Medicinal products must comply with their approved specifications before they are released into the market. Compliance with release specifications can be demonstrated by performing a complete set of tests on the active substance and/or finished product, according to the approved specifications. Under certain conditions, an alternative strategy to systematic end product testing is possible. So far this concept has been mainly applied to sterility testing of terminally sterilised products and has become associated with parametric release applications. Recent guidelines adopted in the ICH context (ICH Q8, Q9 and Q10) have made it possible to apply a similar release decision process to tests other than sterility, this approach has been called Real Time Release Testing (RTRT).
RTRT is a system of release that gives assurance that the product is of intended quality, based on the information collected during the manufacturing process, through product knowledge and on process understanding and control. RTRT recognises that under specific circumstances an appropriate combination of process controls (critical process parameters) together with pre-defined material attributes may provide greater assurance of product quality than end-product testing and the context as such be an integral part of the control strategy. The RTRT principle is already authorised for use as an optional alternative to routine sterility testing of products terminally sterilised in their final container i.e. parametric release. Enhanced product knowledge and process understanding, the use of quality risk management principles and the application of an appropriate pharmaceutical quality system, as defined within ICH Q8,Q9 and Q10 provide the platform for establishing RTRT mechanisms for other applications, for new products as well as established marketed products. Release of a product can be a combination of a RTR approach for certain critical quality attributes (CQAs) and a more conventional evaluation for other CQAs (partial RTR).
This presentation deals with the concepts of Real Time Release Testing. This presentation was compiled from material freely available from FDA , ICH , EMEA and other free resources on the world wide web.
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Real time release testing
1. Presentation prepared by Drug Regulations – a not for
profit organization. Visit www.drugregulations.org for the
latest in Pharmaceuticals.
www.drugragulations.org 1
2. This presentation will cover
◦ What is Real Time Release Testing
◦ Batch Release & RTRT
◦ Organizational approach
◦ Examples
◦ End product testing Vs RTRT
◦ Process control : paradigm shift
◦ Benefits & challenges
◦ Relationship between QbD, PAT, Control Strategy &
RTRT
◦ Control Strategy – Conventional Vs RTRT
◦ ICH and other published examples of RTRT
www.drugragulations.org 2
3. Medicinal products must comply with their
approved specifications before they are released
into the market.
Compliance with release specifications can be
demonstrated by performing a complete set of
tests on the active substance and/or finished
product, according to the approved
specifications.
Under certain conditions, an alternative strategy
to systematic end product testing is possible.
www.drugragulations.org 3
4. So far this concept has been mainly applied
to sterility testing of terminally sterilized
products and has become associated with
parametric release applications.
Recent guidelines adopted in the ICH context
(ICH Q8, Q9 and Q10) have made it possible
to apply a similar release decision process to
tests other than sterility, this approach has
been called Real Time Release Testing (RTRT).
www.drugragulations.org 4
5. 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. ICH Q8(R2)
Typically include a valid combination of measured
◦ Material attributes and
◦ Process controls
www.drugragulations.org 5
6. The exact approach to RTRT will vary depending
on the process requirements.
The RTRT strategy may be based on control of
process parameters, monitoring of product
attributes or on a combination of both at
appropriate steps throughout the process.
Critically, the RTRT strategy should be based on a
firm understanding of the process and of the
relationship between process parameters, in-
process material attributes and product
attributes.
www.drugragulations.org 6
7. Process monitoring may be applied to various
manufacturing steps or unit operations, such as
tabletting, on the basis of appropriate testing at
various stages in the process.
Some parameters/attributes are usually checked
routinely at defined intervals regardless of the
design of the manufacturing process of a tablet.
Uniformity of mass, crushing strength and
disintegration are such examples.
www.drugragulations.org 7
8. The results of a comprehensive set of in-process
tests and controls in these cases may constitute
sufficient grounds for replacing the
corresponding end product testing.
This may also offer greater assurance of the
finished tablet meeting certain criteria in the
specification, without the tests being repeated on
a sample of the finished product, as the amount
of data will in general be substantially larger.
www.drugragulations.org 8
9. If testing of units is part of the RTRT a
sampling strategy should be defined that
provides the number of locations sampled
throughout the batch as well as the number
of dosage units tested at each location.
www.drugragulations.org 9
10. RTRT will, in general, comprise a combination
of process controls which may utilise process
analytical technology (PAT) tools e.g.
