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.

1. The Secret
Names Of
Things

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.

5. FAIR (Meta)data
Findable
Accessible
Interoperable
Reusable

6. Risks & Impacts

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 don’t
realize that they’re 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
customer’s 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
fail—not 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 they’re
viewing.
Embed metadata in the microservices
documentation so that integrators
understand the services they’re using.
Embed metadata in the service layer so
that SDK developers understand the
methods and parameters they’re using.
Embed metadata in the entity layer and
database.
Embed metadata in reports &
presentations, so that the reader
(collaborator/CRO) understands the
results.

17. Pipeline’s Metadata Based Architecture
Metadata
Protein Production Registration
Assay Management Inventory

18. Contact Us
info@aspen.bio
https://youtube.com/@aspenbio
@aspenbio
https://www.linkedin.com/company/aspen-biosciences/
https://www.aspen.bio
18
Booth #822