Strategy and tools for building glycoengineered biobetter MAbs

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Strategy and tools for building glycoengineered biobetter MAbs

Biosimilars/Research | Posted 28/01/2010 0 Post your comment

In an article by Dr Claire Morgan and Dr Daryl Fernandes of Ludger, published in IPI of Autumn 2009, it is shown how both the original drug manufacturers and the designers of follow-on biologics could produce biobetter monoclonal antibodies (MAbs) through glycoengineering. (see also Modify Fc fucosylation and β-galactosylation for biobetter MAbs, Design out NeuGc, Fab glycosylation for biobetter MAbs, Design out Gal-α(1,3)-Gal for biobetter MAbs, When is a glycoengineered biobetter commercially better than a biosimilar? and Ludger’s GTO-QbD: Defining glycovariant biobetter MAbs)

Dr Morgan and Dr Fernandes suggest the following design strategy for building glycoengineered biobetter MAbs: (a) Use a Quality-by-design (QbD)-type paradigm for your drug development - its flexibility and power can simplify many tasks that would be difficult with older paradigms. In particular, ensure that you continually refine your models of the Design Space (DS) and Control Space (CS) both for your copy drug and the original drug - this is essential for demonstrating comparability or otherwise of drug variants. Their GTO-QbD schema is an example of a QbD-based tool that you could use when glycoengineering MAbs; (b) Investigate the mode of action, clinical behaviour and glycan structure-function relationships for the original drug; (c) Identify GCQAs (glycosylation critical quality attributes) for the original drug. These are glycosylation features that significantly influence the safety or efficacy profiles of the therapeutic. GTO-QbD has methods for reliably determining and prioritising GCQAs; (d) Use the GCQA list to work out what non-human or other glycosylation features should be eliminated; (e) Similarly, work out which glycosylation features need to be modified or designed in order to improve the in vivo efficacy in your target patients. Be aware that some glycan features may cause an increase in biological activities in vitro or in vivo animal models but the same effects may not be reflected in the clinic; (f) Draw up a new GCQA list for your hypothetical, ideally glycosylated drug; (g) Select glycoprofiling methods that allow reliable measurement of the GCQAs of your candidate drug and that are suitable for your analytical labs. Ensure that your glycoanalysis scheme complies with current regulatory guidelines – e.g. the 2008 Revised Guideline on Monoclonal Antibodies by the EMEA’s Biologics Working Party (BWP); (h) Test a range of glycoprotein expression systems that could deliver your target glycosylation profile and select the best ones. Generally, choose an expression system related to the one used for the original drug unless you are after a drug with a different glycosylation pattern. Then you could use a totally different expression system with possible cost benefits. A bank of cell lines or other expression systems with diverse glycosylation machinery would be invaluable for this exercise.

Reference:

Claire Morgan and Daryl Fernandes. Designing Biobetter Monoclonal

Antibody Therapeutics By Glycoengineering. International Pharmaceutical Industry (IPI) p. 38-44. Autumn 2009.

Source: Designing Biobetter Monoclonal; International Pharmaceutical Industry (IPI)