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Semelhante a Data-driven drug discovery for rare diseases - Tales from the trenches (CINF 20, ACS National Meeting 2019-03-31)
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Data-driven drug discovery for rare diseases - Tales from the trenches (CINF 20, ACS National Meeting 2019-03-31)
- 1. CINF20 - 31 March 2019 Dr Frederik van den Broek, Elsevier Professional Services Data-driven drug discovery for rare diseases Tales from the trenches
- 2. This is what we are all after in drug discovery… Image: Elsevier
- 3. If drug discovery and development only were that simple… Disease Drug compound
- 4. If drug discovery and development only were that simple… Disease Protein Target Drug compound
- 5. If drug discovery and development only were that simple… Disease Protein Target Drug compound • Cell processes • Regulators • Pathways • … • Bioactivity • Toxicity • Specificity • …
- 6. If drug discovery and development only were that simple… Disease Protein Target Drug compound • Cell processes • Regulators • Pathways • … • Bioactivity • Toxicity • Specificity • … • Availability • Synthesis • PK/PD • … • Genotype • Phenotype • Individual
- 7. If drug discovery and development only were that simple… Disease Protein Target Drug compound • Cell processes • Regulators • Pathways • … • Bioactivity • Toxicity • Specificity • … • Availability • Synthesis • PK/PD • … • Genotype • Phenotype • Individual
- 8. This makes it all a lengthy and costly process Image: https://www.phrma.org/graphic/the-biopharmaceutical-research-and-development-process
- 9. With rare diseases it is even harder Small(er) patient populations leading to • Less (integral) medical and scientific knowledge • Small population for clinical trials • Unawareness with doctors, researchers, policymakers • Smaller potential market size for a drug Image: http://www.campingtourist.com/camping-activities/climbing/difficult-mountains-climb/
- 10. Drug repurposing: a new hope for rare diseases • Less costly and of interest for pharma • Quicker to Phase II/III tests, so hopefully quicker to market • Need reliable information from various sources to find suitable repurposing candidates Image: https://www.starwars.com/news/poll-what-is-the-best-scene-in-star-wars-a-new-hope
- 11. Accelerate with new knowledge and data Disease Protein Target Drug compound • Cell processes • Regulators • Pathways • … • Bioactivity • Toxicity • Specificity • … • Availability • Synthesis • PK/PD • … • Genotype • Phenotype • Individual
- 12. Various initiatives we were recently involved in • Project with Findacure to find drug repurposing candidates for Congenital Hyperinsulinism • Pistoia Hackaton: Elsevier-Findacure challenge on Friedrich’s Ataxia • Sub-network enrichment analysis for neuromuscular disorder pathways • Disease pathway analysis for Huntingdon's Disease • Pistoia Datathon for drug repurposing for rare diseases
- 13. | 13 • A rare genetic disease • Permanently excessive level of insulin in the blood • Develops within the first few days of life • Can lead to brain injury or even death • In the most severe cases the only viable treatment is the removal of the pancreas, consigning the patient to a lifetime of diabetes Congenital hyperinsulinsm (CHI) https://res.cloudinary.com/indiegogo-media-prod- cld/image/upload/c_limit,w_620/v1440424745/uzvnq zhvbpsrtthzxqpu.jpg
- 14. Creating a comprehensive view of CHI • CHI Literature Library • Disease, Target, Pathway, and Compound Analysis • Research Landscape Analysis Information Assets Applied • Content Elsevier’s vast set of literature and patent data • Data normalization Taxonomies and dictionaries to normalize author names, institutions, drugs, targets, and other important terms • Information extraction Finding semantic relationships, targets, pathways, drugs, and bioactivities
- 15. Building and refining the CHI disease model 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
- 16. From pathways to CHI treatments: Automated analysis combines bioassay data with pathway data Mean of activities among these targets Me 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 target to find compounds that have high affinity for them (>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
- 17. Pistoia Hackathon Challenge (2017) Elsevier would like you to demonstrate the ability of deep learning to help Findacure, a UK-based charity, accelerate treatment and clinical research for Friedreich’s ataxia (FRDA). You’ll have access to a heterogeneous set of data related to the disease: biological pathway analysis, associated chemical compounds and bioactivities, potential candidates for drug re-purposing, full- text scientific literature and clinical trial data. Basically, giving others a go with the data sets we worked with on CHI….
- 18. Promising results, but still hard work “We spent most of our time the first day just trying to get our heads around the data, so we could start to find some solutions. Even opening the files was tricky.” The students used various tools to try to extract data from the provided XML files, but it was slow going. Daniel [one of the participants] commented that, “we wound up having to do a lot of things manually, so we could at least read the files in plain text.”
- 19. Sharing disease pathways • Shared curated pathways (with supporting literature references) with rare disease organisations to help their discussions with researchers and fill in potential “blanks” • Comparing gene expression algorithms for the identification of expression regulators • Well-defined datasets, with supporting literature references which resonate with researchers
- 20. Datathon (2019): Applying AI in Drug Repurposing for Rare Diseases
- 21. “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 JJ Guy CTO, Jask (AI co.) ‘Siloed’ Lack of standards Requires labeling and contextPoor quality1 2 3 4 Using the Entellect Platform and Data Curation
- 22. Access, curation of authoritative life science data Integration of disparate data, structured and unstructured Normalized and standardized data with industry standard taxonomies Build custom and off-the- shelf analytics tools ‘Un-siloed’ Harmonized Enriched and linkedQuality Nick Patience Founder, 451 Research Aspuru-Guzik Professor of Chemistry & Machine Learning, Harvard University 1 2 3 4 Using the Entellect Platform and Data Curation
- 23. Adverse Event Person Org Which drugs affect this target? Bio- Activity Pathway Disease Bioprocess Trial Disease Species Target Drug Assay 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 Entellect Platform and Data Curation
- 24. Various teams using various approaches • Semantic data: Target Identification • Semantic data: Small Molecule Binding • Machine Learning − Ensemble Learning − Mol2Vec, Prot2Vec − Network diffusion • Expert collaboration − Virtual docking − Adverse Event profiling “I could work on the important stuff straight away, using all the data”
- 25. Promising results so far (March 2019)
- 26. Aiming to make data-driven drug discovery for rare diseases a little easier… Disease Protein Target Drug compound • Cell processes • Regulators • Pathways • … • Bioactivity • Toxicity • Specificity • … • Availability • Synthesis • PK/PD • … • Genotype • Phenotype • Individual
- 27. Conclusions • Data, data, data… • Data has to be FAIR and of good and trusted provenance as the researchers and clinicians will want to see the “chain of evidence” (beware of black box models) • Data sets also have to be FAIR for each other: enabling the integral approaches repurposing needs have to be linked data sets across siloes and domains to go from disease to target to compound (and back) Image: Sangya Pundir, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=53414062
- 28. Acknowledgements • Maria Shkrob • Jabe Wilson • Anton Yuryev • Matthew Clark • Christy Wilson • Finlay Maclean • Elsevier’s Entellect team • Pistioia hackaton and datathon teams
- 29. Questions? By Malis - https://commons.wikimedia.org/w/index.php?curid=2633354
- 30. Appendix – Datathon approaches