Guidance for Industry 

 
 
Q8, Q9, & Q10 

Questions and Answers 

_____ 
 
Appendix 

Q&As from Training Sessions 

 
 
 
 
 
 
 
 
 
 
U.S. Department of Health and Human Services 

Food and Drug Administration 

Center for Drug Evaluation and Research (CDER) 

Center for Biologics Evaluation and Research (CBER) 

 
July 2012 

ICH 

   
 
Guidance for Industry 

 
Q8, Q9, & Q10 

Questions and Answers 

__________ 
 
Appendix 

Q&As from Training Sessions 

 
 
Additional copies are available from: 

 
Office of Communications 
 
Division of  Drug Information, WO51, Room  2201 
 
Center for Drug Evaluation  and Research  

Food and  Drug Administration 

10903 New Hampshire Ave., Silver Spring, MD 20993-0002 
  
Phone: 301-796-3400;  Fax: 301-847-8714 
 
druginfo@fda.hhs.gov 
  
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm  
 
and/or 

 
 Office of Communication, Outreach  and  

Development, HFM-40 
 
Center for Biologics Evaluation and Research 

 Food and Drug  Administration 

1401 Rockville Pike, Rockville, MD  20852-1448  

http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guidances/default.htm    
(Tel)  800-835-4709 or 301-827-1800  
   
 
U.S. Department of Health and Human Services 

Food and Drug Administration 

Center for Drug Evaluation and Research (CDER) 

Center for Biologics Evaluation and Research (CBER) 

 
July 2012 

ICH 

   
TABLE OF CONTENTS 

 
 
I.	   INTRODUCTION (1)....................................................................................................... 1 
 
II.	   WHAT ARE THE POINTS TO CONSIDER FOR CRITICALITY OF QUALITY 

ATTRIBUTES AND PROCESS PARAMETERS (2)................................................... 2 
 
A. 	  Considerations for Establishing CQAs and CPPs (2.1)..............................................................2
  
B. 	  Relationship of Criticality to Control Strategy (2.2) ..................................................................3 
 
III. 	  WHAT ARE THE POINTS TO CONSIDER FOR CONTROL STRATEGY (3)..... 3 
 
A. 	  Lifecycle of the Control Strategy (3.1).........................................................................................3
  
B. 	  Suitability of Control Strategy at Different Scales (3.2).............................................................4 
 
C. 	  Specifications and Certificate of Analysis (CoA) for Real-Time Release Testing (RTRT) 

(3.3)..................................................................................................................................................5
  
D. 	  Process for a Batch Release Decision (3.4)...................................................................................6
  
IV. 	  WHAT ARE THE POINTS TO CONSIDER FOR LEVEL OF 

DOCUMENTATION IN ENHANCED (QBD) REGULATORY SUBMISSIONS (4)
 
............................................................................................................................................. 7 
 
A. 	  Risk Management Methodologies (4.1)........................................................................................8 
 
B. 	  Design of Experiments (4.2)..........................................................................................................8
  
C. 	  Manufacturing Process Description (4.3) ....................................................................................9 
 
V. 	  WHAT ARE THE POINTS TO CONSIDER FOR ROLE OF MODELS IN 

QUALITY BY DESIGN (QBD) (5)................................................................................. 9 
 
A. 	  Categorization of Models (5.1)......................................................................................................9
  
B. 	  Developing and Implementing Models (5.2)..............................................................................11
  
C. 	  Model Validation and Model Verification During the Lifecycle (5.3).....................................11 
 
D. 	  Documentation of Model-related Information (5.4) .................................................................12 
 
VI. 	  WHAT ARE THE POINTS TO CONSIDER FOR DESIGN SPACE (6)................. 13  

A. 	  Development of Design Space (6.1).............................................................................................13
  
B. 	  Verification and Scale-up of Design Space (6.2)........................................................................14
  
C. 	  Documentation of Design Space (6.3).........................................................................................14 
 
D. 	  Lifecycle Management of a Design Space (6.4) .........................................................................15
  
VII. 	  WHAT ARE THE POINTS TO CONSIDER FOR PROCESS 

VALIDATION/CONTINUOUS PROCESS VERIFICATION (7) ............................ 15 
 
A. 	  General Considerations (7.1) ......................................................................................................16
  
B. 	  Continuous Process Verification (CPV) (7.2)............................................................................16
  
C. 	  Pharmaceutical Quality System (7.3).........................................................................................17 
 
  Contains Nonbinding Recommendations  
1 
Guidance for Industry  
 
Points to Consider for 

Q8, Q9, and Q10 

 
 
This guidance represents the Food and Drug Administrations (FDAs) current thinking on this topic.  It 
does not create or confer any rights for or on any  person and does not operate to bind FDA or the public.  
You can use an alternative approach if the approach satisfies the requirements of the applicable statutes 
and regulations. If you want to discuss an alternative approach, contact the FDA staff responsible for 
implementing this guidance.  If you cannot identify the appropriate FDA staff, call the appropriate 
number listed on the title page of this guidance.  
 
