Quality by Design : Control strategy

Control strategy
Presentation prepared by Drug Regulations – a not for
profit organization. Visit www.drugregulations.org for the
latest in Pharmaceuticals.

www.drugragulations.org 1

Product Profile  Quality Target Product Profile (QTPP)

CQA’s  Determine “potential” critical quality attributes (CQAs)

Risk Assessments  Link raw material attributes and process parameters to
CQAs and perform risk assessment
Design Space  Develop a design space (optional and not required)

Control Strategy  Design and implement a control strategy

Continual  Manage product lifecycle, including continual
Improvement
improvement


Product Profile

CQA’s
 This presentation Part III of the
Risk Assessments series “QbD for Beginners” covers
basic aspects of
Design Space
◦ Control Strategy
Control Strategy

Continual
Improvement


 A control strategy is designed to ensure that
a product of required quality will be produced
consistently.
 The elements of the control which contribute
to the final product quality include
◦ In-process controls,
◦ The controls of input materials (drug substance and
excipients),
◦ Intermediates (in-process materials),
◦ Container closure system, and
◦ Drug products


 Control Strategy
◦ Planned set of controls
◦ Derived from current product and process understanding that
assures process performance and product quality
◦ The controls can include parameters and attributes related to
 Drug substance ,
 Drug product materials and components,
 Facility and equipment operating conditions,
 In-process controls,
 Finished product specifications, and
 The associated methods and
 Frequency of monitoring and control.‟ (ICH Q10)


 In-Process Control (or Process Control):
Checks performed during production in order to monitor
and, if appropriate, to adjust the process and/or to ensure
that the intermediate or API conforms to its specifications
(Q7) Applies similarly to the drug product
 In-Process Tests:
Tests which may be performed during the manufacture of
either the drug substance or drug product, rather than as
part of the formal battery of tests which are conducted
prior to release (Q6A)


 „Real time release testing (RTRT)
is the ability to evaluate and ensure the quality of in-
process and/or final product based on process data,
which typically include a valid combination of measured
material attributes and process controls‟ (Q8(R2))

 Process Analytical Technology (PAT):
A system for designing, analyzing, and controlling
manufacturing through timely measurements (i.e., during
processing) of critical quality and performance attributes
of raw and in-process materials and processes with the
goal of ensuring final product quality (Q8(R2))


 Control strategy derives from management of risk
and should lead to assurance of consistent quality
of product in alignment with the Quality Target
Product Profile (QTPP)
 Control strategy is:
◦ Not a new concept
◦ Not just specifications
◦ Based on product and process understanding and
risk management
◦ While design space is optional, control strategy is
not.


 Every process and product has an associated control
strategy.
◦ There is one overall control strategy for a given product.
◦ There are control strategies for unit operations
◦ It could include some site specific aspects
 For a given product, different approaches for the control
strategy are possible (e.g. in-process testing, RTRT, end
product testing)
 Specifications for API and drug product are still needed
for stability testing, regional regulatory testing
requirements, etc.


 Control strategy and batch release should not be
confused.
Control strategy is a key component, but not the
only element needed for the batch release decision.
 Scale-up, technology transfer and manufacturing
experience can lead to refinements of the control
strategy under the PQS considering regulatory
requirements


 Process for defining the control strategy
◦ What are the quality criteria (QTPP)
◦ Initial design of specific product & process
◦ Assess prior knowledge to understand materials, process and product
with their impact
 Experience with different approaches to control
◦ Risk assessment for process steps and variables
 Assure all CPPs are identified during QRA
◦ Development to further determine what type of controls are
appropriate for each variable
◦ Consider design space, if submitted
◦ Specifications
 Scale-up considerations
 Quality system requirements of control strategy
◦ Implementation, maintenance and updating


 Industry selects control approach based on multiple
factors
◦ Factors may include analytical testing sensitivity,
equipment limitations, etc.

 Regulators evaluate the control strategy and whether
the risk has been adequately controlled

 Inspector reviews the implementation of the control
strategy at site, including adaptation at scale up, and
the adequacy of the site quality system to support it


 These controls should be based on
◦ Product,
◦ Formulation and
◦ Process understanding
 They should include, at a minimum, control
of the critical process parameters and
material attributes.


 A comprehensive pharmaceutical
development approach will generate process
and product understanding and identify
sources of variability.
 Sources of variability that can impact product
quality should be identified, appropriately
understood, and subsequently controlled.


