Q14 Analytical 
Procedure 
Development 
Guidance for Industry 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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) 
 
March 2024 
ICH-Quality 
   
Q14 Analytical 
Procedure 
Development 
Guidance for Industry 
 
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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) 
 
March 2024 
ICH-Quality 
  Contains Nonbinding Recommendations 
FOREWORD 
 
The International Council for Harmonisation of Technical Requirements for Pharmaceuticals 
for Human Use (ICH) has the mission of achieving greater regulatory harmonization 
worldwide to ensure that safe, effective, and high-quality medicines are developed, 
registered, and maintained in the most resource-efficient manner.  By harmonizing the 
regulatory expectations in regions around the world, ICH guidelines have substantially 
reduced duplicative clinical studies, prevented unnecessary animal studies, standardized 
safety reporting and marketing application submissions, and contributed to many other 
improvements in the quality of global drug development and manufacturing and the products 
available to patients.  
 
ICH is a consensus-driven process that involves technical experts from regulatory authorities 
and industry parties in detailed technical and science-based harmonization work that results 
in the development of ICH guidelines.  The commitment to consistent adoption of these 
consensus-based guidelines by regulators around the globe is critical to realizing the benefits 
of safe, effective, and high-quality medicines for patients as well as for industry.  As a 
Founding Regulatory Member of ICH, the Food and Drug Administration (FDA) plays a 
major role in the development of each of the ICH guidelines, which FDA then adopts and 
issues as guidance to industry.  
  Contains Nonbinding Recommendations 
TABLE OF CONTENTS 
 
I.  INTRODUCTION (1) .................................................................................................. 1 
A.  Objective (1.1) .......................................................................................................................... 1 
B.  Scope (1.2) ................................................................................................................................. 2 
II.  GENERAL CONSIDERATIONS FOR ANALYTICAL PROCEDURES (2) ....... 2 
A.  Minimal Versus Enhanced Approaches to Analytical Procedure Development (2.1) ....... 3 
B.  Analytical Procedure Life Cycle (2.2) .................................................................................... 3 
III.  ANALYTICAL TARGET PROFILE (3) .................................................................. 4 
IV.  KNOWLEDGE AND RISK MANAGEMENT IN ANALYTICAL 
PROCEDURE DEVELOPMENT AND CONTINUAL IMPROVEMENT (4) ..... 5 
A.  Knowledge Management (4.1) ................................................................................................ 5 
B.  Risk Management (4.2) ............................................................................................................ 5 
V.  EVALUATION OF ROBUSTNESS AND PARAMETER RANGES OF 
ANALYTICAL PROCEDURES (5) .......................................................................... 6 
A.  Robustness (5.1) ....................................................................................................................... 6 
B.  Analytical Procedure Parameter Ranges (5.2) ...................................................................... 6 
VI.  ANALYTICAL PROCEDURE CONTROL STRATEGY (6) ................................ 7 
A.  Established Conditions for Analytical Procedures (6.1) ....................................................... 8 
VII.  LIFE CYCLE MANAGEMENT AND POSTAPPROVAL CHANGES OF 
ANALYTICAL PROCEDURES (7) .......................................................................... 9 
VIII.  DEVELOPMENT OF MULTIVARIATE ANALYTICAL PROCEDURES: 
ADDITIONAL CONSIDERATIONS (8) ................................................................ 12 
A.  Sample and Sample Population ............................................................................................ 13 
B.  Data Transformation ............................................................................................................. 14 
C.  Variable Selection .................................................................................................................. 14 
D.  Robustness .............................................................................................................................. 14 
E.  Recalibration and Model Maintenance ................................................................................ 14 
IX.  DEVELOPMENT OF ANALYTICAL PROCEDURES FOR REAL TIME 
RELEASE TESTING: ADDITIONAL CONSIDERATIONS (9) ......................... 16 
X.  SUBMISSION OF ANALYTICAL PROCEDURE-RELATED INFORMATION 
(10) ............................................................................................................................... 17 
A.  General Regulatory Considerations and Documentation (10.1) ........................................ 17 
B.  Documentation for the Enhanced Approach (10.2) ............................................................ 17 
C.  Documentation for Multivariate Analytical Procedures (10.3) ......................................... 18 
GLOSSARY (11) .................................................................................................................... 20 
REFERENCES (12) ............................................................................................................... 27 
APPENDIX (13. ANNEX) ..................................................................................................... 28 
i  Contains Nonbinding Recommendations 
A.  Examples of Application of ICH Q14 Principles (13.1 Annex A) ...................................... 28 
1. Measurement of Stereoisomers as Specific Process-Related Impurities in a Small Molecule 
Drug Substance (13.1.1) ............................................................................................................... 30 
2. Measurement of Potency for an anti-TNF-alpha Monoclonal Antibody (13.1.2) .................... 36 
B.  Example of Multivariate Model Life Cycle Components (13.2 Annex B)......................... 44 
 
ii  Contains Nonbinding Recommendations 
Q14 Analytical Procedure Development 
Guidance for Industry1 
 
 
 
This guidance represents the current thinking of the Food and Drug Administration (FDA or Agency) 
on this topic.  It does not establish any rights for any person and is not binding on FDA or the public.  
You can use an alternative approach if it satisfies the requirements of the applicable statutes and 
regulations.  To discuss an alternative approach, contact the FDA office responsible for this guidance 
as listed on the title page.   
 
 
 
I.  INTRODUCTION (1)2 
 
A.  Objective (1.1) 
 
This guidance describes science and risk-based approaches for developing and maintaining 
analytical procedures suitable for the evaluation of the quality of drug substances and drug 
products. The systematic approach suggested in the International Council for Harmonisation 
(ICH) guidance for industry Q8(R2) Pharmaceutical Development (November 2009) (ICH 
Q8(R2))together with principles of the ICH guidance for industry Q9(R1) Quality Risk 
Management (May 2023) (ICH Q9(R1)) can also be applied to the development and  life 
cycle  management of analytical procedures.3 When developing an analytical procedure, a 
minimal (also known as traditional) approach or elements of an enhanced approach can be 
applied. Furthermore, the guidance describes additional considerations for the development 
of multivariate analytical procedures and for real time release testing (RTRT). 
 
This guidance complements the ICH guidance for industry Q2(R2) Validation of Analytical 
Procedures (March 2024) (ICH Q2). 
 
Using the tools described in the ICH guidance for industry Q12 Technical and Regulatory 
Considerations for Pharmaceutical Product Lifecycle Management (May 2021) (ICH Q12), 
the guidance describes principles to support change management of analytical procedures 
based on risk management, comprehensive understanding of the analytical procedure, and 
adherence to predefined criteria for performance characteristics. Knowledge gained from 
application of an enhanced approach to analytical procedure development can provide better 
assurance of the performance of the procedure, can serve as a basis for the analytical 
procedure control strategy and can provide an opportunity for more efficient regulatory 
approaches to related postapproval changes. 
 
 
1 This guidance was developed within the Expert Working Group (Quality) of the International Council for 
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 Assembly at Step 4 of the ICH process, November 2023.  At Step 4 of the process, the 
final draft is recommended for adoption to the regulatory bodies of the ICH regions.  
2 The numbers in parentheses reflect the organizational breakdown of the document endorsed by the ICH 
Assembly at Step 4 of the ICH process, November 2023. 
3 We update guidances periodically. To make sure you have the most recent version of a guidance, check the 
FDA guidance web page at https://www.fda.gov/regulatory-information/search-fda-guidance-documents.  
1  Contains Nonbinding Recommendations 
The guidance also describes submission of analytical procedure development and related life 
cycle information in the common technical document (CTD) format (ICH guideline M4Q(R1) 
The Common Technical Document for the Registration of Pharmaceuticals for Human Use 
(ICH M4Q(R1))).4 Information related to analytical procedure development and knowledge 
may be submitted to regulatory authorities to provide additional evidence that the analytical 
procedure is fit for the intended purpose. While the minimal approach remains a valid 
approach, an applicant can decide to submit additional development data and knowledge, 
which may facilitate regulatory communication for postapproval change management. 
 
B.  Scope (1.2) 
 
This guidance applies to analytical procedures used for release and stability testing of 
commercial drug substances and products, hereafter referred to as products. The guidance can 
also be applied to other analytical procedures used as part of the control strategy (ICH 
guidance for industry Q10 Pharmaceutical Quality System (April 2009) (ICH Q10)) 
following a risk-based approach. The scientific principles described in this guidance can be 
applied in a phase-appropriate manner to analytical procedures used during clinical 
development. 
 
In general, FDAs guidance documents do not establish legally enforceable responsibilities.  
Instead, guidances describe the Agencys current thinking on a topic and should be viewed 
only as recommendations, unless specific regulatory or statutory requirements are cited.  The 
use of the word should in Agency guidances means that something is suggested or 
recommended, but not required.  
 
 
II.  GENERAL CONSIDERATIONS FOR ANALYTICAL PROCEDURES (2) 
 
The goal of development is to obtain an analytical procedure fit for the intended purpose: to 
measure an attribute or attributes of the material with the needed specificity/selectivity, 
accuracy, precision over the reportable range. Details of the performance characteristics 
considered for analytical procedure validation are described in ICH Q2. 
 
In this section the minimal and enhanced approaches to analytical procedure development are 
described. While the minimal approach remains an acceptable approach to development of a 
robust analytical procedure that is fit for the intended purpose, some or all elements of the 
enhanced approach might be used to support development and life cycle management of 
analytical procedures. 
 
In certain cases, an analytical procedure can be applied to multiple products with little or no 
modification of measurement conditions. For a new application of such platform analytical 
procedures, the subsequent development can be abbreviated, and certain validation tests can 
be omitted based on a science- and risk-based justification. 
 
Data gained during the development studies (e.g., robustness data from a design of 
experiments (DoE) study) could be used as part of the validation data for the related 
analytical procedure performance characteristics, and studies do not necessarily need to be 
repeated. 
 
 
4 Available at https://database.ich.org/sites/default/files/M4Q_R1_Guideline.pdf.  
2  Contains Nonbinding Recommendations 
A.  Minimal Versus Enhanced Approaches to Analytical Procedure 
Development (2.1) 
 
Analytical procedure development should include the following elements as a minimum: 
 
  Identifying the attributes of the product which need to be tested 
 
  Selecting an appropriate technology and related instruments or suitable apparatus 
 
  Conducting studies to evaluate analytical procedure performance characteristics such 
as specificity, accuracy, and precision over the reportable range (including the 
calibration model, lower and/or higher range limits) and robustness 
 
  Documenting the analytical procedure including the analytical procedure control 
strategy. 
 
The enhanced approach offers a systematic way of developing and refining knowledge of an 
analytical procedure and demonstrating procedure understanding. Product and process 
understanding informs the quality attributes to be tested. The anticipated performance criteria 
for relevant performance characteristics should be documented in an analytical target profile 
(ATP). In addition to the elements of the minimal approach, an enhanced approach may 
include the following elements as appropriate: 
 
  Conducting risk assessment and evaluating prior knowledge to identify the analytical 
procedure parameters that can impact performance of the procedure 
 
  Conducting uni- or multi-variate experiments and/or modeling to explore ranges and 
interactions between identified analytical procedure parameters 
 
  Defining an analytical procedure control strategy including set points and/or ranges 
for relevant analytical procedure parameters. These could include proven acceptable 
ranges for analytical procedures (PARs) and/or method operable design regions 
(MODRs) 
 
Applying elements of the enhanced approach to development can lead to a better 
understanding of the impact of analytical procedure parameters on the analytical procedure 
performance and more flexibility for life cycle management, such as wider operating ranges 
and a more appropriate set of established conditions (ECs) with associated reporting 
categories. 
 
