Organising Knowledge about Neurodegenerative Disease Mechanisms for the Improvement of Drug Development and Therapy
Organising Knowledge about Neurodegenerative Disease Mechanisms for the Improvement of Drug Development and Therapy

Publications

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Scientific publications

2021

1.) Colin Birkenbihl, Sarah Westwood, Eric Westman, Liu Shi, Alejo Nevado-Holgado, Simon Lovestone, Martin Hofmann-Apitius. ANMerge: A comprehensive and accessible Alzheimer's disease patient-level dataset. Journal of Alzheimer's Disease 2021; 79(1):423-431

2020

Frederic Brosseron, Carl Christian Kolbe, Francesco Santarelli, Stephanie Carvalho, Anna Antonell, Sergio Castro-Gomez, Pawel Tacik, Aishwarya Alex Namasivayam, Graziella Mangon, Reinhard Schneider, Eicke Latz, Ullrich Wüllner, Per Svenningsson, Raquel Sanchez-Valle, Jose Luis Molinuevo, Jean-Christophe Corvol, Michael T. Heneka on behalf of the AETIONOMY study group (including Martin Hofmann-Apitius, Holger Fröhlich and Stephan Springstubbe). Multicenter Alzheimer's and Parkinson's disease immune biomarker verification study. Alzheimer's and Dementia, 16(2):292-304

Sepehr Golriz Khatami, Sarah Mubeen, Martin Hofmann-Apitius. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current Opinion in Neurology, 33(2), 249.

Mohammad Asif Emon, Ashley Heinson, Ping Wu, Daniel Domingo-Fernandez, Meemansa Sood, Henri Vrooman, Jean-Christophe Corvol, Phil Scordis, Martin Hofmann-Apitius, Holger Fröhlich: Clustering of Alzheimer’s and Parkinson’s Disease Based on Genetic Burden of Shared Molecular Mechanism. Scientific Reports volume 10, Article number: 19097. 2020.

Markaki I, Bergström S, Tsitsi P, Remnestål J, Månberg A, Hertz E, Paslawski W, Sorjonen K, Uhlén M, Mangone G, Carvalho S, Rascol O, Meissner WG, Magnin E, Wüllner U, Corvol JC, Nilsson P, Svenningsson P. (2020): Cerebrospinal Fluid Levels of Kininogen-1 Indicate Early Cognitive Impairment in Parkinson's Disease. Mov Disord. 2020 Nov;35(11):2101-2106. doi: 10.1002/mds.28192. Epub 2020 Aug 15.PMID: 33179332

Alder KD, Lee I, Munger AM, Kwon HK, Morris MT, Cahill SV, Back J, Yu KE, Lee FY. Intracellular Staphylococcus aureus in bone and joint infections: A mechanism of disease recurrence, inflammation, and bone and cartilage destruction. Bone. 2020 Dec;141:115568. doi: 10.1016/j.bone.2020.115568. Epub 2020 Jul 31.PMID: 32745687 Review.

Antonell A, Tort-Merino A, Ríos J, Balasa M, Borrego-Écija S, Auge JM, Muñoz-García C, Bosch B, Falgàs N, Rami L, Ramos-Campoy O, Blennow K, Zetterberg H, Molinuevo JL, Lladó A, Sánchez-Valle R. (2020): Synaptic, axonal damage and inflammatory cerebrospinal fluid biomarkers in neurodegenerative dementias. Alzheimers Dement. 2020 Feb;16(2):262-272. doi: 10.1016/j.jalz.2019.09.001. Epub 2020 Jan 6.PMID: 31668967

Emon MA, Domingo-Fernández D, Hoyt CT, Hofmann-Apitius M. PS4DR: a multimodal workflow for identification and prioritization of drugs based on pathway signatures. BMC Bioinformatics. 2020 Jun 5;21(1):231. doi: 10.1186/s12859-020-03568-5.PMID: 32503412 Free PMC article.

