Datathon for Rare Disease Drug Repurposing
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This document provides information about a datathon focused on drug repurposing for rare diseases. It discusses: 1) The goals of the datathon to use data science approaches like machine learning to identify potential drug repurposing opportunities for rare diseases. 2) The partners involved - The Pistoia Alliance, Cures Within Reach, and Mission: Cure. 3) How participants can access data on the Entellect platform and work collaboratively on the datathon challenge to find new treatments for rare diseases.
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Datathon for Rare Disease Drug Repurposing
- 1. Datathon for Drug Repurposing for Rare Diseases 1st October 2018 Dr. Jabe Wilson, Consulting Director, Text and Data Analytics Introduction to the Datathon
- 2. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 3. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 4. Rare Diseases and Repurposing 02.10.2018
- 5. Rare Diseases and Repurposing Chronic pancreatitis • Chronic pancreatitis involves the progressive and permanent destruction of the pancreas, often resulting in exocrine and endocrine insufficiency and chronic pain. The cause is often multifactorial, usually involving structural or genetic abnormalities in children. Meniere's disease • Meniere's disease (i.e., endolymphatic hydrops) is an idiopathic condition of the membranous labyrinth. It is characterized by spontaneous bouts of prolonged vertigo, fluctuating hearing loss and tinnitus. Histologically, the amount of endolymph within the scala media is excessive. The disease primarily affects adults between the ages of 30 and 60 years and is somewhat more common in women. Retinitis Pigmentosa • Retinitis pigmentosa is a group of hereditary disorders characterized by progressive deterioration of vision due to dysfunction, cell loss and eventual atrophy of the retina Night blindness is usually the presenting feature, with gradual deterioration of retinal rods and cones. Obsessive compulsive disorder • Obsessive-compulsive disorder (OCD) is characterized by the presence of either obsessions (uncontrollably recurring and intrusive thoughts) or compulsions (uncontrollably recurring needs to repeat behavior), but commonly both. The symptoms can cause significant functional impairment and/or distress.. 02.10.2018
- 6. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 7. • The Pistoia Alliance: AI/ML Centre of Excellence 02.10.2018 The partners
- 8. • The Pistoia Alliance: AI/ML Centre of Excellence • Cures Within Reach: Supporting Repurposing to Clinical Trials 02.10.2018 The partners
- 9. The partners • The Pistoia Alliance: AI/ML Centre of Excellence • Cures Within Reach: Supporting Repurposing to Clinical Trials • Mission: Cure: A new financing model for curing disease (based on patient outcomes)02.10.2018
- 10. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 11. Why a Datathon - the Datathon approach • Hackathon vs Datathon 02.10.2018
- 12. | 12 Why a Datathon - the Datathon approach
- 13. | 13 Why a Datathon - the Datathon approach Picked relevant pathways (from a collection of 1800 models) Explored functions of proteins using 6.2M pre- text mined relations and embedded Gene Ontology Summarized what is known about CHI mechanism in an overview model
- 14. | 14 Mean of activities among these targets Targets and activities for each compound Drug-likeness metrics for sorting/classification • All compounds that were observed to bind to targets in pathway • Sorted by number of active targets. Too many targets may suggest lack of specificity. Find all targets that could be used to affect the disease state Query for each protein to find compounds that target it (>6 log units) Collate data by compound to summarize the targets/activities related to disease that the compound hits • Compute geometric mean of activities for ranking • Rank by number of targets and geometric mean of activities against targets Step 1 Step 2 Step 3 Why a Datathon - the Datathon approach
- 15. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 16. Data subset available in the Datathon Person Drug Org Assay Action Pathway Disease Implication Trial Adverse Event Target What are the druggable targets in my disease? Species Target: - provenanceName - target - uniprotId - sequence - targetType - label - speciesId - speciesName - geneSymbol Disease: - provenanceName - disease - name Implication: - provenanceName - implication - disease - target - score
- 17. Adverse Event Person Org Which drugs affect this target? Bio- Activity Pathway Disease Bioprocess Trial Disease Species Target Drug Assay Data subset available in the Datathon Substance: - provenanceName - substance - name - compoundType - substanceTypeName - inchiCode - molecularFormula - charge - numberOfAtoms - numberOfComponents - numberOfElements - numberOfFragments - numberOfStructure - molWeightPublishedValue - molWeightPublishedUnit - molWeightStandardValue - mpvalue Bioactivity: - provenanceName - effect - inducedBy - target - targetsCount - bioactivityParameterName - displayValue - publishedValue - publishedUnit - pX Target: - provenanceName - target - uniprotId - sequence - targetType - label - speciesId - speciesName - geneSymbol
