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).
Table 1.
Species represented in the model.
Table 2.
Reactions represented in the model.
Table 3.
Model constants.
Table 4.
Model parameters.
Table 5.
Prior and posterior estimates of model parameters corresponding to the “nominal subject.”
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 [81]–[83] and LDL-CE, VLDL-CE data from references [20], [21]. 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 [20]. 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 [43], Ikewaki et al [45], Schaefer et al [44]. 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 [46] and Santos et al [47]; 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 [48]. 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 [50], Torcetrapib [51] and Anacetrapib [52].
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.
Table 6.
Calibration data: HDL-C and ApoA-I in normal and CETP deficient subjects.
Table 7.
Calibration data: CE in ApoB particles.
Table 8.
Calibration data: CE fluxes.