Biopharmaceutical Consortium Developing Next Generation Bioprocessing Technology Next Generation Biopharmaceutical Process • Continuous Process • Perfusion Cell-Culture / SMB / Continuous • Harvest New Operation Paradigms • PAT / QbD • Contamination Handling • Advanced sensing technology • Batch Process Control Platform Biopharmaceutical Infrastructure • Improvement and optimization • Biosimilar development Providing Scientific and Technology Paths for Biopharmaceutical Industry 1
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Biopharmaceutical Consortium
Developing Next Generation Bioprocessing Technology
Next Generation Biopharmaceutical Process
•Continuous Process
•Perfusion Cell-Culture / SMB / Continuous
•Harvest
New Operation Paradigms
•PAT / QbD
•Contamination Handling
•Advanced sensing technology
•Batch Process Control
Platform Biopharmaceutical Infrastructure
• Improvement and optimization
•Biosimilar development
Providing Scientific and Technology Paths for Biopharmaceutical Industry
1
2
Biopharmaceutical Consortium
Universities (Research/Ed
ucation)
Biopharm Dev. and
Manufacturer
Technology Innovator
and Material Suppliers
Regulator
Biopharmaceutical Consortium - Strategic Expansion of Mass BioManufacturing Center
ERC - NIBST
NSF I/UCRC
University / Industry
Consortium
4th Biopharmaceutical Summit (2015) May 18-22, 2015, Univ. of Massachusetts Lowell
Special Issues in Biopharmaceuticals : Workshop (May 2-22) Advanced Training in Biopharmaceutical : Advanced Training (May 18-29, 2015) Question: [email protected]
3
2014 Engineering Process Analytics (Graduate
Course) Student’s Term Paper Project
4 Slide: Courtesy of 2014 EPA Course
BiOPT - Biologics Optimizer
Raw material characterization
Robust Cell-culture Diagnostics Tools
Robust Predictor of Product Quality Attributes
Downstream Performance Estimator
Design Space Tool
Batch Controller
BiOPT
M1. Raw Material Assessment and Screening
scores on second PC (87.05 %)
-10000 -5000 0 5000 10000
sco
res o
n t
hir
d P
C (
10.7
8 %
)
-4000
-2000
0
2000
4000
Lot #1 Lot #3
Lot #7Lot #6 Lot #4
Lot #5
Lot #2
Lot #8Lot #9
Lot #10
Lot #14
Lot #15
Lot #12
Lot #13
Lot #11
Scores on first PC (72.14%)
-0.010 -0.005 0.000 0.005 0.010
Sc
ore
s o
n s
eco
nd
PC
(1
0.8
9%
)
-0.004
-0.002
0.000
0.002
0.004
Lot #1
Lot #3
Lot #7
Lot #6
Lot #4
Lot #5
Lot #2
Lot #8
Lot #9
Lot #10
Lot #14
Lot #15
Lot #12
Lot #13
Lot #11
Scores on second PC (16.82%)
-8-6-4-202468
Sc
ore
s o
n f
irs
t P
C (
52.3
1%
)
-10
-5
0
5
10
Lot #1
Lot #2
Lot #3
Lot #8
Lot #9
Lot #4
Lot #5
Lot #6
Lot #7
Lot #10
Lot #11
Lot #12
Lot #13
Lot #14Lot #15
scores on second PC (17.08 %)
-4-2024s
co
res o
n f
irs
t P
C (
51.3
8 %
)
-8
-6
-4
-2
0
2
4
6
8
Lot #1
Lot #3
Lot #7
Lot #6Lot #4
Lot #5
Lot #2
Lot #8
Lot #9
Lot #10
Lot #14
Lot #15
Lot #12
Lot #13
Lot #11
scores on second PC (0.33 %)
-0.02 -0.01 0.00 0.01 0.02
sco
res o
n t
hir
d P
C (
0.1
1 %
)
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
Lot #1
Lot #3
Lot #7Lot #6
Lot #4
Lot #5
Lot #2
Lot #8
Lot #9
Lot #10
Lot #14
Lot #15
Lot #12Lot #13
Lot #11
Different spectral measurements
could identify lot-to-lot and
vendor-to-vendor differences,
but their information contents
might be different with each
other, providing complementary
information about the
composition of soy
hydrolysates.
BiOPT
Module 2. Cell-Culture Diagnostics
BiOPT
M3. Product Attribute Predictor
BiOPT
M5. Design Space Exploration
The low risk region is significantly smaller than the corresponding classical sweet spot region
The probability estimation; Presents low risk region in a Sweet Spot type plot
The probability acceptance region = a good estimation of Design
Space
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