ASTM E2891-13
(Guide)Standard Guide for Multivariate Data Analysis in Pharmaceutical Development and Manufacturing Applications
Standard Guide for Multivariate Data Analysis in Pharmaceutical Development and Manufacturing Applications
SIGNIFICANCE AND USE
4.1 A significant amount of data is being generated during pharmaceutical development and manufacturing activities. The interpretation of such data is becoming increasingly difficult. Individual examination of the univariate process variables is relevant but can be significantly complemented by multivariate data analysis (MVDA). Such methodology has been shown to be particularly efficient at handling large amounts of data from multiple sources, summarizing complex information into meaningful low dimensional graphical representations, identifying intricate correlations between multivariate datasets taking into account variable interactions. The output from MVDA will generate useful information that can be used to enhance process understanding, decision making in process development, process monitoring and control (including product release), product life-cycle management and continual improvement.
4.2 MVDA is a widely used tool in various industries including the pharmaceutical industry. To generate a valid outcome, MVDA should contain the following components:
4.2.1 A predefined objective based on a risk and scientific hypothesis specific to the application,
4.2.2 Relevant data,
4.2.3 Appropriate data analysis techniques, including considerations on validation,
4.2.4 Appropriately trained staff, and
4.2.5 Life-cycle management.
4.3 This guide can be used to support data analysis activities associated with pharmaceutical development and manufacturing, process performance and product quality monitoring in manufacturing, as well as for troubleshooting and investigation events. Technical details in data analysis can be found in scientific literature and standard practices in data analysis are already available (such as Practices E1655 and E1790 for spectroscopic applications, Practice E2617 for model validation and Practice E2474 for utilizing process analytical technology).
SCOPE
1.1 This guide covers the applications of multivariate data analysis (MVDA) to support pharmaceutical development and manufacturing activities. MVDA is one of the key enablers for process understanding and decision making in pharmaceutical development, and for the release of intermediate and final products.
1.2 The scope of this guide is to provide general guidelines on the application of MVDA in the pharmaceutical industry. While MVDA refers to typical empirical data analysis, the scope is limited to providing a high level guidance and not intended to provide application-specific data analysis procedures. This guide provides considerations on the following aspects:
1.2.1 Use of a risk-based approach (understanding the objective requirements and assessing the fit-for-use status),
1.2.2 Considerations on the data collection and diagnostics used for MVDA (including data preprocessing and outliers),
1.2.3 Considerations on the different types of data analysis and model validation,
1.2.4 Qualified and competent personnel, and
1.2.5 Life-cycle management of MVDA.
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 health practices and determine the applicability of regulatory limitations prior to use.
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Standards Content (Sample)
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Designation: E2891 − 13
Standard Guide for
Multivariate Data Analysis in Pharmaceutical Development
1
and Manufacturing Applications
This standard is issued under the fixed designation E2891; 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 Barrier Systems (EBS) for Geological Disposal of High-
Level Radioactive Waste
1.1 This guide covers the applications of multivariate data
E178 Practice for Dealing With Outlying Observations
analysis (MVDA) to support pharmaceutical development and
E1355 Guide for Evaluating the Predictive Capability of
manufacturing activities. MVDAis one of the key enablers for
Deterministic Fire Models
process understanding and decision making in pharmaceutical
E1655 Practices for Infrared Multivariate Quantitative
development, and for the release of intermediate and final
Analysis
products.
E1790 Practice for Near Infrared Qualitative Analysis
1.2 The scope of this guide is to provide general guidelines
E2363 Terminology Relating to ProcessAnalytical Technol-
on the application of MVDA in the pharmaceutical industry.
ogy in the Pharmaceutical Industry
While MVDA refers to typical empirical data analysis, the
E2474 Practice for Pharmaceutical Process Design Utilizing
scope is limited to providing a high level guidance and not
Process Analytical Technology
intended to provide application-specific data analysis proce-
E2476 Guide for Risk Assessment and Risk Control as it
dures. This guide provides considerations on the following
Impacts the Design, Development, and Operation of PAT
aspects:
Processes for Pharmaceutical Manufacture
1.2.1 Use of a risk-based approach (understanding the
E2617 Practice for Validation of Empirically Derived Mul-
objective requirements and assessing the fit-for-use status),
tivariate Calibrations
3
1.2.2 Considerations on the data collection and diagnostics
2.2 ICH Standards:
used for MVDA (including data preprocessing and outliers),
ICH-Endorsed Guide for ICH Q8/Q9/Q10 Implementa-
1.2.3 Considerations on the different types of data analysis
tion ICH Quality Implementation Working Group Points
and model validation,
to Consider (R2)
1.2.4 Qualified and competent personnel, and
ICH Q2(R1) Validation of Analytical Procedures: Text and
1.2.5 Life-cycle management of MVDA.
Methodology
1.3 This standard does not purport to address all of the
3. Terminology
safety concerns, if any, associated with its use. It is the
responsibility of the user of this standard to establish appro-
3.1 Definitions—Common term definitions can be found in
priate safety and health practices and determine the applica-
Terminology E2363 for pharmaceutical applications and some
bility of regulatory limitations prior to use.
terms can be found in other standards and are cited when they
are mentioned.
2. Referenced Documents
2 4. Significance and Use
2.1 ASTM Standards:
C1174 Practice for Prediction of the Long-Term Behavior of
4.1 A significant amount of data is being generated during
Materials, Including Waste Forms, Used in Engineered pharmaceutical development and manufacturing activities.The
interpretation of such data is becoming increasingly difficult.
Individual examination of the univariate process variables is
1
This guide is under the jurisdiction of ASTM Committee E55 on Manufacture
relevant but can be significantly complemented by multivariate
ofPharmaceuticalandBiopharmaceuticalProductsandisthedirectresponsibilityof
data analysis (MVDA). Such methodology has been shown to
Subcommittee E55.01 on Process Understanding and PAT System Management,
be particularly efficient at handling large amounts of data from
Implementation and Practice.
Current edition approved Nov. 1, 2013. Published November 2013. DOI:
10.1520/E2891-13.
2 3
For referenced ASTM standards, visit the ASTM website, www.astm.org, or Available from International Conference on Harmonisation of Technical
contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM Requirements for Registration of Pharmaceuticals for Human Use (ICH), ICH
Standards volume information, refer to the standard’s Document Summary page on Secretariat, c/o IFPMA, 15 ch. Louis-Dunant, P.O. Box 195, 1211 Geneva 20,
the ASTM website. Switzerland, http://www.ich.org.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
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E2891 − 13
multiple sources, summarizing complex information into 5.2 MVDA Model:
meaningful low dimensional graphical representations, identi-
5.2.1 As defined in Practice C1174, a model is
...
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