B.  Analytical Procedure Life Cycle (2.2) 
 
Figure 1 (below) depicts elements of the analytical procedure life cycle as related to the 
product life cycle. Analytical procedure development and change management approaches 
are described in this guidance whereas analytical procedure validation is described in ICH 
Q2. Depending on the intended purpose of the analytical procedure and the development 
approach taken, the order and extent of each element could vary, and several elements could 
occur simultaneously. 
 
3  Contains Nonbinding Recommendations 
Figure 1: The Analytical Procedure Life Cycle 
 
 
 
 
III.  ANALYTICAL TARGET PROFILE (3) 
 
Product and process understanding (ICH Q8(R2) and the ICH guidance for industry Q11 
Development and Manufacture of Drug Substances (November 2012)) leads to the 
identification of critical quality attributes (CQAs) requiring analytical measurement for 
control, which may be included in the quality target product profile. Measurement needs can 
be captured in an ATP, which forms the basis for development of the analytical procedure. 
An ATP consists of a description of the intended purpose of the analytical procedure, 
appropriate details on the product attributes to be measured, and relevant performance 
characteristics with associated performance criteria. The ATP includes measurement 
requirements for one or more quality attributes. The ATP drives the choice of analytical 
technology. Multiple available analytical techniques may meet the performance criteria. 
Consideration of the operating environment (e.g., at-line, in-line, off-line) should be included 
in the technology selection. Once a technology has been selected, the ATP serves as a 
foundation to derive the analytical procedure attributes and performance criteria for 
analytical procedure validation (ICH Q2). Formal documentation and submission of an ATP 
is optional but can facilitate regulatory communication irrespective of the chosen 
development approach. 
 
The ATP also facilitates ongoing monitoring and continual improvement of the analytical 
procedure. The ATP is maintained over the life cycle and can also be used as a basis for life 
cycle management to ensure that the existing, revised, or new analytical procedure remains fit 
for the intended purpose. 
 
Illustrative examples of ATPs are provided in Appendix section A (13.1 Annex A). 
 
 
4  Contains Nonbinding Recommendations 
IV.  KNOWLEDGE AND RISK MANAGEMENT IN ANALYTICAL 
PROCEDURE DEVELOPMENT AND CONTINUAL IMPROVEMENT (4) 
 
A.  Knowledge Management (4.1) 
 
As with product and manufacturing process development, knowledge management (ICH 
Q10) plays a critical role in analytical procedure development and during the life cycle of the 
analytical procedure. 
 
Prior knowledge is explicitly or implicitly used for informing decisions during analytical 
procedure development and life cycle management. Prior knowledge can be internal 
knowledge from a companys proprietary development and analytical experience, external 
knowledge such as reference to scientific and technical publications, or established scientific 
principles. 
 
Prior product knowledge plays an important role in identifying suitable analytical techniques. 
Knowledge of best practices, state-of-the-art technologies, and regulatory expectations 
contribute to the selection of the most suitable technology for a given purpose. Existing 
platform analytical procedures (e.g., protein content measurement by ultraviolet 
spectroscopy) can be leveraged to evaluate the attributes of a specific product without 
conducting additional procedure development. 
 
As additional information is obtained, knowledge related to analytical procedures should be 
actively managed throughout the product life cycle. 
 
B.  Risk Management (4.2) 
 
The use of quality risk management (QRM) is encouraged to aid in the development of a 
robust analytical procedure to reduce risk of poor performance and reporting incorrect results. 
Risk assessment is typically performed early in analytical procedure development and is 
updated as more information becomes available. Risk assessment can be formal or informal 
and can be supported by prior knowledge. 
 
Risk assessment tools as described in ICH Q9(R1) Annex 1 can be used to:  
 
  Identify analytical procedure parameters (factors and operational steps) with potential 
impact on its performance, e.g., Appendix section A (13.1 Annex A) Figure 2 
(Ishikawa diagram) 
 
  Assess the potential impact of analytical procedure parameters on the analytical 
procedure performance  
 
  Identify and prioritize analytical procedure parameters to be investigated 
experimentally 
 
  Inform the need and the extent of ongoing monitoring as part of risk review 
 
Risk communication should be used to support continual improvement of the analytical 
procedure performance throughout its life cycle. The outcome of QRM should be 
documented in the relevant parts of the applicants pharmaceutical quality system (PQS) 
(ICH Q10).  
5  Contains Nonbinding Recommendations 
 
 
V.  EVALUATION OF ROBUSTNESS AND PARAMETER RANGES OF 
ANALYTICAL PROCEDURES (5) 
 
A.  Robustness (5.1) 
 
The robustness of an analytical procedure is a measure of its capacity to meet the expected 
performance criteria during normal use. Robustness is tested by deliberate variations of 
analytical procedure parameters, and applicants should consider the duration of the analysis 
(including stability of sample preparations and reagents). Prior knowledge and risk 
assessment can inform the selection of parameters to investigate during the robustness study. 
Those parameters likely to influence procedure performance over the intended period of use 
should be studied. 
 
For most procedures, robustness evaluation is conducted during development. If the 
evaluation of robustness was already conducted during development, it does not need to be 
repeated during validation (as discussed in ICH Q2). Data from validation studies (e.g., 
intermediate precision) can complement robustness evaluation. For some analytical 
procedures with inherent high-parameter variability (e.g., those requiring biological 
reagents), wider parameter ranges may need to be investigated during robustness studies. 
Robustness of multivariate procedures may require additional considerations (see section VIII 
(8)). The outcome of the evaluation of robustness should be documented and also reflected in 
the analytical procedure control strategy. 
 
B.  Analytical Procedure Parameter Ranges (5.2) 
 
Experiments to investigate parameter ranges can provide additional knowledge about the 
analytical procedure performance. The respective analytical procedure attributes and 
associated criteria could be derived from the ATP. Univariate examination of a single 
parameter can establish a PAR for the analytical procedure. 
 
In an enhanced approach, the ranges for the relevant parameters and their interactions can be 
investigated in multivariate experiments (DoE). Risk assessment and prior knowledge should 
be used to identify analytical procedure parameters, attributes, and associated ranges to be 
investigated experimentally. Categorical variables (e.g., different instruments) can also be 
considered as part of the experimental design. 
 
The outcome of development studies should provide an understanding of the relationships 
between analytical procedure parameters (inputs) and the responses of the analytical 
procedure (outputs). Based on the results, fixed set points may be defined for some 
parameters. For others, PARs could be defined while still others could be included into an 
MODR. An MODR consists of combined ranges for two or more analytical procedure 
parameters within which the analytical procedure is shown to be fit for the intended purpose. 
 
Set points, PARs, and/or MODRs of an analytical procedure proposed by the applicant based 
on development and validation data are subject to regulatory approval. Moving within an 
approved PAR or MODR does not require regulatory notification. 
 
Analytical procedure validation for a PAR and/or an MODR is required only for those 
performance characteristics not covered by data from analytical procedure development. For 
6  Contains Nonbinding Recommendations 
practical reasons and following a risk-based approach, it may not be necessary or possible to 
validate the entirety of an MODR. The part of a PAR or an MODR intended for routine use 
(typically the intended operational conditions or the set point) in the analytical procedure 
must be covered by validation data. The extent of validation tests should be justified on a 
case-by-case basis. 
 
For future changes to operational conditions within a PAR or an MODR, an assessment of the 
need for and extent of additional validation tests should be performed. An analytical 
procedure validation strategy (e.g., as part of the analytical procedure validation protocol) 
can define the necessary extent of additional validation. 
 
 
VI.  ANALYTICAL PROCEDURE CONTROL STRATEGY (6) 
 
An analytical procedure control strategy should ensure that the analytical procedure is fit for 
the intended purpose during routine use throughout its life cycle. It consists of a set of 
controls, derived from current understanding of the analytical procedure including 
development data, risk assessment, robustness, and prior knowledge. The analytical 
procedure control strategy should be defined before validation (ICH Q2) and should be 
confirmed after validation has been finalized. 
 
The analytical procedure control strategy includes analytical procedure parameters needing 
control and the system suitability test (SST), which is part of the analytical procedure. The 
analytical procedure should describe the steps necessary to perform each analytical test. This 
can include (but is not limited to) the sample, the reference materials and the reagents, 
sample and control preparations, use of the apparatus, generation of the calibration curve, the 
number of replicates, use of the formulas for the calculation of the reportable results, and 
other necessary steps. The level of detail should enable a skilled analyst to perform the 
analysis and interpret the results (such as the level of detail in a regional pharmacopoeia for a 
similar product). 
 
The SST depends on the type and intent of the analytical procedure and is typically 
conducted with one or more predefined materials (including use of positive and/or negative 
controls). The SST is designed to verify selected analytical procedure attributes. The 
acceptance criteria should be based on analytical procedure performance criteria. The 
components of the SST should be selected using risk assessment as well as knowledge and 
understanding from development data. The test is used to verify that the measurement system 
and the analytical operations associated with the analytical procedure are fit for the intended 
purpose during the time period of analysis and enable the detection of unacceptable 
performance. Validity of the results of the analytical procedure depends on the outcome of 
the SST. In the enhanced approach, a well-designed set of SST parameters and criteria to 
ensure analytical procedure performance could represent an important aspect of risk 
mitigation. For analytical procedures relying on multivariate models, data quality should be 
verified using suitable software tools.  
 
In addition to SST, sample suitability assessment may be required to ensure acceptable 
sample response. A sample and/or sample preparation is considered suitable if the 
measurement response of the sample satisfies predefined acceptance criteria for the analytical 
procedure attributes that have been developed for the validated analytical procedure (often 
used for biologics). In these cases, sample suitability is a prerequisite for the validity of the 
result along with a satisfactory outcome of the SST. Sample suitability assessment generally 
7  Contains Nonbinding Recommendations 
consists of the assessment of the similarity of the response between a reference material and 
the test sample and may include a requirement for acceptable levels of interfering signals 
arising from the sample matrix. For analytical procedures relying on multivariate models, 
sample suitability assessment can be verified using suitable software tools, which check if the 
sample fits within the model space. This is commonly called data quality check. 
 
Ongoing monitoring of selected analytical procedure outputs is recommended to look for any 
trends, in line with PQS expectations. Review of analytical procedure outputs facilitates the 
procedure life cycle management and enables proactive intervention to avoid failures.  
 
A.  Established Conditions for Analytical Procedures (6.1) 
 
In line with ICH Q12, applicants may propose ECs for an analytical procedure. ECs can be 
identified using tools highlighted in section II (2) including risk assessment, prior knowledge, 
and results from uni- and/or multi-variate experimentation. The nature and extent of ECs will 
depend on the development approach, the complexity of the analytical procedure, and a 
demonstrated understanding of how parameters and other factors impact the analytical 
procedure performance. 
 
With a minimal approach to analytical procedure development, the number of ECs may be 
extensive with fixed analytical procedure parameters and set points. 
 
With an enhanced approach, there should be an increased understanding of the measurement 
requirements, the suitability of available technologies, and the relationship between analytical 
procedure parameters and performance. This knowledge facilitates the identification of an 
appropriate set of ECs and related reporting categories (see section VII (7)). ECs can be 
reduced and focused on analytical procedure performance (e.g., acceptable ranges for 
analytical procedure parameters, performance characteristics with associated criteria) when 
justified by analytical procedure understanding (including prior knowledge and 
product/process knowledge) and risk management. 
 
