The Secret Names Of Things.pdf
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According to Deloitte, approximately 50% of biotech/pharma collaborations will fail, part of what leads to that failure is ambiguity in communication. "Where did this result come from?", "What was the protocol used in this experiment?", "What does this value represent?" At Aspen Biosciences, our Pipeline platform makes it easy for drug discovery companies, CROs, and pharmas to collaborate and share data seamlessly, using a common, industry-standard vocabulary.
The Secret Names Of Things.pdf
- 2. The Secret Names Of Things How FAIR (meta)data can make or break drug discovery
- 3. Agenda About Us FAIR Four Areas Where FAIR Metadata Impacts Drug Discovery Operations Next Steps
- 4. About Us Aspen Biosciences is an international consultancy, founded in 2009. We provide software and services to drug discovery and diagnostics companies: custom software development systems integrations technology roadmaps.
- 7. Design, Make, Test, Analyze Therapeutics Academic & Pharmaceutical Collaborators Investors CROs & Service Providers
- 8. Design, Make, Test, Analyze Therapeutics Risks: Interdisciplinary Teams that share hand-off points in a workflow often dont realize that theyre not speaking the same language. Impacts: Wasted time and effort Increased Expense Project Delays AI/ML Models Based On Faulty Data
- 9. CROs & Service Providers Risks: Differences in the way in which a protocol is run at a customers lab and at a CRO can adversely affect both parties. Impacts: Expensive decisions made based on faulty data. Rework needed. Project delays. Loss of confidence
- 10. Pharmaceutical Collaborators Risks: Collaborations between biotech and pharmaceutical companies often revolve around cutting edge new methods, platform technologies, and modalities. Pharmaceutical companies view the value of these collaborations in terms of learnings and potential earnings. Impacts: Transparency is critical in both in terms of what the data represents and how it was generated. A lack of transparency can lead to rework, a difficult working relationship, or the breakdown of the relationship.
- 11. Pharmaceutical Collaborators half of pharmaceutical collaborations failnot because the science falls short, but because poor alliance management leads to missteps, missed deadlines, lack of trust, and ultimately, collapse of the relationship itself Source: Driving high-performance alliances - pharmaceutical alliance management as a model for best practices - Project Management Institute (PMI)
- 12. Collaboration Trends The number of biotech-pharma collaborations continue to increase year over year. Emerging biopharma companies (EBPs) those with an estimated expenditure on R&D of less than $200 million and less than $500 million in revenue annually are responsible for a record 65% of the molecules in the R&D pipeline, up from less than 50% in 2016 and one-third in 2001 Source: Global Trends in R&D 2022 - IQVIA Source: How biopharmaceutical collaborations are fueling biomedical innovation - Deloitte
- 13. Investors Risks: Investors & potential collaborators need to be able to find and analyze pipeline information in order to identify potential investments. Collaborators need to find potential partners for assets. Impacts: missed opportunities
- 14. Next Steps
- 15. Milestones To The Mountaintop Improved Ontologies For Drug Discovery Improved Tools for Managing Ontologies Standardized & Ubiquitous Protocol Management Standardized Ontology for Pharmaceutical Pipelines Metadata In 3rd Party Tools Metadata Integrated At Every Level of Drug Discovery
- 16. Database Microservices Service Layer User Interface Microservices Service Layer Excel Export Filter Excel Report PowerPoint Report Microservices Service Layer Chart Generator & PPT Filter Metadata Database Entity Layer Integrate Metadata Throughout Your Stack Embed metadata in the user interface so that users understand the results theyre viewing. Embed metadata in the microservices documentation so that integrators understand the services theyre using. Embed metadata in the service layer so that SDK developers understand the methods and parameters theyre using. Embed metadata in the entity layer and database. Embed metadata in reports & presentations, so that the reader (collaborator/CRO) understands the results.
- 17. Pipelines Metadata Based Architecture Metadata Protein Production Registration Assay Management Inventory