Work Package 6.1 ‘Pharmainformatics’

1. Work Package 6.1‘Pharmainformatics’

2. WP 6.1 Pharmainformatics Overall Needs Disease Understanding Biochemical/Genetic Context Drug/Ligand Characterisation Adverse Effect Deconvolution

3. WP 6.1 Pharmainformatics Information Continuum Pathology Pathway Target Ligand Ontologies Evolution

4. WP6.1 Coordination Meeting - 1 • June 3-4 Barcelona • IMIM, AZ, Erasmus, ISCIII • Three example disease areas • Interferon pathways in inflammation • Nuclear Hormone Receptors • Complex Regional Pain Syndrome

5. Thiazolidenediones & PPARg Nuclear Hormone Receptors Several compounds marketed and in development for the treatment of dyslipidemias associated with type II diabetes mellitus Clear patterns of adverse events associated with this therapy. Result of Target or Off-Target Effects?

6. Why NHRs? • One of the largest groups of transcription factors • 49 known • They regulate a broad spectrum of processes, including reproduction, development and general metabolism • Tissue distribution of co-factors varies and different ligands induce NHR binding with different co-factors = tissue-specific therapeutics • Receptor cross-talk & Structural similarity • Need for pathway mapping and annotation tools

7. Nuclear Hormone Receptors Primary Question: Can we associate clinical adverse events from PPARg agonists to activities on any of the other nuclear hormone receptors?

8. Ligand library Match against 1º and 2º targets Identify secondary target pathways

9. Secondary target pathways • Reproduction problems • Liver problems • Cardiac problems Adverse events in humans

10. Specific Activities: NHRs Pathology Pathway Target Ligand • Ligand to Target: IMIM and AZ identify relevant ligands and privileged structures. AZ to identify current PPARg agonists and communicate this list to Erasmus. • Target to Pathway: AZ, using the tool PathwayAssist and the target information from the previous step, identifies all pathways related to the targets. Erasmus use Collexis to differentiate or expand the pathways. • Pathway/Target to Genetic Variation and Clinical Adverse Events: Erasmus and AZ link individual members of the identified pathway to specific phenotypic information, i.e., phenotypes of genetic variants and deficiencies etc. Erasmus and AZ investigate whether any of the phenotypes identified from genetic variability are similar to the adverse effect patterns observed in PPARg agonist patients.

11. Complex Regional Pain Syndrome(CRPS) Anke GJ De Bruijn

12. Complex Regional Pain Syndrome(CRPS) Anke GJ De Bruijn

13. Complex Regional Pain Syndrome(CRPS) Anke GJ De Bruijn

14. Complex Regional Pain Syndrome(CRPS) Anke GJ De Bruijn

15. Complex Regional Pain Syndrome(CRPS) Anke GJ De Bruijn

16. CRPS • 72 different names: • Sudeck’s atrophy • Posttraumatic dystrophy • Sympathetic reflex dystrophy • Shoulder-hand syndrome • Veldman criteria, IASP criteria, Bruehl criteria

17. Epidemiology • 1-2% following fracture • 7-35% following Colles’ fracture • 10-26% spontaneous • Other causes: MI, CVA, reperfusion syndrome • 8000 estimated new patients per year in NL • Female : Male ≈ 3:1

18. Complex Regional Pain Syndrome(CRPS) • Significant patient population • Several potential biochemical/physiological lesions • Several drug classes tried in the clinic • Large patient datasets (Trend Project & IPCI Database)

19. Complex Regional Pain Syndrome(CRPS) Primary Question: Can we clarify possible pathological biochemical pathways and their constituent members against which we can then identify ligands that could be useful in understanding/treating CRPS?

20. Complex Regional Pain Syndrome Pathology Pathway Target Ligand • Pathology to Pathway: Erasmus identifies all known pathways related to CRPS through literature mining, and share the information with AZ. • Pathway to Target: The known pathways are associated or expanded by Erasmus and AZ. Both obvious and non-obvious interactions will be investigated. Erasmus will use the knowledge retrieval tool Collexis and AZ the pathway analysis tool PathwayAssist for this purpose. • Target to Ligands/Approved Drugs:AZ and IMIM identify approved drugs or ligands that are active against members of the associated or expanded pathways,with the help of compound/ligand databases. • Target/Pathway to new Target/Pathway: Erasmus verifies in original population database if the found approved drugs are being used, what their indications are, whether it is possible to use them in CRPS or whether patients on these drugs have an altered incidence of CRPS.

21. Timings and Effort Distribution

22. WP 6.1 Timelines

23. WP 6.1 Effort Distribution

24. Activities: project start-present • AZ: Pathway tool evaluation (4 PM) • 34 Databases & 15 Analysis tools • NHR pathway catalog • IMIM: Chemogenomic tool development (4 PM) • Chemical nomenclature system • Development/validation of biochemoinformatics tools • Erasmus: Pathology context – CRPS (1PM)

25. INFOBIOMED NoE Activity code and title: WP 6.1 Indicate the expected involvement of partners in the table below, describing role and estimated effort, expressed in person-months. *: Code as follows: L - Activity Leader. W - Works. I - Provides input. R - Reviews. O - Other (please specify).

26. Technology Issues

27. Early Issues: NHRs • Can we link Chemogenomics tools with pathway tools? • Can we effectively populate a pathway tool – which one and who needs it? • Adverse events – do we need a standard vocabulary? • Are there other sources of data from patients taking PPAR compounds, e.g. national patient registers or clinical study populations? • What are the standard data sources and how can they be accessed and merged? • Genotypes???

28. Early Issues: CRPS • How should a patient be described, e.g. using time-dependent observations as discrete units or the patient’s history as a discrete unit? • Are there existing drug/target databases for all members of identified pathways? • Pathway databases need integration, redundancy checks, and conflict resolutions • Are the pathways ill defined – will data mining be required? • Which is the best way to combine clinical/literature data sources? • Genotypes??

29. Preliminary thoughts on what WP6.1 needs from WP 4 & 5: BMI • WP4: Data interoperability & management • - data storage: preferred format for structural data for both molecules and proteins (pdb,mol2,sdf,xml?) • - data storage: preferred value for pharmacological data for molecules (Ki,pKi,IC50,pIC50,all?) • - integrating approaches: standards on annotation of pathways for the mapping to targets and diseases • - integrating approaches: linking GO to phenotype data • WP5: Methods, Technologies & Tools • - existing methods for the high-throughput systematic construction of protein models • - list of existing publicly accessible databases of protein models • - existing tools on molecule2target2pathway2disease mapping and access to such information continuum

30. Preliminary thoughts on what WP6.1 needs from WP 4 & 5 BMI • WP4 Data: • clinical data: • -how are they represented in the database (classification/free text/lab results/etc) . • - how to query clinical data with specific symptom/disease questions • - definition of patient history (see general details on CRPS in draft proposal) • - how to translate questions to employed clinical classification systems textual data: • - how are diseases described in natural language text • - available thesauri for diseases, drugs, chemicals, molecules, genes/proteins, genetic data: • - cross references of database IDs There is often not a 1-to-1 mapping • - merging or splitting of IDs, how to deal with them • WP5 Methods: • - interfacing different kind of databases • - use of text mining techniques • - mapping of terminologies (part of granularity mentioned above). • - pathway analys tools

31. WP 6.1: Participants (thusfar) Johan van der Lei Marc Weeber Anke GJ De Bruijn Jordi Mestres Lulla Opatowski Scott Boyer Kristina Hettne