◦ Near infrared spectroscopy (NIR) and
◦ Raman spectroscopy (usually in combination with
multivariate analysis),
◦ Together with the control of relevant material
attributes.
www.drugragulations.org 10
11. Spectral data monitored on-line
◦ Controlling content of active substance,
◦ Polymorphism,
◦ water content,
◦ Blending homogeneity,
◦ Particle/powder properties or
◦ Film thickness
could thereby replace end-product testing e.g.
◦ Uniformity of content,
◦ Tablet strength and
◦ Drug dissolution.
www.drugragulations.org 11
12. In active substance manufacturing, RTRT can
apply to
◦ Continuous manufacturing processes, and
◦ Also to discrete unit operations such as
Distillations,
Hydrogenations,
Crystallisations and
All sorts of other chemical reactions or separations
(e.g. diastereoisomers).
www.drugragulations.org 12
13. Real time release testing is “moving the QC
lab into the process” and
“measure the CQAs where they are
generated”
www.drugragulations.org 13
14. Parametric Release: One type of RTRT.
Parametric release is based
on process data (e.g. temperature, pressure,
time for terminal
sterilization) rather than the testing of a
sample for a specific attribute
(ICH Q8 Q&A).
www.drugragulations.org 14
15. 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.
www.drugragulations.org 15
16. Batch release: Approved RTRT may form a
basis but
More aspects needs to be taken into account
in the decision of a Qualified Person to
release a batch.
These aspects could include batch results of
testing for an attribute not subject to RTR as
well as specific GMP requirements.
www.drugragulations.org 16
17. Formulation
Operations Quality
Development
Analytical
Regulatory RTRT Decision
Development
Technology Development Chemometrics
Multi-disciplinary / cross-functional teams are key to RTRt
New skill sets may be needed
www.drugragulations.org 17
18. On-line or in-line measurements and/or controls,
◦ Tablet weight after compression
◦ Particle size measurement after granulation or milling
◦ Moisture measurement during drying
◦ Blend uniformity
Fast at-line measurements,
◦ NIR for tablet assay
◦ Disintegration in lieu of dissolution
Models as surrogate for traditional release tests,
◦ Multivariate model as a surrogate for dissolution
Process signatures
◦ An evolving approach
www.drugragulations.org 18
19. Fixed Output
Input
Process
Disturbance:
Variation
due to
materials or
process Several days latter QC
End Product Testing
www.drugragulations.org 19
20. Process analyzers used to
NIR measure process
Interface parameters and adjust the
process
Adjustable Output
Input
Process
Disturbance:
Variation
due to
Immediate Feed back/
materials or
forward loop
process
www.drugragulations.org 20
21. Reaction developed and
understood during development –
typical tools are IR, NIR and
Raman.
At commercial scale NIR is used to
control the reaction.
Stop the reaction at Maximal API
Concentration
Stopping time differs from Batch to
Batch
Real time release measurement of
the API assay and bi-product
(impurity)
No sampling for in-process control
or end-product testing for this
CQA
www.drugragulations.org 21
22. Holistic Control Strategy e.g.:
Content Uniformity = Blend uniformity + Drug
concentration + Weight control
RTRT
1 = Blend Uniformity
2 = Granule particle size
3 = Weight, Hardness, Potency, Drug
concentration, Identity, Rate–controlling
polymer concentration
www.drugragulations.org 22
23. 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
www.drugragulations.org 23
24. 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
www.drugragulations.org 24
25. The outcome of a high level of process understanding
1. Controlling the process
2. Adjust for variability in raw and in-process materials
3. Increase yield, reduce waste, scrap
4. Reduce the risk of losing a batch
5. Reduced QC test
6. Increased control activity on the manufacturing shop floor
7. Reduced cycle time
8. Real time monitoring of CPPs and CQAs for free (must also be
included in continuous process verification and Annual Product
Review)
9. Quality of the finished product can be measured during
manufacturing – no surprises!
