Transcript Genzyme Process Data - Discovery, Innovation, Adaptive

Turning Data Into Dollars
John W. Rusher, Eli Lilly & Co.
Robert H. McCafferty, Curvaceous Software
Pharma – IT Summit
March 18th, 2004
Pharma IT Summit
The Benefits of Integrating and
Exploiting Data
John W. Rusher
What is the purpose of
collecting and analyzing data?
 To
detect, interpret, and predict qualitative
and quantitative patterns in data, leading
to information and knowledge.
Now, what’s the real objective?
 Reap
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the benefits of our infrastructure
Improve quality, safety and/or efficiency
Process optimization
• Minimize costs
• Maximize throughput
• Minimize risk

Rapid recovery from abnormal situations
 Ultimately,
to increase revenue and/or
decrease expenses
Examples of Real-World
Benefits
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Reducing production cycle times by using on-, in-,
and/or at-line measurements and controls.
Preventing rejects, scrap, and re-processing.
Improving batch disposition process.
Decreasing time to resolve deviations.
Control system optimization.
Development of process knowledge to improve
efficiency and manage variability.
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Using small-scale equipment (to eliminate certain scale-up
issues) and dedicated manufacturing facilities.
Improving energy and material use and increasing capacity.
I collect a bunch of data, isn’t
that enough?
 Data Does Not Equal Knowledge!
 Data and technology are sometimes confused
with knowledge.
 The computer, database management
software, data warehouses, data marts are
equated with information and knowledge.
 These
are data access vehicles, they are
information and not knowledge.
So What is the Difference Among
Data, Information and
Knowledge
I. Spiegler / Information & Management 40 (2003) 533–539
So, for The Techies Out There:
“The Transformation Algorithm”
 “If
data becomes information when they
are organized to add value, then
information becomes knowledge when it is
analyzed to add insight, abstraction, and
better understanding.” – I. Spiegler
Knowledge
Analyze
Potency = 95%
Information
Organize
Data
O = F(I1, I2, …In)
And, for you “Non-Techies” out
there:
The Restaurant Simile
 ‘‘…data
are the symbols on the menu,
information is the understanding of the
restaurant’s offerings, knowledge is the
dinner. You don’t go to the restaurant to
lick the ink or eat the menu’’ (by Lewis
Perelman).
OK, so it sounds great, but it
can take lots of effort…
 Oceans
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of Data
Disparate Systems
Various ways to access
 Data
May be Spread Across Various
Processes
 Many Tools and Techniques for Data
Analysis
 Competing Business Priorities
Oceans of data, Islands of
knowledge
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In support of manufacturing pharmaceuticals,
large volumes of data are collected to:
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Ensure compliance with cGMP, safety, purity, and
quality standards
Track lot history and genealogy
Improve product quality, process reliability, and overall
production performance
Demonstrate to regulatory agencies that
manufacturing systems are in control and reliable
Worth the Effort
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We invest resources to generate and archive data,
but often fail to maximize the value of these data
because it is stored in various and unrelated
databases
$
Process Data
Lab Data Manufacturing Execution
In-Process Process Automation
Analytics
PFDs and Control Logic
Maintenance
DHRs
History
Deviations Change
Control
Data
Acquisition
Data
Aggregation
Data Integration
35
5-HT 1D 1NP
30
25
Metrics
0
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08/09/99
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05/22/00
07/31/00
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11/02/00
11/27/00
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01/08/01
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02/12/01
02/27/01
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15
10
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Control
Charts
Ad-hoc Tables, Figures,
queries Listings for Reg.
Documents
Technical
Reports
Regulatory
Reports
Access &
Analysis
Why Integrate Disparate Data
Systems?
Accessing
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and organizing data for:
Lot disposition
Production control
Root cause investigation
Process optimization/learning
Without
Integrated data we spend an inordinate amount of
time extracting, collating and reformatting data prior to use.
What types of data are needed
to integrate for effective
analysis?
