Where Science, Simulation, and Regulation Converge to Deliver Predictive Healthcare Outcomes
In-Silico Clinical Trials (ISCTs) use AI-driven modeling and physics-based simulation to replicate how real patients respond to medical treatments—digitally, safely, and at scale.
These “virtual trials” combine real-world data, engineering models, and regulatory-grade validation methods (ASME V&V 40 / FDA 2023 CM&S Guidance) to generate credible evidence faster, at lower cost, and with greater patient diversity than traditional studies.
ISCTs do not replace human trials—they enhance them. By simulating thousands of human digital twins before the first patient is enrolled, sponsors can predict outcomes, refine trial design, and submit validated evidence directly to FDA and EMA through our compliant-ready process.
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40–60 % faster and cheaper
Simulations reduce patient enrollment and trial duration
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Regulatory-accepted evidence
Meets FDA/EMA expectations for model credibility and validation.
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Ethically sound
Reduces reliance on animal and early-stage human testing.
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Scalable and reusable
Human digital twins form a library for future trials and submissions.
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Reduced Risk
Virtual trials identify safety issues and performance failures early to minimize costly changes and post-market surprises.
Expanding Your Results with Our Reliable Process Flow.
Data Acquisition & Preparation
Clinical and imaging data are securely de-identified, standardized, and prepared for modeling. Each project begins by defining a Question of Interest and Context of Use—consistent with FDA’s computational modeling guidance—to ensure results are clinically and regulatory relevant.
Model Development
Using validated physics-based and AI-assisted algorithms, our engineers build digital representations of patient anatomy, physiology, and device interaction. All models follow verification and validation best practices defined in ASME V&V 40 and FDA’s 2023 framework.
Virtual Cohort Generation
Hundreds of “virtual patients” are created to mirror the diversity of real-world populations. These human digital twins enable population-level studies that would otherwise be impractical or unethical, aligning with FDA-recognized in-silico trial approaches.
Simulation & Credibility Assessment
Each virtual patient undergoes simulated testing to assess device performance, safety, and variability. Results are verified and cross -checked against experimental or clinical benchmarks to generate the credibility evidence FDA expects in model-based submissions.
Regulatory Evidence Package
The results are compiled into a standardized Credibility Assessment Report suitable for FDA Pre-Sub, IDE, or label-expansion pathways. Deliverables include modeling assumptions, validation summaries, and uncertainty quantification in the FDA-recommended structure.
Lifecycle Reuse & Insights
Models are continuously refined with new real-world data, creating reusable digital assets that inform future designs, post-market surveillance, and label extensions—supporting FDA’s Total Product Life Cycle (TPLC) approach.
Engineering the Future of Clinical Evidence—Virtual Patients, Real Regulatory Impact.
Ready to Redefine Your Next Clinical Trial?
Connect with our team to explore how Human Digital Twin–based evidence can reduce human cohort size, shorten timelines, and strengthen regulatory confidence.