An in-silico model of lipoprotein metabolism and kinetics for the evaluation of targets and biomarkers in the reverse cholesterol transport pathway

Abstract

High-density lipoprotein (HDL) is believed to play an important role in lowering cardiovascular disease (CVD) risk by mediating the process of reverse cholesterol transport (RCT). Via RCT, excess cholesterol from peripheral tissues is carried back to the liver and hence should lead to the reduction of atherosclerotic plaques. The recent failures of HDL-cholesterol (HDL-C) raising therapies have initiated a re-examination of the link between CVD risk and the rate of RCT, and have brought into question whether all target modulations that raise HDL-C would be atheroprotective. To help address these issues, a novel in-silico model has been built to incorporate modern concepts of HDL biology, including: the geometric structure of HDL linking the core radius with the number of ApoA-I molecules on it, and the regeneration of lipid-poor ApoA-I from spherical HDL due to remodeling processes. The ODE model has been calibrated using data from the literature and validated by simulating additional experiments not used in the calibration. Using a virtual population, we show that the model provides possible explanations for a number of well-known relationships in cholesterol metabolism, including the epidemiological relationship between HDL-C and CVD risk and the correlations between some HDL-related lipoprotein markers. In particular, the model has been used to explore two HDL-C raising target modulations, Cholesteryl Ester Transfer Protein (CETP) inhibition and ATP-binding cassette transporter member 1 (ABCA1) up-regulation. It predicts that while CETP inhibition would not result in an increased RCT rate, ABCA1 up-regulation should increase both HDL-C and RCT rate. Furthermore, the model predicts the two target modulations result in distinct changes in the lipoprotein measures. Finally, the model also allows for an evaluation of two candidate biomarkers for in-vivo whole-body ABCA1 activity: the absolute concentration and the % lipid-poor ApoA-I. These findings illustrate the potential utility of the model in drug development.

Figure 1. A schematic representation of the model.

The arrows shown in the diagram denote the processes represented by the model and the boxes with italicized text denote mediators that are explicitly represented. The process arrows are numbered, refering to the reaction number shown in Table 2. The arrows leading from the nascent sphere towards the α-HDL pool represent the 2 scenarios that may occur in the transformation of newly formed particles: they may either enter the α-HDL pool as distinct particles (the dashed arrow) or fuse with the existing ones (solid arrow).

Figure 2. The fit of the model to the calibration data for CETP deficiency: HDL-C (panel A), ApoA-I (panel B), LDL-CE (panel C) and VLDL-CE (panel D).

The data are as shown in Table 6, obtained by pooling HDL-C and ApoA-I data from references – and LDL-CE, VLDL-CE data from references , . The model simulation curves were obtained by decreasing the 3 parameters representing CETP activity from 100% to 0% of those corresponding to the nominal subject.

Figure 3. The fit of the model to the calibration data: CE fluxes.

The data are as shown in Table 8, taken from reference . The model simulation is produced using the point estimate of parameters for the nominal subject.

Figure 4. The fit of the model to the calibration data: FCR of ApoA-I versus HDL-C/ApoA-I ratio.

The data sources are: Brinton et al , Ikewaki et al , Schaefer et al . The piecewise linear fit and the confidence interval are discussed in the Methods section. The model simulation values are indicated by asterisk symbols, for the nominal subject and the heterozygote, homozygote of CETP mutation.

Figure 5. Model validation: simulation of ABCA1 and ApoA-I mutations compared with literature data for HDL-C (panel A) and ApoA-I (panel B).

For the simulation results, the mean and the 95% confidence intervals are plotted. The data sources are Asztalos et al and Santos et al ; the mean ± SD are shown. The model simulations of the mutation cases were obtained by taking the parameter values for the nominal subject and set and to 50% and 0% of the nominal values respectively.

Figure 6. Model validation: Simulation of tracer kinetic experiment with labelled ApoA-I compared to experimental data.

The data are obtained by digitization of tracer kinetics measurements carried out in 4 subjects and shown in Figure 3 of Ikewaki et al . The model simulation corresponds to the nominal subject.

Figure 7. The distribution of RCT rate and HDL-C and their correlation in the simulated virtual population.

By drawing the parameters of the model from an uncorrelated, multivariate normal distribution, a set of 2000 virtual patients is generated and the model simulations of RCT rate and HDL-C are shown. The right-hand axis represents the hypothetical inverse relationship between RCT rate and CVD risk.

Figure 8. The distribution of the clearance of HDL-CE and HDL-C and their correlation in the simulated virtual population.

By drawing the parameters of the model from an uncorrelated, multivariate normal distribution, a set of 2000 virtual patients is generated and the model simulations of HDL-CE clearance rate and HDL-C are shown.

Figure 9. Model predictions for the dependence of HDL measures (HDL-C, panel A; ApoA-I, panel B; HDL size, panel C; HDL particle concentration, panel D; lipid-poor ApoA-I, panel E) and RCT (panel F) on the CETP level.

The model simulation curves were obtained by decreasing the 3 parameters associated with CETP activity from 100% to 0% of those corresponding to the nominal subject. For each prediction, the mean and the 95% confidence intervals are plotted.

Figure 10. Simulation of CETP inhibition on a virtual population with low HDL-C (≤40 mg/dL).

Each virtual patient selected for the treatment simulation had its rate constants associated with CETP activity and decreased to 20% of their original values.

Figure 11. Model predictions for the dependence of HDL measures (HDL-C, panel A; ApoA-I, panel B; HDL size, panel C; HDL particle concentration, panel D; lipid-poor ApoA-I, panel E) and RCT (panel F) on ABCA1 activity.

The model simulation curves were obtained by increasing the parameter representing ABCA1 activity from 100% to 300% of the nominal subject. For each prediction, the mean and the 95% confidence intervals are plotted.

Figure 12. Simulation of ABCA1 up-regulation on a virtual population with low HDL-C (≤40 mg/dL).

Each virtual patient seleted for the treatment simulation had its ABCA1 activity () increased by 100% of its initial value.

Figure 13. Comparison of CETP inhibition with ABCA1 up-regulation: changes in RCT rate (panel A) and biomarkers (ApoA-I, panel B; HDL size, panel C; HDL particle concentration, panel D; LDL-C, panel E) versus the rise in HDL-C.

The nominal subject is taken as the baseline. The model simulation of CETP inhibition is compared with literature data of CETP inhibitors, Dalcetrapib , Torcetrapib and Anacetrapib .

Figure 14. Correlations between HDL-C and HDL-P (panel A), and HDL-C and HDL size (panel B) in a virtual population of 2000 subjects.

Figure 15. Correlations between and absolute concentration of lipid-poor ApoA-I (panel A), and between and % lipid-poor ApoA-I (panel B) in a virtual population of 2000 subjects.

Figure 16. Comparison of two expressions involving remodeling flux: (panel A) and (panel B).

The distributions of absolute and ApoA-I adjusted remodeling flux in the virtual population are plotted against . The simulations of the nominal subject with only the parameter varied are shown as solid lines.