Background: Long, expensive outcomes trials are typically required to quantify cardiovascular (CV) benefit under lipid-lowering therapy (LLT). Mechanistic in-silico trials built on Quantitative Systems Pharmacology (QSP) offer a complementary pathway to anticipate efficacy and inform trial design. Objective: Using a mechanistic Quantitative Systems Pharmacology (QSP) framework, this study aims to prospectively predict the Pelacarsen efficacy on Major Advanced Coronary Event (MACE) outcomes of the HORIZON-Lp(a) study. Methods: We improved a previously published and validated QSP model with new revascularization endpoints, calibrated composite MACE outcomes on FOURIER trial data, validated the model and in particular the Lp(a) - MACE relationship on ODYSSEY-OUTCOMES trial and CGPS cohort data. We then conducted a in silico simulation of the HORIZON-Lp(a) study to prospectively predict MACE outcomes and Pelacarsen efficacy and performed a sensitivity analysis to determine the impact of sources of uncertainty on predictions.Results: We predicted a palette of predictions for 3P-MACE, 4P-MACE and their individual components for different values of median follow-up time and contextual effect in Placebo arm of HORIZON-Lp(a) trial which are currently unknown, as well as for the Lp(a) >= 90 mg/dL subgroup of interest.Conclusions: Within a credibility framework inspired by recent work on ASCVD mechanistic modeling, we delivered pre-readout prospective predictions of the upcoming HORIZON-Lp(a) trial, suggesting a consistent clinical benefit in lowering Lp(a). Validation of results with upcoming trial results will challenge the credibility of the ASCVD model and the robustness of predictive QSP approaches.
Peyronnet et al. (Tue,) studied this question.