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Process Data
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Critical Process Parameters (CPPs)
Criteria for Forward Processing (CFPs)
Release Specifications
Analytical results and controls
Deviations
Changes
Materials
Equipment & Maintenance
Analysis Of Data Across the
Supply Chain:
The Lot Genealogy Issue
Source
Lot
RM A
Lot
RM B
Lot
RM C
Lot
Output
Lot
S10
S09
S08
S11
S12
lot A35
lot B1
S14
S13
S14
S15
lot B3
lot B4
lot B5
S18
lot B6
lot B7
lot C710
lot C709
lot AB0182
S17
lot A37
lot A36
lot B2
S16
lot AB0183
lot AB0184
Perfect Separation vs. Ave.
Data
Source
Lot
RM A
Lot
RM B
Lot
S10
S09
Final
Product
Lot
S11
S12
lot A35
lot B1
S14
S13
S14
lot B3
lot B4
lot AB0182
lot CD0533
S16
S17
S18
lot A37
lot B6
lot B5
lot B7
lot C710
lot C709
lot CD0532
S15
lot A36
lot B2
RM C
Lot
Output
Lot
S08
lot AB0183
lot CD0534
lot CD0535
lot AB0184
lot CD0536
The Tools and Techniques for
Analysis Vary Greatly
 Multivariate
Data Acquisition and Analysis
 Process Analyzers or Process Analytical
Chemistry Tools
 Process Monitoring, Control, and End
Points
 Continuous Improvement and Knowledge
Management
Business Needs Should Direct
Analysis
 If
You are in High Market Business with
little inventory – Focus on Capacity
 If Commodity Market and Have Excess
Capacity – Focus on Costs
 If Highly Regulated – Focus on
Documenting Process Understanding
Example for Pharmaceuticals :
FDA Definition of Process
Understanding
A process is generally considered well
understood when
(1) all critical sources of variability are
identified and explained;
(2) variability is managed by the process;
and,
(3) product quality attributes accurately
and reliably predicted
Making the connection
 Exploratory

analysis
It helps to examine the data graphically to see
how and if things really do go together.
 A poorly
done analysis can make bad
results even worse. (Combining apples
and oranges, Garbage in Concentrated =
Garbage out, etc.).
What’s the Payoff? Potential
Areas for Benefits

Increased understanding of manufacturing
processes and variability by providing integrated
access to process and product data.
 Effectively demonstrate manufacturing
processes are stable and capable.
 Efficiently disposition manufactured product
 Other opportunities
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Broad access to data
Auto-generation of key reports
Data sharing and comparison across sites
Understanding the System
Level of Sophistication
Details Resolved
1st
Principles
HIGH
HIGH
MECHANISTIC
MODELS
MEDIUM
EMPIRICAL
MODELS
MEDIUM
HEURISTIC RULES
LOW
(HISTORICAL) DATA DERIVED FROM
TRIAL-N-ERROR EXPERIMENTATION
LOW
The Need and the Opportunity for Improving Efficiency of U.S. Pharmaceutical Manufacturing: …
References and
Acknowledgements
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I. Spiegler, Knowledge management: a new idea or a recycled concept,
Communications of the AIS 3 (14), 2000, pp. 1–24.
Israel Spiegler, Technology and knowledge: bridging a "generating" gap, Information
& Management, Volume 40, Issue 6, July 2003, Pages 533-539.
FDA CDER Draft Guidance Document: PAT — A Framework for Innovative
Pharmaceutical Manufacturing and Quality Assurance, August 2003, Pharmaceutical
cGMPs
The Need and the Opportunity for Improving Efficiency of U.S. Pharmaceutical
Manufacturing: The Need and the Opportunity for Improving Efficiency of U.S.
