Cleaning Validation for the 21st Century.

1. Statistical Evaluation Using Design of
Experiments of Total Organic Carbon Analysis
(TOC) to Combine Cleaning Agent Method with
Drug Product Methods
g
Peter W l h Nik j Vasoya, M i
P t Walsh, Nikunj V
Mariann N
Neverovitch,
it h
and Antonio Fernandez
Analytical and Bioanalytical Development,
BristolBristol-Myers Squibb Co., New Brunswick NJ
19th November, 2013
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2. Outline

Cleaning Background

Current Practice & Project Goal
j

Design of Experiments (DoE)

Data Analysis & Results

Risk Analysis

Conclusion
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3. Why Do We Clean?
y
Cleaning is important
Equipment
Every time we make a batch of Drug
Product we have a chance of
contamination from the previous batch
We avoid this by cleaning in-between
inruns with detergent. Then in order to
verify it’s clean we take a swab and test it
it s
for API/detergent.
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4. What Is Considered Clean?
Cleaned Equipment
The equipment is considered clean if the swab sample is below
its worst case Permitted Residue Limit (PRL). This is calculated
from the contaminant’s Subject Exposure Limit (SEL) provided
by a toxicologist.
Cleaning Agent Limit:
PRL Limit: 13.5 parts per million Carbon (ppmC)
Cleaning Limit: 1.00 ppmC (Alert Cleaning Limit)
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5. Total Organic Carbon Analysis
g
y
The TOC method is nonnonspecific in that it analyzes
how much organic carbon
is i
i in a solution.
l ti
This means ANY and ALL
contamination will be
accounted for in one
analysis.
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6. Current Project And Project Goal
j
j
Blender (Equipment to clean)
Current Practice
Drug
Product
Cleaning
Agent
Example Settings:
• 40 mL
• pH-7
pH• 0.0 µL/min
Drug Product + Cleaning
Agent
Example Settings:
• 20 mL
• pH-2
pH• 2.0 µL/min
Project Goal
Drug Product and Cleaning Agents
can be swabbed separately due to
different optimal solubility
parameters.
Our Goal: To analyze only one sample for
both Drug Product and Cleaning Agent.
To do this we needed to prove that no
variation occurs when changing the
parameters for the Cleaning Agent.
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7. Design of Experiment (DoE)
g
p
(
)
Full Factorial DoE was conducted with 3 factors, 2 levels,
and 5 replicates in the statistical program Minitab
Run
Order
Water
pH
Diluent
Volume (mL)
Oxidizer Flow
Rate (µL/min)
Cleaning Agent
Results (ppmC)
1
2
15
4.8
0.92
2
7
35
0.0
00
1.00
1 00
3
7
35
4.8
0.94
4
7
15
4.8
0.93
5
2
35
4.8
0.92
6
2
15
0.0
1.01
7
2
35
0.0
1.02
8
7
15
0.0
1.01
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8. Main Effects Plots
DoE results for Cleaning Agent
Little to NO
slope!
Very slight
Variation for
Oxidizer
Lowest
Reading
Observed was
0.15 ppmC
less than
theoretical
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9. Interaction Plots
DoE results for Cleaning Agent
- No angles!
- Lines are
almost
p
y
completely
overlapping!
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10. Q
Quantifying Oxidizer Variation
y g
Oxidizer was individually varied for Cleaning Agent
Oxidizer Variation
2.00
TOC (ppmC)
1.75
1.50
1 50
1.25
1.00
0.75
0.50
0.25
0.00
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Oxidizer Flow Rate (µL/min)
 Average Deviation at 4.8 Oxidizer: 0.08 ppmC less than
theoretical
 Worst case error was observed during DoE with 0.15
ppmC l
C less th theoretical
than th
ti l
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11. Risk Analysis
y
Using alert limit, i.e. 1.00 ppmC which means re-clean the equipment if
reTOC is 1 ppmC or higher. Therefore, the worst case scenario would be
pp
higher.
g
,
accepting a sample at 1 ppm when its contamination is actually 1.15
ppmC.
ppmC. However, the actual PRL Limit is 13.5 ppmC.
13. ppmC.
Therefore the
detergent
g
contamination will
ALWAYS be well
within the PRL limit.
NO RISK!
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12. Conclusion
Based on the data gathered we conclude
there is NO significant variation when
changing parameters for both detergents.
detergents
Therefore, we can combine analysis of
Th
f
bi
l i f
Detergent with API.
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