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Semelhante a DataFAIRy bioassays pilot -- lessons learned and future outlook
Semelhante a DataFAIRy bioassays pilot -- lessons learned and future outlook (20)
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DataFAIRy bioassays pilot -- lessons learned and future outlook
- 1. DataFAIRy bioassays pilot project - lessons learned and future outlook Isabella Feierberg, AstraZeneca Samantha Jeschonek, Collaborative Drug Discovery Nick Lynch, Curlew Research 2021-06-02
- 2. Why DataFAIRy? 2 Substantial investments are being made in AI, ML and FAIR data across life science industry and academia Available metadata in (public domain) data repositories is often insufficient for answering current and future business questions Pharma companies already pay for curation of partially overlapping public domain data (e.g., ChEMBL, papers, chemistry patents) There is a need for FAIR public domain data with high quality annotations using public ontologies and a common data model
- 3. Example Predict kinase selectivity Get all kinase activity/sel ectivity data from ChEMBL, inhouse, collaborato rs Build model Use model Update model with new data 3
- 4. What we want 4 Well annotated data Lots of data High quality data
- 5. Siloed data is not helpful 5 My organization’s data Public data Partner’s data
- 6. The proposed DataFAIRy operational model (2018) 7 Curation and QC by independent domain experts unstructured public data FAIR data DataFAIRy Partners Cost-shared annotation of public domain bioassay descriptions with high quality, using an agreed data model, making data FAIR FAIR = Findable, Accessible, Interoperable, Reusable
- 7. Small molecule bioassays make up a good pilot case 8 Chemogenomic model building Assay development, e.g., assay conditions and tool compounds Enriching public chemogenomics data with FAIR metadata will show impact across the cheminformatics domain Project planning – what is available in the public domain? • • • •
- 8. Roche Project team 9 Rama Balakrishnan Martin Romacker Novartis Anosha Siripala Gabriel Backiananthan BMS Dana Vanderwall AstraZeneca Tim Ikeda Isabella Feierberg Collaborative Drug Discovery Samantha Jeschonek Jason Harris Whitney Smith Pistoia Alliance Vladimir Makarov Thomas Liener
- 9. Feasibility study, guidance for a larger initiative, example creation Pilot project (2020) – Summary 10 Curation of 496 public domain assay descriptions were converted into FAIR information objects using an agreed data model, which was guided by jointly defined business questions. Upload of the metadata to PubChem. Learning points were captured along with recommendations for future endeavors
- 10. Pilot Project - Business questions 11 Biology oriented literature mining for discovery project planning Assay technology oriented Chemistry/tool compound oriented Specific assay conditions Computational chemogenomic modelling (e.g., target activity, ”PAINS”) 1 2 3 4 5 26 initial questions, pruned down to 15, across 5 main categories
- 11. CDD’s BioAssay Express = NLP/ML + Human in the Loop 12
- 12. Pilot Project - Assay selection 13 245 Commercial panel assays: ThermoFisher’s kinase selectivity Z’-lyte panel -Downloaded vendor’s pdf document with assay protocol 42 PubChem NCATS assays – qHTS, large datasets -Assay Description and Assay Protocol sections in plain text on Pubchem page 210 publication assays: ChEMBL assays where the target is EGFR, and the reference is Open Access -Paper/supplementary material, references 1 2 3 100 of these 496 annotated assays were subjected to manual QC by project team members
- 13. Pilot: 100 QC:d assays (~20%) 14 • Learning points are largely extrapolating on the 100 QC:d assays • 89 ChEMBL assays, 5 NCATS assays • 6 ThermoFisher panel assays QC:d
- 14. 15 How well did the pilot assays get annotated?
- 15. Pilot Project – Learnings 16 Review of supplements and citations → High cost. Choose assays wisely. No persistent links exist for commercial assay panel protocols Errors propagate between papers Commercial assay panels were the easiest to annotate (low-hanging fruit) Fully automated is not fully accurate: Benefit from good work practices: audit trail, versioning, iterative QC by experts Need for a common community data standard for future assay publications. 1 2 3 4 5 6 7 Hard and expensive to annotate old assay protocols from literature : A need for published assay protocols to be well-annotated in public databanks and linked to the publication
- 16. Value statement 17 “Richly annotated FAIR bioassay data has been very valuable for an internal data integration project, where it has provided additional terminology aiding the assimilation of the chemogenomics datasets used by the machine-learning models. The extra annotations better harmonise our dataset with those from external partners, enabling the federated platform to provide superior multi-task predictions across range of panels and safety screens in a privacy preserving way” Lewis Mervin, Machine Learning and Cheminformatics Expert, Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca
- 17. Optimize process, data sources, tools, QC within quality constraints. Define quality metrics. Next steps: 18 Define and promote a community standard for assay reporting and publishing --align with vendors, publishers, government agencies. Attract new project members and sufficient funding to start the next phase Scale up (x 10-100) in next steps. Having more partners lowers cost per partner per assay and overhead cost
- 18. 19 Thanks to AstraZeneca Nigel Green David Hayes Tom Plasterer BMS Rick Bishop Janssen Herman van Vlijmen Novartis Fabien Pernot MMV Jeremy Burrows PubChem Evan Bolton ChEMBL Anna Gaulton Andrew Leach Roche Olivier Roche Medicines Discovery Catapult John Overington Mark Davies Pangeadata.ai Vibhor Gupta University of Miami Stephan Schürer BioSci Consulting Scott Wagers Collaborative Drug Discovery Barry Bunin Frank Cole Alex Clark Hande Kücük McGinty (now Univ. Of Ohio) Pistoia Alliance Carmen Nitsche (now at CCDC) Nick Lynch (Now at curlew Research)