Pharmaceutical Manufacturing Technology Initiative, Ajaz S. Hussain, Ph.D., Deputy
Director, Office of Pharmaceutical Science, CDER, FDA,
B. McGarvey, Eli Lilly and Company
R. Plapp, Eli Lilly and Company
W. Hendricks, Eli Lilly and Company
Pharma IT Summit
Making Sense of it All…
Rapidly Wringing Information From
Apparently Indiscriminant Piles Of
Numbers
Robert H. McCafferty
Beyond The Third Dimension
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Typical Industry Practice
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Few High Return Processes Fully Understood
Complex Chain/Hierarchy Of Intricate Unit Processes
Brute Force Numerical Analysis Characterization Method Of
Choice
Human Intelligence Relegated To Back Seat
Jungle Of N-Space Impenetrable
New Process Knowledge Latent In Existing Data
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Key To Extraction Engaging Human Mind… Native Curiosity
Eyes Primary Path Of Information Input To Human Brain
N-Dimensional Visualization Breakthrough Technology
3-Dimensional Status Quo Must Be Broken
Unexpected Consequences
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No Hypotheses, Modeling Assumptions Required… Only
Curiosity
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Increased Insight & Understanding - “It Makes Us Ask Better
Questions”
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More Engineering... Better Conclusions, With Less Effort
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Rapid Visual Learning From Existing Process Data
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Discovery Of “Black Holes”
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Parameter Space Voids Where Desired Performance Never Obtained
Significant Issue For Process Control
Almost Generic In Existence
Business Level Benefit
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Knowledge Sharing Mechanism Across Organization
Traditional Visualization
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Time Trend Displays… Effective Limit Six Variables
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X-Y Plots, Contour Plots, 3-D Surface Views… Good For
Up To Six Variables
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Radar Plots… Adequate For Many Variables, But
Visualization Only (popular in Japan)
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Multiple Regression, PLS, PCA, Dimensionless Groups,
Multivariate SPC
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Reduce Dimensions To Allow Visualization (ideally 2-D) For
Lumped Variable/Reduced Parameter Space
Parallel Coordinates
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Substantial Foundation In N-Dimensional Geometry
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Map N-D Into 2-D Through Coordinate Transform
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Allow Direct Data Visualization And Manipulation
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Many Process Variables Simultaneously (30+)
Mathematically Robust… Zero Information Loss
No Derived Quantities (Re, Nu, PC, etc.) Required
True Visualization
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Otherwise Unobservable Phenomena Easily Seen
Readily Explained
A Single 16-Dimensional Point In Parallel
Coordinates
Visual Analysis
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Patterns Formed When Many Points Plotted
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Human Brain Superlative Pattern Recognizer
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Very Good At Seeing “Bigger Picture”
Eyes Better Than Algorithms
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Knowledge Key To Understanding & Resolving
Issues
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Specialized Training No Longer Gate To Solution
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Process Physics
Mathematics
Statistics
Anyone Can Use It…
Perfect Separation Analysis
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Data Rich Environment
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Oddities, Features, Relationships Readily Visible
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Prone To Overstatement… But Excellent Spotter For More
Refined Examination
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Applying Good, Better, Best Criteria Uncovers Patterns
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Very Quick Form Of Analysis
Perfect Separation Overview
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Black Observations @ Top Of X27 Axis Weak Starting Material
Curious Hole In Center Of X30 (Temporal Variable)
Clear Relationship Between X41 And X42
Best Operating Zone
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“Sweet Spot”
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Where To Operate
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Plant
Process Line
Sector Within Line
Individual Piece Of Manufacturing Equipment
How To Keep It There
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Comprehensive Engineering Analysis… One That Can See
Everything
Visibility Across Entire Engineering Organization
Right Tools In Operational Hands
Averaging Approach Analysis
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Designed To Uncover Best Operating Zone
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Based On Detailed Knowledge Of Lot Geneology
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Averaged Contribution Of Pooled Sub-Lots Calculated
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Substantial Compression Of Available Data… But Very
High Quality Information
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Investigation Keyed By Perfect Separation Observations
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Applying Good, Better, Best Criteria Decorates Gradients
& Reveals Sweet Spots
Averaging Analysis Overview
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Covers First Third Of Biosynthetic Insulin Manufacture… 50 Plus Variables
Note Hole In X2, High Limit For Premium Material On X12 (Temporal Vars)
Possible Duality In Biosynthesis Mechanism Given Hole In X15
Pronounced Sweet Spot In X14 (Environmental Variable)
Geometric Model
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Derived From Best Operating Zone Uncovered During Data Analysis
Incorporates Variable Interactions Inherent In Desirable Operating Region
Excellent Vehicle For Response Surface Visualization… Process
Optimization, Inferential Measurement And Control
Lessons Learned
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Leverage Standing IT Investment
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Harvest New Knowledge From Existing Data
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Databases
Network Infrastructure
Engage Complementary Visualization Technology
Analyze Full Span Of Process Data Available
Capitalize On Engineering Knowledge
Effectively Mine Existing Records
Exploit Gains
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Process Optimization
Problem Resolution
Dynamic Control