I.  INTRODUCTION (1)2  
The ICH Quality Implementation Working Group (Q-IWG) has prepared this Points to Consider 
document covering topics relevant to the implementation of ICH Q8(R2), Q9, and Q10, which 
supplement the existing guidance Q8, Q9, and Q10 Questions & Answers3 and workshop 
training materials4 already produced by this group. They should be considered all together. 
The points to consider are based on questions raised during the ICH Q-IWG training workshop 
sessions in the three regions. The points to consider are not intended to be new guidance. They 
are intended to provide clarity to both industry and regulators and to facilitate the preparation, 
assessment, and inspection related to applications filed for marketing authorizations. 
The development approach should be adapted based on the complexity and specificity of product 
and process; therefore, applicants are encouraged to contact regulatory authorities regarding 
questions related to specific information to be included in their application. 
Using the Quality by Design (QbD) approach does not change regional regulatory requirements 
but can provide opportunities for more flexible approaches to meet them.  In all cases, good 
manufacturing practice (GMP) compliance is expected. 
 
                                                 
1  This guidance was developed within  the Quality Implementation Working Group of the International Conference 
on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) and has  
been subject to consultation  by the regulatory parties, in  accordance with the ICH process.  This  document has been  
endorsed by the ICH Steering Committee at Step 4  of the ICH process, June  2011 and November  2011.  At  Step 4 of  
the process, the final draft is recommended  for adoption to the regulatory bodies of  the European Union, Japan, and 
the United States. 
2 Arabic numbers reflect the organizational breakdown of the document endorsed  by the ICH Steering Committee at 
Step 4 of the ICH process, June 2011 and November 2011.  
 
3  We  update  guidances periodically.  To make sure you  have the most recent version of a guidance, check the CDER  
guidance page  at  http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm. 
 
4 Materials for ICH quality training  on Q8, Q9, and  Q10 are available at www.ich.org under ICH Training. 
  1
  Contains Nonbinding Recommendations  
II. 	 WHAT ARE THE POINTS TO CONSIDER FOR CRITICALITY OF QUALITY 
ATTRIBUTES AND PROCESS PARAMETERS (2) 
Scientific rationale and quality risk management (QRM) processes are used to reach a conclusion 
on what are critical quality attributes (CQAs) and critical process parameters (CPPs) for a given 
product and process 
The quality target product profile (QTPP) describes the design criteria for the product, and 
should therefore form the basis for development of the CQAs, CPPs, and control strategy. 
The information developed to determine CQAs and CPPs will help to:  
   Develop control strategy 
   Ensure quality of the product throughout the product lifecycle  
   Increase product and process knowledge 
   Increase transparency and understanding for regulators and industry 
   Evaluate changes 
 
A. 	 Considerations for Establishing CQAs and CPPs (2.1) 
The introduction of ICH Q9 states that ...the protection of the patient by managing the risk to 
quality should be considered of prime importance.  The QTPP provides an understanding of 
what will ensure the quality, safety, and efficacy of a specific product for the patient and is a 
starting point for identifying the CQAs. 
As part of risk assessment, risk analysis, as defined by ICH Q9 is the qualitative or quantitative 
process of linking the likelihood of occurrence and severity of harm. In some risk management 
tools, the ability to detect the harm (detectability) also factors in the estimation of risk.  
Relationship between risk and criticality: 
	  Risk includes severity of harm, probability of occurrence, and detectability, and therefore 
the level of risk can change as a result of risk management.   
	  Quality attribute criticality is primarily based upon severity of harm and does not change 
as a result of risk management.   
	  Process parameter criticality is linked to the parameters effect on any critical quality 

attribute.  It is based on the probability of  occurrence and detectability and therefore can
  
change as a result of risk management.  

Considerations for identifying and documenting CQAs can include the: 
	  Severity of harm (safety and efficacy) before taking into account risk control and the 

rationale for distinguishing CQAs from other quality attributes; 

	  Link to the patient as described in the QTPP; 
	  Basis on which the CQAs have been developed (e.g., prior knowledge, scientific first 

principles, and experimentation); and 

  2
  Contains Nonbinding Recommendations  
	  Inter-dependencies of the different CQAs. 
Considerations for identifying and documenting CPPs can include the: 
  Risk assessment and experimentation to establish the linkage between potential CPPs and 
CQAs; 
  Basis on which the CPPs have been identified (e.g., prior knowledge, scientific first 
principles, QRM, design of experiment (DoE), and other appropriate experimentation); 
  Interdependencies of the different CPPs; and 
  Selected control strategy and the residual risk. 
CQAs and CPPs can evolve throughout the product lifecycle, for example: 
	  Change of manufacturing process (e.g., change of synthetic route) and 
	  Subsequent knowledge gained throughout the lifecycle (e.g., raw material variability, 

pharmacovigilance, clinical trial experience, and product complaints). 

