Deadline: 08-Oct-2026
The Innovative Health Initiative (IHI) funding opportunity supports research aimed at transforming nonclinical safety assessment for new medicines.
Traditional drug development relies heavily on animal toxicity studies to evaluate potential safety risks. However, advances in artificial intelligence, data science, and alternative testing methods provide opportunities to improve efficiency while maintaining high patient safety standards.
This initiative aims to develop:
- An AI Foundation Toxicology Model
- A transparent weight-of-evidence framework
- Validated artificial intelligence toxicology prediction tools
- Regulatory pathways for New Approach Methodologies (NAMs)
- Sustainable systems for future AI model improvement
Why AI-Based Toxicology Research Matters
Drug safety assessment is a critical stage in medicine development. Traditional approaches often require extensive animal testing, which can be:
- Time-consuming
- Expensive
- Resource-intensive
- Limited in predicting certain human responses
AI-based toxicology models can help improve safety assessment by:
- Analysing large toxicology datasets
- Identifying complex biological patterns
- Predicting potential toxicity outcomes
- Supporting regulatory decision-making
- Reducing unnecessary animal studies
The goal is not simply to replace existing methods but to create scientifically reliable tools that strengthen safety evaluation.
Main Objectives of the Funding Opportunity
The programme aims to achieve several key objectives:
- Develop an AI Foundation Toxicology Model for predicting toxicity outcomes
- Support scientifically justified second-species toxicity testing waivers
- Improve consistency in nonclinical safety assessments
- Reduce dependence on animal testing through validated alternatives
- Strengthen regulatory acceptance of AI-based methodologies
- Promote transparent and explainable artificial intelligence
- Improve patient safety during medicine development
- Establish sustainable approaches for long-term AI model improvement
Key Research Areas
Development of the AI Foundation Toxicology Model
A central objective is the creation of a powerful AI model capable of predicting whether testing in a second animal species provides additional safety information.
The model should:
- Analyse toxicology data from multiple sources
- Predict chronic and sub-chronic toxicity outcomes
- Evaluate differences across species
- Provide probabilistic predictions
- Estimate uncertainty
- Generate explainable results
The model must demonstrate:
- Reliability
- Robustness
- Transparency
- Reproducibility
- Scientific validity
Toxicology Data Collection and Integration
Applicants are expected to collect, organise, and evaluate high-quality toxicology datasets.
Relevant data may include:
- Pharmaceutical toxicity study results
- Structured toxicology datasets
- Unstructured study reports
- Historical safety data
- Additional biological and scientific information
Projects should create secure data systems that enable:
- Collaborative research
- Data analysis
- AI model development
- Regulatory evaluation
Data management approaches must protect confidential information while enabling scientific progress.
Artificial Intelligence Method Development
Projects should assess existing AI approaches and identify suitable methods for toxicology prediction.
Research may include:
- Machine learning algorithms
- Deep learning approaches
- Foundation models
- Explainable AI techniques
- Probabilistic modelling
- Alternative computational approaches
Selected AI methods should support:
- Data traceability
- Model interpretability
- Uncertainty estimation
- Scientific justification
- Regulatory acceptance
Validation of AI Toxicology Predictions
A major requirement is rigorous validation of the AI Foundation Toxicology Model.
Validation activities should demonstrate:
- Accuracy of predictions
- Model performance across different datasets
- Reliability across toxicity outcomes
- Generalisability across species and study durations
Validation should involve collaboration between:
- Pharmaceutical companies
- Academic researchers
- Small and medium-sized enterprises (SMEs)
- Regulatory stakeholders
Development of a Weight-of-Evidence Framework
The programme requires creation of a standardised framework for interpreting AI-generated predictions.
The framework should combine:
- AI model outputs
- Existing scientific evidence
- Toxicology data
- Biological relevance
- Risk assessment information
The framework should evaluate:
- Evidence quality
- Data consistency
- Prediction uncertainty
- Toxicity severity
- Human relevance
The objective is to support transparent and reproducible decisions for regulatory submissions involving second-species toxicity waivers.
Supporting New Approach Methodologies (NAMs)
The initiative supports wider adoption of New Approach Methodologies as alternatives or complements to traditional animal testing.
NAMs may include:
- Artificial intelligence models
- Computational toxicology
- In vitro approaches
- Human-relevant biological models
- Data-driven safety assessment methods
The programme aims to improve confidence in these approaches through scientific validation and regulatory alignment.
Regulatory Adoption and Implementation
Successful projects should consider how AI-based toxicology tools can be integrated into regulatory processes.
Applicants should develop:
- Regulatory guidance strategies
- Implementation recommendations
- Scientific justification approaches
- Communication frameworks
- Adoption pathways
The project should support collaboration with regulatory authorities to ensure practical application.