 Understanding sources of variability and their
impact on downstream processes or
processing, in-process materials, and drug
product quality can provide an opportunity to
shift controls upstream and minimise the
need for end product testing.


 Product and process understanding, in
combination with quality risk management,
will support the control of the process such
that the variability (e.g., of raw materials) can
be compensated for in an adaptable manner
to deliver consistent product quality.


 This process understanding can enable an
alternative manufacturing paradigm where
the variability of input materials could be less
tightly constrained.
 Instead it can be possible to design an
adaptive process step (a step that is
responsive to the input materials) with
appropriate process control to ensure
consistent product quality.


 Enhanced understanding of product performance
can justify the use of alternative approaches to
determine that the material is meeting its quality
attributes.
 The use of such alternatives could support real
time release testing.
 For example, disintegration could serve as a
surrogate for dissolution for fast-disintegrating
solid forms with highly soluble drug substances.
 Unit dose uniformity performed in-process (e.g.,
using weight variation coupled with near infrared
(NIR) assay) can enable real time release testing.


 This can provide an increased level of quality
assurance compared to the traditional end-
product testing using compendial content
uniformity standards.
 Real time release testing can replace end
product testing, but does not replace the
review and quality control steps called for
under GMP to release the batch.


 A control strategy can include, but is not limited
to, the following:
 Control of input material attributes (e.g., drug
substance, excipients, primary packaging
materials) based on an understanding of their
impact on processability or product quality;
 Product specification(s);
 Controls for unit operations that have an impact
on downstream processing or product quality
(e.g., the impact of drying on degradation,
particle size distribution of the granulate on
dissolution);


 Controls for unit operations that have an impact
on downstream processing or product quality
(e.g., the impact of drying on degradation,
particle size distribution of the granulate on
dissolution);
 In-process or real-time release testing in lieu of
end-product testing (e.g. measurement and
control of CQAs during processing);
 A monitoring program (e.g., full product testing
at regular intervals) for verifying multivariate
prediction models.


 A control strategy can include different
elements.
 For example, one element of the control
strategy could rely on end-product testing,
whereas another could depend on real-time
release testing.
 The rationale for using these alternative
approaches should be described in the
submission


 The lifecycle of the control strategy is
supported by
◦ pharmaceutical development,
◦ quality risk management (QRM),and
◦ the pharmaceutical quality system (PQS),
 As described in ICH Q8(R2), Q9, and Q10.


 The control strategy is generally developed
and initially implemented for production of
clinical trial materials.
 It can be refined for use in commercial
manufacture as new knowledge is gained.
 Changes could include acceptance criteria,
analytical methodology, or the points of
control (e.g., introduction of real-time release
testing).


 Additional emphasis on process controls
should be considered in cases where
products cannot be well-characterized
and/or quality attributes might not be readily
measurable due to limitations of testing or
detectability (e.g., microbial load/sterility).


 Consideration should be given to improving
the control strategy over the lifecycle.
 In response to assessment of data trends
over time and other knowledge gained
 Continuous process verification is one
approach that enables a company to monitor
the process and make adjustments to the
process and/or the control strategy, as
appropriate.


 When multivariate prediction models are
used, systems that maintain and update the
models help to assure the continued
suitability of the model within the control
strategy.


 Attention should be given to outsourced
activities to ensure all changes are
communicated and managed.
 The regulatory action appropriate for
different types of changes should be handled
in accordance with the regional regulatory
requirements.


 Different control strategies could be applied
at different sites or when using different
technologies for the same product at the
same site.
 Differences might be due to equipment,
facilities, systems, business requirements
(e.g., confidentiality issues, vendor
capabilities at outsourced manufacturers) or
as a result of regulatory
assessment/inspection outcomes.


 The applicant should consider the impact of
the control strategy implemented on the
residual risk and the batch release process.


 Risk associated with scale-up should be
considered in control strategy development
to maximize the probability of effectiveness
at scale.
 The design and need for scale-up studies can
depend on the development approach used
and knowledge available.


 A risk-based approach can be applied to the
assessment of suitability of a control strategy
across different scales.
 QRM tools can be used to guide these
activities.
 This assessment might include risks from
◦ processing equipment,
◦ facility environmental controls,
◦ personnel capability,
◦ experiences with technologies, and
◦ historical experience (prior knowledge).


