Guidance for Industry: Q7A Good Manufacturing Practice Guidance for Active Pharmaceutical Ingredients

1. Cleaning Validation - A Lifecycle
Approach
Ned Wyman
Principal Scientist, MS&T/Validation
AstraZeneca Operations - Supply Biologics
20 Aug 2015

2. 2
Objectives
Cleaning Validation Overview
Process Validation Lifecycle
Cleaning Validation Life Cycle – Stage 1
Small-scale studies
Biotech specific studies
Cleaning Validation Life Cycle – Stage 2
The initial cleaning validation acceptance criteria
Biotech approach
Cleaning Validation Life Cycle – Stage 3
Statistics and cleaning validation
Trending of cleaning validation data
Summary

3. 3
The AstraZeneca Biologics
Frederick Manufacturing Center
Manufacturing
Facility (~350,000 ft 2)
ISPE’s Facility of
the Year - 2011

4. 4
Cleaning Validation Overview
Guidance for Industry: Q7A Good Manufacturing Practice
Guidance for Active Pharmaceutical Ingredients (Aug 2001)
• Cleaning procedures should normally be validated.
• Validation of cleaning procedures should reflect actual equipment
usage patterns.
• The cleaning validation protocol should describe the equipment to be
cleaned, procedures, materials, sampling methods, acceptable cleaning
levels, parameters to be monitored and controlled, and analytical
methods.
• Equipment cleaning/sanitation studies should address microbiological
and endotoxin contamination for those processes where there is a need
to reduce total microbiological count or endotoxins.

5. 5
Cleaning is a Process
Cleaning validation plays an important role in reducing the
possibility of product contamination from pharmaceutical
manufacturing equipment.
It demonstrates that the cleaning process adequately and
consistently removes product residues, process residues and
environmental contaminants from the manufacturing
equipment / system, so that this equipment / system can be
safely used for the manufacture of specific subsequent
products (which may be the same or a different product).
PDA Technical Report No. 29:
Points to Consider for Cleaning Validation (2012)

6. 6
Process Validation Life Cycle
• Stage 1 – Process Design: The commercial manufacturing process is
defined during this stage based on knowledge gained through
development and scale-up activities.
- Demonstrate a Understanding of the Process
• Stage 2 – Process Performance Qualification (PPQ) or traditional
Process Validation: During this stage, the process design is
confirmed as being capable of reproducible commercial
manufacturing.
- Demonstrate Process Robustness at Commercial-scale
• Stage 3 – Continued Process Verification (CPV): Assuring that
during routine production the process remains in a state of control.
- Demonstrate Understanding of Process Variability at Commercial-Scale
FDA Guidance for Industry:
Process Validation: General Principles and Practices (2011)

7. Stage 1: Cleaning Process Parameters:
• Remember TACT:
 Time
 Action
 Concentration
 Temperature
• Varying the cleaning process attributes directly impacts
cleaning effectiveness
• Varying these attributes directly impacts cleaning effectiveness.
7

8. Stage 1: Equipment Related:
8
• Materials of construction are identified
for all pieces of equipment.
• Equipment is designed/built with proper
slopes to allow for free draining.
• Spray coverage tests are performed for
all systems/equipment with spray
devices installed.
• Identify areas that are difficult to reach
or hard to clean.
Varying these attributes directly impacts cleaning
effectiveness.

9. Stage 1: Product Related:
• Cleaning solutions are selected based
upon the chemical and physical
properties of the in-process materials,
buffers and actives to ensure adequate
removal of the residuals.
• Analytical methods used to test
cleaning samples are validated.
• Product specific small-scale studies
are performed to ensure the cleaning
process will adequately remove
process related residues as well as
demonstrate adequate swab recovery.
9

10. Stage 1: Biotech Inactivation Approach
• Demonstrate that active ingredient is pharmacologically
inactive as a result of the cleaning process:
• Extreme pH (<2 and >13)
• Temperature (60 - 80°C)
• Known degradation/denaturation/inactivation
• Demonstrate that the inactivated product has been
removed below a predefined acceptance limit.
• Consistent with the expectation that the carryover of an extrinsic impurity
into a subsequent batch should be justified from the standpoint of the
safety and efficacy of the product.
• Removes the need to develop product specific immunoassays for cleaning
validation
10

11. Guidance for Designing Denaturation Studies
• Design bench scale experiments to simulate full scale
conditions
• Conditions should be the least conducive (worst-case)
for inactivation
• Simplify the bench scale studies:
• If product inactivation increases with shear rate, then it can be
eliminated
• Ratio of cleaning solution to process soil to be reduced
• Acid wash and rinse steps can be eliminated to minimize
dilution of the process soil
• Include a neutralization step to demonstrate that the
denaturation is not reversible
• Ensure modifications do not result in experimental artifacts
11

