Ranking and evaluation of quality attributes for biosimilarity

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Ranking and evaluation of quality attributes for biosimilarity was a subject discussed by Dr Patrick Lynch at the US Drug Information Association’s Biosimilars Conference [1].

Review committee architect V15a16

According to Dr Lynch, considerations for ranking the criticality of an attribute include:

  • Primary mode of action
  • Biological activities involved in mechanism of action for each sought indication
  • Attributes that collectively contribute to biological activities (including impact on pharmacokinetics/pharmacodynamics and immunogenicity)
  • General properties of the molecule

The highest risk ranking attributes should be used to evaluate clinically relevant mechanism of action for each indication sought. However, it must be noted that some methods evaluate critical attributes, but generate data not amenable to rigorous statistical analyses, such as primary structure and higher order structure.

Dr Lynch gave an outline of the different approaches for statistical evaluation of critical attributes in Figure 1.

Figure 1: Approaches for statistical evaluation of critical attributes

GW 3330G Fig 1 V18H24

CI: confidence interval; MoA: mechanism of action; PD: pharmacodynamics; PK: pharmacokinetics; SD: standard deviation.

In addition, attributes that may not be directly related to the primary mechanism of action, but act collectively to define the safety and efficacy profile of the reference product include:

  • Protein content (other than for products with a narrow therapeutic index or dosed on a linear curve)
  • Certain biological activities
  • complement-dependent cytotoxicity (CDC), antibody-dependent cellular cytotoxicity (ADCC), reverse signalling
  • Affinity to receptors or ligands
  • Glycosylation profile
  • Purity
  • Certain post-translational modifications that may affect activity

According to Dr Lynch, comprehensive assessment of critical attributes is an integral part of FDA’s totality‐of‐evidence approach to evaluate analytical similarity. This helps direct implementation of control strategies to address residual uncertainty when differences are identified.

Dr Patrick Lynch, Product Quality Reviewer at the US Food and Drug Administration (FDA), stated that his presentation reflects the views of the author and should not be construed to represent FDA’s views or policies.

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1. Lynch P. Expectations and approaches for demonstrating analytical similarity. DIA Biosimilars Conference; 24-25 October 2017; Bethesda, Maryland, USA.

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