Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : EmbryoNet-AI (Horizon Europe, appel 2024, Proof of concept grants)
Entité participante : Université de Constance — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

The EmbryoNet-AI project seeks to transform drug discovery and toxicology testing by integrating advanced deep learning technologies to automate phenotypic analysis of embryos and organoids. Traditional drug testing methods rely heavily on animal models, which are time-consuming, costly, and often ethically problematic. EmbryoNet-AI offers a faster, more accurate, and comprehensive solution for evaluating the effects of compounds on biological development, thus significantly enhancing the efficiency of early-stage drug screening. The core innovation is EmbryoNet-AI's ability to analyze complex biological data with precision, providing insights into drug mechanisms that are not only quicker but also more reliable than current methods. This will ultimately reduce the need for animal models in drug testing, addressing both ethical and logistical concerns. The EmbryoNet-AI platform is poised to fill a critical gap in the pharmaceutical and biotech industries, offering a scalable, non-invasive approach that can be easily integrated into existing workflows. With the support of the ERC Proof of Concept grant, we will focus on the further development, validation, and commercialization of the EmbryoNet-AI platform. The project will involve refining our AI models through rigorous testing, building a prototype web interface for user interaction, and collaborating with industrial and academic partners to ensure the platform’s practical utility. Additionally, we will explore intellectual property strategies and assess market readiness, paving the way for wide-spread adoption. By addressing the limitations of traditional phenotyping methods, EmbryoNet-AI aims to accelerate drug discovery, reduce costs, and promote more sustainable research practices in developmental biology and pharmacology. Through ERC funding, we aim to establish EmbryoNet-AI as a ground-breaking tool that will impact both the scientific community and the pharmaceutical industry.
Projet ERC : Ai4Cilia (Horizon Europe, appel 2024, Proof of concept grants)
Entité participante : Helmholtz Zentrum München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HMGU
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

In Ai4Cilia (“Artificial intelligence for Cilia”), we will develop an automated microfluidic AI-powered assay of ciliated cells to detect and classify ciliary beat abnormalities for drug discovery, biomedicine, and disease screening. Rhythmically beating ciliated cells perform important physiological functions in the airways and the reproductive system. Defects of ciliary beat contribute to debilitating diseases, such as to-date uncurable chronic obstructive pulmonary disease (COPD), and underdiagnosed causes of infertility. In the context of our ERC StG MecCOPD, we recently showed that standard video-microscopy recordings of ciliary beat can be used to extract quantitative metrics that clearly identify disease-specific ciliary dysfunction. Therefore, since ciliary beat can be observed in vitro and in minimally invasive biopsies, it could provide a much-needed readout for drug development and improved diagnosis. However, since ciliary beat patterns are highly complex, it takes specialists to analyze them and hence this information is rarely exploited in praxis. To establish ciliary beat as a common readout, we developed a microfluidic platform that standardizes data acquisition from ciliated cells and uses AI for detecting and classifying ciliary beat defects. To bring this approach to the next level and towards market entry, we will address three key objectives in Ai4Cilia to demonstrate (1) hardware feasibility by validating human ciliated cell capture and data acquisition in our microfluidic chip; (2) software feasibility by training our AI algorithm to detect disease-specific ciliary defects and validate sensitivity and specificity of our assay; and (3) business feasibility by assessing customer needs in biomedical and clinical research to define the most efficient go-to-market strategy. We believe this strategy will make ciliary beat a highly sensitive and robust readout for respiratory and reproductive (dys)function in biomedical and clinical research.
Projet ERC : BugGPT (Horizon Europe, appel 2023, Proof of concept grants)
Entité participante : Université de Stuttgart — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Software bugs are a major problem for software developers and users alike, as they cause crashes, security vulnerabilities, and data loss. Unfortunately, identifying and fixing software bugs is among the most expensive and time-consuming tasks in software development, accounting for 28% to 50% of the costs of a billion-dollar industry. The LearnBugs ERC project, on which this proposal is based, has developed ground-breaking techniques to automatically find bugs and to propose suitable bug fixes. These techniques are based on artificial intelligence and deep learning, making them particularly powerful for kinds of bugs missed by traditional software developer tools. However, these techniques are currently only available as research prototypes, and there is a gap to be bridged in order to integrate them successfully into the software development workflow. This Proof of Concept proposal, named BugGPT, aims to make learning-based techniques for finding and fixing software bugs practical and usable by software developers. The project will develop practical tools that enable software developers to automatically find and fix bugs in their code. To this end, we will perform technical development activities that address the questions of where, when, and how to suggest bug fixes. Furthermore, we will perform business development activities to identify potential customers, to evaluate the usefulness of our tools, and to compare potential business models with each other. Overall, BugGPT has the potential to make a significant impact on the software development industry by making learning-based bug finding and fixing practical for software developers. If successful, the project could be the beginning of a commercial product that stirs up the market of software development tools.