Markaki, Ioanna ; Bergström, Sofia ; Tsitsi, Panagiota ; Remnestål, Julia ; Månberg, Anna ; Hertz, Ellen ; Paslawski, Wojciech ; Sorjonen, Kimmo ; Uhlén, Mathias ; Mangone, Graziella ; Carvalho, Stephanie ; Rascol, Olivier ; Meissner, Wassilios G ; Magnin, Eloi ; Wüllner. Cerebrospinal Fluid Levels of Kininogen-1 Indicate Early Cognitive Impairment in Parkinson's Disease. Movement disorders : official journal of the Movement Disorder Society, November 2020, Vol.35(11), pp.2101-2106

Luise Gootjes-Dreesbach, Meemansa Sood, Akrishta Sahay, Martin Hofmann-Apitius, Holger Fröhlich (2020): Variational Autoencoder Modular Bayesian Networks (VAMBN) for Simulation of Heterogeneous Clinical Study Data. Frontiers in Big Data: Medicine and Public Health, doi: 10.3389/fdata.2020.00016.

2019

Herrera-Rivero M, Santarelli F, Brosseron F, Kummer MP, Heneka MT: Dysregulation of TLR5 and TAM Ligands in the Alzheimer's Brain as Contributors to Disease Progression. Mol Neurobiol. 2019 Sep;56(9):6539-6550. doi: 10.1007/s12035-019-1540-3. Epub 2019 Mar 9.PMID: 30852796

Daniel Domingo-Fernandez, Sarah Mubeen, Josep Marin-Llao, Charles Hoyt,Martin Hofmann-Apitius. PathMe: Merging and exploring mechanistic pathway knowledge. bioRxiv doi: https://doi.org/10.1101/451625

Charles Tapley Hoyt, Daniel Domingo-Fernández, Rana Aldisi, Lingling Xu, Kristian Kolpeja, Sandra Spalek, Esther Wollert, John Bachman, Benjamin M. Gyori, Patrick Greene, and Martin Hofmann-Apitius. Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language. bioRxiv doi: https://doi.org/10.1101/536409

Tamás Letoha, Anett Hudák, Erzsébet Kusz, Aladár Pettkó-Szandtner, Ildikó Domonkos, Katalin Jósvay, Martin Hofmann-Apitius & László Szilák. Contribution of syndecans to cellular internalization and fibrillation of amyloid-β(1–42). Scientific Reportsvolume 9, Article number: 1393 (2019)

Carles Falcon; Gemma C Monté-Rubio; Oriol Grau-Rivera; Marc Suárez-Calvet; Raquel Sánchez-Valle; Lorena Rami; Beatriz Bosch; Christian Haass; Juan Domingo Gispert; José Luis Molinuevo. CSF glial biomarkers YKL40 and sTREM2 are associated with longitudinal volume and diffusivity changes in cognitively preserved individuals. Neuroimage Clinical

Laura de Boni and Ullrich Wüllner. Epigenetic Analysis in Human Neurons: Considerations for Disease Modeling in PD. Frontiers in Neurosciences doi: 10.3389/fnins.2019.00276

Carles Falcon; Gemma C Monté-Rubio; Oriol Grau-Rivera; Marc Suárez-Calvet; Raquel Sánchez-Valle; Lorena Rami; Beatriz Bosch; Christian Haass; Juan Domingo Gispert; Jose Luis Molinuevo. CSF glial biomarkers YKL40 and sTREM2 are associated with longitudinal volume and diffusivity changes in cognitively unimpaired individuals. NeuroImage: Clinical

Daniel Domingo-Fernández, Sarah Mubeen, Josep Marín-Llaó, Charles Tapley Hoyt & Martin Hofmann-Apitius. PathMe: merging and exploring mechanistic pathway knowledge. BMC Bioinformatics
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Reagon Karki, Alpha Tom Kodamullil, Charles Tapley Hoyt & Martin Hofmann-Apitius. Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm. BMC Bioinformatics
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Akrishta Sahay, Meemansa Sood, Reagon Karki, Martin Hofmann-Apitius, Holger Fröhlich. Disease Prognosis and Prediction of Interventions using a Bayesian Network Model of Longitudinal, Multi-Modal and Multi-Scale Patient Trajectories. Bioinforamtics - submitted 20.12.2018  

Colin Birkenbihl, Mohammad Asif Emon, Henri Vrooman, Sarah Westwood, Simon Lovestone on behalf of AddNeuroMed, Martin Hofmann-Apitius, Holger Fröhlich, ADNI. Finding comparable subjects across clinical studies for validating an artificial intelligence based Alzheimer‘s disease risk model. Alzheimer's & Dementia - submitted Sept.19

Sarah Mubeen, Charles Hoyt, André Gemünd, Martin Hofmann-Apitius, Holger Fröhlich and Daniel Domingo Fernández. The Impact of Choice of Pathway Database on Statistical Enrichment Analysis and Predictive Modeling Methods. Frontiers in Genetics - accepted

