Discovery to Commercialization of a Drug: The IT Holy Grail and Enabler of the Supply Chain
description
Transcript of Discovery to Commercialization of a Drug: The IT Holy Grail and Enabler of the Supply Chain
BIOTECH SUPPLYBIOTECH SUPPLYBIOTECH SUPPLYBIOTECH SUPPLYOctober 8-9, 2012
Crowne Plaza, Foster City, CA
Discovery to Commercialization of a Drug: The IT Holy Grail and Enabler
of the Supply ChainDavid Wiggin, Program Director,
Healthcare and Life Sciences, Teradata
Great innovations & discoveries have been the result of
• Accidents– Penicillin – Sir Alexander Fleming, 1928
• Persistence / hard work / brute force– Light bulb – Thomas Edison, 1879
• A brilliant mind– Theory of Relativity – Einstein, 1915
We’re intrigued by the notion of ‘the next big thing’!
One from recent memory…
• The year was 1989• The field was electrochemistry• The discovery was almost as good as world
peace - an abundant, safe source of energy!• …Cold Fusion
…but it wasfiction!
Today
• We’re not here to talk about the discoveries themselves
• I’d like to propose that we think about the largest untapped resource at your organization; you have it in great abundance and it holds the answers to the next big thing
• The paradox is that it’s everywhere, but we are all powerless to use it
• The ‘it’ here is data
• The next great discovery from your organization will be the result of analyzing data
A thought experiment…what if
• You could capture all the data from your enterprise, a project cradle to grave (early research projects, research, development, clinical trials through post-market analysis)
• Keep it, regardless of the kind of data (Mass Spec, genomics, machine data, web data,…)
• Integrate it (tie it together) so it’s ready for analysis
• Access/analyze it using the most powerful analytics tools
• On a platform that is flexible, fast, scalable & affordable
Hype Cycle for Life Sciences
For example, Biotech Manufacturing Process Analytics
Supply Demand
Manufacturing Process
Fermentation Process Purification Process Finishing Process
Viral Inactivation
Cell Expan-sion
Cell Cul-ture
CellSeparation/ Ultra Filtration
Blast Freeze
Thaw Chromatographic Columns
Ultra Filtration/ Diafiltration
Blast Freeze
Thaw/ Bulk
Fill and Freeze-Dry
Finishing
Media Prep Buffer Prep
Quality Engineering
Engineering
Finance/Accounting
R1 R2 R3 R4 R 6R5 R 7 R 8 R 9 R 10 R 11 R 12 R 13 R 14
Pro
cure
me
nt
Dem
and
Driven
Su
pp
ly N
etwo
rk
Base Phase SAP MM SAP PP SAP QM ITS SAP FICO
Tech Ops SAP MM SAP WM SAP PP SAP QM ITS MES
Supply Chain SAP MM SAP WM SAP PP APO SCM Data Swch UCB
Engineering SAP MM SAP WM SAP PP SAP PM EDMS DCS
Quality Unit SAP MM SAP WM SAP QM ITS LIMS LIRs
Procurement SAP MM SAP FICO
Finance/Accounting SAP MM SAP FICO
Supply Demand
Manufacturing Process
Fermentation Process Purification Process Finishing Process
Viral Inactivation
Cell Expan-sion
Cell Cul-ture
CellSeparation/ Ultra Filtration
Blast Freeze
Thaw Chromatographic Columns
Ultra Filtration/ Diafiltration
Blast Freeze
Thaw/ Bulk
Fill and Freeze-Dry
Finishing
Media Prep Buffer Prep
Quality Engineering
Engineering
Finance/Accounting
R1 R2 R3 R4 R 6R5 R 7 R 8 R 9 R 10 R 11 R 12 R 13 R 14
Pro
cure
me
nt
Dem
and
Driven
Su
pp
ly N
etwo
rk
Data Sources for Biotech Manufacturing Process Analytics
Output
HEORIntegratedRepository
Pattern AnalysisCluster AnalysisText Analysis
Health Economics & Outcomes ResearchIntegrated Discovery and Intelligence Environment
Strategic and Operational Intelligence
ResearchNetworks
Data Aggregators
Input
RWE Data
Employer Data
Practice Data
Rx Data
Claims Data
HIE
EMR Data
Payer Data
Clinical Data
Partners
R&D
Brand Teams
ManagedMarkets
End Users
LAB
LAB
LAB
LAB
LAB
Capture, Store,Refine
• “To Succeed with Big Data, Start Small” Bill Franks– Select simple analytics that won’t take much time or data
to run– Capture data in ‘one-off’ fashion– Limit data volume, e.g. 1 month data instead of 5 years– A successful prototype paves the way for investing in larger
effort
• Start with a sketch, not a full blueprint
• Choose technologies that can grow with you and help you deliver results
How to get started
BONUS: Proteomics using MPP database to greatly improve protein identification
04/21/23 11
Benefits of Streaming Mass Spec data to MPP platform
• Speeding overall data processing time• Improving the selection of proteins by peak
matching over a broader range of scans• Provision of full traceability of identified
proteins to the data that formed the m/z peak• Facilitates rapid cross-experiment analysis on
a common repository of trace information, built as a by-product of the analysis
04/21/23 12