10. Regulators might be more interested in the beginning but this will
fade as process understanding has been demonstrated – reduced
inspection frequency
www.drugragulations.org 25
27. New – not familiar to many
PAT tools in place (in-line analysers, PAT data
management, multivariate data analysis, process control)
Require new skills and reorganisation of work
Risk associated with implementing PAT
Installation of probes, representative sampling, failure of
instrument, failure of multivariate models, failure in feed
forward & backward controls, etc
Backup strategy must be in place
Models needs frequent update
If RTRT fails it cannot be replaced by end-product testing
Regulators might be very interested in the beginning...
www.drugragulations.org 27
28. QbD
Control
Design Strategy
Space
RTRT
CMA CPP CQA
RTRT
PAT
www.drugragulations.org 28
29. QbD is
really
about QbD and PAT links
the the patient,
patient product and process
Patient
1. Understanding what the patient
needs
2. Designing and developing a product
meeting these needs
Process
3. Designing and developing a Understanding
manufacturing process capable of
delivering the product that meets
these needs
Product Process
www.drugragulations.org 29
30. PAT
RTRT
A systematic approach to development that begins with
predefined objectives
and emphasizes product and process understanding
and process control,
based on sound science and quality
risk management
www.drugragulations.org 30
31. CQA’s
&
CPP’s
In Line
On Line
Process Analytical Technology is:
Analyzers
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
Predictive materials and processes with the goal of
Models
ensuring final product quality
Real Time Real
Testing
www.drugragulations.org 31
32. Quality What is
Product Profile Target Identify critical to
Product CQA the
Profile
CQA’s Patient
QRM
PAT
Risk Assessments
Identify
CMA &
Design Space CPP
Design space Control Strategy
Control Strategy
Continual
Improvement
PAT , PAT RTRT
SOP PAT RTRT 32
33. RTRT, when used, is part of the Control
Strategy
◦ Can include some or all of the final product CQAs
QbD is not directly correlated to RTRT
◦ You can have QbD approaches without RTRT
◦ However, it would be difficult to justify RTRT
without a science and risk based approach
www.drugragulations.org 33
34. Not all Process Analytical Technology (PAT)
leads to RTRT
◦ PAT systems can be designed to control CQAs of
raw materials or in-process materials and not
contribute to RTRT
A design space is not required for RTRT
◦ Having a design space can increase operational
flexibility, without additional regulatory approval
www.drugragulations.org 34
35. 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)
www.drugragulations.org 35
37. Every product MUST have a control strategy
Minimal Enhanced approach
Drug product quality • Drug product quality
controlled primarily ensured by risk-based
by intermediates (in control strategy for
process materials) well understood
and end product and process
product testing • Quality controls
shifted upstream, with
the possibility of real-
time release testing or
reduced end-product
testing
www.drugragulations.org 37
38. Identify CQAs
Identify related CPPs and Material Attributes
(MAs)
Develop the design space for the CPPs and MAs
Develop the control strategy ensuring the CPPs
and MAS are always within the design space
Based on risk-assessment plan how the control
strategy can be implemented
◦ This process starts in development
◦ It is a lifecycle activity and
◦ The Control Strategy can be updated as new knowledge
has been gained
www.drugragulations.org 38
39. NIR, at-line (id
raw materials)
IR, on-line
(purity, assay ) NIR, on-line
(Moisture, purity Assay (HPLC)
Purity, related
Conventional Testing
impurities, ((HPLC)
Residual solvent (GC)
Moisture content (KF)
Heavy Metals
Etc…
ID, Assay, CU (HPLC)
Purity, ((HPLC)
NIR, at-line (id raw Dissolution,
NIR, on-line materials)
Appearance
(reaction id) Moisture content (KF)
Etc
FBRM, on-
line (PSD)
NIR, on-line, blend
homogeneity
NIR, on-line, blend NIR, on-line
homogeneity (assay, CU, ID)
39
41. NIR can be used for RTRT of water
determination
Conventional lab-based NIR system
◦ Validated over range 1 – 6%
Tablets dried and “spiked” to encompass
historical range and regulatory specification
Prepare calibration curve
In line NIR for water content determination
www.drugragulations.org 41
42. CQA: CU, dissolution,
Crystal size during formation - PSD
1. Focuses beam
reflectance
measurements can be
used to measure PSD
2. Measure crystal
diameter
3. Probe inserted into
reactor
www.drugragulations.org 42
43. FBRM used to define the best cooling ramp
FBRM used to measure PSD inline
RTRT of PSD
No sampling and
QC test
www.drugragulations.org 43
44. Mock P 2 example
Design Space