Charles Tapley Hoyt, Daniel Domingo-Fernández, Sarah Mubeen, Josep Marin Llaó, Andrej Konotopez, Christian Ebeling, Colin Birkenbihl, Özlem Muslu, Bradley English, Simon Müller, Mauricio Pio de Lacerda, Mehdi Ali, Scott Colby, Dénes Türei, Nicolàs Palacio-Escat, Martin Hofmann-Apitius. Integration of Structured Biological Data Sources using Biological Expression Language. npj Systems Biology and Applications https://doi.org/10.1101/631812 - submitted May 19

2018

Alan Tucholka, Oriol Grau-Rivera, Carles Falcon, Lorena Rami, RaquelSánchez-Valle, Albert Lladó, Juan Domingo Gispert, José Luis Molinuevo, for the Alzheimer’s Disease Neuroimaging Initiative. Structural connectivity alterations along the AD continuum: reproducibility across two independent samples and correlation with CSF Aβ and tau. Journal of Alzheimer's Disease  61.4: 1575-1587.

Charles Tapley Hoyt Daniel Domingo-Fernández Martin Hofmann-Apitius. BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database (Oxford). 2018 Jan 1;2018. doi: 10.1093/database/bay126.

Tucholka A1, Grau-Rivera O1,2, Falcon C1,3, Rami L2, Sánchez-Valle R2, Lladó A2, Gispert JD1,3, Molinuevo JL1,2; Alzheimer’s Disease Neuroimaging Initiative. Structural Connectivity Alterations Along the Alzheimer's Disease Continuum: Reproducibility Across Two Independent Samples and Correlation with Cerebrospinal Fluid Amyloid-β and Tau. J Alzheimers Dis. 2018;61(4):1575-1587.

Frederic Brosseron†, Andreas Traschütz†, Catherine N. Widmann, Markus P. Kummer, Pawel Tacik, Francesco Santarelli, Frank Jessen and Michael T. Heneka. Characterization and clinical use of inflammatory cerebrospinal fluid proteinmarkers in Alzheimer’s disease. Alzheimer's Research & Therapy (2018) 10 (1):25 DOI 10.1186/s13195-018-0353-3

José Luis Molinuevo, Carolina Minguillon, Lorena Rami and Juan Domingo Gispert. The rationale behind the new Alzheimer’s disease conceptualization: lessons learned during the last decades. Journal of Alzheimer’s Disease, 62(3), 1067-1077

François Mouton‐Liger, Thibault Rosazza, Julia Sepulveda‐Diaz, Amélie Ieang, Sidi‐Mohamed Hassoun, Emilie Claire, Graziella Mangone, Alexis Brice, Patrick P. Michel, Jean‐Christophe Corvol, Olga Corti. Parkin deficiency modulates NLRP3 inflammasome activation by attenuating an A20-dependent negative feedback loop. GLIA (2018) 66.8: 1736-1751

Carles Falcon, Alan Tucholka, Gemma C.Monté-Rubio, Raffaele Cacciaglia, Grégory Operto, Lorena Rami, Juan Domingo Gispert, José Luis Molinuevo. Longitudinal structural cerebral changes related to core CSF biomarkers in preclinical Alzheimer's disease: A study of two independent datasets. NeuroImage: Clinical (2018) 19: 190-201

Charles Tapley Hoyt,Daniel Domingo-Fernández, Nora Balzer, Anka Güldenpfennig, Martin Hofmann-Apitius. A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities. Database (2018) 1: bay050

Skouras S, Falcon C, Tucholka A, Rami L, Sanchez-Valle R, Lladó A, Gispert JD, Molinuevo JL. Mechanisms of functional compensation, delineated by eigenvector centrality mapping, across the pathophysiological continuum of Alzheimer's disease. bioRxiv doi: https://doi.org/10.1101/342246 under review in Neuroimage Clinical

Shashank Khanna, Daniel Domingo-Fernández, Anandhi Iyappan, Mohammad Asif Emon, Martin Hofmann-Apitius & Holger Fröhlich. Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer's Disease and Reconstruction of Relevant Biological Mechanisms. Nature Scientific Reports (2018) 8:11173

Charles Tapley Hoyt, Andrej Konotopez, Christian Ebeling. PyBEL: a computational framework for Biological Expression Language. Bioinformatics 34.4: 703-704

Domingo-Fernández D, Hoyt CT, Bobis-Álvarez C, Marín-Llaó J, Hofmann-Apitius M. ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases. NPJ Syst Biol Appl. 2018 Dec 13;5:3. doi: 10.1038/s41540-018-0078-8. eCollection 2019.