- 18. Person Drug Org Assay Bio- Activity Pathway Disease Implication Trial Adverse Event Target What classes of drugs cause this adverse reaction? Species Data subset available in the Datathon Substance: - provenanceName - substance - name - compoundType - substanceTypeName - inchiCode - molecularFormula - charge - numberOfAtoms - numberOfComponents - numberOfElements - numberOfFragments - numberOfStructure - molWeightPublishedValue - molWeightPublishedUnit - molWeightStandardValue - mpvalue Adverse event: - provenanceName - adverseEvent - disease - inducedBy Citation: - provenanceName - citation - pui - publicationShortName - publicationName - publicationYear
- 19. Search & workflow Visualization Predictive analytics Accelerated data-science driven R&D • Chemistry intelligence • Disease intelligence • Safety intelligence • Efficacy • intelligence • Trial intelligence • Drug intelligence • Commercial intelligence Exploratory Analytics Compound & Reaction Trial Post Market Assay -omics Translational Scientific data from internal external sources Ingest & enrich Connect Serve Entellect is a smart and flexible life sciences platform that powers R&D discovery by using Elsevier’s trusted approach towards data integration and harmonization. Entellect delivers connected and AI-ready data by linking and enriching disparate content against established life science taxonomies. Combined with the option of Elsevier data, the result is a scalable knowledge environment, enabling exploratory and predictive analytics applications
- 20. 02.10.2018 “Machine learning won’t work if your data is rigidly siloed.” “One major challenge is collecting enough reliable information to properly train AI systems. AI is as good as the data.” Nick Patience Founder, 451 Research “Organizations need to make sure that the data being accessed is treated and defined consistently across the sources. Otherwise, virtualization won't work.” “All the major AI advances have been fueled by advances in data sets. The algorithms are easy…. "Collecting, classifying and labeling datasets used to train the algorithms is the grunt work that’s difficult” Aspuru-Guzik Professor of Chemistry & Machine Learning, Harvard University Michael Linhares BIS team leader, Pfizer JJ Guy CTO, Jask (AI co.) Access, curation of authoritative life science data Integration of disparate data, structured and unstructured Normalized and standardized data with industry standard taxonomies ‘Siloed’ Lack of standards Requires labeling and contextPoor quality ‘Un-siloed’ Harmonized Enriched and linkedQuality1 2 3 4 Professional Services Build custom and off-the- shelf analytics tools Discovery Pre-clinical Clinical Post Launch Repurposing drugs – semantic data and machine learning
- 21. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 22. Examples of what can be done We are looking for innovative data science approaches: • Semantic data analysis – reasoning with assertions and numeric data • Machine Learning – looking for patterns beyond the assertions 02.10.2018
- 23. Examples of what can be done 02.10.2018
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- 41. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 42. How do you participate? Please go here: https://www.elsevier.com/promo/entellect/datathon • 1. Info page • 2. Register • 3. Get access (credentials will be emailed) 02.10.2018
- 43. How do you participate? 4. Get team together: • join as a team • find through slack discussion • assigned group on 15th October 5. Access via Elsevier Entellect: Data + Environment to run ML + Jupyter notebooks 6. Collaborate on slack Note: As part of the judging process you will be asked to comment on fellow participants solutions, as well as the Elsevier Data Science team, and representatives from the partner organisations
- 44. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 45. Schedule • October 1: Pistoia Alliance webinar and opening of registration • October 9: Datathon launch event in Boston • October 15: Teams to be selected (later registration is possible for teams) • October – November: Progress webinars (TBC) • November 30: Close of Datathon • Early December (TBC): Judging (involving participants in peer review) • End December: Decisions to be made before the end of the year – and taken forward • March 2019: Spring conference in London with awards ceremony and reports on progress 02.10.2018
- 46. Datathon for Drug Repurposing for Rare Diseases • Rare Diseases and Repurposing • The Partners • Why a Datathon - the Datathon approach • The data to be made available • Examples of what can be done • How do you participate – what to expect • Schedule • Take part 02.10.2018
- 47. Take part Please go here: https://www.elsevier.com/promo/entellect/datathon We are looking for teams to: • share openly their experiences as lessons in best practice; • to participate in on going communications around the Datathon (possible publication); • get involved with the next steps in taking these findings to clinical trial! 02.10.2018
- 48. Spring conference award and progress 02.10.2018
- 49. Thank you