PHARMACEUTICAL PROCESS VALIDATION CURRENT REGULATORY ASPECTS

Md. Saddam Nawaz
QbD, Data Integrity and Validation Practitioner in Pharmaceuticals
gmp-geek.weebly.com
PHARMACEUTICAL
PROCESS VALIDATION
CURRENT REGULATORY ASPECTS

LIFECYCLE APPROACH OF
PHARMACEUTICAL PROCESS
The lifecycle approach is applied linking product and
process development, validation of the commercial
manufacturing process, and maintenance of the process
in a state of control (continued process verification)
during routine commercial production.
1

ORGANIZATIONS IMPLEMENTING
LIFECYCLE APPROACH - REASONS
• Global communication
• Logical approach – development, performance,
and maintenance
• Application to other processes (e.g. cleaning,
analytical method) equipment, facilities, etc.
2

ORGANIZATIONS NOT IMPLEMENTING
LIFECYCLE APPROACH - REASONS
• “Its only a guidance.”
• “Let’s see what happens.”
• “It’s only for USA.”
• “We will consider it if we get observations.”
• “It’s too costly”.
3

HISTORY AND DEVELOPMENT –
LIFECYCLE APPROACH
• Health Canada guidance (2004)
• FDA initial presentations (2005)
• ICH Q 9 (2005)
• ICH Q 10 (2007)
• FDA draft guidance (2008)
• ICH Q 8 (R2) (2009)
• Health Canada revision (2009)
• FDA guidance issued (2011)
• EMA draft guidance (2012)
• WHO draft guidance (2013)
• EMA guidance issued (2014)
• WHO guidance issued (2015)
4

LIFECYCLE APPROACH ENABALER
ICH Q8, Q9 and Q10
• High level guidances
(not prescriptive)
• Science and risk-based
• Encourages systematic
approaches
• Applicable over entire product
lifecycle
• Intended to work together to
enhance pharmaceutical product
quality
Nov 2005 & Nov 2008
5

PHARMACEUTICAL DEVELOPMENT - Q8(R2)
Quality Target Product Profile (QTPP)
Determine “potential” critical quality attributes (CQAs)
Link raw material attributes and process parameters to CQAs and perfor
m risk assessment
Develop a design space (optional and not required)
Design and implement a control strategy
Manage product lifecycle, including continual improvement
CQA’s
Product Profile
Risk Assessments
Design Space
Control Strategy
Continual
Improvement
6

QUALITY RISK MANAGEMENT PROCESS - Q9
Risk Review
RiskCommunication
Risk Assessment
Risk Evaluation
unacceptable
Risk Control
Risk Analysis
Risk Reduction
Risk Identification
Review Events
Risk Acceptance
Initiate
Quality Risk Management Process
Output / Result of the
Quality Risk Management Process
RiskManagementtools
• Flow charts
• Process Mapping
• Check sheets
• Cause and effect diagram
• FTA
• PHA
• Risk ranking and filtering
• FMEA
• FTA
• HAZOP
• HACCP
7

PHARMACEUTICAL QUALITY SYSTEM - Q10 8

PROCESS VALIDATION AND DRUG QUALITY
Effective process validation contributes significantly to assuring
drug quality. The basic principle of quality assurance is that a drug
should be produced that is fit for its intended use. This principle
incorporates the understanding that the following conditions exist:
• Quality, safety, and efficacy are designed or built into the
product.
• Quality cannot be adequately assured merely by in-process and
finished-product inspection or testing.
• Each step of a manufacturing process is controlled to assure
that the finished product meets all quality attributes including
specifications.
9

PROCESS VALIDATION DEFINITION
US FDA 1987 definition of PV:
“…establishing documented evidence which provides
a high degree of assurance that a specific process
will consistently produce a product meeting its pre-
determined specifications and quality attributes”
(old definition – Focusing exclusively on the qualification effort without
understanding the process and ensuring the process is maintained in a state of
control may not lead to adequate assurance of quality.)
10

