Digital Twins & In Silico Trials
Dassault Systèmes SIMULIA (Living Heart Project)
by Dassault Systèmes SE
Physics-based virtual human simulation enabling in silico clinical trials for cardiovascular devices and drugs
Category
Digital Twins & In Silico Trials
Founded
1981
Headquarters
Vélizy-Villacoublay, France
Overview
Dassault Systèmes SIMULIA develops computational simulation software for physics-based modeling of biological systems, with its Living Heart Project representing the most advanced publicly shared cardiac digital twin platform. The Living Heart Human Model is an anatomically accurate, biophysically detailed finite element model of the human heart that simulates electrical conduction, mechanical contraction, and fluid dynamics simultaneously. Built on the Abaqus FEA solver and 3DEXPERIENCE platform, it enables medical device companies to perform virtual clinical testing of cardiac devices — pacemakers, stents, valves, and electrophysiology catheters — before first-in-human studies. Medical device manufacturers, pharmaceutical companies modeling cardiac drug effects, and regulatory agencies including FDA use SIMULIA's in silico models to assess device safety, optimize designs, and study patient-specific cardiovascular mechanics. The FDA has accepted computational modeling submissions for cardiac device approval based on SIMULIA software, establishing a regulatory precedent for in silico clinical trials. Beyond cardiology, SIMULIA's simulation capabilities extend to bone mechanics, respiratory dynamics, and drug delivery device modeling. SIMULIA's competitive advantage is its multi-physics simulation fidelity: unlike statistical digital twin approaches, its finite element models solve coupled electrical-mechanical-fluidic equations grounded in first-principles physics, producing predictions that generalize to novel device geometries and patient anatomies not represented in training data. The Living Heart Project's open consortium model — bringing together medical device companies, pharmaceutical companies, academics, and regulators on a shared platform — has accelerated validation and regulatory acceptance of cardiac in silico methods in ways that proprietary platforms cannot achieve.
Key Features
Organ System Models
Detailed models of cardiovascular, hepatic, renal, and neural organ systems for PBPK simulation.
Trial Design Optimization
AI-optimized trial design including dosing schedules, endpoints, and patient stratification.
Regulatory Evidence Generation
Generate computational evidence packages aligned with FDA guidance for regulatory submissions.
Real-World Data Integration
Calibrate and validate models using real-world clinical data from healthcare systems.
Virtual Patient Modeling
Create digital patient models simulating drug responses across diverse population demographics.
Pros & Cons
Pros
- +Predictive toxicology models identify safety liabilities before first-in-human dosing
- +Integration with real-world clinical data improves model calibration and prediction accuracy
- +Virtual patient models simulate drug responses across diverse population demographics
- +In silico clinical trials reduce time and cost of traditional Phase I-III studies by 30-50%
- +Multi-scale modeling connects molecular interactions to organ-level physiological responses
- +Regulatory acceptance growing with FDA guidance on computational modeling for device and drug evaluation
Cons
- −Computational models cannot fully capture the complexity of human biological variability
- −Requires extensive clinical data for initial model calibration and ongoing validation
- −Adoption requires significant cultural change in organizations accustomed to traditional trial designs
- −Model validation against real clinical data is essential but time-consuming and expensive
- −Regulatory acceptance of in silico evidence varies across jurisdictions and therapeutic areas
Use Cases
Research Workflow Optimization
AI-powered optimization of research workflows to accelerate discovery timelines and improve reproducibility.
Data Analysis & Insights
Machine learning analysis of complex biological datasets to extract actionable insights and identify patterns.
Collaboration & Knowledge Management
Platform-enabled collaboration across distributed research teams with integrated data sharing and knowledge capture.