A manufacturing unit of any product is the key basis that decides its future. If we talk about this in detail regarding biopharmaceutical products and manufacturing, it raises little concern as it is directly linked with the lives of the people. As in this article, we are going to focus our concern mainly on drug products and its manufacturing using Artificial Intelligence.
As simple as that, a well-equipped and controlled manufacturing unit will bring quality-proof products which can determine how vulnerable it is to the patients. Although preclinical and clinical assessments are necessary to check the quality of the product it is mainly the manufacturing unit that comes in direct power. Production costs, process requirements, and data are necessary for any bioprocess control.
Process understanding is the main aspect of any control strategy where the researchers use models to meet the quality expectations. Statistical models are highly used for this purpose where the predictions are based on the available data and thus, the manufacturers use the provided data to process and control.
The only drawback of this mechanism is that it is entirely based on the data which can limit the experimental procedure hence another model is used which is the mechanistic model that is based on principles rather than just data. They are highly used in the industry as they predict scale-up processes and easily run in large manufacturing units.
Angela Li, Ph.D., senior scientist at Sanofi explains the quality and process of mechanistic models. She explains that as the traditional concepts and designs are not well equipped to provide the right information, they are prone to errors and hence voted for the mechanistic model that is starting to employ in day-to-day operations and provide the right approach.
Digital Twins, on the other hand, are in silico models of purposes. As told by Ayazi-Shamlou, Ph.D., vice president of Jefferson Institute for Bioprocessing in Philadelphia, it can answer the “What If” questions and can be highly employed in sectors.
Artificial intelligence is spreading its roots to every sector and will increase its demand in the coming future. At present time, it has a limited role in biopharmaceutical manufacturing but cannot be ignored its role.
With the right use of IT technology and data provided, one can model an entire bioprocess with the connecting links. Although AI technology is not brought completely used in this sector, the researchers are trying their best to include this technology to get the best and desired results.
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