ASTM E2475-23

This document is not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
Designation: E2475 − 10 (Reapproved 2016) E2475 − 23
Standard Guide for
Process Understanding Related to Pharmaceutical
Manufacture and Control
This standard is issued under the fixed designation E2475; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope
1.1 The purpose of this guide is to establish a framework and context for process understanding for pharmaceutical manufacturing
using the principles of quality by design (QbD) (Juran, 1992; FDA/ICH Q8).ICH Q8). The framework is applicable to both active
pharmaceutical ingredient (API) and to drug substance (DS) and drug product (DP) manufacturing. High (detailed) level process
understanding can be used to facilitate production of product which consistently meets required specifications. It can also play a
key role in continuouscontinual process improvement efforts.
1.2 Process Analytical Technology (PAT) is one element that can be used for achieving control over those inputs determined to
be critical to a process. It is important for the reader to recognize that PAT is defined as:
“{a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing)
of critical quality and performance attributes of raw and in process materials and processes, with the goal of ensuring final
product quality. It is important to note that the term analytical in PAT is viewed broadly to include chemical, physical,
microbiological, mathematical, and risk analysis conducted in an integrated manner. The goal of PAT is to enhance
understanding and control the manufacturing process{” (U.S. FDA PAT)
“{a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing)
of critical quality and performance attributes of raw and in process materials and processes, with the goal of ensuring final
product quality. It is important to note that the term analytical in PAT is viewed broadly to include chemical, physical,
microbiological, mathematical, and risk analysis conducted in an integrated manner. The goal of PAT is to enhance
understanding and control the manufacturing process{” (USFDA PAT)
1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility
of the user of this standard to establish appropriate safety and healthsafety, health, and environmental practices and determine
the applicability of regulatory limitations prior to use.
1.4 This international standard was developed in accordance with internationally recognized principles on standardization
established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued
by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
2. Referenced Documents
2.1 ASTM Standards:
E456 Terminology Relating to Quality and Statistics
This guide is under the jurisdiction of ASTM Committee E55 on Manufacture of Pharmaceutical and Biopharmaceutical Products and is the direct responsibility of
Subcommittee E55.11 on Process Design.
Current edition approved Sept. 1, 2016Nov. 15, 2023. Published September 2016December 2023. Originally approved in 2010. Last previous edition approved in 20102016
as E2475 – 10. DOI:10.1520/E2475-10R16.10 (2016). DOI:10.1520/E2475-23.
Juran, J., Juran on Quality by Design: The New Steps for Planning Quality Into Goods and Services, Free Press, New York, N.Y., 1992.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
E2475 − 23
E2281 Practice for Process Capability and Performance Measurement
E2474 Practice for Pharmaceutical Process Design Utilizing Process Analytical Technology (Withdrawn 2020)
E2617 Practice for Validation of Empirically Derived Multivariate Calibrations
2.2 U.S. Government Publications:
ICH Quality Implementation Working Group Points To Consider (R2) ICH-Endorsed Guide for ICH Q8/Q9/Q10 Implementa-
tion
FDA/ICH Q8ICH Q8 Pharmaceutical Development
ICH Q9 Quality Risk Management
FDA/ICH Q10ICH Q10 Pharmaceutical Quality Systems
ICH Q11 Development and Manufacture of Drug Substances
ISO 14971 Medical devices—Application of risk management to medical devices
U.S. FDA PATUSFDA PAT Guidance Document, Guidance for Industry PAT—A Framework for Innovative Pharmaceutical
Manufacturing and Quality Assurance
3. Terminology
3.1 Definitions of Terms Specific to This Standard:
3.1.1 critical inputs, n—critical process parameters and critical raw material attributes for a given process.
American Society for Quality
3.1.2 empirical, adj—any conclusion based on experimental data and past experience, rather than on theory.
3.1.3 expert system, n—an expert system is a computer program that simulates the judgment and behavior of a human or an
organization that has expert knowledge and experience in a particular field.