Projet ERC : TEMPLe (Horizon Europe, appel 2025, Proof of concept grants)
Entité participante : Fundació Clínic per a la Recerca Biomèdica — Pays : Espagne — Rôle : PI
Acronyme de l'entité : FCRB
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Predicting the future clinical behaviour of a patient’s cancer remains a major scientific and clinical challenge. Chronic Lymphocytic Leukaemia (CLL), the most common leukaemia in the Western world, exhibits a broad range of clinical behaviours. The disease begins with a premalignant state that can later progress into an asymptomatic, progressive and highly aggressive form with a poor prognosis. Predicting the risk and timing of progression is a major unmet clinical need in CLL. Through our ERC-SyG grant BCLLatlas and previous studies, we have revealed that DNA methylation plays a key role in cellular identity and memory. These observations indicate that: 1) neoplastic cells carry epigenetic imprints of their cellular origin allowing the distinction of epigenetic subtypes; 2) a mitotic clock traces the proliferative history of cancer cells; and 3) stochastically fluctuating DNA methylation serves as a molecular barcode to track cancer evolutionary dynamics. In CLL, all three nuances of epigenetic memory have a profound and independent prognostic impact. Can these independent prognostic variables be integrated into a single score? In TEMPLe, we propose to leverage the rich information on cellular memory that is encoded within the cancer epigenome by integrating these three variables and generating personalized risk-scores to predict future clinical outcomes of CLL. We will make significant steps towards developing solutions suitable for clinical translation by 1) generating a user-friendly digital platform for risk prediction and prognosis assessment, 2) defining the intellectual property (IP) strategy and best exploitation route in close collaboration with the Technology Transfer Office and IP experts. In the medium term this will improve the clinical management of CLL, and in the long term, this strategy could be applied to other cancers, providing a valuable global asset in precision oncology.
Projet ERC : LeukoBIAS (Horizon Europe, appel 2024, Proof of concept grants)
Entité participante : Helmholtz Zentrum München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HMGU
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Foundation models have revolutionized image processing in healthcare, offering robust performance across various tasks without task-specific training. However, potential biases in these models, especially when applied to critical medical diagnostics such as leukemia detection, remain largely unexplored. LeukoBIAS aims to address this crucial gap by developing a framework for analyzing and mitigating bias in foundation model-based leukemia detection algorithms. Building on my previous work in AI-driven leukemia diagnostics, we will leverage a unique real-world dataset of over 6000 patients from my long-time industry partner, the Munich Leukemia Laboratory, to investigate biases related to sex, age, and other patient characteristics. Our approach combines advanced machine learning techniques, including multiple instance learning and attention mechanisms, with novel bias detection and mitigation strategies. The project consists of three work packages: (i) bias analysis in foundation model-based leukemia diagnostics; (ii) development of bias mitigation techniques for model fine-tuning; and (iii) exploration of intellectual property and commercialization opportunities for bias auditing. The innovative potential of the project extends beyond leukemia diagnostics. We will thus explore the scalability of our approach to other modalities and conduct a comprehensive market analysis to identify potential industry partners. LeukoBIAS will contribute to the scientific understanding of bias in medical AI and pave the way for more equitable and reliable AI-driven diagnostic tools. By addressing the requirements outlined in the EU Artificial Intelligence Act, LeukoBIAS is poised to have a significant impact on the development and deployment of trustworthy AI in healthcare. By providing a framework for bias analysis and mitigation, this project will contribute to more accurate diagnoses, improved patient outcomes, and accelerated innovation in AI-driven medicine.
