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Review
, 56 (suppl_4), iv14-iv29

The Process Defines the Product: What Really Matters in Biosimilar Design and Production?

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Review

The Process Defines the Product: What Really Matters in Biosimilar Design and Production?

Arnold G Vulto et al. Rheumatology (Oxford).

Abstract

Biologic drugs are highly complex molecules produced by living cells through a multistep manufacturing process. The key characteristics of these molecules, known as critical quality attributes (CQAs), can vary based on post-translational modifications that occur in the cellular environment or during the manufacturing process. The extent of the variation in each of the CQAs must be characterized for the originator molecule and systematically matched as closely as possible by the biosimilar developer to ensure bio-similarity. The close matching of the originator fingerprint is the foundation of the biosimilarity exercise, as the analytical tools designed to measure differences at the molecular level are far more sensitive and specific than tools available to physicians during clinical trials. Biosimilar development, therefore, has a greater focus on preclinical attributes compared with the development of an original biological agent. As changes in CQAs can occur at different stages of the manufacturing process, even small modifications to the process can alter biosimilar attributes beyond the point of similarity and impact clinical effectiveness and safety. The manufacturer's ability to provide consistent production and quality control will greatly influence the acceptance of biosimilars. To this end, preventing drift from the required specifications over time and avoiding the various implications brought by product shortage will enhance biosimilar integration into daily practice. As most prescribers are not familiar with this new drug development paradigm, educational programmes will be needed so that prescribers see biosimilars as fully equivalent, efficacious and safe medicines when compared with originator products.

Keywords: biosimilars; comparability; critical quality attribute; manufacturing; process control; regulatory.

Figures

F<sc>ig</sc>. 1
Fig. 1
The biologics manufacturing process and the manufacturing steps that affect final characteristics of biologics Information taken from Ahmed et al. [35].
F<sc>ig</sc>. 2
Fig. 2
Potential mAb variants An IgG antibody schematic is shown, with some potential structural variations resulting from post-translational modifications indicated by symbols. Each symbol is noted in the key with a list of variations. The number of variation sites in each half-antibody × the number of possible variations at each site is in parenthesis. Not all possible variants are described. For example, there are fucosylation variants in glycosylation that were not counted. If one assumes that these variants are independent and if combinations are considered, each half-antibody has 2 × 6 × 4 × 4 × 5 × 5 × 2 = 9600 possible states. If one assumes that both halves of the antibody are independent, there are 96002 ≈ 108 possible states. Reprinted from Kozlowski S, Swann P. Current and future issues in the manufacturing and development of monoclonal antibodies. Adv Drug Deliv Rev 2006;58(5–6):707–22, [37], ©2006, with permission from Elsevier.
F<sc>ig</sc>. 3
Fig. 3
Comparability of the biosimilar with the originator attributes (fingerprint) during the biosimilar development process
F<sc>ig</sc>. 4
Fig. 4
Comparison of the developmental processes for a reference (originator) product and a biosimilar
F<sc>ig</sc>. 5
Fig. 5
Example of an automated, real-time, quality control using multivariate batch process modelling (A) Normal growth. (B) Slow growth. Data from several historical batches are used to correlate parameter levels with product quality, using multivariate modelling techniques, such as principal component analysis (PCA). Of the dozens of parameter inputs, those that strongly correlate with product quality are summarized by a single output (principal component), which describes a large portion of the potential variation in product quality. Historical data are also used to define acceptable limits for each parameter.

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