www.drugragulations.org 44
46. 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
46
47. 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
www.drugragulations.org
47
48. 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
www.drugragulations.org 48
49. 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
www.drugragulations.org
52. 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 analysed in blend by HPLC
- Tablet weight control in compression step
www.drugragulations.org
53. RTRT of Assay and Content Uniformity
• 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)
www.drugragulations.org
55. 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
www.drugragulations.org
56. Multifactorial DOE study of Exp No
1
Run Order
1
API
0.5
MgSt
3000
LubT
1
Hard
60
Diss
101.24
variables affecting dissolution 2
3
14
22
1.5
0.5
3000
12000
1
1
60
60
87.99
99.13
Factors:
4 8 1.5 3000 10 60 86.03
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 6 1.5 12000 1 110 85.47
10 16 0.5 3000 10 110 95.81
[MgSt] 11 20 1.5 3000 10 110 84.38
unit: cm2/g
12 3 1.5 12000 10 110 81
13 10 0.5 7500 5.5 85 96.85
◦ Lubrication time [LubT] unit: min 14
15
17
19
1.5
1
7500
3000
5.5
5.5
85
85
85.13
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 5 1 7500 5.5 60 92.37
20 11 1 7500 5.5 110 90.95
DOE design:
21 12 1 7500 5.5 85 91.95
22 13 1 7500 5.5 85 90.86
◦ RSM design 23 23 1 7500 5.5 85 89
Note: A screening DoE may be used first to
◦ Reduced CCF (quadratic model)
identify which of the many variables have the
◦ 20+3 center point runs
greatest effect
www.drugragulations.org
57. Scaled & Centered Coefficients for Diss at 60min
• Key factors
0
influencing in-vitro
-1
dissolution: -2
- API particle size is -3
the dominating % -4
factor (= CQA of
API)
-5
-6
- Lubrication time has API Mg Lubricatio Tablet Mg St*LubT
MgSt*LubT
Hard
API
MgSt
LubT
n
Particle Stearate Hardness
a small influence Size N=23 SSA
R2=0.986
Blending
R2 Adj.=0.982
(= low risk DF=17 Q2=0.981 time
RSD=0.725 Conf. lev.=0.95
parameter)
MODDE 8 - 2008-01-23 10:58:52
Acknowledgement: adapted from Paul Stott (AZ) – ISPE PQLI Team
www.drugragulations.org
58. 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
www.drugragulations.org
59. 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)
www.drugragulations.org
60. 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
Design risk for dissolution
Space failure
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)
www.drugragulations.org
61. 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
www.drugragulations.org
63. 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
www.drugragulations.org
65. 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
2 7 varied 16 10 V type 40
DOE design
3 10 varied 2 30 V type 40
4 5 varied 16 30 V type 5
5 6 varied 2 10 Drum type 40
6 1 varied 16 10 Drum type 5
7 8 varied 2 30 Drum type 5
8 11 varied 16 30 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
www.drugragulations.org
66. Blend uniformity monitored using a process
analyzer
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
www.drugragulations.org
67. On-line NIR spectrometer 0.045
used to confirm scale up of
mean spectral standard deviation
0.04
blending 0.035
Blending operation complete 0.03
Pilot Scale
when mean spectral std. dev.
Full Scale
0.025
reaches plateau region 0.02
◦ Plateau may be detected
0.015
using statistical test or rules Plateau region
0.01
Feedback control to turn off
blender
0.005
Company verifies blend does
0
0 32 64 96 128
not segregate downstream Revolution Revolutions of Blender
Number of (block number)
◦ Assays tablets to confirm
uniformity
◦ Conducts studies to try to Data analysis model will be provided
segregate API Plan for updating of model available
Acknowledgement: adapted from ISPE PQLI Team
www.drugragulations.org
68. 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.
www.drugragulations.org
69. 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
www.drugragulations.org 69
70. Real Time Release Testing Controls
◦ Blend uniformity assured in blending step (on-line NIR spectrometer
for blending end-point)
◦ API assay is analysed 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)
www.drugragulations.org
71. Before a medicinal product is released for sale,
the Qualified Person responsible for its release
should take into account, among other aspects,
the conformity of the product to its specification.
In the case of approved RTRT, this conformity
would not routinely be supported by results of
end product testing.
Nevertheless a specification has to be established
and each batch of a product should comply with
it if tested.
www.drugragulations.org 71
72. The application for RTRT should be supported by
adequate validation of the RTR test method.
