A multi-scale model for low-density lipoprotein cholesterol (LDL-C) regulation in the human body: Application to quantitative systems pharmacology. (2nd November 2019) - Toroghi, Masood Khaksar; Cluett, William R.; Mahadevan, Radhakrishnan
Abstract: Cardiovascular disease (CVD) is the leading cause of death worldwide. Studies have found that abnormally high low-density lipoprotein cholesterol (LDL-C) levels are the highest risk factor for occurrences of CVD. In this paper, we have developed a mathematical model for LDL-C regulation in the human body. A multi-scale modeling approach has been used to integrate cholesterol synthesis in the human liver with diet cholesterol, LDL receptor trafficking pathways, and proprotein convertase subtilisin/kexin type 9 (PCSK9) function. Dynamic flux balance analysis (dFBA) has been used to integrate the hepatocyte genome-scale metabolic model with the multi-scale model of the LDL-C regulation. In this approach, the hepatocyte genome-scale model has been used to calculate the synthesis of the cholesterol. The resulting estimation has been utilized to estimate VLDL, LDL-C and other lipoproteins. In addition, LDL-C receptor signaling pathway has been integrated to estimate the LDL-C uptake rate in the liver cell. An in silico study has been carried out to demonstrate the potential application of this modelling framework to quantitative systems pharmacology (QSP). In this study, we have created a virtual subject with high levels of LDL-C as representative of a population with high levels of PCSK9 and liver cholesterol synthesis rates. Statin and anti-PCSK9 pharmacodynamic and pharmacokinetic (PD/PK) models have been integrated with the proposed network to estimate LDL-C reduction after drug administration. The simulation results indicate that combination therapy is necessary to reduce the LDL-C levels for this patient. These results are consistent with experimental evidence showing that the low-dose combination therapy may be the best approach to achieve the recommended LDL-C levels for patients with multiple risk factors for coronary heart disease. This novel modelling framework has great potential in quantitative systems pharmacology to improve decision making, reduce the risk of treatment failure, and improve dose selection associated with the LDL-C lowering therapy.