12. Analytical Methods for Denaturation Studies
• Methods should
• Evaluate fragmentation and inactivation of the API at bench scale
• Detect impurities in the cleaning validation sample
• Additional Method Considerations:
• Sodium Dodecyl Sulfate Polyacrylamide gel electrophoresis (SDS-PAGE)
- 4 to 20% gradient corresponds to a molecular weight range of 4 – 250 kDa,
sufficient for most biological APIs.
• Capillary Electrophoresis (CE)
- Greater sensitivity, lower variability
- Higher throughput capability
• SDS-PAGE and CE
- Adequate for demonstrating distinct, size-based separation of protein fragments
• Size Exclusion High Pressure Liquid Chromatography (SE-HPLC)
- Can also be used for size based separation
- Difficult to obtain a distinct size based separation across a wide range of
fragment sizes.
12

13. Product Denaturation Study Trace
13

14. Cleaning Validation Life Cycle – Stage 2
Equipment Related:
• Performed on actual commercial-scale equipment / system.
• Acceptance criteria initially set by equipment capability
• Matrix / Family approach to optimize the number of trials.
Product Related:
• Assess biochemical properties of the product and process
intermediates.
• Verify that the acceptance criteria demonstrate adequate removal.
Cleaning Parameters:
• Verify that the TACT parameters are consistent
•
14

15. 15
Dirty Equipment Removal of most
of product
Removal of any
remaining residual
Cleaning agents/
residuals Removed
Initial water rinse
(cold)
Cleaning agents
Final WFI rinse
(hot)
Swab worst-case
locations
Cleanliness
verified
Stage 1: Cleaning Process: Rinse, Clean,
Rinse, Verify

16. Stage 2: Typical approaches to establish Product
Specific Cleaning Validation Acceptance Criteria
Product Specific approaches:
• 0.1% (1/1000th reduction)
• Health Based Limits:
• LD50
• NOAEL
• ADE/PDE
• TTC
• Minimum therapeutic dose
16

17. Stage 2: Cleaning Limit Determination
17
Clean Equipment
Last rinse = HWFI
Swab worst-case
locations for
residual carbon
Assume residual is
uniformly
distributed over
total surface area
< Final TOC Action
Limit

18. Stage 2: Cleaning Limit Determination
18
Clean Equipment
Last rinse = HWFI
Swab worst-case
locations for
residual carbon
Assume residual is
uniformly
distributed over
total surface area
< Final TOC Action
Limit
TOC AC ≤ X ppm x
Volume of Desorption Solution (mL)
x
1µg/mL
Surface Area Swabbed (cm2) ppm

19. Calculation of a Permitted Daily Exposure (PDE)
19
PDE =
NOAEL x Weight Adjustment
F1 x F2 x F3 x F4 x F5
EMA: Guideline on setting health based exposure limits for
use in risk identification in the manufacture of different
medicinal products in shared facilities (20 Nov 2014)
F1: A factor (2-12) to account for extrapolation between species
F2: A factor of 10 to account for variability between individuals
F3: A factor of 1-10 to account for toxicity studies of short duration
F4: A factor (1-10) that may be applied in cases of severe toxicity
F5: A variable factor that may be applied if the no-effect level was
not established.

20. Typical approach to establish Multi-Product
Cleaning Validation Acceptance Criteria
A common approach to determine the cleaning validation acceptance
criteria has been the Maximum Allowable Carryover (MAC).
20

21. Typical approach to establish Multi-Product
Cleaning Validation Acceptance Criteria
A common approach to determine the cleaning validation acceptance
criteria has been the Maximum Allowable Carryover (MAC).
The MAC calculation is as follows:
Where:
PDE a = Permitted Daily Exposure for Product A
Volume b = Volume of first B batch (or minimum Batch Size of Product B)
Dosage b = Maximum dose of Product B
SSA e = Surface area of shared equipment
21
MAC =
PDE a x Volume b
Dosage b x SSA e

22. PDE/MAC Approaches from a Biotech
Perspective
• Both PDE and MAC based upon the assumption that the
product remains active after cleaning.
• In Biotech, API is typically denatured during the cleaning process
• Calculated acceptance limits are often below the limit of
quantitation (LOQ) of non-specific methods
• Large Surface Areas, small batch sizes and low concentrations in
Biotech while using non-product specific testing (e.g., TOC) to
determine PDE and MAC, the AC could be below the LOQ for TOC
• LOQ for TOC is typically between 0.05 and 0.2ppm
• Use of product specific immunoassays (PSIA)
• Recognizes epitopes; however, epitopes are known to be
denatured by the cleaning process. Therefore, results can be
misleading.
22

