Biosimilarity: How to statistically demonstrate bioequivalence for biosimilars?

Biosimilarity: How to statistically demonstrate bioequivalence for biosimilars?

As a biotech company or quality laboratory involved in the development or manufacture of biosimilars, you are responsible to demonstrate biotequivalence/ biosimilarity of your biosimilars to originator products. In particular at early stages (strain screening, early phase development), the demonstration of bioequicalence/ biosimilarity is an important but highly debated topic. A new EMA paper on comparability testing provides now new insights on current best practices. In this article, we review the main aspects of the new EMA paper on comparability testing and regulatory documents and guidelines [1–3] in their relevance to bioequivalence testing.

What is an appropriate statistical workflow to demonstrate bioequivalence / biosimilarity?

Here we summarize the main aspects. The authors of the reflection paper come up with a workflow which can be summarized in the following steps:

Define general aim
(non-inferiority or equivalence)
Define CQAs
(e.g. continuous, binary)
Define measure of similarity
(e.g. difference in means, ratios, multivariate measure)
Conduct experimental study plan and sampling strategy controlling
for measurement variability
Pre-specify acceptance criteria and check whether
inferential statistical approach can be performed
Perform equivalence/non-inferiority testing
(e.g. TOST testing)
Consideration regarding false positive conclusion
and risk mitigation of non-comparability results

How should I proceed during biosimilar screening to detect biosimilarity?

As a developer of a biosimilar, you first have to screen for a production clone that produces a product similar to the originator. Typically, you measure multiple critical quality attributes of the product. To be cost efficient, you typically compare the analytical results of one strain to the originator population. In this situation it is highly challenging to perform inferential statistics, since you have only one measurement of the clone and need to conclude for that. This is in accordance to the EMA reflection paper, where it is stated that inferential statistics is not always feasible especially when comparing single values to a population.

Especially for biosimilar screenings multiple CQAs are measured which are correlated with each other. Hence, it is a good idea to use multivariate methods that have increased power to detect similarity and potential differences.

 

Multivariate Biosimilarity Analysis for Biosimilar Screening

Quality professionals use Exputec inCyght software for biologics quality data management and data analytics. For the case of biosimilar screening, Exputec together with Vela Laboratories have developed a powerful approach to demonstrate multivariate similarities of individually tested clones/process conditions. It works in the following way:

  1. Selection of the originator group: clones/process conditions to be compared & variables that will be used to assess similarity
  2. As a result, candidate biosimilar clones/process conditions can be categorized into:
      • Similar to a certain confidence (all biosimilar batches in the green area of the example below)
      • Similar, but showing different correlation between analytical methods, such as biosimilar batches 4 and 5 in the example below (e.g. those might be caused by inconsistency between the result of redundant methods)
      • Showing extreme/different behavior than the originator, such as biosimilar batch 7 in the example below
  3. Variables are identified by the app that lead to inconsistent or extreme behavior of biosimilar batches. At this point counteraction can be taken either by re-measuring a sample or stating clones/process conditions as similar or different. As consequence the most similar clones/process conditions can be identified.

Outlier plot showing biosimilar and originator batches with their associated multivariate scores and orthogonal distances. Additionally, also the 97.5% tolerance intervals are indicated as black dashed lines. Score distance of samples acts as a measure for biosimilarity to the originator group. Batches in the green area can be considered similar to the originator.

Contribution plot showing which variables have most contribution to the difference of the biosimilar to the originator group. Especially batch 13 shows differences in endotoxin concentration (Endotox_conc) and Titer.

The solution is available as plug-in for Exputec inCyght data management and analytics software. For more information, contact us and talk to one of our data scientists.

inCyght for GMP Quality labs: Bioequivalence Testing

As a leading analytical service provider and biotech laboratory in the field of Biosimilars, we support many customer projects involving biosimilar clone screening as well as comparability studies. Our clients need to select the right clones with minimal analytical effort and highly consistent data. With inCyght® software, we now have a tool in hand to explore which clones or process conditions perform most similarly to the originator product. Furthermore, we can explore which process parameters and settings achieve the highest biosimilarity score.

Rainer Fedra, Head Analytical Development at VelaLabs GmbH

 

References:

  1. ICH Q5E: Comparability of Biotechnological/Biological Products Subject to Changes in Their Manufacturing Process Available online: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q5E/Step4/Q5E_Guideline.pdf (accessed on Oct 30, 2017).
  2. Guideline on Comparability after a change in the Manufacturing Process- Non-Clinical and Clinical Issues Available online: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500003935.pdf (accessed on Oct 30, 2017).
  3. Comparability Protocols for Human Drugs and Biologics: Chemistry, Manufacturing, and Controls Information Guidance for Industry Available online: https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM496611.pdf (accessed on Oct 30, 2017).