 
B.  Relationship of Criticality to Control Strategy (2.2) 
The identification and linkage of the CQAs and CPPs should be considered when designing the 
control strategy. A well-developed control strategy will reduce risk but does not change the 
criticality of attributes. 
The control strategy plays a key role in ensuring that the CQAs are met and, hence, that the 
QTPP is realized. 
 
III.   WHAT ARE THE POINTS TO CONSIDER FOR CONTROL STRATEGY (3) 
 
A.  Lifecycle of the Control Strategy (3.1) 
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 following points can be considered: 
	   Development of control strategy:  
	   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).  
	   Continual improvement of the control strategy:  
  3
  Contains Nonbinding Recommendations  
 	  Consideration should be given to improving the control strategy over the lifecycle (e.g., 
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.  
	   Change management of 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 for the same product: 
 	  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. 
	   Knowledge management: 
	   Knowledge management is an important factor in assuring the ongoing effectiveness of 
the control strategy. 
	   For contract manufacturing, knowledge transfer in both directions between the parties 
should be considered, particularly for model maintenance and/or updates, application 
of design space, and control strategies incorporating real-time release testing. 
 
B.  Suitability of Control Strategy at Different Scales (3.2) 
 
	   Management of risk on scale-up: 
	   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). See the ICH Q-IWG case study for examples.  
  4
  Contains Nonbinding Recommendations  
	   Scale-up considerations for elements of Control Strategy: 
	   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  
 
C. 	 Specifications and Certificate of Analysis (CoA) for Real-Time Release 
Testing (RTRT) (3.3) 
The purpose of specifications and CoAs remains the same in the case of RTRT, but the way to 
develop them is different. Real-time release  tests(RTRT) are considered to be specification 
testing methods and follow the established regional regulatory requirements for release 
specifications (as interpreted in e.g., ICH Q6A and ICH Q6B) together with other regional 
regulatory requirements (e.g., formats, GMP, batch acceptance decisions). 
The use of RTRT has been addressed (see ICH Q8(R2), section II.E (2.5); ICH Q8, Q9, and Q10 
Q&As, section II.B (2.2)). The following points should be considered when developing a 
specification and CoA for RTRT: 
  Quality attributes: 
	   Not all CQAs need to be included in the specification. 
	   The attribute to be measured (e.g., surrogate for a CQA) can depend on the point of 
testing and/or control (e.g., materials, process steps, process parameters). 
	   Linking of the measured attribute to CQA and QTPP 
  Methods of control: 
	   The type of control used (e.g., models, process analytical technology (PAT), test of 
isolated material, end product test, stability and regulatory test) 
	   Reference to the testing method used, if relevant 
  5
  Contains Nonbinding Recommendations  
	   Validation of control method 
  Acceptance criteria:  
	   Acceptance criteria at control point 
	   Criteria for stability and regulatory testing 
  CoA elements:  
	   Reported results (e.g., values calculated from models, established calibrations, and 
actual test results) 

   Acceptance criteria related to the method used 

   Method references 

 
D.  Process for a Batch Release Decision (3.4) 
Different development approaches lead to different control strategies. Regardless of the control 
strategy, the batch release process should be followed.  For a batch release decision, several 
elements should be considered.  See in the figure below an illustration of the elements of the 
batch release process leading to the batch release decision. 
Elements of  a 
Batch Release Process 
ments  co1m. Rpeligaunlcaet odrayt  a  
uring GMP ele 2c. uSryresntet mba rteclha tmeda nduaftaac ftourr ethde   B ReleasBea tdcehc  ision 
Manufact ontrol Startegy &  o34n. . P tPhrroeod mduucactnt-u-rrefealalcattteuedrd idn dagat tapa  rb  ofarcosemesds         atch  record CoA  bay cmcoarnduifnagc ttuor erer gionaflo  pr rtohcee mduarrekest  
C quality control 
Batch  Batch 
Elements defined regarding outsourced  not released  released 
activities, if applicable 
Batch rejection  Distribution 
 
 
1. 	  Regulatory compliance data:  
There are regional differences in the regulation of batch release across the ICH 
regions (e.g., Qualified Person (EU), Good Quality Practice (Japan), Head of Quality 
Unit (U.S.)) and the manufacturing licensing procedure.  The PQS facilitates 
implementing and managing control strategy and batch release, notably through 
elements of a global approach (corporate/site/contractor).  The PQS elements also 
facilitate regulatory compliance (e.g., changes that call for variation of the marketing 
authorization), including changes at manufacturing sites (e.g., changes regarding 
facilities, utilities, and equipment). 
2. 	  System-related data for the current batch manufactured (e.g., environmental, 
facility, utilities and equipment) 
In the enhanced approach, there is an increased focus on process monitoring, which 
can provide the opportunity to perform continuous process verification.  Any 
  6
  Contains Nonbinding Recommendations  
deviation or atypical event that occurs during manufacturing (e.g., involving the 
manufacturing process, facility, personnel, testing) should be recorded and assessed, 
properly handled under the PQS (including corrective and preventive action 
(CAPA)), and closed out prior to release. 
3.   Product-related data based on the manufacturing process 
Elements of the control strategy are defined and proposed in the marketing 
authorization dossier and agreed to by the regulators.  Manufacturers should define, 
manage, and monitor product-related data from batches manufactured according to 
the control strategy.  These will be regularly assessed and reviewed during audits and 
inspections.  
4.   Product-related data from quality control 
Results from end product testing and/or RTRT provide data based on which a CoA 
can be issued, in compliance with the specification as part of the release decision. 
The batch release process leading to the batch release decision can be performed by more than 
one quality individual depending on the regional regulatory requirements and company policy: 
  Batch release by manufacturer or contractor for internal purposes  
  Batch release by manufacturing authorization holder for the market  
 
