Sandor Szalma (Janssen) gives an overview of this potential Pistoia Alliance working group during the "Dragons' Den" session of the Pistoia Alliance Conference in Boston, MA, on April 24, 2012.

1. Pitching the Future: The Pistoia
Alliance Project Portfolio
Biomarker Exchange Standards
Sándor Szalma & Bryn Williams-Jones April 2012
http://pistoiaalliance.org

2. Rapidly Evolving Pharmaceutical Ecosystem
Proprietary
content
Public provider
content
provider
Big Life CRO Patient
Science organization
Company Pharma
Regulatory
authorities
Academic
group
Service provider
Software vendor
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3. What Is It Not?
3

4. “Research Externalization” - Biomarker
Pharma
Design in vitro Analyze Select in vivo Report
Fully Internal Model
Pharma
CRO 1
Design Select
Pharma 1
Acade
mic
Academic 1 in vitro assay
Pharma 2
in vivo
CRO
Bio
assay
CRO 2
Pharma 3
Data
CRO
Analyze Report
Academic 2
Selectively Integrated Model
4

5. Some Quotes and Distilled Messages
• ‘Capturing data isn’t a problem, getting rich
annotation and curation is’
• ‘this is different to capturing numbers to populate a
prescriptive spec for a clinical data system’
• ‘data generators really need to keep in mind the
statistical limitations of assay types and formats; and
how their data will be used’
• ‘Big Pharma stand to gain more from consistent
standards than the complexity of competing and
complex custom requirements’
• ‘Critical problem is mismatch of mechanistic biology to
clinical observation’
5

6. Complexity
• A significant proportion of the business of CROs is around
biomarkers
– Define a definition for biomarker that holds water
– Customers don‟t always know whether they can
technically/logistically/practically measure what they think is a
biomarker
• Multiplexing
– Biomarker panels/fingerprints
• Very large data integration and consistency issues
• Statistical modelling problems in populations
• Ensuring rich clinical data is captured to allow nuanced questioning
– Different units for different assays, different limits for
different technologies
• Immunoassays in general need very careful handling, and controlled
interpretation
• Clinical chemistry is usually „easier‟
– Each additional marker in a panel brings complications 6

7. The hidden cost of „biomarker‟ research
• Pharma companies commission lots of studies
– Big pharma usually specify own data standards
– CROs or service labs generate data
– Many iterations required to format, exchange and integrate data into
clinical data/biomarker repositories
– Smaller labs struggle to provide data to bespoke templates
• Customer and provider are impacted by lack of data standards
– Significant operational challenges for both in ‘getting the right data the right way’
• ROI – estimate 10% of CRO costs are in data format „massage‟
– Big pharma custom templates are wasteful
– Formatting errors introduce cycles of troubleshooting
– ‘CROs and Customers end up doing lots of unnecessary work’
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8. Connectivity – Outside World
• CDISC and other are working in the clinical biomarker
standards domain – much more on outcomes
• FDA/PhUSE in tox
• Various disease area (eg Alzheimers) or Tox (eg renal)
consortia are developing prognostic/diagnostic markers
• IMI disease and biomarker programmes
• Many companies are watching other initiatives, but
none seem to be in this early data space
• RECOMMENDATION
– Focus on data interchange standards is welcome and doesn‟t
directly overlap with other activities
– ‘something that goes beyond lots of handling in Excel’
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9. Connectivity – Inside Pistoia
• Vocabularies, dictionaries and ontologies
– Bringing the clinical and preclinical world
together to tackle translational vocabs would
have a big impact on the development and
implementation of biomarker standards
9

10. Bottom Line
• Pistoia Biomarker Standard should:
– Focus on molecular data interchange as an ontological and
data standard
• AVOID qualification/validation/disease linkage
– Develop rules around assay data integration and define
how different endpoints are handled
– Develop rules for exclusion of data points
• some put more emphasis on this than inclusion
• Handle limitations of diverse technologies and assay types
– Allow integration of rich data into Oracle Clinical and
other clinical/biomarker databases
– Explicitly reduce data handling cycles between provider
and customer
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11. Where Do We Start?
• Emerging consensus so far…
– Just do it…
– Pick two assays
• RBM-panel & Luminex assay
• Immunoassay
– Develop use cases
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12. Contributing Members / Organizations
• Janssen R&D
– Sándor Szalma, Hans Winkler
• Connected Discovery Ltd
– Bryn Williams-Jones
• BMS
– Al Wang
• ICON
– Andy Brown
• Daiichi Sankyo
– Jim McGurk
• Molecular Connections
– Usha
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