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European Biopharmaceutical Review

Performance Parameters

Diane Meakin and Janette Waterhouse at R5 Pharmaceuticals discuss the ways in which controlling and measuring quality can increase confidence in analytical data

An established quality control system gives confidence in the analytical data obtained to monitor and demonstrate that the quality of process is under control and that products are suitable for their intended use. The objective of any analyst is to consistently obtain reliable analytical data that demonstrates this. To this end, analysts validate analytical methods and procedures, calibrate instrumentation, perform system suitability tests, analyse in-process quality control check samples, and so on.

The requirement of the management of systems on an organisational level within analytical approaches should not be underestimated, and one should not consider using analytical data as a measure of the control of the quality of an object if said data is itself not controlled. Confidence in the accuracy and reproducibility of the data obtained, measured quantitatively, is essential. It goes without saying that the laboratory environment, chemicals and reagents used should be of a suitable standard and ‘under control’.

There are a number of approaches available to the analyst as a means to measure the quality of the analytical data obtained, but these must all originate from validated systems and procedures.


Analytical instrument qualification (AIQ) is the collection of analytical data that is used to demonstrate that the performance of the instrument is suitable for its intended purpose, and contributes to confidence in the use of generated data. For the purpose of this article, it is assumed that the design qualification (DQ), installation qualification (IQ) and operational qualification (OQ) of instrumentation have previously been completed. The completion of performance qualification (PQ) demonstrates the instruments’ continued ability to operate as per its intended use. System suitability criteria and QC checks demonstrate that the instrumental aspect of the analysis is ‘under control’. It should be noted that the completion of maintenance or preventative maintenance, or the relocation of the instrument itself, may require the re-verification of aspects of the OQ to be performed. The completion of annual preventative maintenance and calibration is a prerequisite of any instrumentation system as a demonstration that it is ‘under control’. The training and expertise of the analyst and the practical skill that they demonstrate cannot be underestimated. Human effort can have a direct or indirect impact on quality, in turn affecting the analytical data. The introduction of the variable of a change in analyst should not impact on the quality of the quantitative data obtained.


However, no analysis should be completed without the analytical method and procedures’ performance parameters being challenged by validation. In pharmaceutical analyses, this typically adheres to the ICH guidelines. Analytical methodology in support of a formulation under development should itself be challenged by the variables that the development of a formulation presents, and it is not unusual for reassessment of specificity and recovery to assess the impact of minor reformulation. The ICH guidelines represent a consistent approach to validation of analytics irrespective of origins, the result of which shows that systematic errors have either been identified, eliminated or controlled, and that the method is under control. Consideration should always be given to its applicability and reproducibility in the QC environment.

The control of quality within a development environment is, by its very nature, different to that within routine QC analysis. Performance evaluation of a validated method in regular use is important to ensure continuation of parameters established in the original validation and to demonstrate that the systems remain ‘under control’. Demonstration of system suitability of specific analytical instrumentation techniques goes without saying, irrespective of the environment in which the analytics are taking place.

The use of control materials, primary standards or certified reference materials (CRM) may be used in initial validation and during research and development, being directly traceable to the National Institute of Standards and Technology (NIST). However, use of such materials in routine analysis may be cost prohibitive. The alternative approach of using a positive control secondary standard meets the needs of the analytical approach in a measurement of recovery, without the excessive implication of cost. Typical demonstration of the precision of analytics is the performance of replica analysis. Replicates are routinely used to demonstrate recovery of sample preparation within any analytical method, while negative controls are consistently used in both development and routine analysis as a means to monitor sources of contamination.


One means of continued measurement of quality within routine analysis is the basic concept of the use of internal quality control (IQC) samples containing the identified analyte as performance indicators. IQC samples are stable control samples typically analysed with each batch of analysis, over a period of months, to demonstrate internal reproducibility and repeatability, or accuracy and precision. Coupled with the use of control charts, IQC demonstrate systematic and random effects within the practical application of the analytical method and procedure, and its fitness for purpose. IQC samples are accepted or rejected on the basis of realtime interpretation of the chart data and demonstrate a realistic picture of the variation within the analytical process.

However, the use of IQC relies on the availability of a ‘history’ of analytical data for the individual analytical method and procedure for statistical evaluation, typically following Westgard rules. Therefore, this approach is not appropriate for ad hoc or infrequently performed analysis. The general use of control charts, whether with IQC samples or control materials, offers the ability to assess the positive or negative bias of an analyst’s impact on a method or procedure.


External quality assurance (EQA) is an approach to assure the quality of analytical data routinely used in the analytics of the environmental and food testing industries, and is a system of measurement for the data obtained from blind testing of the sample material. While in these industries it is typically the analyte that is determined, within the pharmaceutical industry, it is the analytical technique that is typically challenged.