ECs could consist of:  
 
  Performance characteristics and associated criteria (e.g., included in an ATP) 
 
  Analytical procedure principle (i.e., the physicochemical basis or specific technology) 
 
  SST and sample suitability assessment criteria 
 
  Set points and/or ranges for one or more analytical procedure parameters 
 
Analytical procedure parameters, which need to be controlled to ensure the performance of 
the procedure, as well as those where the need for control cannot be reasonably excluded 
should be identified as ECs. If the application of analytical procedure performance criteria 
and/or the SST demonstrate that a specific parameter is under control, that parameter or the 
parameter value may not necessarily need to be defined as an EC or may be assigned a lower 
reporting category as appropriate. ECs and related reporting categories are proposed by the 
applicant and assessed by the regulatory authorities for approval based on the scientific 
justification provided. 
 
8  Contains Nonbinding Recommendations 
Use of the enhanced approach should not lead to providing a less detailed description of 
analytical procedures in a regulatory submission. Suitably detailed descriptions of the 
analytical procedures in module 3 of the CTD are expected to provide a clear understanding 
regardless of the approach used to identify ECs for analytical procedures. A description of an 
analytical procedure includes supportive information as well as identified ECs. 
 
Identification of reporting categories for ECs and the utilization of ECs in change 
management are described in the next section. 
 
 
VII.  LIFE CYCLE MANAGEMENT AND POSTAPPROVAL CHANGES OF 
ANALYTICAL PROCEDURES (7) 
 
Changes to analytical procedures can occur throughout the product life cycle and could 
involve modification of existing procedures or a complete replacement including introduction 
of a new technology. Major changes in the performance characteristics or additional 
information on quality attributes could lead to reevaluation of the ATP and/or a new 
procedure. Typically, process knowledge, analytical procedure knowledge, and continual 
improvement are drivers for change. If possible, changes should lead to improved analytical 
procedures in line with best practices and instrumentation. The tools and enablers discussed 
in ICH Q12 are applicable to analytical procedures, irrespective of the development approach 
and consist of: 
 
  Existing risk-based categorization of changes to analytical procedures (in regional 
regulatory framework) 
 
  ECs 
 
  Postapproval change management protocols (PACMPs) 
 
  The product life cycle change management document 
 
  The PQS (documentation of all changes including those not requiring regulatory 
submission) 
 
  Structured approaches for frequent chemistry, manufacturing, and controls  
postapproval changes 
 
If ECs are not proposed in the dossier, any changes should be reported according to regional 
reporting requirements. The use of different elements of the enhanced approach can facilitate 
management and regulatory communication of postapproval changes as compared to the 
minimal approach. 
 
9  Contains Nonbinding Recommendations 
Figure 2: Risk-Based Approach for Identification of ECs and Reporting Categories for 
Associated Changes in the Enhanced Approach 
 
 
1) Including analytical procedure control strategy 
2) In some cases, moderate risk changes proposed by the company may require prior approval based on health 
authority feedback 
ECs = established conditions. 
 
If justified and validated (see section V.B (5.2)), a PAR or an MODR allows movement 
within the approved range(s) to be managed within a companys PQS. Changes outside of the 
approved ranges or expansion of those ranges require regulatory communication. 
 
In cases where ECs are proposed, the risk associated with prospective changes should be 
assessed up front to propose the appropriate reporting category. Factors to consider include 
the criticality of the quality attribute being measured, the complexity of the technology, and 
the extent of the change (see Appendix section A (13.1 Annex A)). Relevant risk reduction 
measures should be identified based on product and process knowledge as well as analytical 
procedure understanding and the proposed analytical procedure control strategy. Finally, the 
level of risk (high, medium, or low) should be assigned. 
 
In general, an understanding of the analytical procedure robustness and/or prior knowledge 
can be used to support risk mitigation associated with future changes. Submitting the 
outcomes of the risk assessments to regulatory authorities when ECs are proposed can help to 
justify reporting categories for future changes to analytical procedures. 
 
Figure 2 summarizes how risk assessment and risk reduction measures can help to identify 
appropriate reporting categories for ECs. The risk associated with changes can be reduced by 
defining relevant performance criteria, which are identified as ECs. Risk reduction is possible 
when sufficient understanding is available (Table 1) to design future bridging studies (Table 
2). Adherence to the ATP and an analytical procedure control strategy ensures that the 
analytical procedure remains fit for the intended purpose after changes. Changes to 
parameters that are not ECs do not require regulatory communication. 
 
10  Contains Nonbinding Recommendations 
The ATP could also form the basis of a PACMP, which would allow changes (e.g., a change 
between technologies) to be reported at a lower reporting category provided that the 
predefined requirements for the change are met.  
 
Examples are provided in Appendix section A (13.1 Annex A) on how appropriate reporting 
categories can be proposed. 
 
Table 1: Relationship Between Knowledge (Understanding), Risk, and Extent of Studies 
for Changes to Analytical Procedures 
 
Risk associated with the change 
h  
g Low                                                                                          High                                          
i
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1)Knowledge                                 Pctp oorr iinaoof rrsi  rtkkmunndaooytwwo prllleyeadd nsggt duee e d oryri v aecdc ofrrodmin g     Isk ntnu-oddweyp lpethlda gsnet u d deyri vaecdco frrdoimng p troio ar  
w             Ctoo an fsitrumdayt oprlayn s t udy according    Isntu-ddeyp pthla snt u dy according to a 
o
L
 
1) As described in International Council for Harmonisation guidance for industry Q10 Pharmaceutical Quality 
System. 
 
When initiating changes to analytical procedures, QRM can be used to evaluate the impact of 
the changes and reconfirm that the originally agreed reporting category is still appropriate. 
The outcome of this risk assessment informs the design and extent of the studies needed to 
support the change including a bridging strategy to demonstrate that the revised or new 
procedure remains fit for the intended purpose. When considering a bridging strategy, a 
greater understanding of the analytical procedure can enable a reduced study design whereas 
a higher risk change may need a more in-depth study (Table 1). The implementation of an 
already validated analytical procedure at a different location, including the concept of 
analytical procedure transfer, should follow a similar bridging strategy (Table 2). 
 
For product and process changes, a reassessment and potential adaptation of the ATP, if used, 
and a reassessment of the suitability of the analytical procedure may be necessary. 
 
If an applicant proposes a new analytical procedure, a comprehensive risk assessment and 
evaluation should be conducted to determine any impact on the performance. The analytical 
procedure control strategy for the new procedure should be established. ECs associated with 
the new procedure should be justified when communicating the change. 
 
11  Contains Nonbinding Recommendations 
Table 2: Examples of Analytical Procedure Change Evaluation 
 
Risk Factor: Extent of  Bridging Strategy  Evidence of the Suitability 
Change  of a New Procedure 
Change of analytical procedure  Full validation of new procedure  Analytical procedure 
principle  performance characteristics are 
And 
(physicochemical/biochemical  evaluated, and criteria are met 
basis)  Comparative analysis of representative  after the change 
samples and reference materials  
And 
And/or 
Results are comparable after 
Demonstration that the analytical  change or differences are 
procedures ability to discriminate  acceptable and potential 
between acceptable and nonacceptable  impact on specification 
results remains comparable  evaluated 
Change within same analytical  Partial or full revalidation of the  Analytical procedure attributes 
procedure principle  analytical procedure performance  are evaluated, and criteria are 
characteristics affected by the change  met after change 
And, as appropriate  And, as appropriate 
Comparative analysis of representative  Results are comparable after 
samples and reference materials  change or differences are 
acceptable and potential 
And/or 
impact on specification 
Demonstration that the analytical  evaluated 
procedures ability to discriminate 
between acceptable and nonacceptable 
results remains comparable 
Transfer of analytical  Partial or full revalidation of the  Analytical procedure attributes 
procedure to a different site  analytical procedure performance  are evaluated, and criteria are 
with no change in procedure  characteristics  met after change 
itself 
And/or  And/or 
 
Comparative analysis of representative  Results are comparable 
samples and reference materials 
 
Or 
 
Justification for not performing 
additional transfer experiments 
 
Table 2 provides examples of data recommended to support a change dependent on the extent 
of the change and the identified risk category. 
 
To support the use of the tools described in this guidance, the companys PQS change 
management process should be effective and in line with recommendations described in ICH 
Q12. 
 
 
VIII.  DEVELOPMENT OF MULTIVARIATE ANALYTICAL PROCEDURES: 
ADDITIONAL CONSIDERATIONS (8) 
 
Multivariate analytical procedures are those where a result is determined through a 
multivariate calibration model utilizing more than one input variable. The considerations 
12  Contains Nonbinding Recommendations 
provided here are for models using latent variables that are mathematically related to directly 
measured variables. Other approaches such as machine learning (e.g., neural networks) or 
optimization techniques could use similar principles although the specific approach may vary 
and will not be discussed in detail. 
 
Whereas this chapter includes aspects unique to multivariate analytical procedures, the 
principles described in other parts of this guidance are also applicable. Development of a 
robust multivariate analytical procedure includes scientifically justified sample selection and 
distribution over the range, sample size, model variable selection, and data preprocessing. 
 
A.  Sample and Sample Population 
 
Multivariate models link measured model variables with values obtained from a validated 
reference analytical procedure or from reference samples. Therefore, samples in multivariate 
analysis consist of input measurements and their corresponding reference values, which are 
numeric values for quantitative measurements (e.g., assay) and classification categories for 
qualitative analytical procedures (e.g., identity). In some cases, one set of input 
measurements could be used for multiple models provided that more than one reference value 
exists. The reference values are determined using reference analytical procedure(s) or 
prepared reference samples with known values. Care should be taken to ensure that 
uncertainty in the reference analytical procedure is sufficiently low in relation to the intended 
performance of the multivariate analytical procedure and that prepared reference samples are 
homogeneous. The approach to the reference analytical procedure(s) or prepared reference 
samples should be explained and justified. 
 
The ranges of multivariate models are typically constructed by data from samples. Therefore, 
a careful strategy for sample selection is essential for obtaining the relevant information from 
the analytical data and contributes to the robustness of the resulting model. Based on the 
analytical procedure and measurement principle, the sample population should encompass the 
sources of variability likely to occur during manufacturing and analysis, such as raw material 
quality, manufacturing process variability, storage conditions, sample preparation, and 
testing. Use of risk assessment tools can help to identify sources of variability with the 
potential to influence the measurements and resulting model outputs. 
 
Obtaining samples with appropriate variability at commercial scale can be challenging. 
Therefore, development laboratory and pilot scale samples are often utilized to provide 
enough variability to improve accuracy and robustness of the model. Inclusion of commercial 
scale samples is recommended to capture variability related to specific equipment and/or 
processing conditions. Careful consideration should also be given to sample distribution in 
the calibration and validation sets because this will influence the models predictive 
capability. 
 
The number of samples used to create a calibration model for quantitative analysis will 
depend on the complexity of the sample matrix and/or interference by the matrix in the 
analyte signal of interest (i.e., for more complex sample matrices generally more samples are 
needed). 
 
