The AETIONOMY Project: An Overview

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1 The AETIONOMY Project: An Overview Dr. Erfan Younesi Fraunhofer SCAI Project start: January 2014 Project run time: 5 years Project partners: 16 Mission Could you please generate a mechanism-based taxonomy of Alzheimer s and Parkinson s Disease and if you could be so kind to validate that taxonomy and in particular its representation of mechanisms underlying the disease in the course of a prospective clinical trial. The aim of that trial is to demonstrate that this taxonomy is suited to identify patient subgroups (based on disease aetiology); we (EFPIA) hope that your excellent work will support future drug development and lays the foundations for improved identification and treatment of patient subgroups

2 AETIONOMY Partners KI UCL UCB EMC BI, Fraunhofer, LUH, UKB AE, LCSB (UL) ICM, SARD Novartis PHI NeuroRad IDIBAPS Add your logo

3 AETIONOMY Partners & Expertise Bioinformatics, BioSemantics & Systems BioMedicine UCL (debono), Karolinska (Tegner), LCSB (Schneider), EMC (van der Lei), Fraunhofer (Hofmann-Apitius) Clinical Research & Imaging ICM (Brice, Corvol), UKB (Heneka, Wüllner), IDIBAPS (Molinuevo), EMC (Niessen), Karolinska (Svenningsson), NeuroRad (Ples) Pharmaceutical Research UCB (McHale), Sanofi (Canard), Novartis (Strohmaier), Boehringer Ingelheim (Quast), Pharmacoidea (Letoha) Ethical, Legal & Patients Advocacy LUH (Forgó), Alzheimer Europe (George), European Brain Council (Baker)

4 The Underlying Concept

5 Diagnostic/therapeutic challenges in neurodegenerative diseases (NDDs) Neurodegenerative diseases are often misclassified as psychiatric diseases! The majority of patients do not fit the classic psychiatric diagnoses! Lack of suitable biomarkers with sufficient specificity and sensitivity More than 12 phase II/III drug failures

6 Example: dementia is common to many NDDs

7 Overlapping symptoms between dementias Am Fam Physician Apr 1;73(7):

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9 Linking molecular events to clinical readouts Do we understand the molecular mechanism underlying each disease subtype? Symptom-based diagnosis requires complementation by mechanism-based information There is a need to stratify patients based on their molecular signatures.

10 Deterministic factors involved in shaping the disease phenotype Younesi, E., & Hofmann-Apitius, M. (2013). From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine. The EPMA journal, 4(1), 23.

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12 In 2011, Kola and Bell published a remarkable paper in Nature Reviews Drug Discovery. With their Call to reform the taxonomy of human disease they proposed a new, mechanismbased classification of human disease. Kola, I., & Bell, J. (2011). A call to reform the taxonomy of human disease. Nature Reviews Drug Discovery, 10(9), Add your logo

13 The fundamental idea behind Call 8 of the Innovative Medicines Initiative (IMI): Current classification of disease is built upon a hierarchical structure with subdivisions of morbid entities assigned based on consensus criteria. The Classification is grouped into: Epidemic diseases Constitutional or general diseases Local diseases arranged by anatomical site Developmental diseases Injuries The origin of the current classification of diseases dates back to William Farr s work in Add your logo

14 What drove the concept approach behind the projects?

15 The Concept The AETIONOMY concept is based on: Comprehensive and systematic harvesting, re-annotation and curation of all relevant public data Generation of semantic frameworks that support the integration of data through metadata annotation Systematic capturing of relevant knowledge and modelling of disease in dedicated, knowledge-based disease models Identification of testable hypotheses representing putative disease mechanisms through data- and knowledge-driven model validation and mining Validation of a selection of testable hypotheses in the course of a prospective clinical study

16 The AETIONOMY Knowledge Base: Organising Data, Models and Knowledge to make them amenable for modelling and mining Data Cube (the axis model ) Causal Relationship Models Taxonomies (ordering principles; metadata) Analysis workflows / visualisation

17 Mechanisms, measurable features and stratification...

18 Mechanisms, measurable features and stratification...

19 There is not a single coherent data set covering all aspects...

20 The Mining & Validation Strategies Literature Public data In-house data from PD and AD cohorts modelling & mining AETIONOMY Disease Models Model instructed mining AETIONOMY hypothesis I AETIONOMY clinical data Unbiased clustering AETIONOMY hypothesis II testing & validation Biological pre-validation in existing cohorts Clinical validation in a prospective cohort Validation in independent cohorts already recruited

21 What the Consortium brought to AETIONOMY

22 Preparatory Work: Fully Curated Omics-Data Based on our work in BMBF-project NEUROALLIANZ we bring to AETIONOMY: Fully curated and re-annotated human and mouse gene expression data Fully curated mirna data Fully curated genetic variance data Fully curated human brain protein-protein-interaction data Omics-information from the scientific literature (extracted by text mining; e.g. SNPs, gene-disease-associations etc.) Younesi, E., & Hofmann-Apitius, M. (2013). Biomarker-guided translation of brain imaging into disease pathway models. Scientific reports, 3.

23 Clinical Biomaterial Collections and Patient Cohorts - Substantial AD biomaterial collections from IDIBAPS and UKB - Substantial PD biomaterial collections from ICM, KI and UKB - Access to large AD and PD patient cohorts - Access to familial PD cases - Experience in conducting clinical studies - Access to longitudinal imaging studies - Access to EMIF (European Medical Information Framework) and observational clinical data

24 The Final Outcome of AETIONOMY

25 AETIONOMY Expected Outcome A knowledge base comprising curated, re-annotated and well-organised data; knowledge and disease models that will sustain over the next 10 years A semantic framework providing the terminology for annotations, text mining services, knowledge-based disease models and class-labels Dedicated, knowledge-based, highly curated, computable disease models that represent major aspects of AD and PD and that are revised regularly New workflows for regular updating and curation / re-annotation of knowledge-based content New workflows for various mining approaches that will be used to identify testable hypotheses

26 AETIONOMY Expected Outcome A set of testable hypotheses suited for (further) validation of mechanism-based classification systems for AD and PD Strategies for the clinical validation of testable hypotheses Partially validated, mechanism-based taxonomies for AD and PD A concept for sustainable operation of the AETIONOMY knowledge base An imaging biomarker testing scenario involving a routine imaging laboratory (NeuroRad) A biotechnology industry target ID and target validation scenario (Pharmacoidea)

27 etriks curation workflow Current status of AETIONOMY knowledgebase 1. Data stewardship 2. Data QC 3. Semantic alignment 4. Data formatting & upload SCAI Database mysql Direct SQL access, API, or JSON? Knowledge base at UL Postgres

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