FDA PROCESS VALIDATION DEFINITION
“Collection and evaluation of data,
from the process design stage
throughout commercial
production, which establishes
scientific evidence that a
process is capable of
consistently delivering quality
products. Process validation
involves a series of activities
over the lifecycle of the product
and process.”
(FDA Guidance to Industry Jan 24, 2011-Focus
on understanding and data collection as well as
analysis)
US FDA 2011 definition of PV:
11

FDA PROCESS VALIDATION GUIDANCE
(General Considerations for Process Validation )
• “Before …commercial distribution to consumers, a
manufacturer should have gained a high degree of assurance
in the performance of the manufacturing
process…consistently produce …”
• Manufacturers should: Understand the sources of variation
• Detect the presence and degree of variation
• Understand the impact of variation on the process and
product attributes
• Control the variation in a manner commensurate with risk
to process and product.”
“…to justify commercial distribution of the product.”
“… use ongoing programs to collect and analyze product
and process data … state if control of the process.”
12

(General Considerations for Process Validation )
• Good project management and good archiving to capture
scientific knowledge.
• Integrated team approach: Process engineering,
industrial pharmacy, analytical chemistry, microbiology,
statistics, manufacturing, and quality assurance.
• Scientific studies throughout the product lifecycle planned,
documented, and approved.
• Greater control over higher-risk attributes.
• Reevaluate risks throughout product/process lifecycle.
• Homogeneity with batch and consistency between
batches are goals of process validation.
13

(Phases of Process Validation)
FDA current thinking on PV revolves around :
• Stage 1 - Process Design -Development
and scale up activities
• Stage 2 - Process Qualification -Demonstrate
reproducible manufacturing through
conformance of PPQ lots
• Stage 3 - Continued Process Verification -
Routine manufacturing and monitoring
of performance
STAGE 1 AND STAGE 3 EMPHASIS – NEW PARADIGM
14

STAGE 1, PROCESS DESIGN
Information from product and process development
and scale up activities forms a basis for defining
commercial manufacturing process. It is expected
that manufacturers:
1. Build and capture process knowledge and understanding
2. Establishing a strategy for process control.
15

• Defining commercial manufacturing process
• API and excipient pharmaceutics
• Quality attributes
• Variability by different component lots, production operators,
environmental conditions, and measurement systems
• Use risk analysis tools to screen variables
• Define unit operations and process parameters
• Lab scale and pilot scale experiments
• Design of experiments (DoE)
• Identify critical process parameters
• Design space
• Normal operating range
• In-process controls
• Establish a strategy for process control
• Process analytical technology (PAT)
General Notes:
16

QbD STORY PER UNIT OPERATION
Process Vari
ables
Design of
Experiments
Quality
Risk Management
Illustrative Examples of Unit Operations:
QTPP
& CQAs
Design
Space
Control
Strategy
Batch
Release
Compression
Real Time
Release testing
(Assay, CU, Dissolution)
Blending
18

QbD EXAMPLE
Assumptions, Literature Review & Prior Knowledge
• API is designated as Amokinol
– Single, neutral polymorph
– Biopharmaceutical Classification System (BCS) class II – low solubility & high
permeability
– API solubility (dissolution) affected by particle size
• Crystallization step impacts particle size
– Degrades by hydrolytic mechanism
• Higher water levels and elevated temperatures will increase degradation
• Degradates are water soluble, so last processing removal point is the aqueous
extraction step
• Degradates are not rejected in the crystallization step
• In vitro-in vivo correlation (IVIVC) established – allows dissolution to be used
as surrogate for clinical performance
• Drug product is oral immediate release tablet
19

QbD EXAMPLE
QTPP and CQAs
Dosage form and strength
Immediate release tablet
containing 30 mg of active ingredient.
Specifications to assure safety
and efficacy during shelf-life
Assay,
Uniformity of Dosage Unit (content uniformity) and
dissolution.
Description and hardness Robust tablet able to withstand transport and handling.
Appearance Film-coated tablet with a suitable size to aid patient
acceptability and compliance.
Total tablet weight containing 30 mg of active ingredient
is 100 mg with a diameter of 6 mm.
Drug Product CQAs
•Assay
•Content Uniformity
•Dissolution
•Tablet Mechanical Strength
CQAs derived using Prior Knowledge (e.
g. previous experience of developing tablets)
CQAs may be ranked using quality risk assessment.
QTPP
20