Sufficient samples should be available for creation of calibration and validation sets of 
appropriate size and variability. Independent samples in the validation set are not 
incorporated in the calibration set or the internal test set. A validation sample set generated 
with samples from independent batches can be used to demonstrate model robustness. 
13  Contains Nonbinding Recommendations 
 
B.  Data Transformation 
 
The selection of the data transformation method(s) can be driven by the type of data, 
instrument or sample, the intended purpose of the model and/or prior knowledge. Caution 
should be exercised when performing any transformation because artifacts can be introduced, 
or essential information can be lost. Any transformation of data should be documented and 
justified. 
 
C.  Variable Selection 
 
Variable selection is performed during model development. For example, wavelength range 
selection is frequently applied in spectroscopic applications to select a region of a spectrum 
that gives the best estimation of the selected chemical or physical property to be evaluated 
(modeled). Variable selection depends on the measurement principle, application, and other 
factors and should be justified. 
 
D.  Robustness 
 
Model development should minimize the prediction error and provide a robust model that 
consistently assures the long-term performance. The robustness should be built into the 
model by including relevant sources of variability related to materials, process, environment, 
instrumentation, or other factors. Sources of variability can be identified from prior 
knowledge and risk assessments and evaluated using statistical tools. Robustness depends on 
multiple factors, e.g., composition of the calibration set, data transformation method, variable 
selection, and the number of latent variables. 
 
Optimization of the multivariate model often requires a trade-off between accuracy and 
robustness. A critical factor is the number of latent variables to be used in the calibration 
model, which ensures the model is optimized for the intended purpose. Selection of the 
number of latent variables occurs during model development and is confirmed during internal 
testing. Too many latent variables can result in model overfitting, potentially resulting in 
decreased robustness and a need for more frequent model updates. Justification for the final 
number of latent variables used should be provided. Diagnostic plots provided by software 
packages can be useful to support the justification.  
 
E.  Recalibration and Model Maintenance 
 
Tracking the calibration model performance is an important part of ongoing monitoring for a 
multivariate analytical procedure. Various statistical tools can be employed as diagnostics to 
ensure that the model assumptions are upheld. For latent variable models, these diagnostic 
tools can include: 
 
  Examination of residuals to determine unmodeled features of the data (e.g., x-
residuals or F-probability) 
 
  Outlier diagnostics to determine if the data is within the bounds of the model 
construction (e.g., Hotellings T-squared or Mahalanobis distance) 
 
Software packages allow for the application of diagnostic tools for model prediction. 
 
14  Contains Nonbinding Recommendations 
Additionally, continued performance of the calibration model should be confirmed on a 
periodic and event-driven basis by comparison of the model predictions with the known 
values of the reference samples or reference analytical procedure results. This confirmatory 
testing helps to ensure that the calibration model continues to perform as expected. Examples 
of events that could trigger confirmatory testing include new known process variability, 
unexpected process events, or scheduled instrument maintenance.  
 
Monitoring of the model can be used to trigger model rebuilding (recalibration) as a part of 
continual improvement. In general, the same considerations hold as for the original model 
building and internal testing. Based on the cause of the model update (e.g., a process shift), 
new data may need to be included, and old nonrelevant data may be taken out. 
 
Once the new calibration model is established, the updated analytical procedure can be 
validated against the same performance criteria as the ones included in the original model. 
Aspects that are not expected to change from the model update may not need to be evaluated 
(e.g., specificity). 
 
Figure 3: Multivariate (MV) Model life cycle 
 
 
PQS = pharmaceutical quality system. 
 
The multivariate model life cycle (Figure 3) is iterative and can be broken down into three 
major components: model establishment, routine use, and model maintenance. 
 
The choice of a multivariate model is based on the analytical procedure requirements and the 
measurement technology selected. Prior to model development, the performance factors for 
15  Contains Nonbinding Recommendations 
the model are defined, including the underlying model assumptions and desired ranges for 
model applicability. An initial risk assessment can be valuable to understand potential 
sources of variability in the materials and process that could affect the model performance 
and, therefore, should be considered during the model calibration. Model development, 
including calibration and internal testing, follows the considerations outlined in this section. 
Once the model is developed, it is validated using independent data not previously used in the 
calibration set. The last step in model establishment is development of a multivariate model 
maintenance plan, which includes the procedures and limits for outlier diagnostics and 
defines the frequency and circumstances for confirmatory testing, if needed. 
 
Routine analysis of the multivariate analytical procedure typically includes monitoring the 
appropriateness of every measurement using outlier diagnostics. Model reassessment can be 
triggered by failure of confirmatory testing or outlier diagnostics to meet the predefined 
criteria, or from data trending indicating potential unacceptable performance of the model, 
the process or the materials being measured (examples of multivariate model life cycle 
components are provided in Appendix section B (13.2 Annex B)).  
 
Model reassessment is performed within the PQS and utilizes knowledge management and 
risk assessment. If unacceptable performance is identified, model development and 
revalidation may be needed, for example, to add samples into the calibration set and remove 
those that are no longer relevant. In some cases, the model may be performing appropriately, 
but additional experience may identify the need to modify the model maintenance plan. In 
other cases, the identified unacceptable performance could be related to the measurement 
system (e.g., a misaligned sample interface), and no model update would be needed. The 
dashed arrows in Figure 3 illustrate reintroduction into the life cycle flow based on the 
potential outcomes of the model reassessment.  
 
 
IX.  DEVELOPMENT OF ANALYTICAL PROCEDURES FOR REAL TIME 
RELEASE TESTING: ADDITIONAL CONSIDERATIONS (9) 
 
RTRT is the ability to evaluate and ensure the quality of in-process and/or final product based 
on process data, which typically include a valid combination of measured material attributes 
and process controls (ICH Q8(R2)). RTRT measurements work in conjunction with all 
elements of the product control strategy (e.g., process monitoring or in-process controls) to 
ensure product quality. RTRT can be applied to drug substances, intermediates, and drug 
products. 
 
RTRT can be based on an appropriate combination of one or more process measurements 
and/or material attributes to provide a value for one or more CQAs and should be specific for 
those CQAs. The relationship between the RTRT approach and the CQAs, as well as 
acceptance criteria, should be fully justified. An RTRT analytical procedure should be 
validated as recommended in ICH Q2 and it should be demonstrated that the process 
measurements have appropriate specificity for the targeted quality attribute. 
 
Sampling and the sample interface are important considerations when designing any on-line 
or in-line analytical procedure, including those used for RTRT. The measurement point(s) 
should be chosen to be representative of the entire material being processed with the sample 
duration or amount appropriately chosen (e.g., relative to a unit dose). Additionally, the 
sample interface should remain consistent over the duration of manufacturing and should be 
robust to expected processing and environmental variations. 
16  Contains Nonbinding Recommendations 
 
The RTRT approach should be included in the product specification (ICH guidance for 
industry Q8, Q9, & Q10 Questions and Answers  Appendix: Q&As From Training Sessions 
(July 2012) (ICH Q8, Q9, and Q10 Appendix). In accordance with the ICH guidances for 
industry Q6A Specifications: Test Procedures and Acceptance Criteria for New Drug 
Substances and New Drug Products: Chemical Substances (December 2000) and Q6B 
Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological 
Products (August 1999), this includes a reference to the analytical procedure(s) and the 
related acceptance criteria. Quantitative RTRT results should be expressed in the same units 
as those for traditional testing. The product specification will typically also include the 
analytical procedures to be used for offline testing. If the dossier includes a registered 
alternative control strategy to RTRT (e.g., traditional end-product testing when process 
analytics are unavailable), the related analytical procedures and when they would be applied 
should also be included in the product specifications. 
 
 
X.  SUBMISSION OF ANALYTICAL PROCEDURE-RELATED INFORMATION 
(10) 
 
A.  General Regulatory Considerations and Documentation (10.1) 
 
The analytical procedure description(s) should be included in the ICH M4Q(R1) CTD section 
3.2.S.4.2 for drug substance or section 3.2.P.5.2 for drug product. Validation data and 
supportive information needed to justify the analytical procedure control strategy should be 
included in the CTD section 3.2.S.4.3 for drug substance or section 3.2.P.5.3 for drug 
product. Other analytical procedures used as part of the control strategy should be included in 
relevant CTD sections (e.g., 3.2.S.2, 3.2.P.3, and 3.2.P.4). The analytical procedure should 
describe the steps in sufficient detail for a skilled analyst to perform the analysis (including 
SST) as elaborated in section VI (6). Submission of validation data should reflect the 
guidance provided in ICH Q2. The performance criteria used in the validation study should 
be included in the submission. In some cases, depending on the intended purpose (e.g., 
dissolution testing) and/or the selected technique, it may be appropriate to submit 
development data as justification. 
 
Where ECs are proposed for analytical procedures as elaborated in section VI (6), the ECs 
should be clearly differentiated from supportive information. Additional development and 
validation information can be included in sections 3.2.S.4.3 and 3.2.P.5.3 to justify ECs and 
their reporting categories. When other life cycle management elements as described in ICH 
Q12 are included in the submission, the applicant should follow the principles described in 
ICH Q12 and section VII (7) of this document. 
 
B.  Documentation for the Enhanced Approach (10.2) 
 
If the approach to development leads to the incorporation of enhanced elements into the 
analytical procedure control strategy, then these should be justified. 
 
Performance characteristics and acceptance criteria (e.g., described in an ATP) and other 
elements of the enhanced approach (e.g., MODRs or PARs), should be described in the 
dossier sections for analytical procedure description (e.g., 3.2.S.4.2 and 3.2.P.5.2). If ECs are 
proposed, then these should also be included in the analytical procedure description, 
17  Contains Nonbinding Recommendations 
accompanied by supportive information. The use of the enhanced approach should not lead to 
providing a less detailed description of analytical procedures in a regulatory submission. 
 
If ECs and related reporting categories are proposed, risk-based categorization of changes 
and corresponding reporting categories should be included in the submission. Justification 
should be given for parameters that are ECs and those that are not ECs, as appropriate (see 
section VI (6)).  
 
Appropriate information from analytical procedure risk assessment and development studies 
to support the proposed life cycle management strategy should be summarized and submitted 
in the regulatory submission sections for analytical procedure validation (e.g., 3.2.S.4.3 and 
3.2.P.5.3). 
 
C.  Documentation for Multivariate Analytical Procedures (10.3) 
 
Development information related to multivariate analytical procedures should be provided 
commensurate with the level of impact of the model (ICH Q8, Q9, & Q10 Appendix). The 
process development section of the dossier (e.g., 3.2.S.2.6 or 3.2.P.2) should include the 
model development information for multivariate models used as part of manufacturing 
development studies or for in-process controls or tests. Supportive development information 
for RTRT multivariate models can be included in either the appropriate analytical procedure 
validation or process development section. 
 
Validation information for analytical procedures used for release of drug substance or drug 
product, including RTRT, should be included in the validation information section of the 
dossier (e.g., 3.2.S.4.3, 3.2.P.5.3). Additionally, these sections should include validation 
information on analytical procedures used as reference analytical procedures. The model 
development, calibration, and validation information can be included directly in the CTD 
section or in an appended document. 
 
