Bio g - ManufacturingIintelligence webinar GBX

© Bioproduction Group | www.bio-g.com
Manufacturing Intelligence in the Life Sciences
5/30/2013
May 2013
Rick Johnston, Ph.D.
Principal, Bioproduction Group
Professor Lee Schruben University of California at Berkeley

Bioproduction Group
Founded in 2007 with an exclusive focus on Biomanufacturing Operations Primary goal of improving Quality, Productivity, Flexibility and Operations in Biomanufacturing World Class “real time” data collection, modeling, and simulation software Technology Assisted Knowledge Generation Tool Specifically designed for the unique needs of Analysts Improving Quality, Productivity, Flexibility and Operations at the World’s Largest Biomanufacturers

Biotech’s current challenge: Information
5/30/2013
3
WAVE OF INVENTIONS REQUIRED FOR RECOMBINANT PROTEIN PRODUCTION
(from Croughan, Plenary Presentation at NSF ERC Meeting, 2004, published in Biotech. Bioeng.,95:220-225, 2006 and reproduced with permission)
1975 1980 1985 1990 1995 2000 Molecular Cell biology Protein Biochemical Mechanical Manufacturing Biology and biochemistry engineering engineering operations and Microbiology and information automation systems

Operations improvements drive the future of biopharmaceutical manufacturing
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4
Key focus for biopharmaceutical companies*
IT Spending by biopharmaceutical companies**
Molecular Biology
Cell biology and microbiology
Protein biochemistry
Biochemical engineering
Mechanical engineering
Manufacturing operations / IT
Cost cutting / standardization
1980
1985
1990
1995
2000
2005
2010
Yearly IT spend ($B)
* NSF ERC Meeting, 2004, published in Biotech. Bioeng.,95:220-225, 2006. ** Frost & Sullivan , 2009
$8.3B
2015
$11.0B
The critical area for biopharmaceutical companies is operational efficiencies and cost cutting, driven by biogenerics
15% / year growth

Why is data so important in biotech manufacturing?
1.Regulatory: A complex regulatory framework requiring high rates of data capture and accuracy
2.Manufacturing Duration: Cycle time for batches is long (end-to-end cycle time can be half a year or longer for mAbs)
3.Variability: Significant “inherent biological variability” that cannot be engineered out of the process
4.Risk Adverse: Very high customer service requirements “No customer goes without”
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5
Biotechs must therefore manage significant variability, while also shielding the effects of that variability from their customers.

Variability in Unit Operations
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Variability in Unit Operation processing times
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 More
Standardized Processing Time (hours)
Percent of values
Company A
Company B
11 hours (+ 100%):
~10% of values above this
Median
processing time
~5.5 hours
7.2 hours (+30%):
~35% of values above
this
* Indicative times only, not from any specific single biotech manufacturer. Comparative durations for times
retained, X and Y scales normalized. Processing step includes inline testing. Rejected batches removed.

Continuous Time Data:
Instantaneous WFI consumption
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WFI Demand During Rituxan 3.5 rpw 2/09/09 to 3/(PI Data: tank level drop plus distillate flow sums)
0
200
400
600
800
1000
1200
1400
0
24
48
72
96
120
145
169
193
217
241
265
289
313
337
361
385
409
434
458
482
506
530
554
578
602
626
650
674
698
723
LPM
WFI consumption (L/min)

Non-stationary processes (i.e. process drift)
5/30/2013
Most modern biomanufacturing data exhibits both significant variability and significant process drift.
CIP Times, 2002 - 2008
Hours

Variability in One Area Cascades Through other Manufacturing Areas
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9
Downstream
“Biopharmaceutical supply chains are unique in high levels of variability in drug discovery, final demand, production output, quality testing, regulations and certification requirements” (1)
(1) Shah N (2004) Pharmaceutical supply chains: key issues and strategies for optimization. Computers and Chemical Engineering 28: 929-941

Focus is on “robust” processes and manufacturing systems
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10
“managing the variance in a manufacturing system may be more important to an organization’s financial performance than managing averages.”*
* Christiansen, Germain, Birou (2007), Variance vs. average: supply chain lead-time as a predictor of financial performance, Supply Chain Management: An International Journal, v12: 5, pp. 349-357
Sam Savage, “The Flaw of Averages”
1.A quantitative understanding of risk can help teams understand its impact and how to address it
2.Managing for the “average” case produces the wrong answers
3.Data can help us understand variability and its impact

Big Data
Data Mining
INFORMATION
Risk Modeling
DECISIONS
Dynamic Models
(Big Noise)
Data Refining
Manufacturing Intelligence – An Overview

“Reporting”
Analytics
What is Manufacturing Intelligence?
5/30/2013 12
Data Historians, Dashboards,
Multivariate Analysis,
Regression etc.
“Monitoring”
Analytics
Real-time control systems,
process optimization, “big
data”, SPC / SQC, etc.
“Predictive”
Analytics
Scheduling, Debottlenecking,
Process and Supply Chain
Optimization, Optimal recovery
REPORTING
What happened?
ANALYSIS
Why did it happen?
MONITORING / CONTROL
What’s happening now?
PREDICTION
What might happen?
The past Now The Future

The more ‘predictive power’, the more valuable to the business.
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REPORTING What happened?
ANALYSIS Why did it happen?
MONITORING / CONTROL What’s happening now?
PREDICTION What might happen?
SIMULATION What is likely to happen?
COMPLEXITY
BUSINESS VALUE
HIGH
LOW
LOW
HIGH

Examples: 1. Distributions / Process Evolution plots
•Provide a simple method of tabulating historical data
•Doesn’t focus on inter-dependencies
•Does allow us limited insight to root cause analysis
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REPORTING What happened?
ANALYSIS Why did it happen?

