Digital Twins & In Silico Trials
InSilicoTrials
by InSilicoTrials Technologies S.p.A.
Cloud-based platform aggregating computational models for regulatory-grade in silico clinical trial simulation
Category
Digital Twins & In Silico Trials
Founded
2017
Headquarters
Trieste, Italy
Overview
InSilicoTrials provides a cloud-based marketplace and simulation platform that aggregates validated computational models for in silico clinical trials across multiple therapeutic areas and medical device categories. The platform hosts models for cardiac electrophysiology, pharmacokinetics/pharmacodynamics (PK/PD), respiratory mechanics, orthopedics, and ophthalmology, allowing drug and device developers to run virtual patient populations and clinical trial simulations without requiring in-house modeling expertise. The platform supports regulatory submission packages by generating traceable, auditable simulation results. Medical device manufacturers, pharmaceutical companies, and clinical trial designers use InSilicoTrials to reduce costs and accelerate timelines for regulatory submissions involving computational evidence. The platform has been used for CE mark submissions in the EU and FDA submissions for cardiac rhythm management devices, with regulatory bodies increasingly accepting computational modeling data under the ISO/IEC 80601 and ASME V&V 40 frameworks. InSilicoTrials operates under a software-as-a-service model with pay-per-simulation pricing for smaller organizations and enterprise licensing for large pharma and device companies. InSilicoTrials differentiates through its model aggregation strategy: rather than developing proprietary models, the company curates, validates, and packages community and partner-developed computational models into regulatory-grade workflows with standardized verification and validation (V&V) documentation. This marketplace approach gives users access to specialized models — such as the TPVC (Total Peripheral Vascular Circuit) cardiac model or validated PK/PD models for specific drug classes — that would take years to develop internally. The company participates actively in EU regulatory working groups shaping the framework for in silico medicine evidence in clinical submissions.
Key Features
Synthetic Control Arms
Generate synthetic control groups reducing the need for placebo groups in rare disease trials.
Multi-Scale Physiological Modeling
Connect molecular interactions to organ-level responses with multi-scale biological models.
In Silico Clinical Trials
Virtual clinical trials reduce time and cost of traditional Phase I-III studies by 30-50%.
Virtual Patient Modeling
Create digital patient models simulating drug responses across diverse population demographics.
Real-World Data Integration
Calibrate and validate models using real-world clinical data from healthcare systems.
Pros & Cons
Pros
- +Predictive toxicology models identify safety liabilities before first-in-human dosing
- +Synthetic control arms reduce the need for placebo groups in rare disease clinical trials
- +Regulatory acceptance growing with FDA guidance on computational modeling for device and drug evaluation
- +Multi-scale modeling connects molecular interactions to organ-level physiological responses
- +In silico clinical trials reduce time and cost of traditional Phase I-III studies by 30-50%
- +Virtual patient models simulate drug responses across diverse population demographics
- +Integration with real-world clinical data improves model calibration and prediction accuracy
Cons
- −Requires extensive clinical data for initial model calibration and ongoing validation
- −Computational models cannot fully capture the complexity of human biological variability
- −Regulatory acceptance of in silico evidence varies across jurisdictions and therapeutic areas
- −Model validation against real clinical data is essential but time-consuming and expensive
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.