For multivariate models used as part of drug substance or drug product specifications, 
including RTRT approaches, the description of the validation approach and results should 
include:  
 
  Description of the validation set with independent samples 
 
  The performance criteria to be met during validation of the multivariate model 
 
  Evaluation of the model validation results against the performance criteria 
 
  Discussion of the relationship between the model performance criteria and the 
attribute specification limits 
 
  High-level overview of the PQS elements for model monitoring and maintenance, 
such as diagnostic tools for determining the appropriateness of the sample data for the 
model and the approach taken when outliers are identified 
 
The description of the analytical procedure used for RTRT should be provided in the CTD 
section 3.2.S.4.2 for drug substance or section 3.2.P.5.2 for drug product and typically 
includes: 
 
18  Contains Nonbinding Recommendations 
  The property or attribute of interest to be determined by the multivariate analytical 
procedure and the desired quantitative ranges or limits 
 
  A description of the measurement principle and pertinent instrument operating 
parameters (e.g., sample presentation, sample interrogation time, measurement 
frequency) 
 
  An overview of how the multivariate model calibration data are obtained (e.g., sample 
preparation approach, reference analytical procedure) 
 
  The type of multivariate model 
 
  A description of reference analytical procedure or high-level description of prepared 
reference samples 
 
  Any calculations needed to adjust the model output into the reported value 
 
Additionally, section 3.2.S.4.2 for drug substance or section 3.2.P.5.2 for drug product should 
include description of any analytical procedures that are part of a registered alternative 
control strategy to RTRT. Information on validation of these alternative analytical procedures 
should be included in the validation information section of the dossier (e.g., 3.2.S.4.3, 
3.2.P.5.3). 
19  Contains Nonbinding Recommendations 
GLOSSARY (11) 
 
Accuracy 
 
The accuracy of an analytical procedure expresses the closeness of agreement between the 
value which is accepted either as a conventional true value or as an accepted reference value 
and the value or set of values measured. (International Council for Harmonisation (ICH) 
guidance for industry Q2(R2) Validation of Analytical Procedures (March 2024) (ICH Q2))1 
 
Analytical procedure 
 
The analytical procedure refers to the way of performing the analysis. The analytical 
procedure should describe in sufficient detail the steps necessary to perform each analytical 
test. (ICH Q2) 
 
Analytical procedure attribute 
 
A technology specific property that should be within an appropriate limit, range, or 
distribution to ensure the desired quality of the measured result. For example, attributes for 
chromatography measurements may include peak symmetry factor and resolution. (ICH 
guidance for industry Q14 Analytical Procedure Development (ICH Q14)) 
 
Analytical procedure control strategy 
 
A planned set of controls derived from current analytical procedure understanding that 
ensures the analytical procedure performance and the quality of the measured result. (ICH 
Q14) 
 
Analytical procedure parameter 
 
Any analytical factor (including reagent quality) or analytical procedure operational condition 
that can be varied continuously (e.g., flow rate) or specified at controllable, unique levels. 
(ICH Q14) 
 
Analytical procedure validation strategy 
 
An analytical procedure validation strategy describes the selection of analytical procedure 
performance characteristics for validation. In the strategy, data gathered during development 
studies and system suitability tests can be applied to validation, and an appropriate set of 
validation tests can be predefined. (ICH Q14) 
 
Analytical target profile (ATP) 
 
A prospective summary of the performance characteristics describing the intended purpose 
and the anticipated performance criteria of an analytical measurement. (ICH Q14) 
 
 
1 We update guidances periodically. To make sure you have the most recent version of a guidance, check the 
FDA guidance web page at https://www.fda.gov/regulatory-information/search-fda-guidance-documents.  
20  Contains Nonbinding Recommendations 
Calibration model 
 
A model based on analytical measurements of known samples that relates the input data to a 
value for the property of interest (i.e., the model output). (ICH Q2) 
 
Control strategy 
 
A 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 and 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 guidance for industry Q10 
Pharmaceutical Quality System (April 2009) (ICH Q10)) 
 
Critical quality attribute (CQA) 
 
A physical, chemical, biological, or microbiological property or characteristic that should be 
within an appropriate limit, range, or distribution to ensure the desired product quality. (ICH 
guidance for industry Q8(R2) Pharmaceutical Development (November 2009) (ICH 
Q8(R2))) 
 
CTD 
 
Common technical document. (ICH guideline M4Q(R1) The Common Technical Document 
for the Registration of Pharmaceuticals for Human Use)2 
 
DoE 
 
Design of experiments. 
 
Established conditions (ECs) 
 
ECs are legally binding information considered necessary to assure product quality. As a 
consequence, any change to ECs necessitates a submission to the regulatory authority. (ICH 
guidance for industry Q12 Technical and Regulatory Considerations for Pharmaceutical 
Product Lifecycle Management (May 2021) (ICH Q12)) 
 
Intermediate precision 
 
Intermediate precision expresses intra-laboratory variations. Factors to be considered should 
include potential sources of variability, for example, different days, different environmental 
conditions, different analysts, and different equipment. (ICH Q2)  
 
Knowledge management 
 
A systematic approach to acquiring, analyzing, storing, and disseminating information related 
to products, manufacturing processes, and components. (ICH Q10) 
 
 
2 Available at https://database.ich.org/sites/default/files/M4Q_R1_Guideline.pdf.  
21  Contains Nonbinding Recommendations 
Method operable design region (MODR) 
 
A combination of analytical procedure parameter ranges within, which the analytical 
procedure performance criteria are fulfilled and the quality of the measured result is assured. 
(ICH Q14) 
 
Ongoing monitoring 
 
The collection and evaluation of analytical procedure performance data to ensure the quality 
of measured results throughout the analytical procedure life cycle. (ICH Q14) 
 
PACMP 
 
Postapproval change management protocol. (ICH Q12) 
 
Performance characteristic 
 
A technology independent description of a characteristic that ensures the quality of the 
measured result. Typically, accuracy, precision, specificity/selectivity, and range may be 
considered. Previous ICH Q2 versions referred to this as validation characteristic. (ICH Q2) 
 
Performance criterion 
 
An acceptance criterion describing a numerical range, limit, or desired state to ensure the 
quality of the measured result for a given performance characteristic. (ICH Q14) 
 
Platform analytical procedure 
 
An analytical procedure that is suitable to test quality attributes of different products without 
significant change to its operational conditions, system suitability, and reporting structure. 
This type of analytical procedure can be used to analyze molecules that are sufficiently alike 
with respect to the attributes that the platform analytical procedure is intended to measure. 
(ICH Q2) 
 
Precision 
 
The precision of an analytical procedure expresses the closeness of agreement (degree of 
scatter) between a series of measurements obtained from multiple samplings of the same 
homogeneous sample under the prescribed conditions. Precision can be considered at three 
levels: repeatability, intermediate precision, and reproducibility. 
 
The precision of an analytical procedure is usually expressed as the variance, standard 
deviation, or coefficient of variation of a series of measurements. (ICH Q2) 
 
Proven acceptable range for analytical procedures (PAR) 
 
A characterized range of an analytical procedure parameter for which operation within this 
range, while keeping other parameters constant, will result in an analytical measurement 
meeting relevant performance criteria. (ICH Q14) 
 
22  Contains Nonbinding Recommendations 
Quality risk management (QRM) 
 
A systematic process for the assessment, control, communication, and review of risks to the 
quality of the drug (medicinal) product across the product life cycle. (ICH guidance for 
industry Q9(R1) Quality Risk Management (May 2023))  
 
Range 
 
The range of an analytical procedure is the interval between the lowest and the highest results 
in which the analytical procedure has a suitable level of precision, accuracy, and response. 
(ICH Q2) 
 
Reportable range 
 
The reportable range of an analytical procedure includes all values from the lowest to the 
highest reportable result for which there is a suitable level of precision and accuracy. 
Typically, the reportable range is given in the same unit as the specification acceptance 
criterion. (ICH Q2) 
 
Working range 
 
A working range corresponds to the lowest and the highest levels of the quality attribute 
to be measured (e.g., content, purity) as presented to the analytical instrument and for 
which the analytical procedure provides reliable results. (ICH Q2) 
 
Real time release testing (RTRT) 
 
The ability to evaluate and ensure the quality of the in-process and/or final product based on 
process data, which typically include a valid combination of measured material attributes and 
process controls. (ICH Q8(R2)) 
 
Reference material 
 
A suitably characterized material, sufficiently homogeneous and stable with regard to one or 
more defined attributes, which has been established to be fit for the intended purpose. 
Reference materials may include national/international reference standards, pharmacopoeial 
reference standards, or in-house primary/secondary reference materials. (ICH Q2) 
 
Repeatability 
 
Repeatability expresses the precision under the same operating conditions over a short 
interval of time. Repeatability is also termed intra-assay precision. (ICH Q2) 
 
Reportable result 
 
The result as generated by the analytical procedure after calculation or processing and 
applying the described sample replication. (ICH Q2) 
 
23  Contains Nonbinding Recommendations 
Reproducibility 
 
Reproducibility expresses the precision between laboratories (e.g., interlaboratory studies, 
usually applied to standardization of methodology). (ICH Q2) 
 
Response 
 
The response of an analytical procedure is its ability (within a given range) to obtain a signal, 
which is effectively related to the concentration (amount) or activity of analyte in the sample 
by some known mathematical function. (ICH Q2) 
 
Revalidation 
 
Demonstration that an analytical procedure is still fit for the intended purpose after a change 
to the product, process, or the analytical procedure itself. Revalidation can involve all (full 
revalidation) or a subset (partial revalidation) of performance characteristics. (ICH Q2) 
 
Robustness 
 
The robustness of an analytical procedure is a measure of its capacity to meet the expected 
performance criteria during normal use. Robustness is tested by deliberate variations of 
analytical procedure parameters. (ICH Q14) 
 
Sample suitability assessment 
 
A sample or sample preparation is considered suitable if the measurement response on the 
sample satisfies predefined acceptance criteria for the analytical procedure attributes that 
have been developed for the validated analytical procedure. (ICH Q14) 
 
Specificity/selectivity 
 
Specificity and selectivity are both terms to describe the extent to which other substances 
interfere with the determination of an analyte according to a given analytical procedure. 
Specificity is typically used to describe the ultimate state, measuring unequivocally a desired 
analyte. Selectivity is a relative term to describe the extent to which particular analytes in 
mixtures or matrices can be measured without interferences from other components with 
similar behavior. (ICH Q2) 
 
System suitability test (SST) 
 
System suitability tests are developed and used to verify that the measurement system and the 
analytical operations associated with the analytical procedure are fit for the intended purpose 
and increase the detectability of unacceptable performance. (ICH Q14) 
 
Validation study 
 
An evaluation of prior knowledge, data, or deliberate experiments (i.e., validation tests) to 
determine the suitability of an analytical procedure for the intended purpose. (ICH Q2) 
 
24  Contains Nonbinding Recommendations 
Validation test 
 
Validation tests are deliberate experiments designed to authenticate the suitability of an 
analytical procedure for the intended purpose. (ICH Q2) 
 
Multivariate Glossary  
 
Calibration set 
 
A set of data with matched known characteristics and measured analytical results. (ICH Q14) 
 
Data transformation 
 
Mathematical operation on model input data to assume better correlation with the output data 
and to simplify the model structure. (ICH Q14) 
 
Independent sample 
 
Independent samples are samples not included in the calibration set of a multivariate model. 
Independent samples can come from the same batch from which calibration samples are 
selected. (ICH Q2) 
 
Internal testing 
 
Internal testing is a process of checking if unique samples processed by the model yield the 
correct predictions (qualitative or quantitative). 
 
