Chris Burgess, PhD, is an analytical scientist at Burgess Analytical Consultancy Limited, ‘Rose Rae,’ The Lendings, Startforth, Barnard Castle, Co Durham, DL12 9AB, UK; Tel: +44 1833 637 446; chris@burgessconsultancy.com; www.burgessconsultancy.co

Chris Burgess

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Nomenclature is important. Data governance, data integrity, and data quality are all widely used terms, but what do they actually mean and how are they connected? The purpose of this article is to provide a structured model for these terms with their definitions and their relationships in the context of analysis and testing within a pharmaceutical quality system.

The crucial concept is that data quality, including data integrity, is only attainable via data governance, as will be illustrated in the proposed model.

In a regulatory context, the establishment of measured values and subsequent reportable results of a predefined quality is an essential activity. Reportable results are then compared with predetermined acceptance criteria and/or standards and specifications. The processes, mechanisms, and control systems necessary to establish measurement values and reportable results of a defined quality are interlinked. These interlinkages may include metrological, procedural, or organizational elements.

The overall proposed model is built up using building blocks akin to the construction of Lego brick models. This approach has been used in previous papers concerning error budgets in measurement uncertainty and Monte Carlo Simulation, and data quality within a lifecycle approach (1–2).

An analytical testing flow diagram, shown in 

, gives a high-level idea of the traceability and subsequent interactions from the marketing authorization (new drug application, NDA, or abbreviated new drug application, ANDA) to the reportable result and data quality.

 A high-level process flow indicating some of the elements of data governance, data quality, and data integrity, as well as the quality management system (QMS).

This process flow has been translated into the Lego brick model shown in 

. This model is based upon a data quality within a lifecycle approach model which has been modified and extended, as will be described in some detail (2).

 Data governance model modified and extended from the data quality within a lifecycle approach model (taken from reference 2, Figure 15).

The crucial concept is that data quality is only attainable via data governance.

The data governance and data quality Lego brick model

Data quality is not an accident but a product of design. It is a combination of data integrity and the functionality and control under the Pharmaceutical Quality System, usually termed the quality management system (QMS). These aspects are underpinned by data governance and qualified information technology (IT) infrastructure services. This is essentially a sandwich structure in which the “filling” is provided by metrological integrity, analytical procedure integrity, and quality oversight. These “fillings” are described below.

 are subject to risk assessments and risk management (3), which will not be discussed here.

The qualified IT infrastructure services with cybersecurity and access control, as seen above, also will not be covered further in this article.

It is, however, necessary to discuss the key elements of data integrity and the Pharmaceutical Quality System which, when combined, generate data quality on the foundation of data governance.

Data governance is the totality of arrangements to ensure that data, irrespective of the format in which they are generated, are recorded, processed, retained, and used to ensure a complete, consistent, and accurate record throughout the data lifecycle (4).

The purpose of an analytical procedure is to provide a reportable result of the analytical characteristic or quality attribute being determined. Analysis and testing require a measurement system, and a procedure for its application to a sample.

Data integrity is underpinned by the first brick, the metrological integrity of the instrument or system’s operational performance, with demonstrable assurance that it is “fit for intended use” within a specific analytical procedure, which is “fit for intended purpose” over the data lifecycle.

Analysis and testing usually involve the use of an apparatus, analytical instrument, or system to make a measurement. Therefore, establishment of “fitness for intended use” for any apparatus, analytical instruments, or systems used in analysis and testing is necessary to ensure metrological integrity over the operational ranges required.

Therefore, it is essential to establish “fitness for intended use” before the analytical procedure is performed. The main resource for instrument and system requirements are the specific monographs and general chapters in the pharmacopeias. In particular, United States Pharmacopeia (USP) has a unique general chapter on the lifecycle processes and requirements for ensuring that any apparatus, analytical instrument, or system is “fit for intended use,” as seen in 

 Data quality outline for an analysis and testing quality control (QC) model using USP references.

analytical instrument and system qualification

calibration over the operational ranges of critical measurement functions

trend analysis to monitor an ongoing state of control.

The second component of data integrity is a validated or verified analytical procedure performed by a trained analyst.

It has been a requirement for more than 20 years that analytical methods and procedures need to be validated or verified (6–7). Recently, these requirements have been updated by the International Council for Harmonisation (ICH) and a new guideline on analytical procedure development issued (8–9). 

 General Chapter <1220> on Analytical Procedure Lifecycle should be consulted (10).

analytical target profile and development

trained analysts and second person review

Particular attention should be taken regarding analyst training and second person review within the laboratory.