Funding Details
Key funding information includes:
- Funding programme: Innovative Health Initiative (IHI)
- Total indicative budget: €53.2 million
- Application process: Two-stage submission process
- Call opening date: 2 July 2026
Expected indicative funding distribution:
- Approximately €9 million
- Approximately €9.2 million
- Approximately €35 million
Final funding allocation will depend on proposal evaluation and selection outcomes.
Application Timeline
Important dates include:
- Call opening: 2 July 2026
- First-stage application deadline: 8 October 2026
- Second-stage full proposal deadline: 21 April 2027
Applicants should prepare submissions according to Horizon Europe and IHI requirements.
Who Is Eligible?
The funding opportunity is open to eligible legal entities that meet Horizon Europe and topic-specific requirements.
Eligible applicants may include:
- Universities
- Research organisations
- Pharmaceutical companies
- Biotechnology companies
- Small and medium-sized enterprises (SMEs)
- International research organisations
- Other eligible legal entities
Applicants may be located:
- Within participating countries
- In eligible non-associated third countries
- Within eligible international organisations
Applicants must:
- Meet Horizon Europe eligibility conditions
- Register in the Participant Register
- Obtain a Participant Identification Code (PIC)
- Complete required administrative processes before grant agreement signing
How the Funding Programme Works
Step 1: Build a Collaborative Research Consortium
Applicants should establish partnerships involving expertise in:
- Artificial intelligence
- Toxicology
- Drug development
- Regulatory science
- Data management
- Computational modelling
Strong collaboration between scientific and industry partners is encouraged.
Step 2: Develop the AI Toxicology Research Strategy
The proposal should explain:
- Scientific objectives
- AI methodology
- Data sources
- Validation strategy
- Regulatory pathway
- Expected healthcare impact
Step 3: Develop and Validate the AI Model
Research teams should:
- Collect and harmonise toxicology datasets
- Train AI models
- Test model performance
- Evaluate uncertainty
- Conduct validation studies
Step 4: Create Regulatory Implementation Tools
Projects should develop:
- Decision-support frameworks
- Guidance documents
- Practical tools
- Recommendations for adoption
Step 5: Ensure Long-Term Sustainability
Applicants should include plans for:
- Model maintenance
- Data governance
- Future updates
- Ethical AI management
- Continued regulatory relevance
Expected Impact of the Programme
The initiative is expected to contribute to:
- Faster medicine development
- Improved drug safety predictions
- Reduced unnecessary animal testing
- Greater regulatory confidence
- More efficient pharmaceutical research
- Better patient protection
- Wider adoption of AI-driven toxicology methods
Common Mistakes Applicants Should Avoid
Applicants should avoid:
- Developing AI models without regulatory consideration
- Using poor-quality or limited datasets
- Failing to address explainability and transparency
- Ignoring uncertainty in AI predictions
- Not involving relevant stakeholders
- Focusing only on technology without implementation planning
Tips for a Strong Proposal
Applicants can strengthen their proposals by:
- Combining expertise from AI and toxicology fields
- Including high-quality, diverse datasets
- Demonstrating explainable AI approaches
- Planning early engagement with regulators
- Defining clear validation strategies
- Addressing ethical and governance issues
- Creating practical implementation pathways
Frequently Asked Questions (FAQ)
What is the Innovative Health Initiative AI Foundation Toxicology Model funding opportunity?
It is an IHI research funding programme designed to develop AI-based toxicology models and frameworks that improve drug safety assessment and support reduced reliance on animal testing.
What is the purpose of the AI Foundation Toxicology Model?
The model aims to predict whether second-species toxicity testing provides additional safety information during chronic and sub-chronic drug development studies.
What are New Approach Methodologies (NAMs)?
NAMs are alternative scientific approaches that can complement or replace traditional animal testing methods, including computational models, artificial intelligence, and advanced biological systems.
How much funding is available?
The total indicative budget is €53.2 million, distributed among selected projects based on evaluation outcomes.
Who can apply?
Any eligible legal entity that meets Horizon Europe requirements may apply, including research institutions, companies, SMEs, and international organisations.
Why is explainable AI important in toxicology?
Explainable AI helps researchers and regulators understand how predictions are generated, evaluate reliability, and make scientifically justified safety decisions.
What role will regulators play in the project?
Regulatory stakeholders will help ensure that AI-based toxicology approaches meet safety requirements and can be adopted in real-world drug development processes.
Conclusion
The Innovative Health Initiative AI Foundation Toxicology Model funding opportunity represents a major step toward modernising medicine safety assessment through artificial intelligence, advanced data analysis, and transparent scientific frameworks.
By combining AI technology, toxicology expertise, regulatory collaboration, and New Approach Methodologies, the programme aims to create safer, more efficient, and more ethical approaches to developing innovative medicines while maintaining strong standards of patient protection.
For more information, visit European Commission.
