Using semi-mechanistic modelling to inform the design of the Ph3 program for a novel PCSK9 antisense oligonucleotide
Knöchel J (1), Nilsson C (1), Carlsson B (2), Hofherr A (2), Johanson P (4), Ueckert S (1), Rydén-Bergsten T (2), Rekic D (1,5)
[1] Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden [2] Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden [3] Early Biometrics and Statistical Innovation, Data Science & Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg MD, USA [4] Research and Late Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden [5] current affiliation: BioPharmaceuticals, Global CVRM, AstraZeneca, Gothenburg, Sweden
Objectives:
Statin treatment is the standard of care for the management of atherosclerotic cardiovascular disease (ASCVD), however, target low density lipoprotein cholesterol (LDL-C) levels are not reached in all patients. The discovery of protein convertase subtilisin/kexin type-9 (PCSK9) as a regulator of the LDL-C receptor has resulted in clinical development of PCSK9 inhibitors a new class of LDL-C lowering therapeutic agents [1-3]. This case study spans both the technical modelling as well as the drug development opportunities of MIDD for a novel PCSK9 antisense oligonucleotide. Here, we focus is on the design of the phase 3 program, which included the design of the pivotal phase 3 study, a head-to-head study, and a model-averaging analysis to inform the design of the cardiovascular outcome study.
Methods:
A semi-mechanistic approach together with the KPD approach [4] was used to model PCSK9 and LDL-C data for 152 subjects from Phase1 and Phase 2 studies [5]. The resulting K-PD model was used to conduct clinical trial simulation to evaluate the effect of AD8233 Q4W dosing on LDL-C steady state levels to inform design of the pivotal Phase 3 study. An established K-PD model for inclisiran [3] was used to perform virtual head-to-head studies.
For the model averaging analysis, the dataset of a previous analysis of cardiovascular outcome for lipid lowering agents (majority statins) was used [6] and extended to include the PCSK9 inhibitor outcome trials [7-8]. This dataset was analysed with 4 different linear models and model averaging was performed with weights based on AIC.
Results:
Based on a previously established KPD model build based on Phase 1 and Phase 2 [5] we performed clinical trial simulations that accounted for uncertainty, variability, trial execution as well as the statistical analysis method for the effect size to design and plan the phase 3 clinical program for AZD8233. These extensive clinical trial simulations provided insight on how drop-out, timepoint of readout, dose frequency and statistical analysis method would impact the final study readout. The final design was assuming a drop-out rate of around 1% per month based on studies with other PCSK9 inhibitors [7-8] using an EMA/FDA Ph3 endpoint approved statistical analysis method and the primary readout was set for week 16. The predicted LDL-C reduction at week 16 in Phase 3 with AZD8233 60 mg Q4W was -69%. Furthermore, it could be seen that the effect of AZ8233 is fast and sustained. To evaluate differentiation for AZD8233, a virtual head-to-head study versus inclisiran was performed which demonstrated a clear superiority of AZD8233 giving an on average 27% larger LDL-C lowering at day 270.
The predicted relative risk reduction (RRR) for AZD8233 on top of statins ranged from 24 to 49% depending on the linear model choice. The model average analysis predicted a RRR for AZD8233 on top of statins of 27% assuming an average 63% LDL-C reduction and a baseline of 130 mg/dL LDL-C.
Conclusions:
This case study demonstrates the power of MIDD to inform the design of a Phase 3 clinical program, analyze the potential for differentiation of a novel PCSK9 inhibitor, and the significant impact of model choices on cardiovascular outcome predictions.
References:
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