QbD EXAMPLE
Rationale for Formulation & Process Selection
• Amokinol characteristics
– BCS class II (low solubility, high permeability)
– Susceptible to hydrolysis
– 30 mg per tablet (relatively high drug loading)
• Direct compression process selected
– Wet granulation increases risk of hydrolysis of Amokinol
– High drug loading enables content uniformity to be achieved without dry granulati
on operation
– Direct compression is a simple, cost-effective process
• Formulation Design
– Excipient compatibility studies exclude lactose due to API degradation
• Consider particle size aspects of API and excipients
– Dual filler system selected and proportions optimised to give good dissolution and
compression (balance of brittle fracture and plastic deformation consolidation mec
hanisms)
– Conventional non-functional film coat selected based on prior knowledge
21

QbD EXAMPLE
Tablet Formulation
22

QbD EXAMPLE
Direct Compression Process
Focus of
Story
23

QbD EXAMPLE
• Impact of formulation and process unit operations on Tablet
CQAs assessed using prior knowledge
– Also consider the impact of excipient characteristics on the CQAs
Drug
substance
particle size
Moisture
content in
manufacture
Blending Lubrication Compression Coating Packaging
- Low risk
- Medium risk
- High risk
Degradation
Content uniformity
Appearance
Friability
Stability-chemical
Stability-physical
in vivo performance
Dissolution
Assay
Initial Quality Risk Assessment
24

QbD EXAMPLE
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 Cma
x 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
25

QbD EXAMPLE
DOE Investigation of factors affecting Dissolution
Multifactorial DOE study of variable
s affecting dissolution
• Factors:
– API particle size [API]
unit: log D90, microns
– Mg-Stearate Specific Surface Area [MgS
t]
unit: cm2/g
– Lubrication time [LubT] unit: min
– Tablet hardness [Hard] unit: N
• Response:
– % API dissolved at 20 min [Diss]
• DOE design:
– RSM design
– Reduced CCF (quadratic model)
– 20+3 center point runs
Exp No Run Order API MgSt LubT Hard Diss
1 1 0.5 3000 1 60 101.24
2 14 1.5 3000 1 60 87.99
3 22 0.5 12000 1 60 99.13
4 8 1.5 3000 10 60 86.03
5 18 0.5 12000 10 60 94.73
6 9 1.5 12000 10 60 83.04
7 15 0.5 3000 1 110 98.07
8 2 0.5 12000 1 110 97.68
9 6 1.5 12000 1 110 85.47
10 16 0.5 3000 10 110 95.81
11 20 1.5 3000 10 110 84.38
12 3 1.5 12000 10 110 81
13 10 0.5 7500 5.5 85 96.85
14 17 1.5 7500 5.5 85 85.13
15 19 1 3000 5.5 85 91.87
16 21 1 12000 5.5 85 90.72
17 7 1 7500 1 85 91.95
18 4 1 7500 10 85 88.9
19 5 1 7500 5.5 60 92.37
20 11 1 7500 5.5 110 90.95
21 12 1 7500 5.5 85 91.95
22 13 1 7500 5.5 85 90.86
23 23 1 7500 5.5 85 89
Note: A screening DoE may be used first to identify
which of the many variables have the greatest effect
26

QbD EXAMPLE
Factors affecting Dissolution
-6
-5
-4
-3
-2
-1
0
API
MgSt
LubT
Hard
MgSt*LubT
%
Scaled & Centered Coefficients for Diss at 60min
N=23 R2=0.986 R2 Adj.=0.982
DF=17 Q2=0.981 RSD=0.725 Conf. lev.=0.95
MODDE 8 - 2008-01-23 10:58:52
API
Particle
Size
Mg
Stearate
SSA
Lubrication
Blending
time
Tablet
Hardness
Mg St*LubT
Key factors influencing
in-vitro dissolution:
- API particle size is the
dominating factor
(= CQA of API)
- Lubrication time has a
small influence
(= low risk parameter)
27