IV. 	 WHAT ARE THE POINTS TO CONSIDER FOR LEVEL OF 
DOCUMENTATION IN ENHANCED (QBD) REGULATORY SUBMISSIONS (4) 
This document is intended to provide suggestions on the type of information and the level of 
documentation that is appropriate to support a proposal for enhanced (QbD) approach.  The type 
of information, as suggested in this document, is considered supportive and is intended to 
facilitate assessment and inspection without increasing the regulatory requirement.  Submitted 
information should be organized in a clear manner and provide the regulators with sufficient 
understanding of the companys development approach; this information will be important to the 
evaluation of the proposed control strategy. Companies might consider, especially for QbD-
containing submissions, an internal peer review process to assure quality, clarity, and adequacy 
of the regulatory submission. 
For submissions containing QbD elements (e.g., RTRT, design space), it is helpful for regulators 
to have a statement by the applicant describing the proposed regulatory outcome and 
expectations. 
It is important to realize that not all the studies performed and/or data generated during product 
development need to be submitted.  However, sufficient supporting information and data should 
be submitted in the application to address the following: 
	  The scientific justification of the proposed control strategy; 
	  The scientific rationale for the studies conducted; 
	  A concise description of methodologies used to conduct these studies and to analyze the 

generated data; and 

	  The summary of results and conclusions drawn from these studies. 
  7
  Contains Nonbinding Recommendations  
 
The following sections include examples of background information that can be considered by 
both companies and regulatory authorities to assure scientific risk-based regulatory decisions.  
 
A.  Risk Management Methodologies (4.1) 
Following determination of the quality target product profile (QTPP) of the product under 
development, the applicant can use quality risk management (QRM, ICH Q9) tools to rank and 
select quality attributes (including material attributes) and/or process parameters that should be 
further evaluated and/or controlled within appropriate ranges to ensure the desired product 
quality. The applicant should consider providing information of sufficient detail to demonstrate 
how the conclusions were reached, which can include: 
	  The scientific rationale for designation of QTPP and identification of corresponding CQAs 
(critical quality attributes); 
	  Material attributes, process parameters, and prior knowledge that were considered during 
risk assessment, preferably provided in a concise/tabulated form;  
	  Relevant known risk factors (e.g., degradation, solubility); 
	  The scientific rationale and basis for the risk assessment as part of risk management and 
experiments that determined the final criticality of quality attributes and process parameters;  
	  Identification of potential residual risk that might remain after the implementation of the 

proposed control strategy (e.g., movements to commercially unverified areas of design 

space) and discussion of approaches for managing the residual risk;  

	  A list of critical and other quality attributes and process parameters;  
	  The linkage between CPPs , CQAs, and the QTPP; and 
	  Comment on the impact of the following on risk assessment:  (a) interaction of attributes and 
process parameters, (b) effect of equipment and scale. 
 
B.  Design of Experiments (4.2) 
The factors to be studied in a DoE could come from the risk assessment exercise or prior 
knowledge. Inclusion of a full statistical evaluation of the DoEs performed at early development 
stages (e.g., screening) is not expected.  A summary table of the factors and ranges studied and 
the conclusions reached will be helpful.  For DoEs involving single- or multiple-unit operations 
that are used to establish CPPs and/or to define a Design Space (DS), the inclusion of the 
following information in the submission will greatly facilitate assessment by the regulators: 
	  Rationale for selection of DoE variables (including ranges) that would be chosen by risk 

assessment (e.g., consideration of the potential interactions with other variables).  

	  Any evidence of variability in raw materials (e.g., drug substance and/or excipients) that 

would have an impact on predictions made from DoE studies. 

	  Listing of the parameters that would be kept constant during the DoEs and their respective 
values, including comments on the impact of scale on these parameters.  
  8
  Contains Nonbinding Recommendations  
	  Type of experimental design used and a justification of its appropriateness, including the 

power of the design. 

	  Factors under study and their ranges can be presented in a tabular format. Submitters should 
indicate if the factors are expected to be scale-dependent. 
	  Reference to the type of analytical methods (e.g., high-pressure liquid chromatography 
(HPLC), near infrared (NIR)) used for the evaluation of the data and their suitability for their 
intended use (e.g., specificity, detection limit). 
	  Results and statistical analysis of DoE data showing the statistical significance of the factors 
and their interactions, including predictions made from DoE studies relevant to scale and 
equipment differences.  
 
