Ensuring the Quality of Biologic Antibodies Manufactured Through Recombinant Protein Expression
Biologics manufacturers can use a recently developed mathematical model that has been developed to identify problems with recombinant protein expression during the antibody manufacturing process. Image credit: Flickr user UCL Mathematical and Physical Science
As the world grows ever more dependent on biologics, ensuring the quality of the manufacturing process is becoming increasingly important. Today, biologic drugs and vaccines are typically manufactured via recombinant protein expression in living cells, but the process is not entirely reliable.1 For instance, proteins may be produced inaccurately, rendering them ineffective against their intended targets. Another potential problem is that these cells may not produce enough of a particular protein. In the event of a disease outbreak, this could leave life science companies and contract manufacturing organizations (CMOs) scrambling to generate enough antibodies to meet the needs of an affected community.
In order to address these problems, a group of microbiologists at the University of Aberdeen recently teamed up with mathematicians and physicists to develop an in silico mathematical model that can replicate the in vivo process of recombinant protein expression. Based on this simulation, researcher can determine what may be going wrong in a particular process and what changes can be made to the cell’s biochemistry in order to improve manufacturing quality and increase antibody yield.
As large life science companies and CMOs start using this new mathematical model, they may need to start making changes to the manufacturing processes of the biologic antibodies they produce. Modern quality-monitoring software can help companies and CMOs determine which manufacturing processes need to be altered, as well as whether or not changes are working. For larger companies, they can also help ensure that new methods are being applied consistently across the organization.
Determining Whether Changes Need to Be Made
For some biologics, it is obvious that the manufacturing process needs to be improved. For example, if the production yield is not sufficiently meeting the market demand, life science companies know that changes need to be made. Similarly, when CMOs are unable to keep up with the requests of contracting companies, they have to find ways to increase production, or they may lose the client. In these cases, there is no question that running the University of Aberdeen researchers’ mathematical model can help, since it identifies possible glitches in the process of recombinant protein expression and can highlight biochemical steps that can be tweaked in order to increase yields.
However, in some cases, the need for process improvement is not always as clear. Researchers may not initially be aware that the process is producing proteins that do not “fit” their targets, since many patients respond differently to biologics, so non-response cannot always be attributed to the drug itself.2 In order to ensure the quality of the manufacturing process, many life sciences companies and CMOs are turning to modern monitoring software, which can help verify that a particular process is efficiently and consistently producing proteins that are folded exactly as scientists expect them to be. When it is not, scientists know that it is time to run the researchers’ mathematical model.
Implementing a Changes to a Manufacturing Process
After running the new mathematical model and determining that process changes need to be made, life science companies face the challenge of implementing them without severely disrupting production. The longer it takes for a system modification to be integrated and verified, the more profits the company loses, since they will not be delivering drugs to market. Modern software helps streamline the adoption of changes, so process modifications become less of a logistical and financial burden. In addition, the software can confirm that the new process remains in compliance with regulatory standards, minimizing the odds of legal sanction or product recall upon implementing a process change.
Another benefit of the software is that it allows for changes to manufacturing processes to be shared across labs, organizations and even with external parties. This is essential for multinational companies that have manufacturing bases in multiple locations, since they must be able to guarantee process consistency, regardless of location, even when they make changes to the manufacturing process. Software also enables information sharing between CMOs and contracting organizations, opening lines of communication so that all parties are on the same page regarding the manufacturing process and the most recent quality-monitoring data.
With this recently published mathematical model, smart corporations should be ready to utilize its potential to identify problems with recombinant protein expression and thereby improve their biologics manufacturing processes. Modern software can help determine which processes may be need to be subjected to the simulation, and once the necessary changes have been identified and implemented, it can confirm the quality and compliance of the new process.
BIOVIA Designed to Cure for Biologics, is a multifaceted quality management program that can meet the needs of both large life sciences companies and small contract management organizations. Whether the company is producing biologics or traditional pharmaceuticals, modern software can significantly enhance the quality and efficiency of the manufacturing process. Contact us today to learn more about this and our other innovative software offerings.
- “Sustainable vaccines and fuels boosted by computer breakthrough,” October 7, 2016, http://phys.org/news/2016-10-sustainable-vaccines-fuels-boosted-breakthrough.html ↩
- “Predictors of response to anti-TNF-a therapy among patients with rheumatoid arthritis: results from the British Society for Rheumatology Biologics Register,” May 16, 2006, http://rheumatology.oxfordjournals.org/content/45/12/1558.short ↩