In Silico Clinical Trials Using Mechanistic Knowledge-Based Model, an Innovative Approach to Accelerate Data Generation and Support Health Technology Assessment

Speaker(s)

Pham E1, Porte S2, Courcelles E2, Peyronnet E2, Wang Y2, Diatchenko A2, Gomez G2, Amarenco P3, Angoulvant D4, Boccara F5, Cariou B6, Mahé G7, Marie-Natacha M8, Bastien A9, Portal L9, Boissel JP2, Bechet E2, Granjeon-Noriot S2, Steg PG10
1Novadiscovery, Lyon, France, 2Novadiscovery, Lyon, Rhone-Alpes, France, 3Department of Neurology and Stroke center, APHP, Bichat Hospital, Université Paris-Cité, Paris, France, 4Cardiology department, Hôpital Trousseau, CHRU de Tours & EA4245 Transplantation Immunologie Inflammation, Université de Tours, Tours, France, 5Sorbonne Université, GRC n°22, C2MV-Complications Cardiovasculaires et Métaboliques chez les patients vivant avec le Virus de l'immunodéficience humaine, Inserm UMR_S 938, Centre de Recherche Saint-Antoine, Institut Hospitalo-Universitaire de Cardio-mé, Paris, Ile de France, France, 6Nantes Université, CHU Nantes, CNRS, Inserm, l'institut du thorax, Nantes, France, 7Vascular Medicine Unit, CHU Rennes, Univ Rennes CIC1414, Rennes, France, 8Novartis, Rueil Malmaison, 92, France, 9Novartis, Rueil-Malmaison, France, 10Université Paris-Cité, AP-HP, Hôpital Bichat, and INSERM U-1148/LVTS, Paris, France

Presentation Documents

OBJECTIVES: Demonstrating cardiovascular (CV) benefits with lipid-lowering therapy (LLT) requires long-term randomized clinical trials (RCT) with thousands of patients. Innovative approaches such as in silico trials applying a disease computational model to virtual patients receiving alternative treatments provide a valuable option to complement RCTs by rapidly generating supplementary comparative effectiveness data and facilitating drug value demonstration to health technology assessment (HTA) bodies.

This study aimed at building a computational model of atherosclerotic cardiovascular disease (ASCVD). Once validated, the model will be used to run in silico clinical trials to compare the benefit of inclisiran, an siRNA targeting PCSK9 mRNA, vs other LLT on CV events in patients with ASCVD.

METHODS: A mechanistic computational model of ASCVD was built from an extensive literature review, combining mechanisms of lipoprotein metabolism, with atherosclerotic plaque evolution leading to clinical events. Impact of ASCVD risk factors and standard-of-care LLTs were also integrated. A panel of 6 multidisciplinary clinical experts validated modelling hypotheses and the strategy of calibration/validation by selecting relevant RCTs and registry data. Calibration is the process of determining the values of unknown model parameters such that the model reproduces relevant data. A virtual population was generated to account for inter-patient variability.

RESULTS: The model was calibrated to reproduce the pharmacokinetics of atorvastatin, rosuvastatin and ezetimibe, the clinical benefit of combinations of LLTs, including evolocumab and inclisiran and its surrogate markers of efficacy as observed in landmark trials such as FOURIER and ORION 10.

CONCLUSIONS: The model is successfully calibrated. Next step is model validation before using it to predict long term effect of inclisiran on CV events. In silico clinical trials will potentially provide evidence of the clinical benefit of Inclisiran earlier than RCTs. Therefore, the acceptance of this new emerging approach by the HTA bodies could accelerate patient access to innovative drugs.

Code

SA1

Topic

Health Policy & Regulatory, Methodological & Statistical Research, Study Approaches

Topic Subcategory

Decision Modeling & Simulation, Reimbursement & Access Policy

Disease

Cardiovascular Disorders (including MI, Stroke, Circulatory), Drugs