Comparative quantitative systems pharmacology modeling of anti-PCSK9 therapeutic modalities in hypercholesterolemia

Abstract

Since the discovery of proprotein convertase subtilisin/kexin type 9 (PCSK9) as an attractive target in the treatment of hypercholesterolemia, multiple anti-PCSK9 therapeutic modalities have been pursued in drug development. The objective of this research is to set the stage for the quantitative benchmarking of two anti-PCSK9 pharmacological modality classes, monoclonal antibodies (mAbs) and small interfering RNA (siRNA). To this end, we developed an integrative mathematical model of lipoprotein homeostasis describing the dynamic interplay between PCSK9, LDL-cholesterol (LDL-C), VLDL-cholesterol, HDL-cholesterol (HDL-C), apoB, lipoprotein a [Lp(a)], and triglycerides (TGs). We demonstrate that LDL-C decreased proportionally to PCSK9 reduction for both mAb and siRNA modalities. At marketed doses, however, treatment with mAbs resulted in an additional ∼20% LDL-C reduction compared with siRNA. We further used the model as an evaluation tool and determined that no quantitative differences were observed in HDL-C, Lp(a), TG, or apoB responses, suggesting that the disruption of PCSK9 synthesis would provide no additional effects on lipoprotein-related biomarkers in the patient segment investigated. Predictive model simulations further indicate that siRNA therapies may reach reductions in LDL-C levels comparable to those achieved with mAbs if the current threshold of 80% PCSK9 inhibition via siRNA could be overcome.

Keywords: atherosclerosis; cholesterol metabolism; lipoproteins; plasma proprotein convertase subtilisin/kexin type 9; quantitative modeling; translational pharmacology.

Fig. 1.

Structural elements and interactions captured in the mechanistic QSP model (A) and model predictions of mean plasma LDL-C dynamics under treatment with anti-PCSK9 mAbs (B) and siRNA (C). Comparisons of plasma LDL-C levels measured at weeks 0–16 for RG-7652 data and weeks 0–34 (colored dots with error bars; mean ± standard deviation) for inclisiran in clinical studies versus corresponding model-based simulations (line with shaded area; mean ± 95% confidence prediction interval of the model). All values were normalized to absolute LDL-C values from each dosing arm. Experimental data from (27, 30). Values are presented in percentage change from baseline.

Fig. 2.

Model-based predictions illustrating the dynamic interplay among PKs (A), key biomarkers, plasma PCSK9 (B), and LDL-C (C). Mean plasma PK, PCSK9, and LDL-C responses to the various compounds and dosing regimens were simulated using the model (blue line) for 36 weeks of consecutive treatment. The median (green solid line) and interquartile range (green dashed line) were calculated for quasi-steady-state periods between doses (shaded gray area).

Fig. 3.

Model simulations using various dosing schedules of alirocumab, evolocumab, and inclisiran. Comparison of plasma PCSK9 and LDL-C levels for the different compounds. Peak and trough concentrations were estimated for weeks 34–36 of a Q2W regimen and for weeks 32–36 of a Q4W regimen. For a continuous infusion, last-point concentrations at week 36 of treatment were analyzed. Colored lines with shaded area represent mean model prediction with a 95% confidence interval. Boxes with numbers reflect the position of the marketed doses (for alirocumab and evolocumab) or phase 3 dose (inclisiran) on the curves. The blue dotted line represents model-based interpolation of plasma PCSK9 reduction for siRNA-based compounds. Values are presented in percentage change from baseline.

Fig. 4.

Lipoprotein and lipid biomarker responses following plasma LDL-C lowering under different anti-PCSK9 therapies. Predose values of apoB, non-HDL-C, TC, Lp(a), HDL-C, TGs, and LDL-C from clinical data on alirocumab, evolocumab, RG-7652 (red dots), as well as inclisiran with ALN-PCS (blue dots) were compared with model-generated values (green dashed curves). Values are presented as percentage change from baseline.