23. Biologics Cleaning criteria approach is different
due to denaturation of the active material.
23
EMA “Guideline on setting health based exposure limits for use in risk
identification in the manufacture of different medicinal products in shared
facilities” (released 20-Nov-14) states:
5.3 Therapeutic macromolecules and peptides
Therapeutic macromolecules and peptides are known to degrade and denature
when exposed to pH extremes and/or heat, and may become pharmacologically
inactive. The cleaning of biopharmaceutical manufacturing equipment is typically
performed under conditions which expose equipment surfaces to pH extremes
and/or heat, which would lead to the degradation and inactivation of protein-based
products. In view of this, the determination of health based exposure limits using
PDE limits of the active and intact product may not be required.
EudraLex, Volume 4, “EU Guidelines for GMP for Medicinal Products for Human
and Veterinary Use, Annex 15: Qualification and Validation” (released 30-Mar-15)
states:
10.6.1 Therapeutic macromolecules and peptides are known to degrade and denature
when exposed to pH extremes and/or heat, and may become pharmacologically
inactive. A toxicological evaluation may therefore not be applicable in these
circumstances.

24. Biologics approaches to establish Cleaning
Validation Acceptance Criteria is different
Biopharmaceutical approaches:
• Proteins are degraded/denatured/inactivated by cleaning solutions at
elevated temperatures
• As each facility has unique characteristics and products
manufactured, variables to consider at each facility are also unique.
• These approaches are not intended to be inclusive of all acceptable
methods to determine cleaning limits
• Examples of alternative approaches that are scientifically based:
• WFI Limits
• Safety Factor
• Toxicology Threshold
24

25. Cleaning AC: WFI Specs
• Equipment can not be cleaner than the last solution to contact their
surfaces.
• The last solution of the cleaning process is Purified Water (typically
WFI).
• Purified Water contains an allowable and known/monitored amount of
organic carbon (≤ 500 ppb TOC ).
Maximum Surface Residual TOC (ng TOC/cm2) =
Equipment Volume (mL) x Purified Water Alert Limit (ng TOC/mL)
Equipment Surface Area (cm2)
Residual TOC Swab Limit (µg TOC/swab) =
Maximum Surface Residual TOC x SSA (cm2/swab) x 1 µg /1000 ng
Note that the approximate amount of carbon in protein is 50%
25

26. Cleaning AC: WFI Specs (example)
Maximum Surface Residual TOC (ng TOC/cm2) =
Equipment Volume (mL) x Purified Water Alert Limit (ng TOC/mL)
Equipment Surface Area (cm2)
= 25,842 mL x 250 ng TOC/mL
3,916.45 cm2
= 1649.58 ng TOC/cm2
Residual TOC Swab Limit (µg TOC/swab) =
Maximum Surface Residual TOC x SSA (cm2/swab) x 1 µg /1000 ng
= <1649.58 ng TOC/cm2 x 25 cm2/swab x 1 µg /1000 ng
= <41 µg TOC/swab (assuming 25 cm2 area swabbed)
Remember that the approximate amount of carbon in protein is 50%
26

27. Cleaning AC: Safety Factor
• The cleaning verification limit determined by the Safety Factor
Approach calculates the reduction of the inactivated product at the
acceptance criteria level as an organic impurity in the Drug Substance
or Drug Product.
• This organic impurity limit is 0.10% which is the equivalent to a Safety
Factor of 1,000
Safety Factor =
Concentration (mg/mL) x 1 ppm x 1000 µg x 50 %
TOC Limit (ppm) x 1 µg/mL x 1 mg
Where the approximate amount of carbon in protein is 50%
Residual TOC Swab Limit =
TOC Limit (ppm) x 1 µg x Volume desorption solution (mL)
ppm x Surface Area swabbed (cm2)
27

28. Cleaning AC: Safety Factor (example)
Safety Factor =
Concentration (mg/mL) x 1 ppm x 1000 µg x 50 %
TOC Limit (ppm) x 1 µg/mL x 1 mg
= 100 mg/mL x 1 ppm x 1000 µg x 50 %
2 ppm x 1 µg/mL x 1 mg
= 25,000
Where the approximate amount of carbon in protein is 50%
Residual TOC Swab Limit =
TOC Limit (ppm) x 1 µg x Volume desorption solution (mL)
ppm x Surface Area swabbed (cm2)
= 2 ppm x 1 µg x Volume desorption solution (mL)
ppm x 25 cm2
= 60 µg / 25 cm2
28