 Complexity of product and process
 Differences in manufacturing equipment,
facilities and/or sites
 Raw materials:
◦ Differences in raw material quality due to source or
◦ batch-to-batch variability
 Impact of such differences on process
controls and quality attributes


 Process parameters:
◦ Confirmation or optimization
◦ Confirmation of the design space(s), if used

 In-process controls:
◦ Point of control
◦ Optimization of control methods
◦ Optimization and/or updating of models, if used


 Product specification:
◦ Verification of the link to QTPP
◦ Confirmation of specifications (i.e., methods and
acceptance criteria)
◦ Confirmation of real-time release testing (RTRT), if
used


 To base the release of a product on product and
process understanding rather than on end product
testing alone and/or on the results of batch analysis.
 This implies
◦ Understanding the science around the product and
process
 Identifying the parameters (critical) of active,
excipients, process influencing the quality
◦ Establishment of a control strategy –risk based- which
 monitors the important parameters influencing the
CQAs;
 gives the basis for RTR or reduced end product
testing.

www.drugragulations.org
36

 Example: Sterilisation
◦ Injectables: compliance with the specification
“sterile”:
 via parametric release rather than with the
conventional Ph.Eu. “Sterility test”;
 monitoring of critical parameters (time, pressure,
temperature, ….)
 Example: dissolution
◦ Release parameters e.g.
 Particle size active substance and/or excipients
 Hardness of the tablet
 ……..

37

Sample
& Sample Sample
Test & &
Test Test

API
Pass
Excipient Blend Screen Blend Tablet or
Fail
Excipient

Fixed processes

Quality Criteria met if:
• Meets specification(s) (off-line QC tests)
• GMP Procedures followed

John Berridge, Pfizer www.drugragulations.org
38

Characterise Adaptive
processes

API
100%
Excipient Blend Screen Blend Tablet
Pass
Excipient
Real
PAT PAT time
release

Standards and acceptance criteria for a PAT/QbD approach are not
the same as a “Test to Document Quality” approach

39

 Minimal Approaches
◦ Drug product quality controlled primarily by
intermediates (in-process materials) and end product
testing
 Enhanced, Quality by Design Approaches
◦ Drug product quality ensured by risk-based control
strategy for well understood product and process
◦ Quality controls shifted upstream, with the possibility
of real-time release testing or reduced end-product
testing

40

Example Control Strategy for
Real Time Release Testing
NIR Spectroscopy
NIR Monitoring Laser Diffraction (At-Line)
Blend Uniformity Particle Size • Identity
• Assay
Raw materials & • API to Excipient
API dispensing ratio
• Specifications
based on product

Roller Tablet Pan
Dispensing Blending Sifting
compaction Compression Coating


 Liquid product, used to determine mix time
 CQA related to mix uniformity
 CPP‟s (Critical Process Parameters) included agitator speed,
time after addition of one ingredient until the addition of
another, solution temperature, and recirculation flow rate.
 Process analyzer used was a refractometer
 Resulted in cost savings and quality enhancement

SCADA,
User
Mix
Interface
RI Tank
Sensor

Control Data
System Historian
Pump


Example from ICH case study
Blending Process Control Options
Decision on conventional vs. RTR testing

Key message: Both approaches to assure blend uniformity are valid in combination
with other GMP requirements

ICH-GCG ASEAN, Kuala Lumpur, 26-28 July 2010 slide 43

Example from ICH case study

ICH-GCG ASEAN, Kuala Lumpur, 26-28 July 2010 slide 44

ICH Quality Implementation Working Group - Integrated Implementation Training Workshop

Breakout B: Control Strategy

www.drugragulations.org slide 45


Breakout B: Control Strategy

RTRT of Assay and Content Uniformity
• Finished Product Specification – use for stability, regulatory
testing, site change, whenever RTR testing is not possible
- Assay acceptance criteria: 95-105% of nominal amount (30mg)
- Uniformity of Dosage Unit acceptance criteria
- Test method: HPLC
• Real Time Release Testing Controls
- Blend uniformity assured in blending step (online NIR spectrometer for
blending end-point)
- API assay is analyzed in blend by HPLC
- Tablet weight control in compression step
• No end product testing for Assay and Content Uniformity (CU)
- Would pass finished product specification for Assay and Uniformity of
Dosage Units if tested because assay assured by combination of blend
uniformity assurance, API assay in blend and tablet weight control (if blend
is homogeneous then tablet weight will determine content of API)



Case Study

Example:
Real Time Release Testing (RTRT)
for Dissolution



Case Study

Developing Product and Process
Understanding
Investigation of the effect of API particle size on
Bioavailability and Dissolution
Drug Substance with particle size D90 of 100
microns has slower dissolution and lower
Cmax and AUC
In Vivo In Vitro correlation (IVIVC) established
at 20 minute timepoint