QbD EXAMPLE
Dissolution: Design Space
Diss (% at 20 min)
Area of potential risk
for dissolution failure
Design
Space
• Response surface plot for effect of API particle size and
magnesium stearate specific surface area (SSA) on dissolution
Graph shows interaction between
two of the variables: API particle size
and magnesium stearate specific
surface area
API particle size (Log D90)
28

QbD EXAMPLE
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
29

QbD EXAMPLE –
(Quality Risk Assessment)
Impact on Assay and Content Uniformity CQAs
© ICH, November 2010
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 humi
dity (GMP control)
Drug
substance
particle size
Moisture
content in
manufacture
Blending Lubrication Compression Coating Packaging
- Low risk
- Medium risk
- High risk
Degradation
Content uniformity
Appearance
Friability
Stability-chemical
Stability-physical
in vivo performance
Dissolution
Assay
30

QbD EXAMPLE -
Blending Process Control Options
Decision on conventional vs. RTR testing
31

QbD EXAMPLE -
Blending Process Control Option 1
DOE for the Blending Process Parameter Assessment
to develop Design Space
– Factors Investigated:
Blender type, Rotation speed, Blending time, API Particle size
DOEdesign
Experiment
No.
Run Condition
Blending time
(minutes)
Rotation speed
(rpm)
Blender
Particle size D90
(m)
1 2 varied 2 10 V type 5
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
32

QbD EXAMPLE -
Blend uniformity monitored using a process analyser
© ICH, November 2010
–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
33

QbD EXAMPLE -
Blend uniformity monitored using a process analyser
On-line NIR spectrometer used to co
nfirm scale up of blending
Blending operation complete when
mean spectral std. dev. reaches platea
u region
– Plateau may be detected using st
atistical test or rules
Feedback control to turn off blender
Company verifies blend does not seg
regate downstream
– Assays tablets to confirm unifor
mity
– Conducts studies to try to segreg
ate API
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0 32 64 96 128
Revolution (block number)
meanspectralstandarddeviation
Pilot Scale
Full Scale
Plateau region
Number of Revolutions of Blender
BAD FLOW BLEND GOOD FLOW BLEND
34

QbD EXAMPLE –
Content Uniformity
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.
35

QbD EXAMPLE – RTRT
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 D
osage Units if tested because assay assured by combination of blend unif
ormity assurance, API assay in blend and tablet weight control (if blend is
homogeneous then tablet weight will determine content of API)
36

QbD EXAMPLE –
Overall Control Strategy
Input materials meet specifications and are tested
– API PSD
Assay calculation
– Verify (API assay of blend by HPLC) X (tablet weight)
– Tablet weight by automatic weight control (feedback loop)
• For 10 tablets per sampling point, <2% RSD for weights
Content Uniformity
– On-line NIR criteria met for end of blending (blend homogeneity)
– Tablet weight control results checked
Dissolution
– Predictive model using input and process parameters for each batch cal
culates whether dissolution meets acceptance criteria
– Input and process parameters are all within the filed design space
• Compression force is controlled for tablet hardness
37

QbD EXAMPLE –
Drug Product Specifications
Use for stability, regulatory testing, site change, whenever RTR testing is not possi
ble
– Assay acceptance criteria: 95-105% of nominal amount (30mg)
– Uniformity of Dosage Unit acceptance criteria
– Test method: HPLC
Input materials meet specifications and are tested
– API PSD
Assay calculation (drug product acceptance criteria 95-105%)
– Verify (API assay of blend by HPLC) X (tablet weight)
– Tablet weight by automatic weight control (feedback loop)
• For 10 tablets per sampling point, <2% RSD for weights
Content Uniformity (drug product acceptance criteria meets compendia)
– On-line NIR criteria met for end of blending (blend homogeneity)
– Tablet weight control results checked
Dissolution (drug product acceptance criteria min 85% in 30 minutes)
– Predictive model using input and process parameters for each batch calculates whether dissolution me
ets acceptance criteria
– Input and process parameters are all within the filed design space
• Compression force is controlled for tablet hardness
Water content (drug product acceptance criteria NMT 3 wt%)
– Not covered in this case study
38