Internal testing serves as means to establish the optimal number of latent variables, estimate 
the standard error, and detect potential outliers. (ICH Q2) 
 
Internal test set 
 
A set of data obtained from samples that have physical and chemical characteristics that span 
a range of variabilities similar to the samples used to construct the calibration set. (ICH Q14) 
 
Latent variables 
 
Mathematically derived variables that are directly related to measured variables and are used 
in further processing. (ICH Q2) 
 
Model maintenance 
 
The process of ensuring continued model performance over the life cycle of a multivariate 
model, which often includes outlier diagnostics and resulting actions for model 
redevelopment or change in the maintenance plans. (ICH Q14) 
 
Model validation 
 
The process of determining the suitability of a model by challenging it with independent test 
data and comparing the results against predetermined performance criteria. (ICH Q2) 
25  Contains Nonbinding Recommendations 
 
Multivariate analytical procedure 
 
An analytical procedure where a result is determined through a multivariate calibration model 
utilizing more than one input variable. (ICH Q2) 
 
Outlier diagnostic 
 
Tests that can identify unusual or atypical data in a multivariate analytical procedure. (ICH 
Q14) 
 
Reference analytical procedure 
 
A separate analytical procedure used to obtain the reference values of the calibration and 
validation samples for a multivariate analytical procedure. (ICH Q2) 
 
Reference sample 
 
A sample representative of the test sample with a known value for the property of interest, 
used for calibration. (ICH Q14) 
 
Validation set 
 
A set of data used to give an independent assessment of the performance of the calibration 
model. (ICH Q2) 
26  Contains Nonbinding Recommendations 
REFERENCES (12) 
 
International Council for Harmonisation (ICH) guidance for industry Q2(R2) Validation of 
Analytical Procedures (March 2024)1 
 
ICH guidance for industry Q6A Specifications: Test Procedures and Acceptance Criteria for 
New Drug Substances and New Drug Products: Chemical Substances (December 2000) 
 
ICH guidance for industry Q6B Specifications: Test Procedures and Acceptance Criteria for 
Biotechnological/Biological Products (August 1999) 
 
ICH guidance for industry Q8(R2) Pharmaceutical Development (November 2009) 
 
ICH guidance for industry Q9(R1) Quality Risk Management (May 2023) 
 
ICH guidance for industry Q10 Pharmaceutical Quality System (April 2009) 
 
ICH guidance for industry Q11 Development and Manufacture of Drug Substances 
(November 2012) 
 
ICH guidance for industry Q12 Technical and Regulatory Considerations for Pharmaceutical 
Product Lifecycle Management (May 2021) 
 
ICH guidance for industry Q8, Q9, & Q10 Questions and Answers  Appendix: Q&As From 
Training Sessions (July 2012)) 
 
ICH guideline M4Q(R1) The Common Technical Document for the Registration of 
Pharmaceuticals for Human Use2 
 
 
1 We update guidances periodically. To make sure you have the most recent version of a guidance, check the 
FDA guidance web page at https://www.fda.gov/regulatory-information/search-fda-guidance-documents.  
2 Available at https://database.ich.org/sites/default/files/M4Q_R1_Guideline.pdf.  
27  Contains Nonbinding Recommendations 
APPENDIX (13. ANNEX) 
 
A.  Examples of Application of ICH Q14 Principles (13.1 Annex A) 
 
The examples provided in this appendix are for illustrative purposes only. Other approaches 
are possible. They suggest how the concepts described in the International Council for 
Harmonisation (ICH) guidance for industry Q14 Analytical Procedure Development (ICH 
Q14) could be applied and should not be used as a template or the sole basis for a regulatory 
submission. 
 
The examples have been created to illustrate: 
 
  How analytical procedure performance characteristics, derived from the product 
context and knowledge, could be summarized in an analytical target profile (ATP) 
 
  How performance characteristics described in the ATP could be applied to select a 
suitable analytical technology, guide the development of an analytical procedure, and 
help define the analytical procedure control strategy  
 
  How to identify established conditions (ECs) for analytical procedures developed 
using elements of the enhanced approach 
 
  How quality risk management (QRM) and the adherence to associated criteria for 
relevant performance characteristics and/or the subsequent execution of a bridging 
study can ensure the postchange quality of the measured result and help to justify the 
respective reporting categories for ECs and the postapproval change management of 
analytical procedures 
 
As described in section IV (4) of ICH Q14, QRM can be used to evaluate the impact of 
prospective changes for analytical procedures. The list below describes examples of risk 
factors and risk reduction measures to identify the risks associated with the changes to an 
analytical procedure. The outcome of the risk assessment (risk level: high, medium, or low) 
feeds into the design and extent of the studies needed to support the change.  
 
Selected Risk (risk factors) 
 
  Relevance of the test  
 
 Potential clinical impact of the measured attribute (efficacy, safety, 
pharmacokinetics, and immunogenicity) (e.g., controlling critical quality attribute 
(CQA) versus non CQA)  
 
 Extent of knowledge of the attribute 
 
 Attribute ensured by other elements of the control strategy (testing or process 
control) 
 
  Complexity of the technology 
 
 Platform technologies 
28  Contains Nonbinding Recommendations 
 Novel versus established technology (e.g., in pharmacopoeias) 
 Several attributes reported as a sum (e.g., charge variants for large molecules) 
 Biological assays, cell-based assays, immunochemical assays 
 Multiattribute analytical procedure 
 Multivariate analytical procedure 
 
  Extent of the change 
 
 Change of one or several parameters outside the already proven acceptable 
ranges 
 
 Change of the analytical procedure within existing analytical procedure 
performance characteristics and associated criteria 
 
 Change to a new analytical procedure using a different technology 
 
 Change to analytical procedure performance criteria (e.g., due to tightening a 
specification limit) 
 
Risk reduction 
 
Risk reduction is defined in the ICH guidance for industry Q9(R1) Quality Risk Management 
(May 2023) as actions taken to lessen the probability of occurrence of harm and the severity 
of that harm.1 
 
Different kinds of knowledge can lead to reduction of risk, for example: 
 
  Product and process knowledge 
 
 Knowledge about quality attributes of the drug substance/drug product and 
acceptable ranges of CQAs 
 
 Well-justified analytical procedure performance criteria cover/link to CQAs and 
their acceptable ranges 
 
 Evidence to control the CQAs through the process parameter settings 
 
 Knowledge of the degradation pathways demonstrated by the analysis of relevant 
stressed samples 
 
 Other product knowledge (e.g., impurity profile, particle size, distribution) 
 
  Analytical procedure understanding and analytical procedure control strategy 
 
 Knowledge about analytical procedure parameters and their impact on 
measurement performance 
 
1 We update guidances periodically. To make sure you have the most recent version of a guidance, check the 
FDA guidance web page at https://www.fda.gov/regulatory-information/search-fda-guidance-documents.  
29  Contains Nonbinding Recommendations 
 
 Proven analytical procedure robustness (e.g., harmonized procedures 
(compendial tests)) 
 
 Enhanced analytical procedure understanding (e.g., design of experiments (DoE) 
studies) supporting justification of acceptable ranges (e.g., proven acceptable 
ranges for analytical procedures (PAR), method operable design region (MODR)) 
 
 Other knowledge from development of analytical procedure 
 
 System suitability test (SST) ensures relevant analytical procedure attributes 
 
 Ongoing monitoring of analytical procedure output 
 
 Clear link between signal and CQA to be measured (e.g., peak characterization 
available, specificity) 
 
  Bridging strategy for changes to analytical procedures 
 
 Availability of reference material, relevant historical and or stressed samples to 
support analytical procedure output assessment against performance criteria 
(demonstrated ability to control the CQA) 
 
 Comparison to output of previous analytical procedure (understanding and 
acceptance of risk for potential differences) 
 
 Demonstrated understanding of risks associated with parameter changes and 
potential interactions with other parameters 
 
 Prior experience with similar changes, analytes or technologies including 
platform analytical procedures 
 
1.  Measurement of Stereoisomers as Specific Process-Related Impurities in a Small 
Molecule Drug Substance (13.1.1) 
 
Introduction and Background 
 
Sakuratinib Maleate is a small molecule drug substance (DS) with multiple chiral centers. 
The chirality of the molecule, its degradation pathway, and the impurities are well 
characterized. From this knowledge and the established manufacturing process controls, the 
six stereoisomers (Impurity A-F) were found to be potentially present in the final product.  
 
Table 1: Analytical Target Profile 
 
Intended Purpose 
Q  uantitation of the six stereoisomers A-F in Sakuratinib Maleate DS for release testing 
Link to CQA (Stereoisomeric Purity)  
The analytical procedure should allow for the quantitation of the individual stereoisomers A-F and determination of 
the total sum to verify the CQA Stereoisomeric Purity 99.0% 
Characteristics of the Reportable Results  
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Performance  Acceptance Criteria  Rationale 
Characteristics 
Accuracy  80120% average recovery of spiked DS with Impurities  For example, at a 
A-E (specified at NMT 0.1% each)  specification level of 0.1%, 
90110% average recovery of spiked DS with Impurity F  20% bias would lead to a 
( specified at NMT 0.5%)  variation of the analytical 
Precision  Intermediate Precision RSD:  result of 0.02%, which was 
Impurities AE 15%  found acceptable for a release 
Impurity F 10%   decision. 
In a similar fashion, values 
for precision were derived. 
The recovery criteria for 
accuracy were set with 
respect to the reported result 
and taking into consideration 
any correction or response 
factors 
Specificity  Analytical procedure should be able to quantitate impurities  Potential interference with 
AF in presence of other likely process-related substances  quantitation of specified 
or DS degradation products with an acceptable bias of not  impurities by other regular 
more than 0.02%   components in the sample 
Reportable Range  Impurities AE: at least 0.050.12%  Reporting threshold to 120% 
Impurity F: at least 0.050.6%   of specification limit  
DS= drug substance; CQA = critical quality attribute; NMT = not more than; RSD = relative standard deviation. 
 
Technology Selection 
 
Multiple analytical technologies are available for the separation of stereoisomers. In this 
example, high-performance liquid chromatography (HPLC) was chosen because development 
studies showed good potential for separation of stereoisomers. As detection mode, ultraviolet 
(UV) detection was selected because it was known that the molecule had sufficient UV 
absorption properties. 
 
Analytical Procedure Development 
 
The chiral HPLC procedure for quantitation of stereoisomers was developed using enhanced 
principles. Below is a summary of the activities conducted during enhanced development. 
 
  Established an understanding of the chemistry, process, and impurities that have 
potential to be present in the DS 
 
  Made available reference materials for development and validation 
 
  Conducted risk assessment and evaluating prior knowledge to identify the analytical 
procedure parameters that can impact performance of the procedure 
 
  Conducted modeling and multivariate experiments including robustness testing to 
explore ranges and interactions between identified analytical procedure parameters 
 
  Defined analytical procedure control strategy based on procedure understanding 
including set points for relevant analytical procedure parameters and SST 
 
Analytical Procedure 
 
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For the purpose of this example, a summary of the analytical procedure is provided below. 
This does not reflect the entirety of the analytical procedure description in the dossier. 
 
Table 2: Summary of the Analytical Procedure Description  
 
Column  Chiral Column, Amylose tris-(3,5-dimethylphenylcarbamate), Immobilized 
on Porous, Spherical, Silica Particles, 4.6 mm ID x 250 mm, 3 m 
Mobile Phase  n-hexane / ethanol / TFA (80/20/0.1) 
Flow Rate  1 mL/min 
Column temperature  30C 
Detection  UV 214 nm 
Injection volume  5 L 
Standard/sample  1.0 mg/mL 
concentration 
System Suitability Tests  Controlled Parameters 
 
Resolution between critical peak pair: DS Main Peak  Column, Temperature*, Mobile Phase, Flow Rate 
and Impurity D  2.0 
S/N at QL; DS at 0.05% >10  Injection Volume, Column, Mobile Phase, 
Standard/Sample Concentration, Detection 
Wavelength 
Repeatability of injection of DS at 0.5% level  5%  Injection Volume, Mobile Phase 
* For example, the retention time models built from data collected during analytical procedure development 
screens were used to assess the robustness of temperature and other parameters that could potentially affect the 
performance characteristics (e.g., specificity). The in silico robustness was verified experimentally by 
confirming resolution at the center point and design points that generated the minimum and maximum main 
peak retention time. 
mm = millimeter; m = micrometer; mL = milliliter; min = minute; UV = ultraviolet; nm = nanometer; L = 
microliter; mg = milligram; DS = drug substance; S/N = signal to noise. 
 
