The document describes an optimal design of experiments (DOE) approach for cell culture media optimization. It discusses screening significant factors using a custom design with fewer experiments than a conventional approach. It then presents a case study where an optimal approach identified interactions and found the media optimization pathway with fewer experiments compared to conventional methods.

1. An efficient Design of Experiment (DOE) approach
to cell culture upstream media optimization
Mayank Garg
Manager-MSAT
Biologics Development Center
Dr. Reddy’s Laboratories Ltd.
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GE Bioprocess Symposium
Bangalore

2. • Prerequisites
• Rational DOE approach
• Conventional vs Optimal
• Stepwise evaluation of approach
• Case study
Overview
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3. Well defined target
Navigator
Appropriate tools
Prerequisites
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4. Conventional approach
Plackett Burman Fl/Fr factorialSteepest movement RSM
Custom Design Augmentation Steepest movement RSM
Optimal approach
DOE Approach
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5. Plackett Burman
(FrF)
Fl/Fr factorialSteepest movement RSM
Custom Design
(FrF) Augmentation Steepest movement RSM
All factors
Point of max
response
All significant
factors
Insignificant factors
Sig. factors
w/o interaction
Sig. factors
With interactions
All sig. factors
+ interactions
Point of max
response
Optima
Optima
Insignificant factors
DOE Approach
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6. DOE Approach
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• Expectations from a good Design
 Number of Experiments to be low
 Design efficiency to be high
 Average variance of prediction to be low
 Relative variance of coefficient to be low
 Power of design to be high
• Expectations from results to rely interpretation
 Model should fit with:
 High significance ( low p value: <0.05)
 High correlation (R2 and adjusted R2)
 No lack of fit

7. Screening: Sig. Factors
Conventional approach
25%
Plackett Burman (Fr Fct)
11F, 2L (12 runs)
Fixed matrix and number of run
Custom design (Fr Fct)
11F, 2L (16 runs)
Custom matrix and number of runs
Optimal approach
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8. 294%
24%
Conventional approach Optimal approach
Screening: Sig. Factors
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9. Path to optima: How far we are ???
• Fit first order
• No lack of fit
•Lack of fit for first order
i≤j
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Factor
Response
•Far from optima
•Near Optima
•Fit for second order
•No lack of fit

10. Path to optima: Steepest movement
Steepest movement
Post screening
• Not efficient when applied to:
• Main effects (if) having
• Interaction
• Curvature
Steepest movement
Post Interaction identification
• Very efficient when applied to :
• Only main effects having
• No interaction
• No curvature
Conventional approach Optimal approach
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11. Full factorial: 4F, 2L (16 runs) Augmentation: 4F, 2L (8 runs)
3%
11%
6%
Path to optima: Sig. factors + Interactions
Conventional approach Optimal approach
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30%

