruud; Ommen, Ben van; Rietjens, Ivonne M. C. M.; Graaf, Albert A. de

doi:10.1016/j.bbalip.2011.02.002

Cited by 7 publications

(14 citation statements)

References 59 publications

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“…Thus, both models indicate a large contribution to plasma HDL coming from the liver. This has also been observed in animal studies and in our previous mouse work ( 12 ).…”

Section: Discussionsupporting

confidence: 69%

“…These eight submodels have been selected from a larger set of 65,536 alternative submodels. Each of the suitable submodels has been selected on the basis of a correct prediction of a higher or lower plasma cholesterol level of fi ve knockout mouse strains compared with the wild-type controls ( 12 ). For the human situation, not enough data were available to apply an identical selection procedure.…”

Section: Methodsmentioning

confidence: 99%

“…Equations were slightly altered compared with the mouse model: for the human model, reaction rates were expressed as mmol/man/day, where "man" refers to a standard human, leading to more simple equations ( 12 ). For practical reasons, reaction rates (expressed by i ) were numbered according to the numbering in Fig.…”

Section: Mathematical Model Formulationmentioning

confidence: 99%

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A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

Pas

Woutersen

Ommen

et al. 2012

Journal of Lipid Research

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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).

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“…Thus, both models indicate a large contribution to plasma HDL coming from the liver. This has also been observed in animal studies and in our previous mouse work ( 12 ).…”

Section: Discussionsupporting

confidence: 69%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

Pas

Woutersen

Ommen

et al. 2012

Journal of Lipid Research

Self Cite

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“…Subsequently, parameters were adjusted to reflect cholesterol pools and fluxes measured in man. Finally, a feedback term of hepatic cholesterol on LDL-uptake was added in order to be able to describe the effect of treatment with a statin [75]. The model makes a distinction between free cholesterol and cholesteryl ester in intestine and liver, which would suggest that this distinction is necessary in order to understand cholesterol dynamics.…”

Section: Whole Body Models Of Cholesterol Metabolismmentioning

confidence: 99%

“…The final model was constructed from an unweighted average of the models that could correctly predict this shift. Parameters were calculated by solving the steady state solutions, using steady state fluxes obtained from the literature [73]. This model was later adapted to describe cholesterol metabolism in man [74].…”

Section: Whole Body Models Of Cholesterol Metabolismmentioning

confidence: 99%

Evaluating computational models of cholesterol metabolism

Paalvast¹,

Kuivenhoven²,

Groen³

2015

Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biolog

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Predicting individual responses to pravastatin using a physiologically based kinetic model for plasma cholesterol concentrations

Pas

Rullmann

Woutersen

et al. 2014

J Pharmacokinet Pharmacodyn

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We used a previously developed physiologically based kinetic (PBK) model to analyze the effect of individual variations in metabolism and transport of cholesterol on pravastatin response. The PBK model is based on kinetic expressions for 21 reactions that interconnect eight different body cholesterol pools including plasma HDL and non-HDL cholesterol. A pravastatin pharmacokinetic model was constructed and the simulated hepatic pravastatin concentration was used to modulate the reaction rate constant of hepatic free cholesterol synthesis in the PBK model. The integrated model was then used to predict plasma cholesterol concentrations as a function of pravastatin dose. Predicted versus observed values at 40 mg/d pravastatin were 15 versus 22 % reduction of total plasma cholesterol, and 10 versus 5.6 % increase of HDL cholesterol. A population of 7,609 virtual subjects was generated using a Monte Carlo approach, and the response to a 40 mg/d pravastatin dose was simulated for each subject. Linear regression analysis of the pravastatin response in this virtual population showed that hepatic and peripheral cholesterol synthesis had the largest regression coefficients for the non-HDL-C response. However, the modeling also showed that these processes alone did not suffice to predict non-HDL-C response to pravastatin, contradicting the hypothesis that people with high cholesterol synthesis rates are good statin responders. In conclusion, we have developed a PBK model that is able to accurately describe the effect of pravastatin treatment on plasma cholesterol concentrations and can be used to provide insight in the mechanisms behind individual variation in statin response.

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A physiologically-based kinetic model for the prediction of plasma cholesterol concentrations in the mouse

Cited by 7 publications

References 59 publications

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

Evaluating computational models of cholesterol metabolism

Predicting individual responses to pravastatin using a physiologically based kinetic model for plasma cholesterol concentrations

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