2017

Charles Hoyt. PyBEL: a Computational Framework for Biological Expression Language". Oxford University Press Bioinformatics Online.

Mufassra Naz. Systematic Analysis of GWAS Data Reveals Genomic Hotspots for Shared Mechanisms between Neurodegenerative Diseases. Journal of Alzheimer Disease & Parkinsonism.

Reagon Karki. Comorbidity analysis between Alzheimer's disease and Type 2 Diabetes Mellitus based on shared pathways and the role of T2DM drugs. IOS Press.

Alpha Tom Kodamullil. Of mice and men: comparative analysis of neuro-inflammatory mechanisms in human and mouse using cause-and-effect models. IOS Press.

Anandhi Iyappan. Neuroimaging Feature Terminology (NIFT): a controlled terminology for the annotation of brain imaging features. IOS Press.

Daniel Dominog-Fernandez. Multimodal Mechanistic Diseases (NeuroMMSig): a web server for mechanism enrichment. Oxford University Press Bioinformatics Online.

Alpha Tom Kodamullil. Tracing investment in drug development for Alzheimer disease. Nature.

2016

Naz, M., Kodamullil, A.T., & Hofmann-Apitius, M. (2016). Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases. Briefings in bioinformatics, 17(3), 505-516.

Iyappan, A., Kawalia, S. B., Raschka, T., Hofmann-Apitius, M., & Senger, P. (2016). NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease. Journal of Biomedical Semantics, 7(1), 45.

Iyappan, A., Gündel, M., Shahid, M., Wang, J., Li, H., Mevissen, H. T., ... & Younesi, E. (2016). Toward a Pathway Inventory of the Human Brain for Modeling Disease Mechanisms Underlying Neurodegeneration. Journal of Alzheimer's Disease, (Preprint), 1-18.

Wüllner U, Kaut O, deBoni L, Piston D, Schmitt I. (2016).
DNA methylation in Parkinson’s disease
More: Accepted for publication in Journal of Neurochemistry

2015

Hofmann-Apitius, M. (2015). Is dementia research ready for big data approaches?. BMC medicine, 13(1), 1.

Hofmann-Apitius, M., Alarcón-Riquelme, M. E., Chamberlain, C., & McHale, D. (2015). Towards the taxonomy of human disease. Nature Reviews Drug Discovery, 14(2).

Malhotra, A., Younesi, E., Sahadevan, S., Zimmermann, J., & Hofmann-Apitius, M. (2015). Exploring novel mechanistic insights in Alzheimer’s disease by assessing reliability of protein interactions. Scientific reports, 5.

Younesi, E., Malhotra, A., Gündel, M., Scordis, P., Page, M., Müller, B., ... & Hofmann-Apitius, M. (2015). PDON: Parkinson’s disease ontology for representation and modeling of the Parkinson’s disease knowledge domain. Theoretical Biology and Medical Modelling, 12(1), 1.

Kodamullil, A. T., Younesi, E., Naz, M., Bagewadi, S., & Hofmann-Apitius, M. (2015). Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis. Alzheimer's & Dementia, 11(11), 1329-1339.

Martin Hofmann-Apitius, Marta E. Alarcón-Riquelme, Chris Chamberlain and Duncan McHale (2015).
Towards reforming the taxonomy of human disease. In Nature Reviews - Drug Discovery, Vol. 14, 75 - 76.
More: http://www.nature.com/nrd/journal/v14/n2/full/nrd4537.html

2014

Hunter, P. J.; de Bono, B. (2014). Biophysical constraints on the evolution of tissue structure and function.
In: The Journal of Physiology, Vol. 592, Issue 11.
More: http://onlinelibrary.wiley.com/doi/10.1113/jphysiol.2014.273235/abstract

Molinuevo et al., Neurobiol Aging. White matter changes in preclinical Alzheimer's disease: a magnetic resonance imaging-diffusion tensor imaging study on cognitively normal older people with positive amyloid b protein 42 levels, 2014 Dec;
35(12):2671-80. doi: 10.1016/j.neurobiolaging.2014.05.027.
Epub 2014 Jun 6.
More: http://www.ncbi.nlm.nih.gov/pubmed/25002037