Analytical Procedure Validation 
 
After the analytical procedure development was finalized and the analytical procedure control 
strategy established, a validation study was planned and completed according to the ICH 
guidance for industry Q2(R2) Validation of Analytical Procedures (March 2024) (ICH Q2).  
 
Description of Established Conditions, Reporting Categories, and Justifications 
 
The applicant proposed and justified established conditions and reporting categories, as part 
of the submission. For the purpose of this example, Table 3 describes the proposed ECs, their 
proposed reporting categories, and examples of parameters that are not ECs. 
 
Note: The extent of ECs and associated reporting categories listed in this table depend on the 
extent of knowledge gained, information and justification provided in the dossier. The dossier 
is subject to regulatory review. The information provided in this example is only part of the 
knowledge available that will be submitted and is provided for illustrative purposes only. The 
extent of ECs (EC or not EC designation), actual reporting categories, and data 
requirements may differ by region. Depending on the nature and extent of the change (e.g., 
change to a different technology), a postapproval change management protocol (PACMP) 
may be required. 
 
Table 3: Evaluated Risk, Proposed Established Conditions, and Proposed Reporting 
Categories  
32  Contains Nonbinding Recommendations 
 
Established Condition  Overall  Proposed  Comment 
Risk  Reporting 
Category  Category1) 
Performance Characteristics and  High  P  A  The performance characteristics and criteria 
Criteria as Described in the ATP:  ensure the quality of the reportable result and 
Accuracy, Precision, Specificity,  link to the CQA. 
Range (see Appendix section A (13.1  If widening of the performance criteria is 
Annex A), Table 1)  necessary, it will be reported as PA. 
Technology: Chiral Liquid  Medium  N  M  A technique that meets the performance 
Chromatography   characteristics and criteria ensures the quality of 
  the reportable result and link to the CQA. 
Suitable chiral separation technique to  There is a strong understanding between product 
meet performance characteristics  knowledge, intended purpose, and the analytical 
 defined in ATP  procedure performance established to enable the 
design of future bridging studies. 
A change resulting in a widening of the 
specification acceptance criteria might require a 
higher reporting category.  
System Suitability Test (SST) and  Medium  NL/NM  SST was developed for the LC procedure based 
parameter-control relationship (see  on a risk analysis and ensures adherence to the 
Appendix section A (13.1 Annex A)  performance characteristics and criteria. Control 
Table 2)  relationships were established through prior 
  knowledge (general principles of technique) and 
   
during procedure development. 
 
If the SST criteria are widened, the reporting 
   
category would be higher. 
LC Column: Amylose tris-(3,5- Low  NL/NM  The LC column, mobile phase components and 
dimethylphenylcarbamate),  mode of detection are the main parameters, 
immobilized on porous, spherical  defining the separation mechanism and 
silica particles  detection. Changing these parameters may result 
  in the need to adapt the SST.  
Mobile Phase Components: n-Hexane, 
Ethanol, TFA 
 
Method of detection: UV 214 nm 
 
The other analytical procedure parameters defined as ECs are omitted for the purpose of this example. 
 
The following conditions are examples of parameters that are not ECs2): 
 
Ratio of mobile phase components: n- Low  -  These parameters are controlled by the SST. 
Hexane/Ethanol/TFA (80/20/0.1)      Robustness testing supported by modeling was 
  performed at the center point and the extrema 
Instrumental conditions:  that generated the minimum and maximum main 
Temperature: 30C  band retention time. 
 
Column length, packing particle size 
       
Preparation of test solutions and  Low  -   The performance over the working range has 
reference materials: 1 mg/mL DS in  been demonstrated through the linearity 
mobile phase  experiments during validation. 
1) PA: prior approval, NM: notification moderate; NL: notification low (as per definitions in the International 
Council for Harmonisation guidance for industry Q12 Technical and Regulatory Considerations for 
Pharmaceutical Product Lifecycle Management (May 2021)). 
2) Depending on the region, some of this information is included in an approval letter. 
ATP = analytical target profile; CQA = critical quality attribute; LC = liquid chromatography; UV = ultraviolet; 
nm = nanometer; EC = established condition; mg = milligram; mL = milliliter; DS = drug substance. 
 
33  Contains Nonbinding Recommendations 
Change Management and Bridging Strategy 
 
The change described below is an example of one that could occur during the life cycle of a 
product and analytical procedure. When the product was initially submitted and approved, 
supercritical fluid chromatography (SFC) was not selected as the analytical technique due to 
unavailability in the commercial facility. Years after approval, the applicant desires to change 
the technique to SFC because it is a more environmentally friendly technology and is now 
available at the commercial site. 
 
For this change, following development of the SFC procedure the applicant will perform a 
structured risk assessment to evaluate potential impact on the performance characteristics and 
the link to CQA (stereoisomeric purity) as defined in the ATP. As an outcome of the risk 
assessment, experimental bridging studies to demonstrate adherence to the performance 
characteristics and associated criteria will be performed. Validation of the new analytical 
procedure and comparative analysis of representative samples and reference materials will be 
performed. 
 
The applicant should not implement the new analytical procedure using the predefined 
reporting category unless adherence to the performance characteristics and associated criteria 
defined in the ATP is demonstrated during the bridging studies. If the precondition of 
adherence to the ATP cannot be met, a higher reporting category would apply. 
 
The example in Appendix section A (13.1 Annex A) Figure 1 illustrates a postapproval 
change in technique as well as the steps an applicant would follow when implementing the 
change. The information in the table above (ECs and reporting categories) would need to be 
agreed upon up front with the regulatory authority. 
 
34  Contains Nonbinding Recommendations 
Figure 1: Example of Work Process of Applicant to Change an Approved Analytical 
Procedure 
 
35  Contains Nonbinding Recommendations 
HPLC = high-performance liquid chromatography; SFC = supercritical fluid chromatography; CQA = critical 
quality attribute; EC = established condition; SST = system suitability test. 
ICH Q14 = International Council for Harmonisation guidance for industry Q14 Analytical Procedure 
Development. 
 
2.  Measurement of Potency for an anti-TNF-alpha Monoclonal Antibody (13.1.2) 
 
Introduction and Background  
 
The example presented refers to the measurement of the relative potency of the drug, in this 
case an anti-TNF-alpha monoclonal antibody, in drug substance and in drug product at 
release and for stability testing. 
 
Assumptions for the example: 
 
  Mode of action: the neutralization of the biological activity of soluble TNF-alpha by 
preventing TNF-alpha from binding to the TNF-alpha receptor 
 
  Fc-effector functions are out of scope 
 
  Specification limits for the relative potency: 80 percent to 125 percent compared to 
reference material 
 
  Potency assay to be developed is able to detect a change and/or a shift in potency 
upon forced degradation 
 
36  Contains Nonbinding Recommendations 
Table 4: Analytical Target Profile 
 
Intended purpose 
Measurement of the potency of an anti-TNF-alpha monoclonal antibody in drug substance and in drug product at 
release and for stability testing.  
Link to CQA (biological activity) 
The mode of action of the drug is the neutralization of the biological activity of soluble TNF-alpha by preventing 
TNF-alpha from binding to the TNF-alpha receptor. Target acceptance criteria: 80% to 125% relative potency1) 
Characteristics of the reportable result 
Performance  Acceptance Criteria  Rationale 
Characteristics 
Accuracy  Accuracy is assessed via a linearity experiment that covers  Parameters are assessed based 
the reportable range. No trend in relative bias is observed  on compendial guidance  
over the tested relative potency range   
  The acceptance criteria are 
The 95% confidence interval of the slope of the fitted 
determined considering the 
regression line between theoretical and measured potency 
intended purpose of the 
falls within a range of 0.8 to 1.25 
measurement 
 
The upper and lower 90% confidence interval for the relative   
bias calculated at each potency level is not more than 20%1)  Selected performance 
Precision  Upper 95% confidence interval for the average intermediate  characteristic ensures that the 
precision across levels across the reportable range (95% CI %  intended analytical procedure 
geometric coefficient of variation) is not more than 20%1)   delivers the quality of the 
reportable result 
Specificity  Analytical procedure is specific for the intended mechanism  Critical characteristic of a 
of action of the active ingredient  bioassay to ensure specificity 
towards the targeted 
biological activity 
No interference from relevant process-related impurities or  For example, process-related 
matrix components  and matrix components do 
not significantly affect the 
characteristics of the dose-
response curve 
Assay is stability indicating, i.e., capable of detecting a  To ensure that the product 
change in potency and/or a change in the shape of the dose- remains within specification 
response curve, confirmed using forced degraded samples   over its shelf life  
Reportable  The potency range is the range that meets accuracy and  Stated range for which the 
range  precision. It should include the specification range (80% to  required accuracy and 
120% of the specification range in this case corresponding to  precision characteristics are 
64% to 150% for a specification of 80% to 125% relative  demonstrated 
potency1)) 
1) Individual values are just an example and can be different from product to product. 
CQA = critical quality attribute; CI = confidence interval. 
 
Technology Selection 
 
Binding assays and cell-based bioassays are suitable technologies for the measurement of the 
relative potency of an anti-TNF-alpha monoclonal antibody when considering the ATP 
above. The two assays rely on the binding of the anti-TNF-alpha monoclonal antibody to the 
soluble TNF-alpha. While the signal of a binding assay directly measures the binding, the 
cell-based assay may target a later stage event in the signaling cascade. Out of different 
formats of cell-based assay, the cell-based proliferation assay was chosen because it is widely 
used and a well-characterized cell line was available. 
 
37  Contains Nonbinding Recommendations 
Analytical Procedure Development 
 
The development of the analytical procedure described has been performed using an 
enhanced approach and was based on extensive knowledge of the molecule and relative 
potency assays, considering the following points for example: 
 
  Performance characteristics and associated criteria for the analytical procedure 
defined in the ATP. 
 
  Extensive analytical procedure understanding gained from prior knowledge and 
development studies guided by QRM principles. Factors considered during risk 
assessment are shown in Appendix section A (13.1 Annex A) Figure 2: e.g., 
 
 The cell and its performance (cell density, cell viability, number of passages) 
 Stability indicating properties using forced degradation samples 
 
  Robustness evaluation was conducted, and its outcome was reflected in the analytical 
procedure control strategy.  
 
Figure 2: Ishikawa Diagram 
 
 
 
Analytical Procedure 
 
For the purpose of this example, a summary of the analytical procedure is provided below. 
This does not reflect the entirety of the procedure description in the dossier. 
 