C. 	 Manufacturing Process Description (4.3) 
While preparing regulatory submissions, applicants should consider: 
	  Regional regulatory requirements with regard to the level of detail in describing 

manufacturing processes. 

	  Describing the proposed design space, including critical and other parameters studied, and 
its role in the development of the control strategy.  
	  Manufacturing changes should be managed in accordance with regional regulatory 
requirements.  Where relevant, applicants can also consider submitting postapproval change 
management plans or protocols to manage postapproval manufacturing changes based on 
regional requirements. 
 
V. 	 WHAT ARE THE POINTS TO CONSIDER FOR ROLE OF MODELS IN 
QUALITY BY DESIGN (QBD) (5) 
A model is a simplified representation of a system using mathematical terms.  Models can 
enhance scientific understanding and possibly predict the behavior of a system under a set of 
conditions.  Mathematical models can be used at every stage of development and manufacturing. 
They can be derived from first principles reflecting physical laws (such as mass balance, energy 
balance, and heat transfer relations), or from data, or from a combination of the two.  There are 
many types of models and the selected one will depend on the existing knowledge about the 
system, the data available, and the objective of the study.  This document is intended to highlight 
some points to consider when developing and implementing mathematical models during 
pharmaceutical product development, manufacturing and throughout the product lifecycle.  Other 
approaches not described in this document can also be used. 
 
A. 	 Categorization of Models (5.1) 
Models can be categorized in multiple ways.  The categorization approaches used throughout this 
document are intended to facilitate the use of models across the lifecycle, including 
development, manufacturing, control, and regulatory processes.  
  9
  Contains Nonbinding Recommendations  
For the purposes of regulatory submissions, an important factor to consider is the models 
contribution in assuring the quality of the product.  The level of oversight should be 
commensurate with the level of risk associated with the use of the specific model.  The following 
is an example of such a categorization: 
1. 	 Low-Impact Models:  
These models are typically used to support product and/or process development (e.g., 
formulation optimization).  
2. 	 Medium-Impact Models:  
Such models can be useful in assuring quality of the product but are not the sole 
indicators of product quality (e.g., most design space models, many in-process controls). 
3. 	 High-Impact Models: 
A model can be considered high impact if prediction from the model is a significant 
indicator of quality of the product (e.g., a chemometric model for product assay, a 
surrogate model for dissolution). 
For the purpose of implementation, models can also be categorized on the basis of the intended 
outcome of the model.  Within each of these categories, models can be further classified as low, 
medium or high, on the basis of their impact in assuring product quality. 
Some examples of different categories based on intended use are: 
  Models for supporting process design:  
This category of models includes (but is not limited to) models for formulation optimization, 
process optimization (e.g., reaction kinetics model), design space determination, and scale-
up. Models within this category can have different levels of impact.  For example, a model 
for design space determination would generally be considered a medium-impact model, 
while a model for formulation optimization would be considered a low-impact model.  
  Models for supporting analytical procedures:  
In general, this category includes empirical (i.e., chemometric) models based on data 
generated by various Process Analytical Technology (PAT)-based methods, for example, a 
calibration model associated with a near infrared (NIR)-based method.  Models for 
supporting analytical procedures can have various impacts depending on the use of the 
analytical method.  For example, if the method is used for release testing, then the model 
should be high-impact. 
  Models for process monitoring and control: 
This category includes, but is not limited to: 
  Univariate Statistical Process Control (SPC)  or Multivariate Statistical Process Control 
(MSPC)-based models:  
These models are used to detect special cause variability; the model is usually derived 
and the limits are determined using batches manufactured within the target conditions.  If 
an MSPC model is used for continuous process verification along with a traditional 
method for release testing, then the MSPC model would likely be classified as a medium-
impact model.  However, if an MSPC model is used to support a surrogate for a 
traditional release testing method in an RTRT approach, then the model would likely be 
classified as a high-impact model. 
  10
  Contains Nonbinding Recommendations  
   Models used for process control (e.g., feed forward or feedback).  
Data-driven models should be developed through appropriately designed experiments. 
These models are typically medium-impact or high-impact.  For example, a feed forward 
model to adjust compression parameters on the basis of incoming material attributes 
could be classified as a medium-impact model. 
 
B.  Developing and Implementing Models (5.2) 
The following steps, if applicable, can be followed in a sequential manner, but occasionally, it 
might be appropriate to repeat an earlier step, thus imparting an iterative nature to this process.  
The overall steps are: 
1. 	  Defining the purpose of the model. 
2. 	  Deciding on the type of modeling approach (e.g. mechanistic or empirical) and the 
possible experimental/sampling methodology to be used to support the model 
development.  
3. 	  Selecting variables for the model; this is typically based on risk assessment, underlying 
physicochemical phenomena, inherent process knowledge, and prior experience. 
4. 	  Understanding the limitations of the model assumptions to:  
a. Correctly design any appropriate experiments;  
b. Interpret the model results; and  
c. Include appropriate risk-reduction strategies. 
5. 	  Collecting experimental data to support model development. These data can be collected 
at laboratory, pilot, or commercial scale, depending on the nature of the model.  It is 
important to ensure that variable ranges evaluated during model development are 
representative of conditions that would be expected during operation. 
6. 	  Developing model equations and estimating parameters, based on a scientific 

understanding of the process and collected experimental data. 

