Evaluating change during pharmaceutical product development and manufacture—comparability and equivalence

Abstract

In the pharmaceutical industry it is necessary to manage changes to processes or analytical methods to ensure that the quality of drug products is maintained. This often includes an assessment of similarity using data. Scientists may be asked to show that output before and after a change is or will be 'equivalent', without clarity on what exactly is required. In the area of analytical methods, the authors have found it useful to promote the differentiation between equivalence and a wider set of comparability approaches. This paper discusses equivalence and comparability, when such approaches should be applied and provides examples of applications to analytical methods. The discussion is then widened to other application areas such as processes including the appropriateness of the approaches and aspects to be considered when designing equivalence studies. Change management, the design of studies and appropriate statistical analysis can be challenging. However, use of the terminology and methodology exemplified here has enabled scientific project teams to focus on appropriate goals and the means to achieve them with great benefits. Copyright

Use of 27G needles improves sensitivity and performance of ATCC anaerobe reference microorganism detection in BacT/Alert system

“…In a recent publication, Chatfield et al. 36 suggested another way to differentiate these two concepts, where analytical method equivalency is restricted to a formal statistical analysis to evaluate similarities in method performance characteristics.…”

Assessing the efficacy of environmental restoration via noninferiority: A Bayesian perspective

“…Noninferiority testing procedures, as well as equivalence testing procedures, have been widely utilized in the medical and pharmaceutical fields but are less commonly discussed in business and industrial statistics. There are several examples of work on noninferiority and equivalence testing in business and industrial statistics . Noninferiority testing may also be particularly useful in situations where one wishes to demonstrate that the quality of a product is not appreciably worse than the same product when say, a more expensive component is replaced by a less expensive component, or perhaps to demonstrate equivalence of experimental designs (eg, a convenient design is not appreciably worse than a D‐optimal design).…”

Innovation and quality technology

“…Design of experiments, reliability engineering and statistical quality control methods can make these industries more productive and provide safer, more efficient and more reliable products. Just in this volume we have published a number of potentially useful articles on these topics; for examples, see Chatfield et al 3, Emmett et al 4, Rand and McLinn5, Steenackers et al 6, and Ulusoy et al 7.…”