|Title||A physiologically based kinetic model for the prediction of plasma cholesterol concentrations in mice and man|
|Author(s)||Pas, N. van de|
|Source||University. Promotor(en): Ivonne Rietjens; Ruud Woutersen, co-promotor(en): A.A. de Graaf. - [S.l.] : S.n. - ISBN 9789461731258|
Sub-department of Toxicology
|Publication type||Dissertation, internally prepared|
|Keyword(s)||cholesterolmetabolisme - klaring (plasma) - dierfysiologie - mannen - cholesterol metabolism - clearance - animal physiology - men|
|Categories||Toxicology (General) / Comparative Physiology|
An increased plasma cholesterol concentration is associated with increased risk of cardiovascular disease. However, individuals vary largely in their response to cholesterol lowering drugs and 40% of them, do not reach their cholesterol-lowering target. Development of novel therapies, for example combinations of existing drugs, can be accelerated by more mechanistic understanding of cholesterol metabolism. This understanding can be improved using computational models.
This thesis describes the development, validation, and analysis of a physiologically based kinetic (PBK) model for the prediction of plasma cholesterol concentrations in humans. For this purpose, first a PBK model for the mouse was set up, calibrated and validated, using ensemble modeling. Then the mouse model was converted to a model for humans. It describes the 21 most influential physiological reactions affecting cholesterol concentrations in 8 pools, including liver, HDL, and non-HDL. The model was parameterized using literature data and validated using clinical data for human mutations and drug interventions, taken from literature.
The model was applied to find properties that determine the individual response to drugs. The processes: hepatic cholesterol synthesis, peripheral cholesterol synthesis, and hepatic cholesterol esterification were major determinants of the non-HDL-C response to the cholesterol-lowering drug pravastatin.
We conclude that plasma cholesterol concentrations and effects of genetic polymorphisms and drugs thereupon can be predicted in silico and thatPBK modeling can provide novel mechanistic insights.