Projet ERC : grabRNA (Horizon Europe, appel 2022, Proof of concept grants)
Entité participante : Université libre d'Amsterdam — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

The progression of RNA-based cellular applications in molecular therapy has been greatly hindered due the difficulty of delivering RNA across biological barriers. Interest has therefore grown in the development of carrier systems which can facilitate RNA delivery in a controlled manner. Herein, we use peptide-based, binders to stabilise and deliver double-stranded RNA. The main goals of this project are the identification of lead candidates with robust cell delivery in relevant cell lines, and to be fit for partnering with a company and/or securing follow-up resources.
Projet ERC : BioChord (Horizon Europe, appel 2023, Proof of concept grants)
Entité participante : Fondazione Ri.MED — Pays : Italie — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

The human heart is among those organs suffering from a limited capacity to self-regenerate. Heart diseases continue to be a leading cause of mortality with suboptimal therapeutic options. Thus, deploying new therapeutic strategies for tissue repair is a primary objective in modern cardiac medicine. Valvular heart diseases, such as those related to the Mitral Valve (MV), are the leading causes of cardiovascular morbidity and mortality worldwide, affecting 5.1% of the 65+ years old population. In particular, MV regurgitation, one of the most common valvopathies, affects over 24 million people worldwide. Since the fundamental nature of MV regurgitation is mechanical, caused by physical, irreversible damage to the restraining force structures of the valvular apparatus, the only effective treatment is limited to the surgical approaches that include MV repair or replacement. However, when the regurgitation is due to Chordae Tendinae (CT) elongation or rupture, several studies have demonstrated the substantial advantages of MV repair, using a substitute for the damaged CT vs. MV replacement. Currently, the expanded polytetrafluoroethylene (ePTFE) has become the standard approach in CT repair/replacement1. However, several complications (rupture, calcification, detachment, fibrosis, and slippage) have been observed. Hence, there continues to be an urgent need to develop better MV repair techniques that are simple, effective, and durable. In this proposal, we intend to further advance and validate BioChord, the first ever polymeric bioengineered regenerative CT, designed to first repair the MV by replacing the diseased CT, providing mechanical support to the valvular apparatus, then to be restored by the patient tissue, progressively becoming a functional tendon-like structure that, as a native CT, while connecting the valve leaflet to the papillary muscle, will sustain the valvular apparatus with blood and nutrients.
Projet ERC : ProBER (Horizon Europe, appel 2022, Proof of concept grants)
Entité participante : École polytechnique Chalmers — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Care of chronic wounds constitutes an enormous socioeconomic burden to the European Healthcare system calling for new strategies to support healing for wounds at risk. As a restored epithelium will outperform any artificial scaffold in protecting the underlying tissue, fast re-epithelialization is the primary goal. In culture models of wound healing, we have shown that direct current stimulation (DCs) makes epithelial ruptures close up to three times faster, and that the key to success is dosage. This aspect is overlooked in previous clinical studies. Typically, DCs thresholds established in cultures are lost in translation, because of the lack of technologies able to support DCs on the skin. In ProBER we will change this. We will exploit technologies for making conformal, metal free, biocompatible DCs electrodes for skin developed in the ERC StG SPEEDER. By focussing on personalisation of the bioelectronic wound dressing we will ensure that the same efficient parameter space established in culture models can be precisely replicated on actual wounds. Our goal is a fabrication pipeline with wound dressings tailored to the wound. We will collect information on wound size and shape using LIDAR 3D scanning, and feed this, as well as impedance measurements, to an automated personalisation algorithm. Personalisation extends beyond fabrication as the ProBER wound dressing will include integrated impedance sensors to ensure that DCs is adapted to the healing progress. In ProBER, we will prepare our promising concept for subsequent clinical studies, which requires a functional concept for fabrication and proof-of-concept demonstration. We will co-develop our wound dressing together with stakeholders in healthcare, and work to protect IP, recruit strategic partners and form alliances for clinical translation.