QbD EXAMPLE – SUMMARY
Iterative risk assessments
Initial QRA
PHA
FMEA FMEA FMEA
API
Crystallization
Blending
Lubrication
Compression
API PSD
Lubricant
Lubrication time
Hardness
Content
uniformity
Beginning
Design
Space
Control
strategy
Blending time
Lubricant
amount
Lubrication time
Pressure
Tablet weight
API PSD model
Blending time
Feedback control
Mg stearate SSA
Lubrication time
Pressure
Automated
Weight control
Blend
homogeneity
High Risk Medium Risk Low Risk
API PSD
39

Conclusion
• Better process knowledge is the outcome of QbD development
• Use Quality Risk Management proactively
• Multiple approaches for experimental design are possible
• Multiple ways of presenting Design Space are acceptable
– Predictive models need to be confirmed and maintained
• Real Time Release Testing (RTRT) is an option
– Opportunity for efficiency and flexibility
40

Challenges For The Manufacturer
• Additional efforts, resources, equipment and time
required to generate the knowledge and information
needed to design a commercial process with an effective
process control
• Additional time and effort, and technology tools,
including advanced statistic packages required to
acquire, process and manage data
• Potential increase in a number of scale up studies
41

STAGE 2, PROCESS QUALIFICATION
Process design is evaluated as being suitable for
reproducibly manufacturing commercial batches. This
stage has four elements:
Part 1
1. Design of a facility and qualification of utilities and
equipment
Part 2
2. Process performance qualification
3. PPQ protocol
4. PPQ protocol execution and report
42

General Notes:
• Confirmation at commercial scale of process design information
• Qualification of equipment, utilities, facilities
• Process Performance qualification
• Conclusion that process consistently produces quality product
• Conformance batches
• All support systems, documents, training, personnel, etc. in
place
• Target / nominal operating parameters within design space
• Additional testing
• Decision to “release process” for routine commercial
manufacturing
43

Design and qualification of facility, utilities and equipment
ASTM E2500-07 Lifecycle Phases
44

Design and qualification of facility, utilities and equipment
VMP
a) Validation policy.
b) The organizational structure for validation activities.
c) Summary of the facilities, systems, equipment, processes on
site and the current validation status.
d) Template formats to be used for protocols and reports.
e) Planning and scheduling.
f) Change control and deviation management for validation.
g) Handling of acceptance criteria
h) References to existing documents.
i) An assessment of the resources required.
j) The ongoing validation strategy, including revalidation and /
requalification, where applicable.
k) Confirmation that the materials used for validation are of the
required quality and suppliers are qualified to the appropriate
level.
45

Process Performance Qualification
• PPQ Protocol: Higher level of sampling, testing, and scrutiny of process
performance.
• Protocol should address:
– Operating parameters, processing limits, and raw material inputs
– Data to be collected and how evaluated
– Test to be performed and acceptance criteria
– Sampling plan – sampling points, number of samples, frequency
– Statistical methods used
– Statistical confidence levels (Process Capability)
– Provisions to address deviations and non-conformances
– Facility, utility, and equipment qualification
– Personnel training
– Status of analytical method validation
– Review and approval by appropriate departments and quality unit
46

Example: Sampling plan – sampling points
47

Process Capability, CpK
48

Example : Process Capability Tablet Hardness
49

PPQ LOTS
“The PPQ lots should be manufactured under normal
conditions by personnel expected to routinely perform each
step of each unit operation in the process. Normal operating
conditions should cover the utility systems (air handling and
water purification), material, personnel environment, and
manufacturing procedures.”
PQ report:
– Discuss all aspects of protocol
– Summarize and analyze data as specified in protocol
– Evaluate unexpected observations and additional data
– Summarize and discuss non-conformances
– Describe corrective actions or changes
– Clear conclusions
– Approval by appropriate departments and quality unit
50

• Since the approach to PPQ should be based on sound
science, objective measures such as statistical metrics
should be used.
• Managing deviations, aberrant test results, or other
information that has bearing on the validity of the
process.
51

