Innovative Health Initiative JU Call 13

Home Funding Calls for proposals An AI Foundation Toxicology Model and Framework to Support Waiving a Second Species in Drug Safety Studies An AI Foundation Toxicology Model and Framework to Support Waiving a Second Species in Drug Safety Studies HORIZON-JU-IHI-2026-13-two-stage-02 Topic Call for proposal Internal navigation General information Topic description Topic updates Conditions and documents Budget overview Partner search announcements Start submission Topic Q&As Get support General information Programme Horizon Europe (HORIZON) Call Innovative Health Initiative JU Call 13 (HORIZON-JU-IHI-2026-13-two-stage) Type of action HORIZON-JU-RIA HORIZON JU Research and Innovation Actions Type of MGA HORIZON Action Grant Budget-Based [HORIZON-AG] Open For Submission Deadline model two-stage Opening date 02 July 2026 Deadline dates 08 October 2026 17:00:00 Brussels time 21 April 2027 17:00:00 Brussels time Topic description Expected Impact: The action under this topic is expected to achieve the following impacts: Faster and more informed decision-making through the use of an AI-driven NAM (AI Foundation Toxicology Model) and increased efficiency through rapid processing of vast amounts of data [1]. Increased consistency and standardisation in a NAM-based approach, specifically an AI model, used by industry in the efficient development, testing and production of safe and effective innovative health technologies, improving industrial competitiveness. Regulatory adoption of a NAM-enabled second species waiver model (AI Foundation Toxicology Model) and weight-of-evidence framework, in line with recommendations and more consistent global decision-making on waiving second species testing. Reduction in animal use, accelerated timelines and lower costs, enhancing the competitiveness of the European health industry through economical and ethical benefits. Improved public health as patients will benefit from safe and effective medicines developed faster using validated NAMs. The action is expected to contribute to the EU Directive (2010/63/EU) [2] on the protection of animals used for scientific purposes and the implementation of the 3Rs principles to replace, reduce and refine the use of animals. The action is expected also to consider and contribute to EU programmes, initiatives and policies on New Approach Methodologies (NAMs) such as the future European Research Area (ERA) action on accelerating NAMs to advance biomedical research and testing of medicinal products and medical devices. [1] Transforming animal study toxicology reports into structured, harmonized data using large language models [2] European Directive on the protection of animals used for scientific purposes Expected Outcome: The action under this topic must contribute to all of the following outcomes: A validated Artificial Intelligence (AI) Foundation Toxicology Model* that provides transparent probabilistic predictions for industry and regulator stakeholders to determine when a second species in chronic (>90 days) and sub-chronic (90 days) small molecule medicine repeat-dose studies is unlikely to provide additional safety relevant information, including risks of missed toxicity, organ-specific findings, and divergence in No Observed Adverse Effect Level (NOAEL). The goal would be to enable waiving the need for two species chronic testing for small molecules and other modalities e.g. oligonucleotides. A standardised, transparent weight-of-evidence framework for industry, regulator and academic stakeholders that enables reproducible assessment of evidence quality, consistency, relevance, and uncertainty across regulatory submissions, supporting the wider adoption of the AI Foundation Toxicology Model and New Approach Methodology (NAM)-based toxicology strategies in general. Functional tools, templates, and training materials that support the real-world implementation, sustainability and evolution of the foundation model and weight-of-evidence framework including guidance on explainability, provenance, governance, ethical use, and alignment with AI requirements, tailored to industry, regulator and academic stakeholder needs. Enhanced industry and regulator stakeholder confidence in second species waiver applications, particularly for small molecule medicines, supported by empirical, calibrated evidence and a framework enabling predictable adjudication, more consistent global waiver decision-making and timely progression of medicine development without compromising patient safety. This confidence should be gained through the model and framework’s application for regulator validation and acceptance, with the longer-term goal, beyond the action’s scope, of a revision of the regulatory guidelines ICH M3(R2) [1] taking onboard the future project’s outcomes. This topic should provide the opportunity to extend this confidence to waiving chronic testing in a single species beyond small molecule medicines and to other study types. * An AI Foundation Toxicology Model is a large-scale, pre-trained machine learning system that learns generalisable representations of toxicological biology and chemistry from diverse, multimodal datasets, enabling broad applicability across safety assessment tasks with minimal task-specific training. [1] ICH M3(R2). Nonclinical safety studies for the conduct of human clinical trials and marketing authorization for pharmaceuticals. In: International Conference on Harmonisation (ICH). Topic M3(R2); 2009. Scope: Regulatory guidelines e.g. ICH M3(R2) for nonclinical safety assessment of new small molecule medicines have historically required toxicity studies in two species, typically one rodent and one non-rodent, to maximise the likelihood of detecting adverse effects, improve the translatability to humans and guide patient dosing and monitoring. This two species approach is increasingly challenged by ethical considerations, long and costly study durations, limited reproducibility, and uncertainty regarding human