Examples: 2. Statistical Process Control
•Allow us to understand whether a process is drifting “out of control”
•Provide process capability (a measure of our manufacturing capability to remain within tolerances)
•Broadly supported by ICH Q10 guidelines
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MONITORING / CONTROL What’s happening now?

Examples: 3. Robust Process Simulators
•A sandbox to analyze a manufacturing process that allows “cheap” experimentation
•Based on Monte Carlo (long range planning) or Discrete Event Simulations (manufacturing systems)
5/30/2013
16
Cumulative effect of variability
Each line represents a possible “future state” of the world
Simulation of drug substance produced (kg) by month
SIMULATION What is likely to happen?

Production process data are not independent or identically distributed as assumed in many statistical models.
• Serial Dependencies e.g. yield drift
• Mixtures in the data e.g. hard (repair) and soft (calibration) maintenance
• Cross Dependencies e.g. resource contention and concurrency
• Non-stationarity e.g. shift effects and campaign changeover
Operations Data vs. Laboratory Data

Equipment is subject to unplanned down-time. Data fits identical Time to Repair (TTR) distributions.
IID TTR (T Histogram)
T
0
25
50
75
100
125
150
0
29
58
87
116
145
174
203
232
261
290
319
348
377
406
435
464
493
522
551
580
Count
Count
90% Soft/Hard TTR (T Histogram)
T
0
24
48
72
96
120
144
1
31
61
91
121
151
181
211
241
271
301
331
361
391
421
451
481
511
541
571
601
For example: maintenance

But these identically distributed TTR times are not
independent (mixtures) which has a huge KPI impact
Work in Process (WIP) plots – estimates are off by 10X
iid TTR (WIP vs. Time)
Time
0
50
100
150
200
250
0 10000 20000 30000 40000 50000
WIP
aveW=4.9
90% SOFT/HARD TTR (WIP vs. Time)
Time
0
50
100
150
200
250
0 20000 40000 60000 80000 100000
WIP
aveW=44.3
Independent TTR Dependent TTR
For example: maintenance

© Emerson Process Management | www.syncadesuite.com
Confirmation Activities
Dynamics and Non-stationarity

Scatter Plot Q1 vs Q2
Data Paths

Connecting the dots….

State Dependent Queue Queue
Interarrival Time
Data paths

Law textbook example (frist Q in red)
Data paths

1st and 4th Queue “flight paths” over time
Data paths

Data paths

Local Optimization – Hawthorne Effect
Source: M. Pidd

Case Study: Improving Throughput at a bio manufacturer with no capital investment
•Facility using process historians (to obtain data)
•Built process model including constraints in the facility
–Pro-A / MabSelect, CEX Chromatography, Viral Filtration, CEX Chromatography, UF/DF
–Included Media / Buffer Prep and transfer, HTST, CIPs and SIPs, product and vessel clean expiration, WFI contraints, all tanks and chrom skids, etc.
•Goal: to allow more than one batch into downstream at a time
•Secondary Goal: to evaluate a ‘streamlined’ process which would do several steps in-line “semi-continuous” for a new CMO product
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29

What’s an overlap analysis?
• Maximize the number of batches in downstream
• Improves throughput
5/30/2013 30
Pro-A CEX Viral AEX UFDF Freeze / Storage
Buffer Prep
Other supporting
feeds (WFI,
NaOH, steam)
One batch every 8 days

What’s an overlap analysis?
• Maximize the number of batches in downstream
• Improves throughput
5/30/2013 31
Pro-A CEX Viral AEX UFDF Freeze / Storage
Buffer Prep
Other supporting
feeds (WFI,
NaOH, steam)
One batch every 5 days

0
5
10
15
20
25
30
35
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
% of Time
Days Between Batches
Current Baseline Run-Rate is 16 Days*
* Exact details redacted
95th Pctile: 16 days between batches
3/18/2013
32
With the current configuration, the maximum baseline run-rate is 16 days

0
5
10
15
20
25
30
8-8.5
8.5-9
9-9.5
9.5-10
10-10.5
10.5-11
11-11.5
11.5-12
12-12.5
12.5-13
% of Time
Run-Rate after fixing shared equipment (WFI and NaOH)*
95th Pctile: 12.5 days between batches
3/18/2013
33
With Hydroxide and WFI constraints removed, the model is running at 12.5 days between batches.

0
5
10
15
20
25
30
7.5-7.8
7.8-8.1
8.1-8.4
8.4-8.7
8.7-9
9-9.3
9.3-9.6
9.6-9.9
9.9-10.2
10.2-10.5
% of Time
Run-Rate after fixing shared buffer tank*
95th Pctile: 10 days between batches
3/18/2013
34
With Hydroxide and WFI constraints removed, and fixing one additional issue, the facility can run at a 10 day run rate.

How did this measure up to other models?*
•Bio-G repeated the same problem, but using SME estimates + SuperPro (no variability)
•We then compared this result against the actual changes once implemented in the facility
5/30/2013
35
Item
SME Estimate
Simulated
Actual
Baseline
“14 days”
16 days
16 days
“Chrom Skid 1” to 12.8 days
WFI / NaOH to 12.5 days
12.0 days
“Buffer Prep tank A” to 11.6 days
“UFDF operation” to 10.5 days
Shared buffer tank to 10 days
9.5 days

•Production Data is messy
–Dependency and non-stationarity can have a greater impact than distribution shape.
•Hawthorne Effect – Local optimization
•Static Monte Carlo can tell you about the past
•Dynamic Models can tell you about likely futures.
•Sensitivity Analysis with Dynamic Models can identify the critical few important data relationships
Review

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