General Notes:
• Activities to assure process remains in validated state
• A statistician or a person trained in statistical process control should:
–develop data collection plan
–decide on the use of appropriate statistical methods for measuring process
stability and capability
–carry out trending and analysis of process data
–wherever possible, quantitative tools should be used
• Annual Product Review (establish process history based on ongoing process
performance)
• Trend and assess data
• Study OOS and OOT (Out of Trend) data
• Timely monitoring of critical operating and performance parameters.
• Monitor product characteristics, materials, facilities, equipment, and SOP changes
• Continuous monitoring of process parameters and product attributes at PPQ
level should be used to establish variability estimates
52

Statistical Process Control
53

Batch to Batch : Capability Analysis
54

• Variability estimates should be used to establish sampling
and monitoring frequency
• The collected process data should be used to optimize or
improve the manufacturing process
• Production operators and quality unit personnel should be
encouraged to provide feedback on process performance
• Regular meetings between the quality unit and production
staff should be held to evaluate data, trends etc.
55

ITEMS TO BE REVIEWED
• Product and process data
• Relevant process trends
• Quality and records of incoming materials or components
• Monitor facilities, equipment, and SOP changes
• In-process material
• Finished products
• Defect complaints
• Study OOS and OOT (Out of Trend) data
• Deviations
• Yield variations
• Batch records
• Adverse event reports
• Production operator and quality staff feedback
These should help identify
possible product / process
improvements
56

• Have to have appropriate software capabilities in place
to access, aggregate, analyze and report data on-demand by
end users in a meaningful context, such as one that allows
correlation of upstream parameters with downstream
process outcomes
• Staff member(s) trained in statistics
• On demand access to data
• Regular meetings between quality unit and production
personnel to discuss process performance
• Knowledge management
57

PV FUNDAMENTAL CONCEPTS
• Scientific and technical basis in development
(Stage 1)
• Validation (Stage 2 ) confirms stage 1 development
• Acceptable (passing) results are expected.
• Validation is not
– R&D, Final stage of development process
– Optimization, Fine-tuning, or Debugging
• Monitor and maintain validated state throughout product
lifetime (Stage 3).
60

APPLICATIONS OF PV GUIDANCE
Process
• Manufacturing
• Packaging
• Cleaning
• Analytical
• Other
 All processes must be appropriately designed and developed,
demonstrate performance, and be monitored and
maintained.
 Processes must be continually improved.
61

APPLICATIONS OF PV GUIDANCE
FSE Qualification
• Equipment
• Facilities
• Utilities
• Control systems
• Computer systems
• Others
 All qualified equipment must be appropriately designed
and developed, demonstrate performance, and be
monitored and maintained.
 Qualified equipment must be continually Improved.
62

IMPLEMENTATION OF PV GUIDANCE
Team Approach
 Integration of Vendor, Validation, R&D, Production,
Engineering and QA functions
 Full support of senior management, are essential elements
for success.
63

Lifecycle Approach Status, 2018:
Lifecycle approach globally accepted
FDA, 2011
EMA, 2012-2016
WHO, 2013-2015
ASEAN, 2013
European Commission, 2014-2015
65Md. Saddm Naw
az
64

References
• FDA/ICH, (CDER and CBER), Q8(R2) Pharmaceutical Development, guidance for industry, Nove
mber 2009.
• FDA/ICH, (CDER and CBER), Q9 Quality Risk Management, guidance for industry, June 2006.
• FDA/ICH (CDER and CBER) Q10 Pharmaceutical Quality System, guidance for industry, April 20
09.
• ASTM E2500-07 Standard Guide for Specification, Design, and Verification of Pharmaceutical and
Biopharmaceutical Manufacturing Systems and Equipment.
• WHO, Guidelines on good manufacturing practices: validation, Appendix 7: non-sterile process vali
dation, WHO Technical Report Series No. 992, 2015
• EMA, Guideline on process validation for finished products - information and data to be provided i
n regulatory submissions, September 21, 2016
• Eudralex volume 4 (GMP guidelines), Annex 15 (Qualification and validation).
67

“Without change, there will be a constant waste,
during change there will be increased costs, but
after the improvement, margins will be higher
and the increased costs get recouped”
- J. M. Juran
THANK YOU FOR YOUR
ATTENTION!

PHARMACEUTICAL PROCESS VALIDATION CURRENT REGULATORY ASPECTS

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