An alternative approach to EQA is the use of inter-laboratory studies, in which a number of participating laboratories analyse the same material and statistically evaluate the data obtained. This approach would be particularly suitable for QC monitoring post-analytical method transfer to support continual performance monitoring over multi-site QC laboratories.

Notwithstanding this, the control of quality within an analytical function can be demonstrated via statistical analysis of the analytical data obtained. This data, however, cannot provide a measure of quality. The measurement of quality is carried out by the measurement of uncertainty. All measurements or aspects of analysis are affected by a certain amount of error – individual or accumulative. Uncertainty measurement is a statement as to what the size of the error in data may be, and allows for the assessment of the reliability of the data for fitness for purpose by determining a probability or level of confidence, typically reported as an expanded measure of uncertainty of two standard deviations, or a 95 per cent confidence level. Analysis and management of the critical control points within an analytical method and procedure (this is typically completed during robustness validation) can minimise and reduce uncertainty, but never eliminate it completely. An estimate of uncertainty for a particular test should encompass all possible sources of uncertainty, including random and systematic. Of the two types of uncertainty present in analytics, it is the random component that causes the greatest headache when calculating. Random components cannot be measured empirically; the only way a quantified value can be obtained is by an experimental repeatability study. A classic example of such a random effect is the use of a pipette; despite rigorous training, each analyst will dispense slightly different volumes with each use, the differences in which will not be known until the measurement is made. Other sources of uncertainty may be more easily measurable and, typically, certificates of analysis supplied from UKAS accredited calibration laboratories will detail the uncertainty measurement of the calibration process. Contributions to uncertainty are derived in many different ways, but expressed in the consistent unit of relative standard deviation. Measurement of quality should only be undertaken on analytical methods and procedures deemed ‘under control’.


The control and measurement of quality is essential in order to guarantee a level of confidence in analytical data both internally and externally. Within any proactive quality system it is beneficial to be able to make a direct measure of the quality of data to demonstrate that systems remain ‘under control’. To the client, or those outside of the analytical field, this also gives a confidence level for the reported data. The use of established quality systems, protocols and validated instrumentation in any analytical environment is essential for the first line of establishment of control. The initial validation of any analytical method is a determination of the overall method performance parameters, the elimination or complete eradication of sources of error, the removal or control of significant influencing factors prior to use, and the demonstration of an ‘in control’ process fit for purpose. Subsequent changes in methodology, however slight, require a review of the impact of those changes on the validation data and reassessment if required.


The analytical methodology and the frequency of completion of the analytics is a determinant of the applicability of the utilisation of alternative approaches to quality control. Ad hoc or infrequently performed analysis, by definition, does not lend itself to internal in-house control samples. External reference standards are the obvious alternative for the continued assessment of the control of quality for this analysis. The completion of negative controls and replica analysis both have their place in the control of quality, irrespective of the frequency or origins of the analysis.

Due to the diversity of the industry in which we operate, EQA does not offer the scope to the pharmaceutical industry that is currently available to the analytics of the environmental and food industries. The measurement of uncertainty – a prerequisite of analytical approach and quality assessment in food analysis – has not to date infiltrated the pharmaceutical industry as a means to measure the quality of analytical data. Does the food industry take the lead in this aspect of measurement of quality, or have they overburdened themselves with an aspect of quality measurement that is incorporated into other aspects of QC? Uncertainty measurement gives a statistical evaluation of the quality of analytical data being generated, subsequent evaluation, and the significance of increasing or decreasing standard deviations can demonstrate just how ‘under control’ your quality system is.

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Diane Meakin is an analytical chemist by training with an extensive career in analytical chemistry, working to both ISO 17025 and GMP in the food and pharmaceutical industries. She joined R5 Laboratories in 2006 as the Section Leader for food analysis and became the Laboratory Manager of the Analytical teams of both food and pharmaceuticals at R5 Pharmaceuticals in 2007, overseeing the amalgamation of the ISO 17025 and GMP Quality systems. In 2009, Diane moved into the Business Development team to provide deep analytical understanding in this group and to assist client projects. 

Janette Waterhouse is an analytical chemist by training, and has worked as an Executive Director in the contract analytical business for both Quintiles, Edinburgh and Cardinal Healthcare. She successfully created and grew the Pharmaceutical Analysis department at Quintiles to over 100 staff in five years. Janette has also conducted transfers and lab audits worldwide as the Analytical International Technical Executive at Glaxowellcome, designing new labs internationally. She is currently the COO and a founder director of R5 and is part of the R5 nominated “Management Team of the Year ” at the British Venture Capital Association National Awards 2010.

Diane Meakin
Janette Waterhouse
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