7. 	  Validating the model, as appropriate (see section V.C (5.3)). 
8. 	  In certain cases, evaluating the impact of uncertainty in model prediction on product 
quality and, if appropriate, defining an approach to reduce associated residual risk (e.g., 
by incorporating appropriate control strategies (this can apply to high-impact and 
medium-impact models)). 
9. 	  Documenting the outcome of model development, including model assumptions, and 
developing plans for verification and update of the model throughout the lifecycle of the 
product. The level of documentation would be dependent on the impact of the model (see 
section V.D (5.4)). 
 
C.  Model Validation and Model Verification During the Lifecycle (5.3) 
Model validation is an essential part of model development and implementation.  Once a model 
is developed and implemented, verification continues throughout the lifecycle of the product. 
  11
  Contains Nonbinding Recommendations  
The following elements can be considered for model validation and verification and generally 
are appropriate for high-impact models.  In the case of well-established first principles-driven 
models, prior knowledge can be leveraged to support model validation and verification, if 
applicable.  The applicability of the elements listed below for medium-impact or low-impact 
models can be considered on a case-by-case basis. 
	   Setting acceptance  criteria for the model relevant to the purpose of the model and to its 
expected performance.  In setting the acceptance criteria, variability in sampling procedure 
(e.g., for blending) could also be considered.  In situations where the model is to be used to 
support a surrogate for a traditional release testing method, the accuracy of the model 
performance versus the reference method could be considered.  For example, a multivariate 
model (e.g. a partial least squares (PLS) model), when appropriate, can be used as a surrogate 
for traditional dissolution testing. In this case, the PLS model should be developed in terms of 
in-process parameters and material attributes and can be used to predict dissolution.  One of 
the ways to validate and verify model performance in this case would be to compare accuracy 
of prediction of the PLS model with the reference method (e.g., a traditional dissolution 
method). 
	   Comparison of the accuracy of calibration versus the accuracy of prediction.  This can often 
be approached through internal cross-validation techniques using the same data as the 
calibration data set. 
	   Validating the model using an external data set (i.e., a data set from experiments/batches not 
used for model-building). 
	   It can be beneficial to verify the prediction accuracy of the model by parallel testing with 
the reference method during the initial stage of model implementation, and this testing can be 
repeated throughout the lifecycle, as appropriate.  If models are used to support a design space 
at commercial scale or are part of the control strategy, it is important to verify the model at 
commercial scalefor example, if a calibration model associated with an NIR-based method 
is developed at the laboratory scale and the method is then transferred to and used in 
commercial scale. In addition, the data sets used for calibration, internal validation, and 
external validation should take into account the variability anticipated in future routine 
production (e.g., a change in the source of raw material that might impact NIR prediction).  
Low-impact models typically do not call for verification. 
Approaches for model verification can be documented according to the PQS of the company and 
can include the following:  a risk-based frequency of comparing the models prediction with that 
of the reference method, triggers for model updates (e.g., because of changes in raw materials or 
equipment), procedures for handling model-predicted Out of Specification (OOS) results, 
periodic evaluations, and approaches to model recalibration. 
 
D.  Documentation of Model-related Information (5.4) 
The level of detail for describing a model in a regulatory submission is dependent on the impact 
of its implementation in assuring the quality of the product.  For the various types of models, the 
applicant can consider including: 
  12
  Contains Nonbinding Recommendations  
1.  Low-Impact Models: 
A discussion of how the models were used to make decisions during process 
development. 
2.  Medium-Impact Models:  
Model assumptions, a tabular or graphical summary of model inputs and outputs, relevant 
model equations (e.g., for mechanistic models) either in the submission or via a 
reference, statistical analysis where appropriate, a comparison of model prediction with 
measured data, and a discussion of how the other elements in the control strategy help to 
mitigate uncertainty in the model, if appropriate. 
3. High-Impact Models: 
Data and/or prior knowledge (e.g., for established first principles-driven models) such as 
model assumptions, appropriateness of the sample size, number and distribution of  
samples, data pretreatment, justification for variable selection, model inputs and outputs, 
model equations, statistical analysis of data showing fit and prediction ability, rationale 
for setting of model acceptance criteria, model validation (internal and external), and a 
general discussion of approaches for model verification during the lifecycle. 
 
