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

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etwo

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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

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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

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