29. Cleaning AC: Threshold of Toxicology Concern
• Denatured biopharmaceutical product fragments may be considered
to be Class I chemicals with a residual soil threshold of 100 µg / day.
• Compounds that are not likely to be potent, highly toxic or
carcinogenic a
Acceptable Residual Limit (ARL) (µg/cm2)
= 100 µg/day x minimum batch size (mg)
Dose (mg/day) x surface area (cm2)
Residual TOC Swab Limit (µg TOC/swab)
= Acceptable Residual Limit (µg/cm2) x SSA (cm2/swab) x 50%
Where the approximate amount of carbon in protein is 50%
a Reference: JVT Nov 2012 Vol 18 Methodology for Assessing Product Inactivation During Cleaning
Part II: Setting Acceptance Limits of Biopharmaceutical Product Carryover for Equipment Cleaning
29

30. Cleaning AC: Threshold of Toxicology Concern
(example)
Acceptable Residual Limit (ARL) (µg/cm2)
= 100 µg/day x minimum batch size (mg)
Dose (µg/day) x surface area (cm2)
= 100 µg/day x 400,000 mg
50,000 µg/day x 28,573 cm2
= 28 µg/cm2
Residual TOC Swab Limit (µg TOC/swab)
= Acceptable Residual Limit (µg/cm2) x SSA (cm2/swab) x 50%
= 28 µg/cm2 x 25 cm2/swab x 50%
= 700 µg/swab (assuming 25 cm2 area swabbed)
Where the approximate amount of carbon in protein is 50%
30

31. Cleaning Validation Life Cycle – Stage 3
Equipment Related:
• Maintain matrix / family approach.
• Periodic re-validation against established acceptance criteria to verify
the system is still within the validated state.
• Assess process variability based upon historical data
• Develop Alert (Control) Limits as appropriate.
Product Related:
• Evaluate new products against worst-case soilant:
• Small-scale coupon studies
• Large-scale verification studies
• Perform additional validation studies as required.
Cleaning Parameters:
• Maintain parameters in validated state
• Modifications handled through the change control system
31

32. CPV and Statistics in General
• Statistics are to be used to determine the amount of
variability within a process.
• Process Capability (Cp, Cpk) and Process Performance
(Pp, Ppk) are typically good tools used to perform this
assessment:
• Cpk is “short-term” in that assesses data within a sub-group
• Ppk is “long-term” in that assesses data within the complete group
• Establish Control Limits and follow trending rules (e.g.,
Western Electric)
• These statistical tools
are associated with a
normally distributed
dataset.
32

33. Cleaning AC: Performance Control Limit
• Performance Control Limits may be considered once cleaning
validation studies have been completed and routine cleaning
consistently meets established acceptance limits.
• Performance Control Limit approach should not change the
rationale for acceptable cleaning validation/verification acceptance
criteria.
• Performance Control Limit establishes a limit that may be more
reflective of the performance of the cleaning process.
• The Performance Control Limit may be considered an Alert Limit:
 Enables detection of a change in the performance of the cleaning
process
 Enables proactive investigation into a potential cleaning process issue.
33

34. Cleaning Validation and Statistics –
Can they co-exist?
The challenge with the data typically from effective cleaning processes
is that the data are not normally distributed.
34

35. Western Electric Rules don’t really apply
35
• One data point outside ± 3 s
• Two out of last three points outside on same side of ± 2 s
• Four out of last five points outside on same side of ± 1 s
• Eight points in a row on same side of center line
• Six consecutive points in an increasing
or decreasing direction
• Fifteen consecutive points within
one sigma zone
• Fourteen consecutive points
alternating direction
• Eight consecutive points
outside one sigma zone

36. Can the Cleaning Data be made “Normal”
• Even though the cleaning validation data are not normal; theoretically,
the data may be made normal. The data maybe transformable (i.e.,
other statistical models may be used).
• A control limit at three standard deviations from the mean ensures a
false out of tolerance (OOT) rate of 0.27% or 0.0027 (the alpha rate).
• Since dataset contains an excessive number of zero values (or <LOQ),
the “0” values should be removed and the alpha rate adjusted
accordingly.
• The Box-Cox method computes the lambda value to optimize normality
using the following equation:
Ytransformed = Yoriginal^ lambda – 1
lambda
Where,
Yoriginal is each TOC value, which must be greater than 0 (e.g., LOQ)
36

37. Normal Assessment of TOC Swab Data
Data from: Methodology for Assessing Product Inactivation During Cleaning and
Setting Cleaning Verification Limits – BPOG Multi-Product presentation
The top is a
histogram of
the original
TOC dataset.
A normal
probability plot
of the same
non-normal
phenomenon
(bottom)