Second person review is essential in ensuring data quality.

Quality control is the guardian of scientific soundness, whereas the quality assurance function is the guardian of compliance. To perform this duty of care, the quality assurance function requires a robust and comprehensive quality management system that enshrines the elements to provide and perform the necessary quality oversight over the data lifecycle.

Quality oversight involves both reviewing and auditing activities of QC but also includes the Pharmaceutical Quality System implementation itself to ensure that it is up to date (11).

Quality oversight covers key areas such as:

Much has been written on this topic, particularly regarding the three ALCOA models and the meanings of their acronyms (4). These acronyms are summarized below and illustrated in 

 Pictorial representation of the three ALCOA models.

It must be possible to identify the individual or computerized system that performed a recorded task, and when the task was performed. This also applies to any changes made to records, such as corrections, deletions, and changes, where it is important to know who made a change, when, and why.

All data, including any associated metadata, should be unambiguously readable throughout the lifecycle. Legibility also extends to any changes or modification to the original data made by an authorized individual so that the original entry is not obscured.

Data should be recorded on paper or electronically at the time the observation is made. All data entries must be dated and signed by the person entering the data.

The original record is the first capture of information, whether recorded on paper (static) or electronically (usually dynamic, depending on the complexity of the system). Data or information originally captured in a dynamic state remain in that state.

Records need to be a truthful representation of facts to be accurate. No errors in the original observation(s) and no editing are allowed without documented amendments or audit trail entries by authorized personnel. Accuracy is assured and verified by a documented review including review of audit trails.

All data from an analysis, including any data generated including original data, data before and after repeat testing, reanalysis, modification, recalculation, reintegration, and deletion. For hybrid systems, the paper output must be linked to the underlying electronic records used to produce it. A complete record of data generated electronically includes relevant metadata.

Data and information records should be created, processed, and stored in a logical manner that has a defined consistency. This includes policies or procedures that help control or standardize data (such as chronological sequencing, date formats, units of measurement, approaches to rounding, significant digits, etc.).

Data are recorded in a permanent, maintainable, authorized media form during the retention period. Records should be kept in a manner such that they continue to exist and are accessible for the entire period during which they are needed. They need to remain intact as an indelible and durable record throughout the record retention period.

Records should be available for review at any time during the required retention period, accessible in a readable format to all applicable personnel who are responsible for their review, whether for routine release decisions, investigations, trending, annual reports, audits, or inspections.

Data should be traceable though the lifecycle. Any changes to data or metadata should be explained and should be traceable without obscuring the original information. Timestamps should be traceable to a trusted time source. Metrological standards and instrument or system qualification should be traceable to international standards wherever possible.

Data quality is a combination of data integrity and overall control as part of the pharmaceutical quality system.

An example of a quality control data quality outline for analysis and testing, using examples from USP, is illustrated in 

Data quality cannot be assured without a data governance structure supported by a qualified IT infrastructure services with cybersecurity and access control.

The proposed Lego brick model provides a structural framework for assuring data quality over the lifecycle.

A short glossary of definitions of key terms is appended.

I wish to thank Bob McDowall and Oscar Quatrocchi for their review and helpful comments.

The totality of arrangements to ensure that data, irrespective of the format in which they are generated, are recorded, processed, retained, and used to ensure a complete, consistent, and accurate record throughout the data lifecycle (16).

Data integrity is the degree to which data are complete, consistent, accurate, trustworthy, reliable, and that these characteristics of the data are maintained throughout the data lifecycle. The data should be collected and maintained in a secure manner, so that they are attributable, legible, contemporaneously recorded, original (or a true copy), and accurate. Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices (16).

All phases in the life of the data (including raw data), from initial generation and recording through processing (including transformation or migration), use, data retention, archive and retrieval, and destruction (17).

The assurance that data produced is exactly what was intended to be produced and fit for its intended purpose (17).

Articles

Data governance, data integrity, and data quality are all widely used terms, but what do they actually mean and how are they connected?

Data Governance, Data Integrity, and Data Quality: What’s the Connection?

Regulations require drug manufacturers determine the detection limit (DL) and quantitation limit (QL) of impurities in APIs and drug products. There are multiple ways of doing so, some of which are described in the International Conference on Harmonisation (ICH) guideline, ICH Q2 (R1) Validation of Analytical Procedures: Text and Methodology. Primarily, these methods are based upon the statistical uncertainties from a linear least squares regression calibration curve. However, different calculation methods give different answers. The following describes an example and a recommended best practice method.