3.1.3.1 Discussion—
Typically, such a system contains a knowledge base containing accumulated experience and a set of rules for applying the
knowledge base to each particular situation that is described to the program. Sophisticated expert systems can be enhanced with
additions to the knowledge base or to the set of rules.
3.1.4 first principles, n—a calculation is said to be from first principles, or ab initio, if it starts directly at the level of established
laws of physics and does not make assumptions such as model and fitting parameters.
3.1.5 mechanistic, adj—(1) of, or relating to, theories that explain phenomena in purely physical or deterministic terms: a
mechanistic interpretation of nature.
3.1.6 process capability, n—statistical estimate of the outcome of a characteristic from a process that has been demonstrated to
be in a state of statistical control. E2281
3.1.7 process inputs, n—the combination of all process parameters and raw material attributes for a given process.
3.1.8 process understanding, v—to recall and comprehend process knowledge such that product quality can be explained logically
or scientifically, or both, as a function of process inputs and respond accordingly.
3.1.9 quality attribute, n—a physical, chemical, biological, or microbiological property or characteristic of a product.
3.1.10 residual error, n—the difference between the observed result and the predicted value (estimated treatment response);
Observed Result minus Predicted Value. E456
3.1.11 uncertainty, n—an indication of the variability associated with a measured value that takes into account two major
components of error: (1) bias, and (2) the random error attributed to the imprecision of the measurement process. E456
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www.access.gpo.gov.
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4. Process Understanding
4.1 From physical, chemical, biological, and microbiological perspectives, a process is considered to be well understood when:
(1) All significantcritical sources of variability in process inputs are identified and explained,
(2) variability is managed by the process, and
(3) The effect of these sources of variability on product quality attributes can be accurately and reliably estimated based on the
inputs to the process, andpredicted over the design space established for materials used, process parameters, manufacturing,
environmental, and other conditions.
(3) Significant process parameters are continuously managed and controlled to ensure that the process must produce product
which is continuously within required specifications to the user specified required degree or confidence.
4.2 A well-controlled process is a process where the risk of producing Well-controlled processes result in the probability of product
not meeting required specifications at a level that is below the maximum acceptable level of risk limit as predetermined by the user.
Accordingly, process understanding requires the comprehension and recall of process knowledge sufficient for the logical,
statistical, or scientific understanding, or combination thereof, of how significant process parameters and raw material attributes
attributes of raw and in-process materials relate to, or impact the quality attributes of, the product being produced. Sufficient
process understanding should be achieved to reduce risk to an acceptable level for the patient, manufacturer, or any other
stakeholder.
4.3 A Lifecycle Commitment (Development and Commercial Manufacture):
4.3.1 Process understanding is fundamental to QbD. It is important to realize that due to commercial realities (for example, finite
resources, time, and money), a process will typically be commissioned as soon as the degree of process understanding is sufficient
to permit operation of the process with an acceptably low, user specified, level of risk of producing out of specification product.
While it may be appropriate to commission a process once this minimum degree of process understanding is achieved, the risk that
the process may transition out of control steadily increases over time (for example, process drift), and could exceed the maximum
acceptable risk without warning, unless an ongoing program to enhance process understanding is in place.
4.3.2 Accordingly, the developmentimprovement of process understanding should be treated as an ongoing process.exercise.
Learning should continue throughout the product and process life cycle to improve the level of process understanding to include
process parameters and other factors (for example, environmental, changes of scale, changes in raw materials, changes in
personnel) which may have changed or which may have newly emerged since the time the process was first commissioned. from
the initial design of the chemical or biological DS or DP through manufacturing of the unit dose to final packaging. Work to
enhance process understanding continuously throughout the life cycle of the product and process can provide assurance that the
process will continue to have an acceptably low risk of producing out of specification results.
4.3.3 Manufacturers are encouraged to continuously monitor and improveshould have an ongoing program for monitoring and
improving upon their operations to enhance product quality.
4.4 Process Understanding for the Whole Process:
4.4.1 For each product, process understanding covers the process from the initial design of the chemical or biological drug
substance DS through manufacturing of the unit dose or device to final packaging. In addition, the critical quality attributes of the
raw materials will in turn become inputs to the drug product DP manufacturing process, as will process parameters.