38. Box-Cox Assessment of TOC Swab Data
Performance Limits are then back-calculated to the original scale
using the transformed dataset and the equation below:
Yoriginal = (Ytransformed * lambda +1)(1/ lambda)
The Box-Cox
transformed
data are
presented in
the top plot.
The Box-Cox
transformed
data are
normally
distributed
(bottom)

39. Transformation of real-life data
39

40. Does Transformation work in this example?
40
The data were evaluated by the Anderson-Darling normality
test, which indicated that the data significantly deviate from
each of the theoretical distributions (all p-values < 0.05)

41. Where to go from here:
• Since the data do not follow a Normal, Exponential,
Weibull, or Gamma distribution, one-sided
parametric tolerance limits (95% and 99%) could not
be estimated.
• Instead, estimate potential Control (and potentially
the Alert) Limits using the empirical distribution
function such as the 99.9th and the 99th percentiles.
• Evaluate the potential Limits against the process and
data to determine if appropriate and adjust as
necessary.
41

42. Develop Control (Alert) Limits
42
Acceptance Criteria:
≤ 150 µg/25 cm2
99.9% percentile:
≤ 95 µg/25 cm2
99 % percentile:
≤ 15 µg/25 cm2
Do either of these Control Limits appear to be appropriate?

43. Refine Control (Alert) Limits
43
Acceptance Criteria:
≤ 150 µg/25 cm2
99.9% percentile:
≤ 95 µg/25 cm2
99.7% percentile:
≤ 60 µg/25 cm2
99.5% percentile:
≤ 37 µg/25 cm2
99 % percentile:
≤ 15 µg/25 cm2
These additional percentiles provide more options for the analysis

44. What’s the next step?
Now that the Control Limit has been accepted (99.5%
percentile which equates to ≤ 37 µg/25 cm2), data can be
evaluated from various perspectives
• Maintain the scientifically derived Acceptance Criteria
• Perform further assessment of historical data to identify trends
• Assess future results against the Control Limit to identify
potential trends before exceeding the Cleaning Validation
Acceptance Criteria
• Potentially apply modified trending rules such as:
• One data point above the Control Limit
• A number of data points from same data set above the Alert Limit
• A number of points from consecutive data sets for a specific piece of
equipment or family/matrix above the Alert Limit
44

45. Trend Cleaning Data by Time
45
Acceptance Criteria:
≤ 150 µg/25 cm2
99.9% percentile:
≤ 95 µg/25 cm2
99.7% percentile:
≤ 60 µg/25 cm2
99.5% percentile:
≤ 37 µg/25 cm2
99 % percentile:
≤ 15 µg/25 cm2
2010
2011
2012
2013
2014
2015

46. Trend Cleaning Data by Campaign
46
Start-up
Campaign
1
Campaign
2
Campaign
3
Campaign
4
Campaign
5
Campaign
6
Campaign
7
Acceptance Criteria:
≤ 150 µg/25 cm2
99.9% percentile:
≤ 95 µg/25 cm2
99.7% percentile:
≤ 60 µg/25 cm2
99.5% percentile:
≤ 37 µg/25 cm2
99 % percentile:
≤ 15 µg/25 cm2

47. Trend Cleaning Data by Product vs. Time
47
Start-up
Product
A
Product
A
Product
B
Product
A
Product
C
Product
D
Product
A
Acceptance Criteria:
≤ 150 µg/25 cm2
99.9% percentile:
≤ 95 µg/25 cm2
99.7% percentile:
≤ 60 µg/25 cm2
99.5% percentile:
≤ 37 µg/25 cm2
99 % percentile:
≤ 15 µg/25 cm2

48. Trend by Sample Location
48
• Sample location 7 as well as locations 9, 13 and 15 are of interest
(and maybe 6, too)
• Sample locations 1, 3, 8, 11 and 14 are well controlled
• Sample locations 5 and 10 are not included in this cleaning family

49. 49
Summary
Cleaning is a process
• Cleaning is a process; therefore, utilizing the Lifecycle
Approach for cleaning validation is appropriate.
• Discussed Equipment, Process and Cleaning
Parameters over the course of the Cleaning Validation
Lifecycle:
• Examined how the need of the small-scale studies support
the at-scale validation studies.
• Stressed the importance of the tracking/trending of the
cleaning data following the initial validation studies.
• Highlighted the need to be open to assess the cleaning
validation data from various perspectives.
• Traced the Biotech approach over the entire cleaning
validation lifecycle.

50. 50
Questions?
wymann@medimmune.com