View the original article published in the November 2021 issue of 

Chris Burgess is managing director, Burgess Analytical Consultancy Limited, Barnard Castle, Co Durham, UK.

Different methods to calculate limits for impurities will give different answers. The prediction interval method may be ideal.

From PharmTech: Detecting and Determining Quantitation Limits for Impurities

For pharma companies and regulators to have meaningful discussion on the results of analyses, they should first agree on the meaning of the terms used. As detailed in Part 1 of this paper, the pharmaceutical industry currently does not have a globally agreed nomenclature for sampling and testing. 

Sampling and Testing Nomenclature for Pharmaceutical Analysis: A Simplified Approach

Pharmaceutical Technology Regulatory Sourcebook eBook

Inconsistent sampling and testing processes that may result from this lack of standardized practices can lead to errors, resulting in data integrity issues and potentially incorrect decisions about pharmaceutical quality.

In the second part of this paper, the author proposes a simplified overall process flow with associated definitions for pharmaceutical sampling and testing.

Pharmaceutical Technology’s September 2021 Regulatory Sourcebook eBook

When referring to this article, please cite it as C. Burgess, “Sampling and Testing Nomenclature for Pharmaceutical Analysis: A Simplified Approach," 

Pharmaceutical Technology Regulatory Sourcebook

A simplified process flow with associated definitions for pharmaceutical sampling and testing is proposed. 

In pharma applications, consistent sampling and testing nomenclature is critical for data integrity and compliance with specifications and regulatory standards. Inconsistent sampling and testing processes can lead to errors, resulting in data integrity issues and potentially incorrect decisions about pharmaceutical quality.

Sampling and Testing Nomenclature for Pharmaceutical Analysis: A Review of Current Practices

Guidance documents within the pharma industry and other regulated sectors, however, offer conflicting information. This review of regulatory and compendial standards reveals the scope of the problem.

This article provides a review of available analytical nomenclature, recommended naming for individual steps, and terms used in the process flows. Part two of this paper proposes a simplified approach for sampling and testing nomenclature.

When referring to this article, please cite it as C. Burgess, “Sampling and Testing Nomenclature for Pharmaceutical Analysis: A Review of Current Practices," 

Inconsistent sampling and testing processes can lead to errors, resulting in data integrity issues and potentially incorrect decisions about pharmaceutical quality. 

The title of this article is deliberatively provocative. Pharmacopoeias contain the legal standards for the quality of medicines with monographs for active ingredients, excipients, and finished products. Analysts should understand how a monograph, together with the associated general notices and general chapters, relates to their responsibilities under good manufacturing practices for ensuring data integrity. However, many pharmacopeial tests therein involve classical chemical analysis (e.g., color, appearance, titration) and rely on an analyst’s observation with little, if any, documented evidence for review. Hence, do pharmacopoeias inadvertently facilitate data integrity violations? 

In this article the authors look at the following topics:

Interaction of the monographs, general notices, and general chapters

Examination of a monograph and the interpretation into an analytical procedure to see how the testing structure in a pharmacopeia can inadvertently facilitate data integrity violations

Practical application of the requirements that data are attributable, legible, contemporaneous, original, and accurate (ALCOA) as well as complete, consistent, enduring, and available (ALCOA+) to pharmacopeial analysis to ensure data integrity.

Pharmaceutical Technology's Regulatory Sourcebook


eBook: Regulatory Sourcebook, October 2020
October 2020
Pages: 30–40

When referring to this article, please cite it as C. Burgess and R.D. McDowall, “Do Pharmacopoeias Inadvertently Facilitate Data Integrity Violations?"

 Pharmaceutical Technology Regulatory Sourcebook 

Analysts should understand how a monograph, together with the associated general notices and general chapters, relate to their responsibilities under good manufacturing practices.

Do Pharmacopoeias Inadvertently Facilitate Data Integrity Violations?

	The reportable value from an analytical procedure has three elements in its error structure: the error of the procedure itself, the error associated with the sampling of the batch, and the error associated with the manufacturing process itself. The first two errors need to be controlled and be sufficiently small as to ensure that any out-of-specification (OOS) result is unlikely to be from these causes. To do this, the developed procedure must enable the target measurement uncertainty (TMU), as defined in the analytical target profile, to be achieved on a routine basis via the analytical control strategy. The United States Pharmacopeial Convention (USP) Validation and Verification Expert Panel has been working on a new general chapter regarding analytical procedure lifecycle management (1–5).
	
