Nova In Silico


Developer of mechanistic, knowledge-driven computational models and a collaborative clinical-trial simulation platform that generates virtual patients and virtual populations to simulate trial protocols, support trial-design optimisation, and provide in silico evidence for R&D, regulatory interactions, and market-access use cases.

Industries

biotechnology
clinical-trials
health-care

Nr. of Employees

medium (51-250)


Products

Collaborative clinical-trial simulation platform (digital twin engine)

A multi-user platform that manages scientific knowledge, assembles mechanistic disease and treatment models, generates virtual patients, and runs large-scale trial simulations with visualisation and analysis tools.


Services

End-to-end creation, calibration, simulation and analysis of knowledge-based disease and treatment models for sponsor projects.

Supporting regulatory interactions and health-technology assessment submissions with model-based evidence and synthetic control approaches.

Calibration workflows, parameter estimation, and development of fast surrogate models to accelerate fitting of complex mechanistic models.

Expertise Areas

  • In silico clinical trials
  • Quantitative systems pharmacology (QSP)
  • Virtual patient and digital twin generation
  • Clinical trial design optimisation and mathematical optimisation
  • Show More (5)

Key Technologies

  • Mechanistic modelling (ODE/PDE-based QSP)
  • Virtual patient/digital twin engines
  • Virtual population generation and calibration
  • Surrogate modelling (ML-based, Gaussian processes)
  • Show More (7)

News & Updates

Announcement of a collaboration to transform clinical trials using advanced AI-powered simulations.

Report on prospective prediction of LDL‑C outcomes for a Phase 3 lipids trial using an in silico clinical trial.

Explainer article describing modelling and simulation methods and their application in drug development.

Discussion of how the COVID-19 pandemic accelerated the use of simulated trials alongside or prior to conventional studies, and the role of MIDD in regulatory initiatives.

Overview addressing misconceptions about in silico trials, the knowledge basis of QSP models, their use-cases and regulatory context.

Article describing FDA discussions on including obese patients in trials and the potential for modelling to support dosing and diversity considerations.

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