Early time points in the dissolution profile
are not as critical due to PK results



Case Study
Developing Product and Process
Understanding: DOE Investigation of factors affecting Dissolution
Multifactorial DOE study of Exp No
1
Run Order
1
API
0.5
MgSt
3000
LubT
1
Hard
60
Diss
101.24
2 14 1.5 3000 1 60 87.99
variables affecting dissolution 3 22 0.5 12000 1 60 99.13
4 8 1.5 3000 10 60 86.03
• Factors: 5 18 0.5 12000 10 60 94.73

- API particle size [API] 6
7
9
15
1.5
0.5
12000
3000
10
1
60
110
83.04
98.07
unit: log D90, microns 8 2 0.5 12000 1 110 97.68

- Mg-Stearate Specific Surface Area
9
10
6
16
1.5
0.5
12000
3000
1
10
110
110
85.47
95.81
11 20 1.5 3000 10 110 84.38
[MgSt] 12 3 1.5 12000 10 110 81
unit: cm2/g 13 10 0.5 7500 5.5 85 96.85

- Lubrication time [LubT] unit: min 14 17 1.5 7500 5.5 85 85.13

-
15 19 1 3000 5.5 85 91.87
Tablet hardness [Hard] unit: N 16 21 1 12000 5.5 85 90.72
17 7 1 7500 1 85 91.95
• Response: 18 4 1 7500 10 85 88.9

- % API dissolved at 20 min [Diss]
19
20
5
11
1
1
7500
7500
5.5
5.5
60
110
92.37
90.95
21 12 1 7500 5.5 85 91.95
• DOE design: 22 13 1 7500 5.5 85 90.86

- RSM design
23 23 1 7500 5.5 85 89

- Reduced CCF (quadratic model)
Note: A screening DoE may be used first to identify
- 20+3 center point runs
which of the many variables have the greatest effect



Case Study

Factors affecting Dissolution
Scaled & Centered Coefficients for Diss at 60min

• Key factors influencing
0

-1

in-vitro dissolution:
- API particle size is the
-2

dominating factor -3

(= CQA of API) % -4

-5

- Lubrication time has a -6

small influence

MgSt*LubT
Hard
API

MgSt

LubT
API Mg Lubrication Tablet Mg St*LubT
(= low risk parameter) Stearate Blending Hardness
Particle
Size N=23 SSA
R2=0.986 R2time
Adj.=0.982
DF=17 Q2=0.981 RSD=0.725 Conf. lev.=0.95

MODDE 8 - 2008-01-23 10:58:52

Acknowledgement: adapted from Paul Stott (AZ) – ISPE PQLI Team



Case Study

Predictive Model for Dissolution
• Prediction algorithm
- A mathematical representation of the design space for
dissolution
- Factors include: API PSD D90, magnesium stearate
specific surface area, lubrication time and tablet
hardness (linked to compression pressure)
Prediction algorithm:
Diss = 108.9 – 11.96 × API – 7.556×10-5 × MgSt – 0.1849 × LubT –
3.783×10-2 × Hard – 2.557×10-5 × MgSt × LubT



Case Study

Predictive Model for Dissolution
• Account for uncertainty
- Sources of variability (predictability, measurements)
• Confirmation of model
- compare model results vs. actual dissolution results for batches
- continue model verification with dissolution testing of production
material, as needed
Batch 1 Batch 2 Batch 3

Model prediction 89.8 87.3 88.5

Dissolution testing 92.8 90.3 91.5
result (88.4–94.2) (89.0-102.5) (90.5-93.5)



Case Study

Dissolution: Design Space
• Response surface plot for effect of API particle size
and magnesium stearate specific surface area (SSA)
on dissolution
Diss (% at 20 min)

Area of potential risk
Design for dissolution failure
Space
Graph shows interaction between
two of the variables: API particle
size and magnesium stearate
specific surface area
API particle size (Log D90)
Acknowledgement: adapted from Paul Stott (AZ)



Case Study

Dissolution: Control Strategy
• Controls of input material CQAs
- API particle size distribution
- Control of crystallisation step
- Magnesium stearate specific surface area
- Specification for incoming material

• Controls of process parameter CPPs
- Lubrication step blending time
- Compression pressure (set for target tablet hardness)
- Tablet press force-feedback control system

• Prediction mathematical model
- Use in place of dissolution testing of finished drug product
- Potentially allows process to be adjusted for variation in API particle size,
for example, and assure dissolution performance