The relationship between the RTR test, including
acceptance criteria, and the end product test and
associated specification should be well
understood and, where applicable, supported by
substantial comparative data at commercial scale
(parallel testing).
www.drugragulations.org 72
73. When RTRT has been approved this should be
routinely used for batch release.
In the event that the test results of RTRT fail or
are trending toward failure, RTRT may not be
substituted by end-product testing.
Any failure should be investigated and trending
should be followed up appropriately.
Batch release decisions will need to be made
based on the results of these investigations, and
must comply with the content of the marketing
authorization and current GMP requirements.
www.drugragulations.org 73
74. Attributes (e.g. uniformity of content) that is
indirectly controlled by approved RTRT
should still appear in the Certificate of
Analysis for batches.
The approved method for end-product
testing should be mentioned and the results
given as ”Complies if tested” with a footnote:
”Controlled by approved Real Time Release
testing”.
www.drugragulations.org 74
75. In case of equipment failure the control strategy
provided in the application should include a
contingency plan specifying the use of alternative
testing or monitoring approaches on a temporary
basis.
In this situation, the alternative approach could
involve use of end-product testing or other options,
while maintaining an acceptable level of quality.
Testing or monitoring equipment breakdown needs
to be managed in the context of a deviation under
the Quality Management System and can be covered
by GMP.
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76. In principle, end product testing should not be substituted
for failure of an RTRT release method. The failure should
be investigated and followed up appropriately.
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77. When RTRT is applied, the attribute that is indirectly
controlled (e.g. sterility, uniformity of content)
together with a reference to the associated test
procedure, should still be included in the
specification as “Conforms if tested”.
The relationship between end-product testing,
material attributes, process monitoring and
acceptance criteria, should be fully explained and
justified.
In addition, the use of any prediction models should
be fully explained, justified and verified at the
commercial site.
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78. Batch release is the final decision to release
the product to the market regardless of
whether RTR testing or end-product testing is
employed.
End-product testing involves performance of
specific analytical procedures on a defined
sample size of the final product after
completion of all processing for a given batch
of that product.
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79. Results of real-time release testing are handled
in the same manner as end-product testing
results in the batch release decision.
Batch release involves an independent review of
batch conformance to predefined criteria through
review of testing results and manufacturing
records together with appropriate good
manufacturing practice (GMP) compliance and
quality system, regardless of which approach is
used.
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80. Real-time release testing does not necessarily
eliminate all end-product testing.
For example, an applicant can propose RTR
testing for some attributes only or not all.
If all critical quality attributes (CQAs) (relevant for
real-time release testing) are assured by in-
process monitoring of parameters and/or testing
of materials, then end-product testing might not
be needed for batch release.
Some product testing will be expected for certain
regulatory processes such as stability studies or
regional requirements.
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81. Product specifications (see ICH Q6A and Q6B)
still need to be established and met, when
tested.
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82. Even where RTR testing is applied, a stability
monitoring protocol that uses stability
indicating methods is required for all
products regardless of the means of release
testing (see ICH Q1A and ICH Q5C).
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83. RTR testing, if utilized, is an element of the
control strategy in which tests and/or
monitoring can be performed as in-process
testing (in-line, on-line, at-line) rather than
tested on the end product.
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84. Traditional sampling plans for in-process and
end-product testing involve a discrete sample
size that represents the minimal sampling
expectations.
Generally, the use of RTR testing will include
more extensive on-line/in-line measurement.
A scientifically sound sampling approach
should be developed, justified, and
implemented.
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85. In principle the RTR testing results should be
routinely used for the batch release decisions
and not be substituted by end-product testing.
Any failure should be investigated and trending
should be followed up appropriately.
However, batch release decisions should be made
based on the results of the investigations.
The batch release decision should comply with
the content of the marketing authorization and
GMP compliance.
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86. In-process testing includes any testing that
occurs during the manufacturing process of
drug substance and/or finished product.
Real-time release testing includes those in-
process tests that have a direct impact on the
decision for batch release through evaluation
of critical quality attributes.
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87. RTR testing can be based on measurement of
a surrogate (e.g., process parameter, material
attribute) that has been demonstrated to
correlate with an in-process or end-product
specification (see ICH Q8(R2); Annex, section
II.E (2.5)).
