By Vivienne Raper, PhD

A pioneering research and training institute is promoting the benefits of measuring multiple quality attributes simultaneously during biologics manufacturing. Ireland’s National Institute for Bioprocessing Research and Training (NIBRT), in collaboration with the USA MAM Consortium, has developed a standardized sample preparation strategy for multi-attribute method quality control (QC) that they hope companies will adopt.

“We see [multi-attribute methods] as a philosophy rather than a pure analytical method,” explains Jonathan Bones, PhD, principal investigator at NIBRT. “If people embrace [MAM], we believe the benefits are huge.”

Traditionally, Bones explains, quality control involves one analytical method applied to answering one question. This is “an inefficient process when you think about it,” he says, as products worth millions can have to wait for data to come back at different times depending on when the analyses were run.

In contrast, MAM involves carrying out multiple analyses simultaneously but only processing the required data using targeted processing methods. If further data is needed later, for example, if an additional attribute needs to be monitored, he says, existing data can simply be reprocessed—rather than analyzing everything again.

Single set of parallel analyses

Conducting a single set of parallel analyses, he says, is much more efficient than the traditional method of analysis.

“If you have a molecule requiring ten assay measurements, that might require the time of let’s say eight people. If you consolidate those analyses, [the time of] those eight people can be repurposed to different things,” he notes.

MAM is also more feasible than in the past, according to Bones, due to massive work by instrument manufacturers to make their technology more accessible to non-specialist users.

“There was a stage where mass spectrometry [for example] was seen as a complex characterization tool […] but that has now changed,” he explains.

Bones, who spoke at the BioProcess International Conference in September, presented on using MAM to test comparability between biosimilars and an original branded product. He told the audience that NIBRT had conducted a study comparing Humira®, the innovator version of monoclonal antibody adalimumab, to biosimilar products. By looking at the product quality attributes (PQAs) that define Humira, the researchers could see the variance in PQAs among biosimilars.

The aim of the work was to define a series of “red/green” flags, which could be aided using MAM, and used by companies to more simply assess the quality of their biosimilar batch.

“Our motivation was to show what [MAM] could do,” says Bones. “By finding the differences between [Humira and other adalimumab] products, so we could understand them and simplify comparability analyses, which may help streamline process development and manufacture.”

NIBRT has also carried out a study with MAM on monoclonal antibody glycosylation data, looking at all glycoforms, which a company could use to produce a report for decision-making during in-process product testing. “MAM can add value by increasing throughput to get [products] out the door quicker,” Bones says, adding that he and his colleagues are now looking into applying MAM to other therapeutic classes.

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