12. Full factorial: 4F, 2L (16 runs) Augmentation: 4F, 2L (8 runs)
12%
33%
Conventional approach Optimal approach
Path to optima: Sig. factors + Interactions
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13. All Main effects
• Not efficient
• Significantly high no. of Experiments
Main effect having interaction
(Post steep movement of main
effects, having no interactions)
• Very efficient
• Low number of experiments
Optimization: Response Surface
Conventional approach Optimal approach
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14. • Objective : To improve productivity
• Product : A Mab
• Cell line : CHO
• Strategy : Feed composition optimization
Bolus feed (serum free chemically defined)
11 components (grouped and individual)
• Approach : DOE
• Scale : 4 x 3, 500 ml bench top reactors
Introduction Case study
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15. X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 Y
-1 -1 -1 1 -1 1 -1 1 -1 -1 -1
-1 -1 -1 1 1 1 1 -1 -1 1 -1
0 0 0 0 0 0 0 0 0 0 0
1 -1 1 1 1 -1 -1 -1 1 -1 -1
0 0 0 0 0 0 0 0 0 0 0
1 1 -1 1 -1 1 1 -1 1 -1 1
1 -1 1 -1 -1 1 1 1 -1 -1 1
1 1 1 -1 -1 1 -1 -1 -1 1 -1
-1 -1 1 -1 1 1 -1 1 1 1 1
1 -1 -1 -1 1 -1 -1 -1 -1 -1 1
1 1 -1 -1 1 -1 1 1 -1 1 -1
-1 1 1 1 -1 -1 -1 -1 -1 1 1
-1 1 1 1 1 -1 1 1 -1 -1 1
-1 1 -1 -1 -1 -1 -1 1 1 -1 -1
1 -1 1 1 -1 -1 1 1 1 1 -1
-1 -1 -1 -1 -1 -1 1 -1 1 1 1
1 1 -1 1 1 1 -1 1 1 1 1
0 0 0 0 0 0 0 0 0 0 0
-1 1 1 -1 1 1 1 -1 1 -1 -1
Screening: Sig. Factors Case study
• Factors : 11
• Levels : 2 (-1,+1)
• Response : Yield
• Design : Custom
(D optimal)
• Center points : 3
• No. of exp : 19
Experimental Design
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X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 Y
-1 -1 -1 1 -1 1 -1 1 -1 -1 -1 1.182
-1 -1 -1 1 1 1 1 -1 -1 1 -1 1.227
0 0 0 0 0 0 0 0 0 0 0 1.000
1 -1 1 1 1 -1 -1 -1 1 -1 -1 0.727
0 0 0 0 0 0 0 0 0 0 0 0.955
1 1 -1 1 -1 1 1 -1 1 -1 1 1.091
1 -1 1 -1 -1 1 1 1 -1 -1 1 0.909
1 1 1 -1 -1 1 -1 -1 -1 1 -1 0.773
-1 -1 1 -1 1 1 -1 1 1 1 1 1.000
1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 0.909
1 1 -1 -1 1 -1 1 1 -1 1 -1 0.591
-1 1 1 1 -1 -1 -1 -1 -1 1 1 1.182
-1 1 1 1 1 -1 1 1 -1 -1 1 1.136
-1 1 -1 -1 -1 -1 -1 1 1 -1 -1 0.682
1 -1 1 1 -1 -1 1 1 1 1 -1 0.545
-1 -1 -1 -1 -1 -1 1 -1 1 1 1 0.864
1 1 -1 1 1 1 -1 1 1 1 1 1.273
0 0 0 0 0 0 0 0 0 0 0 1.091
-1 1 1 -1 1 1 1 -1 1 -1 -1 1.091

16. Fitted model analysis
Screening: Sig. Factors Case study
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17. Parameter estimate analysis
Screening: Sig. Factors Case study
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18. X1 X4 X6 X11 Y
-1 1 1 -1 1.182
-1 1 1 -1 1.227
0 0 0 0 1.000
1 1 -1 -1 0.727
0 0 0 0 0.955
1 1 1 1 1.091
1 -1 1 1 0.909
1 -1 1 -1 0.773
-1 -1 1 1 1.000
1 -1 -1 1 0.909
1 -1 -1 -1 0.591
-1 1 -1 1 1.182
-1 1 -1 1 1.136
-1 -1 -1 -1 0.682
1 1 -1 -1 0.545
-1 -1 -1 1 0.864
1 1 1 1 1.273
0 0 0 0 1.091
-1 -1 1 -1 1.091
-1 1 1 1
1 1 -1 1
-1 1 -1 -1
1 1 1 -1
1 -1 1 -1
-1 -1 -1 -1
1 -1 -1 1
-1 -1 1 1
• Factors : 4
• Levels : 2 (-1,+1)
• Response : Yield
• Design : Augmentation: Custom
(D optimal)
• No. of exp : 8
Experimental Design
Path to optima: Sig. Factors + Interactions Case study
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X1 X4 X6 X11 Y
-1 1 1 -1 1.182
-1 1 1 -1 1.227
0 0 0 0 1.000
1 1 -1 -1 0.727
0 0 0 0 0.955
1 1 1 1 1.091
1 -1 1 1 0.909
1 -1 1 -1 0.773
-1 -1 1 1 1.000
1 -1 -1 1 0.909
1 -1 -1 -1 0.591
-1 1 -1 1 1.182
-1 1 -1 1 1.136
-1 -1 -1 -1 0.682
1 1 -1 -1 0.545
-1 -1 -1 1 0.864
1 1 1 1 1.273
0 0 0 0 1.091
-1 -1 1 -1 1.091
-1 1 1 1 1.364
1 1 -1 1 1.136
-1 1 -1 -1 1.182
1 1 1 -1 0.727
1 -1 1 -1 0.455
-1 -1 -1 -1 0.909
1 -1 -1 1 0.909
-1 -1 1 1 1.091