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88. Is there a potential for the measured CQA to
change downstream from the measurement point?
For example,
◦ Blend desegregation
◦ Loss of weight (e.g., chipping) after weighing step
◦ Hydrolytic degradation during aqueous film coating
Is identity determined at a point that is visually
unique?
◦ Mitigation of potential human and/or system error
◦ Unique identifiers on the intermediate when measured
(e.g., embossing, size, shape)
Risk assessment is valuable to exploring potential
failure modes
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89. Probe/sample location representative of entire
vessel
Sample frequency representative of entire batch
Sample acquisition time
◦ Suitable for system dynamics/mixing
Sample volume/mass
◦ Determine amount of sample measured
◦ Representative of unit dose
Sample interface
◦ Remains constant over the process (e.g., no fouling)
◦ Environmental factors (e.g., temperature, humidity)
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90. Specification still required in an RTRT approach
◦ (CFR §314.50(d) and CFR § 211.165(a))
Should be representative of actual measurement
◦ Can include in-process measurements (e.g., NIR
measurements for assay of uncoated tablets)
◦ Can include surrogate measurements (e.g., models for
dissolution)
◦ Methods should be appropriately validated (including
models used as surrogate measurements)
Alternatives can be included for stability
monitoring
Appropriate statistical criteria for large sample
sizes
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91. Calibration models for spectroscopic analysis
◦ NIR, Raman, FTIR
◦ Typically use chemometric models
Surrogate models for time consuming measurements
◦ Dissolution models relating process parameters and/or
material attributes to dissolution
Design space models
◦ Surface response plots
◦ Mechanistic models
Process control models
◦ Tunable controllers for individual unit operations
◦ Statistical process control & multivariate statistical process
control
Other models
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92. Calibration data
◦ Include potential sources of variance (e.g., operating conditions,
raw materials, scale)
◦ Cover intended areas of operation/design space
◦ Appropriate distribution of spectra over the analysis range
Model development
◦ Appropriate data pre-treatment
◦ Appropriate spectral ranges
◦ Number of model factors justified (avoid overfitting)
Model validation
◦ Internal validation using subsets of calibration data
◦ External validation using an independent data set
Robust and representative reference method
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93. NIR model results may change with time as new
sources of variability are introduced.
◦ Changes in raw material suppliers, process or analyzer
changes
Evaluation of outliers as part of maintenance.
◦ Can detect bad spectra or interface problems
◦ Usually implemented through examination of residuals
Procedures in place to monitor and update the model
◦ Done under the manufacturer’s quality system
◦ Include frequency and methods of periodical model
evaluation
Depth of validation done on updated model,
depending on level of change
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94. Robust calibration model
◦ Use an appropriate reference method
◦ Include variations in raw materials
◦ Cover the entire design space
Include an independent dataset for validation
Demonstrate model performance at
commercial scale
◦ Understand and work within the model limitations
and model assumptions
◦ Compare model results to a reference method for a
statistically acceptable number of batches
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95. Develop and document procedures on how to
evaluate and update the calibration model
◦ How to deal with OOS results
◦ Develop criteria for model re-calibration
Verify or recalibrate the model for process changes:
◦ Revising the operating ranges
◦ Change in raw materials
◦ Change in manufacturing equipment or measuring
instrument
Include plans for model maintenance/update in the
firm’s Quality System
◦ Tracking/trending (for process monitoring) included within
the Quality System
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96. Level of detail in submission should depend on the
importance of the model to the overall control strategy
Low Impact Model (e.g., Models for development)
◦ General discussion of how model was used to make decisions
during process development
Medium Impact Model (e.g., Design space models)
◦ More detailed information about model building, summary of
results and statistical analysis
◦ Discussion of how the model fits into the control strategy
High Impact Model (e.g., RTRT models)
◦ Full description of data collection, pretreatment and analysis
◦ Justification of model building approach
◦ Statistical summary of results
◦ Verification using data external to calibration set
◦ Discussion of approaches for model maintenance and update
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97. Quality What is
Product Profile Target Identify critical to
Product CQA the
Profile
CQA’s Patient
QRM
PAT
Risk Assessments
Identify
CMA &
Design Space CPP
Design space Control Strategy
Control Strategy
Continual
Improvement
PAT , PAT RTRT
SOP PAT RTRT 97