38  Contains Nonbinding Recommendations 
Table 5: Analytical Procedure Description 
 
Unit Operation  Description 
Cell preparation  Prepare a suspension of WEHI-164 cells containing 1x106 cells per 
milliliter, using assay medium containing 2 g/mL of actinomycin D 
Reference solution and test  Included in analytical procedure description in the dossier but not listed 
solution preparation  in this table  
Plate preparation   
Plating cells 
Absorbance measurement 
Calculations 
Solutions and reagents  WEHI-164 cells (ATCC), TNF-alpha solution of suitable concentration, 
preparation  assay and culture medium including components and concentrations-
Actinomycin D, Tetrazolium salt WST-8 
Analytical Procedure Control Strategy 
System suitability test  1. The dose-response curve obtained for the reference standard curve 
corresponds to a sigmoid curve with upper and lower plateaus 
corresponding to cell only control and cell + TNF-alpha control, 
respectively 
2. The dose-response curve obtained for the test sample corresponds to a 
sigmoid curve with upper and lower plateaus corresponding to cell 
only control and cell treated with TNF-alpha control, respectively. 
3. The coefficient of determination calculated for each standard curve 
(r2) is not less than 0.97 * 
4. Maximum value (cell only) to minimum value (TNF-alpha control) 
ratio: minimum 3.0* 
Sample suitability assessment  Assessment of similarity/parallelism: 
- The upper asymptote ratio (A /A ): 0.81.2* 
std test
- The lower asymptote ratio (D /D ): 0.81.2* 
std test
- The Hill slope ratio (B /B ): 0.81.2* 
std test
- The upper to lower asymptote ratio ((DA) /(DA) ): 0.81.2* 
std test
* The ways of assessing of similarity/parallelism as well as individual values are just examples and can be 
different from product to product. 
g = microgram; mL = milliliter. 
 
Analytical Procedure Validation 
 
After the analytical procedure development was finalized and the analytical procedure control 
strategy established, a validation study was planned and completed according to the 
recommendations in ICH Q2. 
 
Description of Established Conditions, Reporting Categories, and Justifications 
 
The applicant proposed and justified ECs and reporting categories as part of the submission. 
For the purpose of this example, Appendix section A (13.1 Annex A) Table 6 describes a 
portion of the proposed ECs, their proposed reporting, and an example of a parameter that is 
not an EC. 
 
Note: The extent of ECs and associated reporting categories listed in this table depend on the 
extent of knowledge gained, information and justification provided in the dossier. The dossier 
is subject to regulatory review. The information provided in this example is only part of the 
knowledge available that will be submitted and is provided for illustrative purposes only. The 
extent of ECs (EC or not EC designation), actual reporting categories, and data 
requirements may differ by region. Depending on the nature and extent of the change (e.g., 
change to a different technology), a PACMP may be required. 
39  Contains Nonbinding Recommendations 
 
Table 6: Evaluated Risk, Proposed Established Conditions, and Proposed Reporting 
Categories 
 
Established Condition  Overall  Proposed  Comment 
(EC)  Risk  Reporting 
Category  Category1) 
Performance characteristics  high  PA  The performance characteristics and criteria 
and associated criteria as  ensure the quality of the reportable result and link 
defined in the ATP  to the CQA. Widening of performance 
(Appendix section A (13.1  characteristics and criteria could have an impact 
Annex A) Table 4)  on the control of the CQA. 
Technology (principle)  high or  PA or NM  Adherence to performance characteristics and 
cell-based assay  medium  criteria ensured by control strategy and defined 
bridging strategy (see below) to assess impact of 
changes. 
Change would be reported as NM if no impact of 
the change on the specification acceptance criteria 
and as PA if there is an impact on the specification 
acceptance criteria. 
Analytical procedure control strategy elements (SST 1-4, sample suitability assessment) 
System suitability test   medium  NM2)  Performance of the analytical procedure is ensured 
(see Appendix section A  by: 
(13.1 Annex A) Table 5)   Direct control of individual analytical procedure 
Sample suitability  medium  NM2)  steps through analytical procedure control strategy 
assessment   elements listed in Appendix section A (13.1 
(see Appendix section A  Annex A) Table 5 (and the dossier) 
(13.1 Annex A) Table 5)   Defined analytical procedure control strategy 
elements which ensures the adherence to the ATP 
 Adherence to the performance characteristics 
and criteria after a change of analytical procedure 
control strategy elements 
 
If assurance of performance of the analytical 
procedure cannot be demonstrated, the change 
needs to be reported as PA. 
40  Contains Nonbinding Recommendations 
Established Condition  Overall  Proposed  Comment 
(EC)  Risk  Reporting 
Category  Category1) 
Cell Preparation 
Cell line:  medium  NM  Based on demonstrated understanding of the mode 
WEHI-164 cells (ATCC)  of action (link to CQA) the suitability of the 
responsive cell line will be confirmed by 
responding to the TNF-alpha (survival of the cell 
in presence of the drug and cell death without 
drug). 
 
Adherence to ATP ensured by control strategy and 
defined bridging strategy (see below) to assess 
impact of changes. 
 
System suitability test ensures the suitability of the 
cell line and its performance (number of passages, 
confluency, cell counting, cell viability, signal 
amplitude, shape of the response curve). 
 
 
 
 
 
 
Preparation of cells:  low  NL  Sufficient cell performance to detect changes in 
sub culturing  the quality of the drug is ensured by: 
Medium composition:  low  NL    System suitability covers the suitability of the 
RPMI 1640, L-glutamine,  cell preparation (number of passages, 
heat-inactivated foetal  confluency, cell counting, cell viability, signal 
bovine serum, and a suitable  amplitude, shape of the response curve) 
antibiotic    Changes in cell metabolism that impact 
Preparation of a suspension  low  NL  performance of the analytical procedure and 
of WEHI-164 cells  link to CQA will be detected 
containing 1x106 cells per    Changes that lead to insufficient cell 
milliliter, using assay  performance will not be implemented as they 
medium containing 2  could have an impact on the defined 
g/mL of actinomycin D.   performance characteristics and would require 
  prior approval 
 
  Analytical procedure control strategy ensures 
  adherence to performance characteristics and 
  criteria. The extent of the bridging study will 
  depend on the extent of the change.  
The other analytical procedure parameters defined as ECs are omitted for the purpose of this example 
The following is an example of a parameter that is not an EC: 
Plating format   low  -  No impact on assay output based on development 
data. 
1) PA: prior approval, NM: notification moderate; NL: notification low (as per definitions in the International 
Council for Harmonisation guidance for industry Q12 Technical and Regulatory Considerations for 
Pharmaceutical Product Lifecycle Management). 
2) Based on regional requirements the proposed reporting category may need to be elevated to PA. 
ATP = analytical target profile; CQA = critical quality attribute; g = microgram; mL = milliliter. 
 
41  Contains Nonbinding Recommendations 
Change Assessment and Bridging Strategy 
 
For every change, the applicant will perform a risk assessment to evaluate potential impact on 
the performance characteristics and the link to CQA (biological activity) as defined in the 
respective ATP. The outcome of the risk assessment informs the extent of the bridging 
studies used to demonstrate adherence to the performance characteristics and associated 
criteria. These can include, if necessary, full or partial revalidation of the analytical procedure 
performance characteristics affected by the change and/or comparative analysis of 
representative samples and reference material. 
 
The applicant should not implement the new analytical procedure using the predefined 
reporting category unless adherence to the performance characteristics and associated criteria 
defined in the ATP are demonstrated during the bridging studies. If the precondition of 
adherence to the ATP cannot be met, a higher reporting category would apply. 
 
The example in Appendix section A (13.2 Annex A) Figure 3 illustrates a postapproval 
change in the cell preparation from subculture to ready-to-use cells and includes the steps an 
applicant would follow when actually implementing the change. 
 
The ECs and reporting categories (see Appendix section A (13.1 Annex A) Table 6) would 
need to be proposed following ICH Q14 Figure 2 and agreed up front with the regulatory 
authority. 
 
42  Contains Nonbinding Recommendations 
Figure 3: Example of Work Process of Applicant to Change an Approved Analytical 
Procedure  
 
 
CQA = critical quality attribute; EC = established condition. 
ICH Q14 = International Council for Harmonisation guidance for industry Q14 Analytical Procedure 
Development. 
 
43  Contains Nonbinding Recommendations 
B.  Example of Multivariate Model Life Cycle Components (13.2 Annex B) 
 
  Example 1  Example 2  Example 3 
Model  On-line NIR to determine blending  Measurement of content uniformity  Glucose Raman model used for qualitative 
Description  ranges to achieve blend uniformity  and assay of uncoated tablets by NIR  identification testing on incoming raw 
during development  used for product release  material release for GMP use 
Model Category - Low Impact  Model Category - High Impact  Model Category - High impact 
User requirements  Defined model requirements (e.g.,  Defined model requirements (e.g., ATP) 
ATP) 
Risk  Initial assessment based on existing  Formal risk assessment based on  Formal risk assessment with knowledge 
Assessment  knowledge, laboratory and pilot  knowledge gained during initial  gained during initial development 
studies, or DoE, as appropriate  development  
Model  Scientifically sound approach based  Formal design-based approach (e.g.,  Formal design-based approach covering 
Development -   on laboratory and pilot data and  DoE) covering appropriate ranges of  appropriate ranges of relevant variability 
previous experience  relevant variability sources with  sources (raw material, lots, packaging, 
Calibration 
established acceptance criteria that are  instrument-to-instrument, user, software 
suitable for the intended purpose  limitation) with established acceptance 
criteria that are suitable for the intended 
purpose. Establish an identification 
threshold that has the same probability of 
detection as the existing analytical 
procedure and a suitable alternative 
analytical procedure should the Raman 
analytical procedure fail 
Validation   Assess specificity and robustness,  Full validation covering applicable  Full validation covering applicable 
optionally assess linearity and/or  performance characteristics across  performance characteristics across 
precision  reportable ranges with established  reportable ranges with established 
acceptance criteria (ICH Q2)  acceptance criteria (ICH Q2). Include 
establishing suitable comparability of 
Raman procedure to existing analytical 
procedure for release (can be reference 
analytical procedure) 
Performance  Routine monitoring - maintain data  Routine monitoring - maintain data  Routine monitoring - maintain data 
Monitoring  sources (instruments), automation  sources (instruments), automation  sources (instruments), automation 
connectivity, and data integrity  connectivity, and data integrity  connectivity, and data integrity 
Real-time diagnostics - implement  Real-time diagnostics - implement  Real-time diagnostics - implement routine 
initial diagnostics to confirm model  routine diagnostics to confirm model  diagnostics to confirm model performance 
performance in real time  performance in real time  in real time 
Periodic monitoring - if applicable,  Periodic monitoring - compare model  Periodic monitoring - compare model 
compare model predicted results to  predicted results to reference  predicted results to reference analytical 
reference analytical procedure at a  analytical procedure at a frequency  procedure at a frequency that is 
frequency that is scientifically  that is scientifically justified or on an  scientifically justified or on an event-
justified or on an event-driven basis,  event-driven basis  driven basis 
as needed 
Model  Model Update - updates are common  Model Update - updates should be  Model Update - updates should be 
Maintenance  during the process development  triggered based on Model Monitoring  triggered based on Model Monitoring and 
stage as new experimental data  and Maintenance Strategy  Maintenance Strategy 
becomes available 
Change management per PQS  Change management per PQS, with  Change management per PQS, with 
regulatory communication as required  regulatory communication as required 
NIR = near infrared; GMP = good manufacturing process; ATP = analytical target profile; DoE = design of 
experiments; PQS = pharmaceutical quality system. 
ICH Q2 = International Council for Harmonisation guidance for industry Q2(R2) Validation of Analytical 
Procedures. 
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