Genzyme Process Data
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Transcript Genzyme Process Data
Heads Up Analysis Vs High Dimensionality
Problems: Using Data Visualization as an
Analytical Tool In Advanced Solutions Applied to
Manufacturing Data
Gloria Gadea-Lopez, Ph.D., Genzyme Corporation
Robert H. McCafferty, Curvaceous Software
International Forum for Process Analytical Chemistry
January 15, 2004
IFPAC 2004
Application of Parallel Cordinates as
Data Mining Tool at Genzyme
Gloria Gadea-Lopez, Ph.D.
Introduction
Genzyme Corporation is the leading
manufacturer of sterile Sodium Hyaluronate
Unique viscoelastic properties that make it the
ingredient of choice for ophtalmic applications
and post-surgery antiadhesion products
Produced under a proprietary Genzyme
fermentation and purification method that
yields highly purified, exceptional quality
material.
Sodium Hyaluronate (HA)
Produced by Genzyme’s
Advanced Biomaterials since
1984
In its natural form, HA typically
exists as a sodium salt (sodium
hyaluronate), which can form a
highly viscous fluid (viscoelastic)
with exceptional lubricating
qualities.
Plays an important role in a
number of physiological functions
including, cells protection and
lubrication, maintenance of the
structural integrity of tissues,
transport of molecules, and fluid
retention and regulation.
Sodium Hyaluronate - Applications
Genzyme's Seprafilm®, which is used during surgery as an adjunct
intended to reduce the incidence, extent, and severity of post-surgical
adhesion formation in the abdominopelvic cavity.
Genzyme's HA is also being used in commercially available ophthalmic
products.
Established and potential applications for HA include:
Ophthalmology
Soft tissue implants
Wound healing
Viscosupplementation of joints
Bone regeneration
Surface coatings
Moisturizing agents
Adhesion prevention
Cell preservation
Drug delivery
Immunomodulation
Making More and Better HA
Process with a lot of history
Existing legacy of previous facility
Lessons to be applied to new expanded facility
Exercise with Curvaceous Software
Data from 150 batches, 70 variables
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Raw material lot information
Process conditions for each unit operation
Cycle times per batch
Properties of the final product (Quality Control data for
release).
Using Parallel Coordinates - Objectives
Discover “hidden” relationship among process
variables that lead to
Higher yield
Viscoelastic properties in optimum range
Prevent process conditions in ranges that lead to
adverse results
Optimize raw material properties
Work with suppliers to ensure consistent properties of
key raw materials.
Collecting the Data
Information
from diverse sources
Batch records
Raw material information
Final QC (Certificate of Analysis)
Process
history resides in MERLIN,
Genzyme’s custom process database.
Biotech Processes are not “Flat
File” Friendly
Fill/Finish
Purification
Purification
Recovery
BioReactor
LOT301
LOT401
LOT302
LOT402
LOT303
LOT403
LOT201
Final
Product
QC Data
LOT101
LOT202
Lot Pooling
Lot Splitting
In-Process Data
Raw Materials
Effects on
Final
Product
Quality?
Typical Data Analysis
Using
ODBC, link Merlin and JMP (SAS),
Excel, Chart FX, QC Charter
Linear regression models
Process Capability
Multivariate with some key properties
Control Charts (Shewhart)
Need
comprehensive analysis. We know
there is more……
What N-Space Really Looks Like… And
How To Make It Pay In Spades
Robert H. McCafferty, Curvaceous Software
Limited
Beyond The Third Dimension
Typical Industry Practice
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
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
Parallel Coordinates
Substantial Foundation In N-Dimensional Geometry
Map N-D Into 2-D Through Coordinate Transform
Allow Direct Data Visualization And Manipulation
Many Process Variables Simultaneously (30+)
Mathematically Robust… Zero Information Loss
No Derived Quantities (Re, Nu, PC, etc.) Required
True Visualization
Otherwise Unobservable Phenomena Easily Seen
Readily Explained
A Single 16-Dimensional Point In Parallel
Coordinates
Visual Analysis
Patterns Formed When Many Points Plotted
Human Brain Superlative Pattern Recognizer
Very Good At Seeing The Big Picture
Eyes Better Than Algorithms
No Absolute Requirement To Understand
Process Physics
Mathematics
Statistics
Process Knowledge Real Key
Anyone Can Contribute
Applying Good, Better, Best Criteria Uncovers Patterns
Sweet Spots - Concentrations Of Yellow - Appearing
High X14 (Biosynthesis End Criteria) Clearly Best For Product
Nearly All Premium Production From One X3 Level… High X5
Best
Delving Into More Variables...
Curious “Hole” In Two Process Variables
Obvious Bad Range - Poison Zone - For Another
Clear Relationship Between Two Others, High Values Favored
Looking At Final Process Results
Different Modes Of Process Operation Plainly Visible
Clear Sweet Spot Relationships For Key Time Variables
Fair Correlation Between Lab Measurement & Final Rheology
Moving To Dynamic Approach… Best Operating Zone
Selecting Desired Results (X75) Reveals Pattern Of Behavior
Pattern Inherently Incorporates Process Variable Interactions
Process Camera
Pattern Of Process Variable Interactions (Red Lines) Used To
Derive Working Limits (Green Lines)… Exploited For Control
Genzyme Lessons
Leverage Standing IT Investment
Harvest New Knowledge From Existing Data
Databases
Network Infrastructure
Engage Complementary Visualization Technology
Analyze Full Span Of Process Data Available
Capitalize On Engineering Knowledge
Effectively Mine Existing Records
Exploit Gains
Problem Resolution
Response Surface Visualization
Process Optimization
Dynamic Control