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The Innovative Medicines Initiative (IMI), was a European public-private partnership (PPP) undertaking intended to improve the drug development process, facilitate biomarker development, accelerate clinical trial timelines, improve success rates, and generally increase the competitiveness of European pharmaceutical sector research. Through the IMI, pharmaceutical research interests and the research agenda of the EU are supported by academic partnership and financed by both the pharmaceutical companies and public funds. Since its inception, the IMI has funded dozens of research partnerships focused on solving the core problems that have consistently obstructed the translation of research into clinical success. In this post-mortem review paper, we focus on six research initiatives that tackled foundational challenges of this nature: Aetionomy, EMIF, EPAD, EQIPD, eTRIKS, and PRISM. Several of these initiatives focused on neurodegenerative diseases; we therefore discuss the state of neurodegenerative research both at the start of the IMI and now, and the contributions that IMI partnerships made to progress in the field. Many of the initiatives we review had goals including, but not limited to, the establishment of translational, data-centric initiatives and the implementation of trans-diagnostic approaches that move beyond the candidate disease approach to assess symptom etiology without bias, challenging the construct of disease diagnosis. We discuss the successes of these initiatives, the challenges faced, and the merits and shortcomings of the IMI approach with participating senior scientists for each. Here, we distill their perspectives on the lessons learned, with an aim to positively impact funding policy and approaches in the future.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374208 | PMC |
http://dx.doi.org/10.3389/fneur.2023.1174079 | DOI Listing |
Artif Intell Med
November 2025
Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea; Ajou Translational Omics Center, Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon 16499, Republic of Korea; Department of Biomedical Science, Graduate School of Ajou Univers
Neurodegenerative diseases involve progressive neuronal dysfunction, requiring identification of specific pathological features for accurate diagnosis. Although cerebrospinal fluid analysis and neuroimaging are commonly employed, their invasiveness and high-cost limit widespread clinical use. In contrast, blood-based biomarkers offer a non-invasive, cost-effective, and accessible alternative.
View Article and Find Full Text PDFNeural Netw
August 2025
Department of Industrial Engineering, Ajou University, Suwon, 16499, Republic of Korea; Department of Artificial Intelligence, Ajou University, Suwon, 16499, Republic of Korea. Electronic address:
Dementia encompasses diverse subtypes with distinct characteristics, including cognitive functions, cerebrospinal fluid biomarkers, and neuroimages. Neuroimaging-based diagnosis is advantageous due to low variability and minimal invasiveness, ensuring safe and accurate outcomes. Recently, integrating multimodal neuroimages with machine learning techniques has enhanced diagnostic precision.
View Article and Find Full Text PDFF1000Res
May 2025
Faculty of Production Engineering, University Bremen, Badgasteiner Str. 3, Bremen, 28359, Germany.
Ongoing digitalization and data-driven developments in materials science and engineering (MSE) emphasize the growing importance of reusing research data and enabling machine accessibility, which requires robust data management and consistent semantic data representation. Ontologies have emerged as powerful tools for establishing interoperable and reusable data structures from inconsistent data structures. Despite advancements in semantic data representation for specific applications, integrating application ontologies with primary data repositories, such as electronic lab notebooks (ELNs), to feed world data remains an open task.
View Article and Find Full Text PDFBrief Bioinform
May 2025
Department of Physiology, Ajou University School of Medicine, Worldcup-ro 164, Yeongtong-gu, Suwon, 16499, Republic of Korea.
Neurodegenerative diseases involve progressive neuronal dysfunction, requiring the identification of specific pathological features for accurate diagnosis. While cerebrospinal fluid analysis and neuroimaging are commonly used, their invasive nature and high costs limit clinical applicability. Recently advances in plasma proteomics offer a less invasive and cost-effective alternative, further enhanced by machine learning (ML).
View Article and Find Full Text PDFJ Neuroeng Rehabil
May 2025
Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Background: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagnosis, treatment planning, and monitoring, yet current clinical tools are largely subjective, relying on self-report and clinician judgment via traditional assessment scales. EEG has emerged as a promising, non-invasive modality for capturing neural correlates of depression.
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