VI.  WHAT ARE THE POINTS TO CONSIDER FOR DESIGN SPACE (6) 
A.  Development of Design Space (6.1) 
A design space can be updated over the lifecycle as additional knowledge is gained. Risk 
assessments, as part of the risk management process, help steer the focus of development studies 
and define the design space.  Operating within the design space is part of the control strategy.  
The design space associated with the control strategy ensures that the manufacturing process 
produces a product that meets the Quality Target Product Profile (QTPP) and Critical Quality 
Attributes (CQAs). 
Since design spaces are typically developed at small scale, an effective control strategy helps 
manage potential residual risk after development and implementation.  When developing a 
design space for a single-unit operation, the context of the overall manufacturing process can be 
considered, particularly immediate upstream and downstream steps that could interact with that 
unit operation.  Potential linkages to CQAs should be evaluated in design space development. 
In developing design spaces for existing products, multivariate models can be used for 
retrospective evaluation of historical production data.  The level of variability present in the 
historical data will influence the ability to develop a design space, and additional studies might 
be appropriate. 
Design spaces can be based on scientific first principles and/or empirical models.  An 
appropriate statistical design of experiments incorporates a level of confidence that applies to the 
entire design space, including the edges of an approved design space.  However, when operating 
the process near the edges of the design space, the risk of excursions from the design space could 
be higher because of normal process variation (common cause variation). The control strategy 
helps manage residual risk associated with the chosen point of operation within the design space.  
When changes are made (e.g., process, equipment, raw material suppliers), results of risk review 
  13
  Contains Nonbinding Recommendations  
can provide information regarding additional studies and/or testing that might verify the 
continued applicability of the design space and associated manufacturing steps after the change. 
Capturing development knowledge and understanding contributes to design space 
implementation and continual improvement.  Different approaches can be considered when 
implementing a design space (e.g., process ranges, mathematical expressions, or feedback 
controls to adjust parameters during processing (see also Figure 1d in ICH Q8(R2)).  The chosen 
approach would be reflected in the control strategy to assure the inputs and process stay within 
the design space. 
 
B.  Verification and Scale-up of Design Space (6.2) 
Although the entire design space does not have to be reestablished (e.g., DoE) at commercial 
scale, design spaces should be initially verified as suitable prior to commercial manufacturing.  
Design space verification should not be confused with process validation.  However, it might be 
possible to conduct verification studies of the performance of the design space scale-dependent 
parameters as part of process validation.  Design space verification includes monitoring or 
testing of CQAs that are influenced by scale-dependent parameters.  Additional verification of a 
design space might be triggered by changes (e.g., site, scale, or equipment).  Additional 
verification is typically guided by the results of risk assessments of the potential impacts of the 
change(s) on design space. 
A risk-based approach can be applied to determine the design of any appropriate studies for 
assessment of the suitability of a design space across different scales.  Prior knowledge and first 
principles, including simulation models and equipment scale-up factors, can be used to predict 
scale-independent parameters.  Experimental studies could help verify these predictions. 
 
C.  Documentation of Design Space (6.3) 
Information on design space can be accommodated in the common technical document (CTD) in 
different presentation formats.  Some examples of format and location in the document are 
covered in ICH Q8(R2). Inclusion of a clear statement of the proposed design space and the 
location of the filed information (hyperlinked, where possible) in regulatory submissions should 
be considered to facilitate the regulatory process. 
Some aspects of the design space that could be considered for inclusion in the regulatory 
submission: 
	   The design space description, including critical and other relevant parameters. The design 
space can be presented as ranges of material inputs and process parameters, graphical 
representations, or through more complex mathematical relationships. 
	   The relationship between the inputs (e.g., material attributes and/or process parameters) and 
the CQAs, including an understanding of the interactions among the variables.  
	   Data supporting the design space, such as prior knowledge, conclusions from risk assessments 
as part of QRM, and experimental studies with supporting data, design assumptions, data 
analysis, and models. 
  14
  Contains Nonbinding Recommendations  
	   The relationship between the proposed design space and other unit operations or process 
steps. 
	   Results and conclusions of the studies, if any, of a design space across different scales. 
	   Justification that the control strategy ensures that the manufacturing process is maintained 
within the boundaries defined by the design space. 
 
D.  	Lifecycle Management of a Design Space (6.4) 
The control strategy used for implementation of a design space in production depends on the 
capabilities of the manufacturing site.  The batch records reflect the control strategy used. For 
example, if a mathematical expression is used for determining a process parameter or a CQA, the 
batch record would include the input values for variables and the calculated result.  
As part of the technology transfer of a design space to a site and throughout the lifecycle, it is 
important to share the knowledge gained during development and implementation that is relevant 
for using that design space both on the manufacturing floor and under the PQS of the company 
or site. This knowledge can include results of risk assessments, assumptions based on prior 
knowledge, and statistical design considerations.  Linkages among the design space, control 
strategy, CQA, and QTPP are an important part of this shared knowledge.  
Each company can decide on the approach used to capture design space information and 
movements within the design space under the applicable PQS, including additional data gained 
through manufacturing experience with the design  space.  In the case of changes to an approved 
design space, appropriate filings should be made to meet regional regulatory requirements. 
Movement within the approved design space, as  defined in the ICH Q8(R2) glossary, does not 
call for a regulatory filing.  For movement outside the design space, the use of risk assessment 
could be helpful in determining the impact of the change on quality, safety, and efficacy and the 
appropriate regulatory filing strategy, in accordance with regional requirements. 
 