4.4.2 Fig. 1 schematically illustrates that the performance of any process output (Y) is a function of the inputs (X), which can be
classified into one of six categories (that is, operator, equipment, measurements, methods, materials, and environmental
conditions).
4.4.3 Comprehensive understanding of the relationships of the process inputs and operating parameters to quality attributes of the
resulting product is fundamental to developing a successful risk mitigation or control strategy, or both. Identification of critical
process parameters (CPPs) and critical raw material attributes (CMAs) should be carried out using suitable experimental and
investigative techniques. An understanding of these critical inputs (CPPs and critical raw material attributes), CMAs), and their
monitoring and control, is essential when designing a process that is able to consistently and reliably deliver product of the desired
quality.
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FIG. 1 Input, Process, and Output Diagram
4.4.4 One common method for achieving the desired state is through multivariate analysis and control. The acceptable operating
envelope of the critical inputs defines the relationship between the design space, control strategy and operating range(s).input
ranges and product quality.
4.4.5 Note that for raw materials, in addition to inherent variability, an additional source of variability derives from the potential
for adulteration. This requires that manufacturers understand their incoming supply chain and suppliers quality systems, and
include methods to detect adulteration of materials in addition to confirming identity as necessary, bearing in mind that adulteration
may be difficult to detect by standard methods. It also requires that manufacturers use suppliers that are aware of these concerns
and are prepared to implement their own precautionary measures, and to permit transparency into their respective supply sources.
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4.5 Tools of Process Understanding:
4.5.1 Process understanding begins with process design (Practice E2474) and usually a structured, small scale development
program which focuses on efficiently delivering a product meeting that meets the required specifications. Tools that may be applied
during development and after commercialization include:
(1) Scientific theory,
(2) Prior knowledge,
(3) Risk analysis,
(4) Design of experiments,
(5) Simulation of unit operations,
(6) Selection of a suitable technology platform,
(7) Mathematical models,
(8) Validated empirical/statistical models,
(9) Appropriate instrumentation, and
(10) Appropriate analytical methods.
4.5.2 The measurement technologies include but are not limited to spectroscopic, acoustic, or other rapid sensor technologies. The
development of these and other advanced techniques will continue to enable or enhance predictive control for commercial
pharmaceutical processes.
4.5.2 The measurement technologies encompass offline, atline, online, and inline technologies. Online and inline measurement
technologies can include but are not limited to spectroscopic, acoustic, or other rapid sensor technologies. The development of
these and other advanced techniques will continue to enable or enhance predictive control for commercial pharmaceutical
processes.
The ability to measure process parameters and quality attributes inline, online, or atline in real time can contribute to process
understanding and the ability to control the process. These technologies offer the development scientist, commercial production
engineer and manufacturing personnel the opportunity for additional insight. This is achieved through the increased measurement
frequency and availability of more comprehensive data.
5. Process Knowledge
5.1 Process knowledge is the cornerstone of process understanding. There are various levels of process knowledge, and these are
listed from lowest to highest state of understanding:
(1) Descriptive knowledge (what is occurring?),
(2) Correlative knowledge (what correlations are empirically observed?),
(3) Causal knowledge (empirical, what causes what?),
(4) Mechanistic knowledge (explanations for observed causality), and
(5) First principles knowledge (underlying physical, chemical, and biological phenomena of the mechanistic explanations).
5.2 Process knowledge is the accumulated facts about the process. This accumulated knowledge is generally embodied in a model
of the process. Accordingly, process model is often used synonymously with process knowledge.
5.3 Process understanding is demonstrated by the extent to which process knowledge can be used to predict and control the process
outcomes; a well understood process will combine knowledge from various sources to ensure a well controlled process and
consistent product quality.
5.4 At any point in time for any manufacturing process, the level of understanding will likely be a combination of various levels
of understanding. As more knowledge is obtained throughout the lifecycle of a product, the relative contribution to understanding
of the various levels is likely to change.
5.5 Prior knowledge is any knowledge that may be available through previous experience. Prior knowledge may c
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