	For many existing procedures, however, the knowledge base regarding these three components is lacking. Therefore, one must resort to performing designed experiments to separate these variance components.
	Consider the analysis of a drug product for an analyte, X, from an established manufacturing process and procedure that has a registered specification of 95-105% of claim. The USP analytical procedure uses a single sample from a batch and a singlet determination. This product has a history of frequent OOS results.
	Examination of the last 10 batches, which are without adverse trend, reveals 100.0% is retained and 2.977 is replaced by 2.98. The process capability report based on these values is shown in 

. Ten batches are used for illustrative purposes, and for reliable values of Cpk, Ppk, etc., many more batches would be needed.
	Based on the available information, however, it would be expected that 9.3% of batches would be OOS. 
	The issue now becomes one of identifying the size of the contributory sources of variance. In this instance, there are three: the analytical testing procedure itself, VT; the sampling process, VS; and the production process, VP. Because these sources of variation are independent, the total variance, V, is simply the sum of the individual variances.

	One can design a simple experiment to estimate the various contributions by:
	
	Sampling and testing 10 batches once; V =VT+VS+VP
	From one batch, sampled 10 times, test each sample once; V =VT+VS
	From one sample, test it 10 times; V =VT.
	
	For this example process, the results are shown in 


	It is critical to estimate the size of variance contributions of the analytical testing procedure, the sampling process, and the production process. 
	
	Contributory variances can be estimated using simple arithmetic (


	It is immediately apparent that the majority of the variation (59.4%) is coming from the process itself and about one-third from the analytical testing procedure. The sampling variance contributes only a little. One can calculate the individual standard deviations and, with the means draw, the overall picture of the variability (

	From an analytical perspective, the ideal but unobtainable situation would be to a target measurement uncertainty of zero. Therefore, the question becomes one of how small a TMU for the procedure would be needed. In addition, would replication help to avoid OOS results due to the testing variance?

	First, however, one should be confident that this simple approach is consistent by using a different approach.

	Confirmation of the error structure using Monte Carlo Simulation

	There are three components to the error structure coming from the testing, the sampling, and the process itself. A value from each of the three distributions can be selected, at random, as shown in 

and for each of these values, the error associated with each value is calculated.

	The analytical testing error, eT, would be the mean (99.3) minus the value selected and similarly for sampling, eS, and manufacturing, eP’ using the appropriate mean values of 100.6 and 100.0, respectively. Note that these errors may be both positive and negative.

	Then the observed reportable value would be (100+eT+ eS+ eP). If the Monte Carlo Simulation is used (6),  then the results from 1 million iterations should give data that closely match those found in Figure 1, which they do. The result is shown in 


	As the sampling and production variations are not controllable during testing, an attempt might be made to increase replication and/or to improve the target measurement uncertainty. However, 

 clearly demonstrates that increased replication does not give a major reduction in % OOS of the reportable values. Even if there were zero testing error, the predicted % OOS of the reportable values is still 4% (

	It is critical to estimate the size of variance contributions of the analytical testing procedure, the sampling process, and the production process. In this simple approach, the analytical testing variance is estimated from the repeatability, which is smaller than the intermediate precision, which would be a better estimate. A more complex design can be made to estimate the intermediate precision. 

	In this example, the combined production and sampling variance is so large that the process will always produce more than 4% OOS reportable values in the long term, irrespective of the testing variance. The production standard deviation needs to be reduced to approximately 1% and the testing to the same; and the sampling standard deviation needs to be reduced to 0.6 before a Ppk of 1.1 is obtained, predicting about 0.1% of OOS reportable values. Control strategies need to be in place to achieve these values.

	1. G. P. Martin et al., “Proposed New USP General Chapter: The Analytical Procedure Lifecycle <1220>,” 

 43(1) 2017.
	2. G. P. Martin et al., “Lifecycle Management of Analytical Procedures: Method Development, Procedure Performance Qualification and Procedure Performance Verification,” 

 39(5) 2013.
	3. K L Barnett et al., “Analytical Target Profile: Structure and Application Throughout the Analytical Lifecycle,” 

 42(5) 2016.
	4. C. Burgess et al., “Fitness for Use: Decision Rules and Target Measurement Uncertainty,” 

42(2) 2016.
	5. E. Kovacs et al., “Analytical Control Strategy,” 

42(5) 2016.
	6. Companion by Minitab v5.1.1.0 

	When referring to this article, please cite it as C. Burgess, "Reportable Values: Where is the Variation Coming From?," 

This article looks at a simple structured approach to assigning variance contributions and to assuring that the analytical procedure is fit for purpose.