Previous publications

2015

Bagewadi, S., Adhikari, S., Dhrangadhariya, A., Irin, A. K., Ebeling, C., Namasivayam, A. A., ... & Senger, P. (2015). NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases. Database, 2015, bav099

2014

Malhotra, Ashutosh; Younesi, Erfan; Bagewadi, Shweta; Hofmann-Apitius, Martin (2014). Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer’s disease. In: GenomeMedicine
More: genomemedicine.com/content/6/11/97

Malhotra, A., Younesi, E., Gündel, M., Müller, B., Heneka, M. T., & Hofmann-Apitius, M. (2014). ADO: a disease ontology representing the domain knowledge specific to Alzheimer's disease. Alzheimer's & Dementia, 10(2), 238-246.

Younesi, E. (2014). A Knowledge-based Integrative Modeling Approach for In-Silico Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease (Doctoral dissertation, Universitäts- und Landesbibliothek Bonn).

Fluck, J., & Hofmann-Apitius, M. (2014). Text mining for systems biology. Drug discovery today, 19(2), 140-144.

2013

Malhotra, A., Younesi, E., Gurulingappa, H., & Hofmann-Apitius, M. (2013). ‘HypothesisFinder: ’A Strategy for the Detection of Speculative Statements in Scientific Text. PLoS computational biology, 9(7), e1003117.

Shahid, M., Shahzad Cheema, M., Klenner, A., Younesi, E., & Hofmann‐Apitius, M. (2013). SVM based descriptor selection and classification of neurodegenerative disease drugs for pharmacological modeling. Molecular Informatics, 32(3), 241-249.

Younesi, E., & Hofmann-Apitius, M. (2013). A network model of genomic hormone interactions underlying dementia and its translational validation through serendipitous off-target effect. J Transl Med, 11, 177.

Younesi, E., & Hofmann-Apitius, M. (2013). Biomarker-guided translation of brain imaging into disease pathway models. Scientific reports, 3.

2011

Kola, I.; Bell, J (2011). A call to reform the taxonomy of human disease, In: Nature Reviews Drug Discovery 10, 641-642 (September 2011) | doi:10.1038/nrd3534

Articles in magazines

2014

Malhotra, Ashutosh; Younesi, Erfan; Bagewadi, Shweta; Hofmann-Apitius, Martin (2014). Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer’s disease. In: GenomeMedicine
More: genomemedicine.com/content/6/11/97

McHale, D.; Hofmann-Apitius, M. (2014). AETIONOMY: Reclassifying Alzheimer‘s disease to find new drug targets. In: Dementia in Europe, Issue 17.
More: http://www.alzheimer-europe.org/Publications/Dementia-in-Europe-Magazines

Press release 2015

Innovative Medicines Initiative Alzheimer’s disease projects launch joint platform

  • IMI Alzheimer’s disease projects AETIONOMY, EMIF and EPAD have a combined budget of €138 million and jointly address many key challenges for medicines research and development.
  • Collaboration via IMI Alzheimer’s Disease Research Platform will enable faster progress.
  • The platform will have a global reach through a Memorandum of Understanding between IMI and the Global Alzheimer’s Platform (GAP).
  • Announcement comes at 12th International Conference on Alzheimer's and Parkinson's Diseases and Related Neurological Disorders (AD/PD 2015) and in wake of major WHO conference on dementia.

Brussels, Belgium, 19 March 2015 – Today, the Innovative Medicines Initiative (IMI) and its AETIONOMY, EMIF and EPAD projects are proud to announce the creation of the IMI Alzheimer’s Disease Research Platform. The platform will facilitate collaboration between the three projects, helping them to deliver results faster. At the same time, IMI and the Global Alzheimer’s Platform (GAP) are announcing their plans to sign a Memorandum of Understanding to accelerate Alzheimer’s drug development by building a global, standing, trial-ready platform for Alzheimer’s drug development.

The announcements come during a symposium held at the 12th International Conference on Alzheimer's and Parkinson's Diseases and Related Neurological Disorders (AD/PD 2015), and in the wake of a major World Health Organization (WHO) conference on dementia.

Dementia already affects over 35 million people globally, and as populations age, this figure is set to rise to over 115 million by 2050. The disease places a huge and growing burden on health and social care systems and on the families and carers of those affected. Yet despite decades of research, there is still neither treatment nor cure for the disease.