19. Fitted model analysis
Path to optima: Sig. Factors + Interactions Case study
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20. Parameter estimate analysis
Significant terms
X1, X4, X6, X11
X1*X11
Path to optima: Sig. Factors + Interactions Case study
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21. Model re-fitting for significant terms: Stepwise regression
Path to optima: Steepest movement Case study
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22. Model re-fitting for significant terms: Stepwise regression
Path to optima: Steepest movement Case study
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23. Y = 0.963– 0.119 X1 + 0.108 X4 + 0.059 X6 + 0.116 X11 + 0.085 X1*X11
ΔX4 = 0.50 unit
ΔX6 = (0.059/0.108)*0.5 = 0.273 unit
Steps
Coded
X4
Coded
X6
Respons
e
Origin 0 0 1.000
Δ 0.500 0.273 ---
Origin + 2Δ 1.000 0.546 1.120
Origin + 4Δ 2.000 1.092 1.317
Origin + 6Δ 3.000 1.638 1.474
Origin + 8Δ 4.000 2.184 1.561
Origin + 9Δ 4.500 2.457 1.415
Origin + 7Δ 3.500 1.911 1.588
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Response
Origin + n delta
Re-fitting model equation
Path to optima: Steepest movement Case study
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24. Pattern X1 X11 Y
00 0 0
00 0 0
+− 1 -1
00 0 0
−+ -1 1
0A 0 1.41421
a0 -1.41421 0
00 0 0
0a 0 -1.41421
A0 1.41421 0
00 0 0
−− -1 -1
++ 1 1
• Factors : 2
• Levels : 2 (-1,+1)
• Response : Yield
• Design : RSM
• Axial points : (a, A)
• Center points : 6
• No. of exp : 13
Experimental Design
Optimization: Response Surface Case study
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Pattern X1 X11 Y
00 0 0 1.473
00 0 0 1.418
+− 1 -1 0.891
00 0 0 1.455
−+ -1 1 1.000
0A 0 1.41421 1.345
a0 -1.41421 0 1.091
00 0 0 1.473
0a 0 -1.41421 0.964
A0 1.41421 0 1.218
00 0 0 1.509
−− -1 -1 1.182
++ 1 1 1.527

25. Fitted model analysis
Optimization: Response Surface Case study
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26. Parameter estimate analysis
Optimization: Response Surface Case study
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27. Contour plot
Optimization: Response Surface Case study
Surface plot
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28. Results Case study
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 48 96 144 192 240 288 336 384
NomalizedIVCC
Age (hours)
Before Feed optimization
After feed optimization
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0.0
0.5
1.0
1.5
2.0
2.5
Before Feed optimization After Feed optimization

29. Summary and Conclusions
• Set Target
• Define Strategy
• Select Appropriate Statistical Tools
• Rationalize Approach
• Evaluate Design At Every Step
• Scrupulously Execute And Accurately Analyze
• Interpret data coalescing Statistical & Scientific knowledge
• Verify and confirm resutls
• Enjoy
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30. 3-4 March 2011 30 of 31Mayank Garg, DRL India

31. 3-4 March 2011 31 of 31Mayank Garg, DRL India
Thank You