Similarity by Design: EMA Reflection Paper on statistical Methodology for comparative Assessment of QAs

Following the draft concept paper published in 2013 the European Medicines Agency (EMA) recently released a draft Reflection paper on statistical methodology for the comparative assessment of quality attributes in drug development. The deadline for comments is March 31, 2018. A European Medicines Agency's public workshop at the end of the 12-month public consultation phase will provide the opportunity for further discussion of the content of this reflection paper and its implications. According to the EMA, a longer than usual consultation period will allow companies to come forward to EMA via interaction with the Scientific Advice Working Party with proposals and also alternative approaches that are not discussed in this document. 

The EMA says that "the reflection paper has been written to provide current regulatory considerations regarding statistical aspects for the comparative assessment of quality attributes where these are used, or are proposed for use, in drug development and Marketing Authorization Applications". The current 24-page document does not contain explicit guidance on which statistical approaches are most suitable. It rather tries to establish a framework and a common language to facilitate future discussions among stakeholders. 

"The reflection paper provides current regulatory considerations regarding statistical aspects for the comparative assessment of quality attributes in the settings of pre- and post-manufacturing change, biosimilar development as well as generics´ development", the agency says. "It raises open issues from a methodological perspective addressing questions related to comparison objectives, sampling strategies, sources of variability, acceptance ranges and statistical analysis approaches to conclude on the similarity of two drug products based on quality attribute data."

Three areas of interest, where the comparative evaluation of drug product’s quality characteristics plays an important role, are identified from a regulatory perspective:  

  • drug development,
  • drug lifecycle,
  • decision making processes potentially leading to marketing authorization.

It is also supposed to emphasize further discussion of realistic requirements to demonstrate 'similarity on the quality level' in the different contexts mentioned above.

Comparison of empirical data from quality characteristics of drug products (quality attributes, QAs) is of importance in many areas of drug development. Furthermore, "there are at least three areas where the comparative evaluation of quality characteristics plays a major role in decision making on the manufacturer's as well as on the regulator's side:

  • the comparison of a particular drug product in versions pre- and post-manufacturing change,
  • the comparison of a candidate biosimilar product to a reference medicinal product,
  • the comparison of a candidate generic product to the reference medicinal product."

In these three areas, many different methodological approaches for the comparison of QAs are followed and often require regulatory assessment, EMA says. "In many instances, the suggested comparison approach contains statistical elements in order to support the assertion that the quality profile of two (versions of a) drug products can be considered similar. This frequently involves the definition of 'similarity'-criteria, mostly based on information regarding known or expected variability of quality data associated with the underlying manufacturing processes. However, conclusions drawn from comparative data analyses (e.g. "a manufacturing change has not substantially altered the product quality") are often based on rather limited information available, e.g. a small number of manufacturing batches."

The EMA states, the reflection paper should be read in conjunction with all other relevant guidelines, especially with the current versions of the ICH Guidelines Q5E, Q8-Q11 (Q12). Other relevant mentioned Guidelines are: 

The draft paper also includes an Appendix which aims at assisting during the planning of tasks related to QAs' data comparison, but also at helping assessors to scrutinize suggested approaches in this context. It suggests to follow the 11 bullet points presented below in a top-down manner to better identify which limitations could hamper to continue with inferential statistical analysis strategy:

  • "General description of comparison setting/comparison objectives,
  • Given the QAs of interest, categorization of QAs regarding scale of measurement,
  • For each QA, decision upon the characteristic/parameter of interest by which underlying data distributions will be compared,
  • Translation to statistical objectives, e.g. deciding upon one- or two-sided comparison approach per QA,
  • Identification of the unit of observation; at the same time exploration of potential sources of variability in QAs' data to be retrieved,
  • Consideration for which potential sources of variability the data analyses can be controlled for,
  • Sampling strategy,
  • Definition of metric/method to describe difference/distance between the chosen parameters,
  • Evaluation whether the so chosen setup for QA data comparison would allow for inferential statistical approach,
  • Pre-specification of an acceptance range for the analysis of each QA separately,
  • Consideration regarding the risk for a false positive conclusion on similarity based on the similarity decision criteria defined."

For more detailed information please go to the EMA Website.

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