Projet ERC : FERADICATE (Horizon Europe, appel 2025, Proof of concept grants)
Entité participante : Helmholtz Zentrum München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HMGU
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Despite significant advancements in the molecular characterization of certain types of cancer, many of these discoveries have yet to translate into substantial improvements in clinical outcomes. While targeted therapies are available, patients harbouring common oncogenic driver mutations often experience relapse. Prognosis is even poorer for those without identifiable oncogenic driver mutations. In cases with high PDL1 expression, single-agent immunotherapy typically leads to disease progression, whereas patients with low expression levels require additional toxic chemotherapy to achieve comparable outcomes. This underscores an urgent medical need for more effective therapeutic strategies for specific cancer subtypes. Ferroptosis, a form of necrotic cell death marked by the iron-dependent oxidative destruction of cell membranes, offers a pharmacologically tractable avenue for treatment. The strong reliance of certain tumour subtypes on cellular systems to suppress ferroptosis creates exciting opportunities for innovative therapies. We recently identified ferroptosis suppressor protein 1 (FSP1) as the second mainstay in ferroptosis suppression, functioning independently of the main regulator GPX4. Since the genetic or pharmacologic inhibition of FSP1 alone is sufficient to suppress certain tumour types in the in vivo context, FSP1 emerges as a compelling drug target. Here, we aim to further optimize the activity, selectivity, and pharmacokinetic profile of our newly identified FSP1 inhibitor family. We also seek strive to expand our patent portfolio to ensure broad IP coverage. Our primary objective is to identify a new lead compound—or potentially a preclinical candidate—for cancer treatment, thereby advancing FSP1 inhibitors toward preclinical and clinical development. This program has the potential to deliver a substantial value proposition and create opportunities for out-licensing or co-development partnerships.
Projet ERC : LOOP-TMS (Horizon Europe, appel 2025, Proof of concept grants)
Entité participante : Université Eberhard Karl de Tübingen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

We aim to develop and validate adaptive closed-loop brain stimulation using advanced real-time electroencephalography-transcranial magnetic stimulation (EEG–TMS) technology guided by probabilistic deep artificial neural networks. TMS is performed routinely worldwide in tens of thousands of healthcare institutions for treating a wide variety of brain disorders. However, current TMS protocols are limited by small therapeutic effect sizes and large portions of non-responders. A significant problem is that one size does not fit all: existing TMS protocols are non-personalized, operate without information of instantaneous brain states, function in an open-loop manner, and lack adaptability. To address this, we have developed brain state-dependent stimulation in the ongoing ERC Synergy project ConnectToBrain. Real-time EEG–TMS has enabled reliable prediction of high- vs. low-excitability brain states and coupling of TMS to a prespecified state. While this is major progress, brain state-dependent stimulation is still open-loop and non-adaptive. The key innovation in our current proposal is implementing truly adaptive closed-loop algorithms that continuously monitor TMS effects and optimize stimulation parameters in real-time to maximize beneficial long-term changes in targeted brain networks. Recent advances in machine learning now make this sophisticated approach possible. This project represents a significant leap beyond ConnectToBrain by creating a user-independent, fully automated, adaptive closed-loop real-time EEG-TMS system ready for further testing in clinical trials. Our technology has breakthrough potential as a paradigm-shifting innovation in non-invasive therapeutic brain stimulation, addressing an urgent societal and medical need for more effective, individualized treatment of brain disorders.
Projet ERC : SNAP-ADAR Kit (Horizon Europe, appel 2022, Proof of concept grants)
Entité participante : Université Eberhard Karl de Tübingen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

RNA base editing is a promising approach to manipulate genetic information in a transient and dosable way, which breaks new ground in basic Life Science research and drug discovery. In a preceding ERC CoG action, we have engineered the SNAP-ADAR tool, which allows for the particularly precise, efficient and programmable adenosine-to-inosine RNA base editing. However, the broad usage of this tool is somewhat hampered by the need for designing the chemically modified guideRNAs, which currently includes some peptide bond-forming chemistry and nucleic acid gel purification. In this PoC action, we aim to simplify the tool to make it available for a larger section of the Life Science community. Specifically, we aim to simplify the chemistry and the compounds to such an extent that a SNAP-ADAR kit is obtained, which enables the Life Scientist to perform the required chemistry without special equipment or chemistry skills. Furthermore, the kit would include a reliable and simple protocol to perform efficient and precise editing reactions in mammalian primary cells. Profit could particularly be generated from the selling of the chemically stabilized guideRNAs. We envision that the product would find application in the modulation of protein function like signaling cues and metabolic pathways and may foster the target screening and target validation in drug discovery efforts, which are currently ongoing in the field of RNA base editing. The action also contains an analysis of the IP landscape and - at the later stage of the action - we aim to involve industrial partners for commercialization.