VII. 	 WHAT ARE THE POINTS TO CONSIDER FOR PROCESS 
VALIDATION/CONTINUOUS PROCESS VERIFICATION (7) 
These points to consider are intended to illustrate how using principles from ICH Q8(R2), Q9, 
and Q10 can support an alternative process validation approach and are applicable to drug 
substance and drug product. They emphasize a more holistic approach to Process Validation 
across the product lifecycle, including continuous process verification (CPV). 
The main objective of process validation is to confirm that a process will consistently yield a 
product meeting its predefined quality criteria.  This can be achieved in different ways, including 
a traditional approach, CPV, or a combination of  these.  There are different regional regulatory 
approaches to process validation. However, the concepts in this document generally are 
universally accepted, as is the appropriate use of Quality Risk Management (QRM) principles in  
this context.  
 
  15
  Contains Nonbinding Recommendations  
A.  General Considerations (7.1) 
In the traditional process validation approach, the focus is on a limited number of batches at 
discrete time-points during the product lifecycle (e.g., at technology transfer or when changes are 
introduced). These batches are manufactured at commercial scale using the control strategy with 
an increased level and frequency of sampling.  This validation approach generally remains 
appropriate, even if enhanced pharmaceutical development has been conducted. 
Knowledge gained from development is the foundation for process validation.  During 
technology transfer, site changes, and scale-up, the control strategy can be further developed as 
new variables are encountered in the commercial manufacturing environment.  In many cases, 
new knowledge will be gained, often leading to modification  of the control strategy and 
improvements to the process, thereby having an impact on process validation.  This lifecycle 
approach to process validation recognizes that elements of process validation begin with 
knowledge gained during development, and continues through technology transfer and 
throughout the commercial manufacturing phase of a product. 
A risk-based approach can be used to determine the plan for process validation studies to ensure 
that process understanding is considered and that the areas of risk are addressed.  
 
B.  Continuous Process Verification (CPV) (7.2) 
ICH Q8(R2) describes CPV as an approach to process validation that includes the continuous 
monitoring and evaluation of manufacturing process performance. Process validation protocols 
can use CPV for the initial and ongoing commercial production.  CPV can also facilitate the 
evaluation of manufacturing process changes.  
CPV can enhance the evaluation of the manufacturing process when it provides substantially 
more information on process variability and control. 
CPV can be applied to an entire process, or to portions of a process, together with traditional 
process validation approaches. 
For initial process validation, CPV generally is more appropriate when an enhanced development 
approach has been applied. However, it can also be used when extensive process knowledge has 
been gained through commercial manufacturing experience.  
CPV can use in-line, on-line or at-line monitoring or controls to evaluate process performance.  
These are based on product and process knowledge and understanding.  Monitoring can also be 
combined with feedback loops to adjust the process to maintain output quality.  This capability 
also provides the advantage of enhanced assurance of intra-batch uniformity, fundamental to the 
objectives of process validation. Some process measurements and controls in support of real-
time release testing (RTRT) can also play a role in CPV. 
Some advantages of CPV: 
	   Replaces the emphasis on the first few commercial-scale validation batches with enhanced 
assurance of product quality in many, or even all, batches;  
	   Provides the foundation for a robust process performance and product quality monitoring 
system, increasing product and process knowledge and facilitation of continual improvement 
opportunities for process and product quality;  
  16
  Contains Nonbinding Recommendations  
	   Enables earlier detection of manufacturing-related problems and trends; 
	   Provides immediate feedback of the effect of a change, thereby facilitating the management of  
changes; 
	   Provides a higher assurance of an ongoing state of control, as more data from CPV provide 
higher statistical confidence for ongoing monitoring and trending; 
	   Is particularly suited to the evaluation of continuous manufacturing processes; and 
	   Contributes to the verification of the design space, if used, throughout the product lifecycle. 
 
C.  Pharmaceutical Quality System (7.3) 
The PQS strengthens the link between the product lifecycle stages, thereby facilitating the 
process validation lifecycle approach.  Data, information, and knowledge from process 
performance and product quality monitoring, as described in ICH Q10, support the lifecycle 
validation approach and the continual improvement of the product and process. 
Quality risk management, as an enabler for the PQS, contributes to process validation as 
follows:  
	   Risk assessment tools are useful in developing the process validation plan.  This can also be 
useful for the evaluation of the effect of changes.  
	   Statistical tools support monitoring and trending of process performance to assure a state of 
control. 
Regardless of the approach to process validation, equipment and facilities should be suitably 
qualified, including computerized systems and control methods, as called for by GMP.   
Similarly, personnel involved in process validation activities should be appropriately trained and 
qualified. 
 
  17