STriTuVaD · Project

Virtual Patient Simulations That Cut Tuberculosis Clinical Trial Costs and Timelines

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Imagine you need to test a new TB treatment, but running a full clinical trial requires thousands of real patients and costs a fortune. This project built a computer simulator that creates realistic "virtual patients" — digital twins that respond to treatments the way real people would. By mixing data from real patients with these virtual ones, drug developers can get reliable results with far fewer real participants. Think of it like a flight simulator for medicine: you test dangerous scenarios digitally before risking real lives and real budgets.

By the numbers

One third of world population

People infected with TB globally

Thousands

Patients typically required for phase III TB clinical trials

Partners in the consortium

Countries represented (ES, IN, IT, NL, UK, US)

Total project deliverables produced

The business problem

What needed solving

Running clinical trials for tuberculosis treatments is brutally expensive, especially phase III trials that require thousands of patients to prove a therapy prevents TB recurrence. With one third of the world's population infected and multi-drug resistant strains spreading, there is an urgent need for faster, cheaper ways to test new treatments — particularly host-reaction therapies that could shorten treatment duration.

The solution

What was built

The project built and validated a computational modeling platform — an extended Universal Immune System Simulator — that generates virtual TB patients and predicts their response to host-reaction therapies. The final deliverable was a validated computational modeling framework and the final release of the in silico clinical trial model, supported by 18 total deliverables.

Audience

Who needs this

Pharmaceutical companies running TB drug and vaccine clinical trialsContract Research Organizations (CROs) specializing in infectious disease trialsBiotech startups developing immune-modulating therapies for TBRegulatory consultants advising on adaptive clinical trial designsGlobal health organizations funding TB treatment development

Business applications

Who can put this to work

Pharmaceutical

enterprise

Target: Pharma companies developing tuberculosis or infectious disease treatments

If you are a pharma company running clinical trials for TB host-reaction therapies — this project developed a validated computational model that generates virtual patients and predicts treatment responses. By combining virtual patients with real trial data using Bayesian adaptive design, you could significantly reduce the thousands of patients typically needed for phase III trials, cutting both costs and timelines.

Contract Research Organizations

mid-size

Target: CROs managing infectious disease clinical trials

If you are a CRO managing TB clinical trials and struggling with patient recruitment across multiple countries — this project built an in silico-augmented trial approach tested across a consortium spanning 6 countries including India where TB is endemic. The validated simulation platform could help you design smarter trials that reach statistical significance with fewer physical patients.

Biotech

SME

Target: Biotech startups developing immune-based therapies

If you are a biotech company working on immune-modulating treatments and facing prohibitive phase III costs — this project extended a Universal Immune System Simulator specifically for TB therapy endpoints like time to inactivation and recurrence. The Bayesian adaptive design lets you combine simulated and real patient data to demonstrate efficacy without running massive, expensive trials.

Frequently asked

Quick answers

How much could this reduce our clinical trial costs?

The project specifically targets the cost problem of phase III trials that require thousands of patients. By augmenting real patient data with virtual patients through Bayesian adaptive design, fewer real participants are needed. Based on available project data, specific cost reduction percentages were not published, but the approach directly addresses the 'huge costs' of large-scale TB trials described in the project objectives.

Can this scale beyond tuberculosis to other diseases?

The underlying technology is the Universal Immune System Simulator, which was extended for TB clinical trials. Based on available project data, the core simulation engine models immune system responses broadly, suggesting potential applicability to other infectious diseases and immune-based therapies. However, each new disease area would require validation with disease-specific clinical data.

What is the IP situation and how can we license this technology?

The project was coordinated by ETNA BIOTECH SRL in Italy, a private company, with 8 partners across 6 countries. IP arrangements would be governed by the consortium agreement. Interested parties should contact the coordinator through SciTransfer to discuss licensing of the validated computational modeling platform.

Has the model been validated against real clinical data?

Yes. The final deliverable explicitly describes a 'validated computational modeling framework.' The project's approach was to establish predictive accuracy against individual patients recruited in actual trials, then use the model to generate virtual patients. This validation against real patient outcomes is a key differentiator.

What endpoints does the simulation cover?

Based on the project objectives, the model covers two critical endpoints for host-reaction therapy trials: time to inactivation (how quickly the treatment works) and incidence of recurrence (whether TB comes back). These are the standard efficacy measures for TB treatment trials.

How does this integrate with our existing clinical trial infrastructure?

The in silico-augmented approach is designed to complement, not replace, real clinical trials. It uses Bayesian adaptive design to combine virtual patient predictions with observations from physical patients. Based on available project data, this means it layers on top of your existing trial design rather than requiring a complete overhaul.

Consortium

Who built it

The STriTuVaD consortium brings together 8 partners from 6 countries, with a deliberate geographic spread that includes India — where TB is endemic — alongside European and US institutions. The mix of 4 universities, 2 industry players, and 1 research organization reflects a research-heavy project (25% industry ratio), coordinated by Italian private company ETNA BIOTECH SRL. The inclusion of 1 SME and the involvement of Indian partners gives the consortium direct access to high-burden TB populations critical for model validation. For a business buyer, the key signal is that this was academically rigorous with real industry involvement, but commercialization would likely need a stronger industry push.

THE UNIVERSITY OF SHEFFIELDparticipant · UK
UNIVERSITA DEGLI STUDI DI CATANIAparticipant · IT
STICHTING TUBERCULOSIS VACCINE INITIATIVEparticipant · NL
ALMA MATER STUDIORUM - UNIVERSITA DI BOLOGNAparticipant · IT

How to reach the team

ETNA BIOTECH SRL (Italy) — contact through SciTransfer for introduction

Order a report on this consortium

Next steps

Talk to the team behind this work.

Want to explore how virtual patient simulations could reduce your TB clinical trial costs? SciTransfer can connect you directly with the team that built and validated this platform.

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