Case Study

Example 2:
Real Time Release Testing (RTRT)
for Assay and Content Uniformity



Case Study

Quality Risk Assessment
Impact on Assay and Content Uniformity CQAs
• QRA shows API particle size, moisture control, blending and lubrication
steps have potential to affect Assay and Content Uniformity CQAs
- Moisture is controlled during manufacturing by facility HVAC control of
humidity (GMP control)
Drug Moisture
substance content in Blending Lubrication Compression Coating Packaging
particle size manufacture
in vivo performance
Dissolution
Assay
Degradation
Content uniformity
Appearance
Friability
Stability-chemical
Stability-physical

- Low risk
- Medium risk
- High risk


Case Study

Blending Process Control Options
Decision on conventional vs. RTR testing



Case Study

Process Control Option 1
DOE for the Blending Process Parameter Assessment to
develop a Design Space
- Factors Investigated:
Blender type, Rotation speed, Blending time, API Particle size
Blending time Rotation speed Particle size D90
Experiment Run Condition Blender
(minutes) (rpm) ( m)
No.
1 2 varied 2 10 V type 5
DOE design

5 6 varied 2 10 Drum type 40
9 3 standard 9 20 V type 20
10 12 standard 9 20 Drum type 20
11 9 standard 9 20 V type 20
12 4 standard 9 20 Drum type 20



Case Study

Blend uniformity monitored using a process analyser
• Control Strategy to assure homogeneity of the blend
- Control of blending
end-point by NIR
and feedback control
of blender
- API particle size

In this case study, the
company chooses to use
online NIR to monitor blend
uniformity to provide
efficiency and more flexibility



Case Study

Blend uniformity monitored using a process analyser
• On-line NIR spectrometer used 0.045
to confirm scale up of blending

mean spectral standard deviation
0.04
• Blending operation complete 0.035
when mean spectral std. dev. 0.03
Pilot Scale
Full Scale
reaches plateau region 0.025
- Plateau may be detected 0.02
using statistical test or rules
0.015
• Feedback control to turn off 0.01
Plateau region
blender 0.005
• Company verifies blend does 0
not segregate downstream 0 32 64 96 128

- Assays tablets to confirm
Revolution Revolutions of Blender
Number of (block number)

uniformity Data analysis model will be provided
- Conducts studies to try to Plan for updating of model available
segregate API Acknowledgement: adapted from ISPE PQLI Team



Case Study

Tablet Weight Control in Compression Operation

Conventional automated control of Tablet Weight using feedback loop:
Sample weights fed into weight control equipment which sends signal to filling
mechanism on tablet machine to adjust fill volume and therefore tablet weight.



Case Study

RTRT of Assay and Content Uniformity
• Real Time Release Testing Controls
- Blend uniformity assured in blending step (on-line NIR spectrometer
for blending end-point)
- API assay is analyzed in blend by HPLC
- API content could be determined by on-line NIR, if stated in filing
- Tablet weight control with feedback loop in compression step

• No end product testing for Assay and Content
Uniformity (CU)
- Would pass finished product specification for Assay and Uniformity of
Dosage Units if tested because assay assured by combination of
blend uniformity assurance, API assay in blend and tablet weight
control (if blend is homogeneous then tablet weight will determine
content of API)


Process C ontrol Philosophy - Paradigm Shift
Conventional approach - lab based
End of phase testing of quality, to reduce the risk in
m oving to the next stage

O btain raw Mix active and Press tablets Package
m aterials excipeints

P.A.T approach - process based, at-line or on-line

O btain raw Mix active and Press tablets Package
m aterials excipeints

Continuously or m ore frequently test quality during each
phase, to rem ove the risk in m oving to the next stage

Granulation Fluidized Bed
Dispensation Dryer

Scale
Water Content – NIR
Identity-NIR Extent of Wet Air Particle size – FBRM
Massing - Power
Consumption
Raw Materials
Blending
Blend Homogeneity -
NIR

Multivariate Model (predicts
Disintegration)
Tableting
Content Uniformity NIR
Unit Operations
Attributes Packaging
Controls


Product Profile  Quality Target Product Profile (QTPP)

CQA’s  Determine “potential” critical quality attributes (CQAs)

Risk Assessments  Link raw material attributes and process parameters to
CQAs and perform risk assessment
Design Space  Develop a design space (optional and not required)

Control Strategy  Design and implement a control strategy

Continual  Manage product lifecycle, including continual
Improvement
improvement


Quality by Design : Control strategy

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