Biomarker Exchange Standards


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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.

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  • Only had a pdf version so need a better paste!
  • Still need to talk to Hans, Ying is working offline to collate Roche Feedback, Sorana from AZ
  • Biomarker Exchange Standards

    1. 1. Pitching the Future: The Pistoia Alliance Project Portfolio Biomarker Exchange StandardsSándor Szalma & Bryn Williams-Jones April 2012
    2. 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 2
    3. 3. What Is It Not? 3
    4. 4. “Research Externalization” - Biomarker Pharma Design in vitro Analyze Select in vivo Report Fully Internal Model Pharma CRO 1 Design SelectPharma 1 Acade mic Academic 1 in vitro assayPharma 2 in vivo CRO Bio assay CRO 2Pharma 3 Data CRO Analyze Report Academic 2 Selectively Integrated Model 4
    5. 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. 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. 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’ 7
    8. 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’ 8
    9. 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. 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 10
    11. 11. Where Do We Start?• Emerging consensus so far… – Just do it… – Pick two assays • RBM-panel & Luminex assay • Immunoassay – Develop use cases 11
    12. 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 12