9. Why? Six Reasons
1. Potency assays are key in making medicines
2. Bioassays are very variable
3. Statistics will help you understand your data
4. Understanding your data will reveal if control
exists
5. Your level of control allows you to judge RISK
6. Regulators globally require it 9
10. The Regulator & Assay Control
Regulators have been asking for this for years! QbD
1. Pharmaceutical cGMPs for the 21st
Century
2. PAT
3. ICH Q2: Validation of Analytical
Procedures
4. ICH Q8: Pharmaceutical Development
5. ICH Q9: Quality Risk Management
6. ICH Q10: Quality Pharmaceutical
Systems
10
15. Bioassays will always be variable
You can improve it
- by understanding it
- Focusing effort in right places
- This brings control
- You can manage expectations
- This is understood by regulators
15
16. Why assay variation matters?
product variation +
A few unsatisfactory
assay variation + batches may even
inaccuracy pass specification
due to a combination
of assay method and
process variability
Many satisfactory OOS batches likely to fail (potentially costing £Ms)
because of combination of assay method & process inaccuracy & variation
16
17. Our Control Strategy
What does the scientist need to achieve?
Define i.e. selectivity, accuracy, precision linearity
Identify & prioritise analytical CNX parameters
Measure
Control Noise eXperimental
parameters parameters parameters
Analyse
Fix & control e.g., MSA, e.g., DoE
Input into
Precision Regression
Method Method
Improve
Ruggedness Robustness
Method Control Strategy & reduce Risk prior to
Control Validation → Routine Use & Continuous Improvement
17
23. QC Which Tools?
UCL
Stage 4 Technology
QC Tools
CELLULA, Shewhart chart,
YES Transfer
LCL CUSUM
NO
TIME
Stage 1:
Qualification Tool
Stage 3:
Fishbone, Minitab
Validation Tools
Nested, CELLULA
Precision
Stage 2:
Accuracy
Linearity etc.
Development Tools
DX8, JMP, Minitab
Design
24. What’s Appropriate Knowledge?
• Learning takes time
• Will you use it often enough?
• It’s not an academic pursuit
• Activities must add value
do what’s necessary
24
26. Define & Scope
How is the assay performing? Prec/TOL2-sided = 6 x 16.76
100
= 1.01
26
27. Parameters (e.g. 15)
pDNA
NaCl
pH
Tube Length
Time
Seeding Density
Ratio of Transfection
Temperature
Agitation and level
Vector – type, conc
Addition Order
28. Q. How Many parameters?
Q. Which parameters?
Q. What ranges?
A. Existing knowledge
A. Common sense
A. Practical limits
29. Define & Scope
Drill down - map out assay - build understanding & scope
Assay Flow
29
30. Define & Scope
Drill down & map out assay to build understanding & scope
Attention is focused
toward key steps
and the parameters
involved in these
steps
Cause & Effect Diagram (Fishbone) helps think your assay through
Identify & prioritise analytical CNX parameters 30
31. Scope & Screen
Scope ranges with simple experiments
Scoping Experiments
Explore mildest
to most forcing
conditions
31
35. Building Understanding
Factorial Design 2400 2600
1300
900 1800
Estimates effects at
different conditions to
estimate interactions 350 600
250
300 500
Design of Experiments
DOE
35
36. Optimisation
Optimise the parameters that
survived the initial screening
work towards a
Robust Optimum
36
37. Simulations
The tools allow you to simulate scenarios using the data you’ve built up
Visual simulation of expected performance relative to specification
37
39. Validate & Verify
The evaluation of robustness should be considered
during the development phase and should show the
reliability of an analysis with respect to deliberate
variations in method parameters ICH Q2B, 1994
Method stretch…what if?
Ideal Settings
Control Space
Design Space
39
41. Working within the control
boundaries will keep the
assay under control
Even if you go outside
the control boundaries,
the assay will have
enough flexibility to
deal with it without an
OOS
41
42. Summary - Data Driven Development
Scope Screen Optimize Verify QC/TT
Transfer to QC to
validate on batches
& bring into routine
use
Explore mildest Identify few potential Estimate & utilize
to most forcing key parameters interactions to move Rattle the cage to
conditions Focus on vital few & towards optimum deliver a design
narrow ranges conditions space
44. Precision
It may be considered at three levels:
1. Repeatability
2. Intermediate precision
3. Reproducibility
ICH Q2A, 1994
45. Repeatability
1 analyst in 1 laboratory on 1 day injecting 6 times
Summary Statistics
Number of Standard Coefficient Lower 95% CI Upper 95%
Values Mean Deviation of Variation for Mean CI for Mean
t30 PS 6 223.27 6.43 2.88% 216.52 230.02
45
46. Intermediate Precision
• 1 analyst in 1 laboratory on
• 1 day
• injecting 6 samples
• each tested 6 times
As well as sample variation, this study still provides
information on repeatability
46
47. Intermediate Precision
So we compare the mean values for each sample
(over replicate results per sample)
Variance Components
Factor df Variance % Total
Sample 5 27.8535 21%
Repeat 30 102.6361 79%
35 130.4896 100%
Standard
Mean Deviation RSD
47
216.24 11.4232 5.28%
48. and the others…..?
Precision within a laboratory but with
different analysts, on different days, with
different equipment…reflects the real
conditions within one laboratory
ICH Q2A 1995
48
49. Intermediate Precision
Data collect using several analysts using several instruments
over several days:
Y
56000
55500
55000
54500
Peak Area
54000
53500
53000
52500
52000
0 5 10 15 20 25
Sample
49
51. Intermediate Precision
better examined looking at multiple
sources of variation within an assay
want to understand
major sources of
variation such as
sample, prep,
analyst etc.
51
53. Intermediate Precision
Can also perform Unbalanced designs
One operator performs multiple injections on single
preparation;
Two operators perform single injections on multiple
preparations
53
54. Reproducibility
multiple laboratories; typically run as an inter-
laboratory cross-over study, with each participating
lab sending samples to every other lab and
analysing all samples (including own)
…. sent to and analysed by other lab
A B C
Samples from A
laboratory:
B
C
54
55. Reproducibility
Can use analysis of variance (ANOVA) to look for
differences or biases between labs
Alternatively look for “analytical equivalence”
56. Risk Management
The level of effort, formality and documentation..
..should be commensurate with the level of risk
ICH Q9
Evaluation of the risk to quality should be based on
scientific knowledge & ultimately link to the
protection of the patient
Is the bioassay fit for purpose and under control?
56
57. Before & After
How is the assay performing? P/TOL2-sided = 6 x 16.76
100
= 1.01
57
59. Risk Management
Method Understanding, Control and Capability (MUCC)
Understand impact of variation
upon risk…
Risk Understanding?
Capable?
Management
Loop
Statistical
Capability
Process Control
& Precision
(SPC) Charts
Control? 59
61. Risk Management
P/TOL2-sided = 6 x 6.99
100
I-MR Chart of t30 PS
Summary Report
= 0.42
Is the process mean stable? I Chart
Evaluate the % of out-of-control points. Investigate out-of-control points.
0% > 5% 225
UCL=220.77
Yes No
210
0.0%
t30 PS
_
X=199.87
195
Comments
180 LCL=178.96
The process mean is stable. No data points are out of control
1 6 11 16 21 26 31 36 41 46
on the I chart. 61
Observation
62. Summary
1.Build a good basic understanding of
stats but don’t need to become guru
2.Involve a statistician, at least at the
beginning
3.Build understanding of your bioassay
(QbD) – it’s a must
4.Get to grips with Bioassay Variability
62