The challenge of developing new, effective treatments for dementia is simply too great for any organisation to tackle alone, and so IMI has launched a number of projects that bring together leading experts from the pharmaceutical industry, universities, small biotechs, and patient organisations from across Europe and beyond. The three projects in the new IMI Alzheimer’s Disease Research Platform have a combined budget of €138 million and address complementary areas of Alzheimer’s disease research.

AETIONOMY is paving the way towards a new approach to the classification of neurodegenerative diseases, particularly Alzheimer’s and Parkinson’s diseases, thereby improving drug development and increasing patients’ chances of receiving a treatment that works for them.

EMIF is developing a common information framework of patient-level data that will link up and facilitate access to diverse medical and research data sources, opening up new avenues of research, particularly in the fields of Alzheimer’s disease and obesity.

EPAD is pioneering a new, more flexible approach to clinical trials of innovative Alzheimer’s disease treatments designed for people who have the disease but have not yet developed dementia.

‘The European Union has a long tradition of fostering research collaboration,’ said Jean Georges, Executive Director of Alzheimer Europe, which is a partner in all three projects. ‘The creation of the IMI Alzheimer’s Disease Research Platform is another great example of European research projects working together to improve our understanding of dementia and to give hope to the 8.4 million Europeans affected by dementia of a cure of the condition in the future. Alzheimer Europe is delighted to support all three projects by representing the views of people with dementia and their carers in the research consortia and by making the research results available to the wider general public.’

The projects are keen to collaborate more closely with other Alzheimer’s research projects around the world. The global reach of the platform will be aided by the signature of a Memorandum of Understanding between IMI and the Global Alzheimer’s Platform (GAP). In addition, international collaboration is already built into the IMI projects. For example, EPAD, EMIF and the Medical Research Council’s Dementias Platform UK are already linked and a number of EPAD partners are directly involved in other initiatives such as GAP.

Irene Norstedt, IMI Acting Executive Director commented: ‘Alzheimer’s disease is a global challenge that requires a global solution, and it is in this spirit that the IMI Alzheimer’s Disease Research Platform is reaching out to other initiatives on Alzheimer’s disease around the world. Everyone working on Alzheimer’s disease needs to pull together if we want to deliver results that will help us to end the suffering caused by this terrible disease.’

Find out more: http://www.imi.europa.eu/content/press-release-imi-ad-platform

Press release 2014

AETIONOMY organizes knowledge about dementia to develop new drugs and therapies

Thursday, February 06, 2014

BRUSSELS. In January the AETIONOMY consortium started a project aiming to develop a new way to classify Alzheimer’s and Parkinson’s disease. The 5-year-project is funded by the Innovative Medicines Initiative (IMI), a joint undertaking between the European Union and the pharmaceutical industry association EFPIA. The new classification will be generated using data derived from a wide range of new biological approaches and will be based on the underlying causes of the disease. Currently, Alzheimer’s disease and Parkinson’s disease are classified by their symptoms and severity but it is clear that this does not represent the many different causes of these diseases. It has been widely recognised that within these broad disease groups there are sub-groups where the different causes result in the symptoms of memory loss or movement disorder.

The AETIONOMY project will involve the collection of all available data including clinical data, imaging and genetic data and will create a new way to combine all the data together to look for patterns which could identify sub-groups of patients with similar causes of their disease. The project will run for the next 5 years and will include a Clinical Study, which aims at a validation of the “mechanism-based taxonomy” generated in the course of the first years.

AETIONOMY is a collaboration of 17 partners across 11 countries and is led by Professor Duncan McHale from UCB Pharma and Professor Martin Hofmann-Apitius from the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI). The collaboration is funded as part of the IMI Taxonomy Call (Call 8) which aims at improving the way we classify diseases to ensure patients get the right drugs and to improve how we find new drugs. The collaboration includes 4 EFPIA Pharmaceutical companies (UCB, Novartis, Sanofi-Aventis and Boehringer Ingelheim), 2 SMEs, 9 Academic institutions and 2 patient advocacy groups.

Project info *
Start date: 01/01/2014, Duration: 5 years, IMI funding: € 8.0 million, EFPIA in-kind: €8.0 million, Other: €1.8 million, Total cost: €17.8 million

The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° [115568], resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.

Find out more: 8th Call for proposals topic text:

(p. 45 onwards)