relevance [1], [2]. In parallel, scientific progress in mechanistic toxicology, advanced in vitro systems (e.g. organoids and micro-physiological systems), multi-omics technologies, and AI based modelling is enabling NAMs to help reduce reliance on animal models without compromising patient safety. Within the context of this topic, the AI Foundation Toxicology Model will be considered a NAM, facilitating NAM-enabled second species waivers. In addition, the policy landscape across global jurisdictions is evolving rapidly. The European Commission is preparing a roadmap towards phasing out animal testing for chemical assessment safety, expected in 2026 [3]; the European Medicines Agency (EMA) has increased its focus on emerging NAMs through horizon-scanning and dedicated working groups [4]; the UK Government has published the Replacing Animals in Science strategy [5] and the United States Food and Drug Administration (FDA) has published a dedicated roadmap for reducing animal testing in preclinical safety studies [6]. Collectively, these initiatives highlight the need for robust evidence, data integration, validated methods and coordinated stakeholder action to support credible alternatives to animal testing. A weight-of-evidence approach can eliminate unnecessary animal tests by integrating and assessing diverse data sets as well as determining whether the available evidence is sufficient or additional nonclinical testing is required to address potential safety concerns. The value of this evidence depends on its quality, consistency, human relevance, and the nature and severity of observed effects. Currently, the absence of standardised, data-driven methods means decisions can vary across reviewers and jurisdictions, creating an uncertainty which discourages changes from being made to the current process. A more structured, reproducible, validated, and evidence-based approach would support consistent, transparent, and well-justified decision-making on whether second species studies could be waived. This topic aims to address and standardise the waiver process for second species chronic and sub-chronic small molecule general toxicology studies by developing a transparent, AI-driven Foundation Toxicology Model. The model should be underpinned by diverse, standardised, high quality in vivo datasets, encompassing all modalities of medicines, and supported by a robust weight-of-evidence framework. The most appropriate single species should be selected early in the drug development programme based on target affinity and functions, metabolite profiling, etc. The main context of use of the AI Foundation Toxicology Model is to predict whether testing in a second species will provide additional safety-relevant information. If toxicity predictions are equal across rodent and non-rodent species, priority should be given to the reduction of non-rodent chronic toxicity testing. Although the context of use for this topic should be focused on waiving second species testing for chronic and sub-chronic small molecule studies, the project’s actions, sustainability plan plus the ongoing development and evolution of the model should provide the opportunity to assess appropriate single species selection and encompass the broadening of the context of use to enable the waiving of single species chronic testing in small molecules and other modalities, such as oligonucleotides. Therefore, relevant data sources and their provision should enable this broader and longer-term goal. Selection of the appropriate single animal species for preclinical safety evaluations for biotechnology-derived pharmaceuticals in line with ICH S6 [7] should be considered within the scope of this project if the relevant data provision is sufficient to enable this functionality. This topic should build upon, among others, the outcomes, learnings, data sharing structures, and methodologies of previous IMI/IHI projects that improve the predictability, feasibility and reliability of pre clinical safety assessments — such as BigPicture, eTRANSAFE , eTOX , imSAVAR , and VICT3R — as well as other relevant European, international and national initiatives such as Animal free safety assessment of chemicals: Project cluster for implementation of novel strategies (ASPIS) and NC3Rs-led efforts like the Virtual Second Species [8] initiative and the Two Species project [9]. Preliminary analysis from the NC3Rs Two Species project predicts that it would be possible to avoid conducting studies in non-rodents (e.g. dog or non-human primate) in >60% of the small molecule drugs that enter Phase II globally each year [10]. By fostering collaboration among pharmaceutical companies, regulatory agencies, academic institutions, SMEs, and patient advocacy groups, the action generated by this topic should seek to enhance patient safety, reduce reliance on animal testing, streamline medicine development timelines, lower associated costs, and boost the global competitiveness of the pharmaceutical industry within the European Union. To fulfil this aim, the proposal should: 1. Consolidate and evaluate data sources Identify, curate, and assess the quality and suitability of data from multiple pharmaceutical companies and other organisations within the consortium with a priority focus on general toxicology studies (14-day, 28-day, 90-day and chronic toxicity studies) in the form of structured toxicity data in CDISC SEND [11] format as well as in unstructured study reports. Secondary focuses for additional data should also be considered, including in vivo toxicity study data, chemistry (e.g. structure information, where permitted), pharmacokinetic and exposure data, mechanistic in vitro systems, in silico models, multi-omics datasets, licensed content, prior consortia outputs, and public regulatory databases. Develop a new database to host and analyse data, encompassing principles to support a common or federated data model that enables sensitive data preservation and multis [... συνέχεια στην επίσημη πρόσκληση]