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Randi Hernandez was science editor at BioPharm International from September 2014 to May 2017.
Richard D. Braatz, PhD, will discuss using mathematical models to design a continuous drug manufacturing plant and the differences between batch and continuous operations for biologics.
Richard D. Braatz, PhD, Edwin R. Gilliland professor of chemical engineering at Massachusetts Institute of Technology, recently spoke to BioPharm International about where the industry is in terms of end-to-end continuous processes for biologics medications, batch vs. continuous flow, mathematical models to design processes and bioprocessing plants, the importance of testing critical quality attributes (CQAs) within end-to-end bioprocessing loops, and the benefits of using chemically defined media within bioreactors.
To hear more about these and other closely related topics, attend Braatz’s upcoming presentation, “Continuous Manufacturing of (Bio)Pharmaceuticals,” to be held on Wednesday, May 17, from 4:00–5:30 pm as part of the Drug Manufacturing track at CPhI North America 2017. To learn more about this particular track and CPhI North America 2017, visit http://schedule.cphinorthamerica.com/track/drug-manufacturing.
BioPharm: Who (company, individual, or academic institution) has designed the ‘most feasible’ model of end-to-end continuous processing for biopharmaceuticals to date?
Braatz: The most feasible model was published by Genzyme (a Sanofi company) in late 2015 by Rahul Godawat, Konstantin Konstantinov, Mahsa Rohani, and Veena Warikoo: "End-to-End Integrated Fully Continuous Production of Recombinant Monoclonal Antibodies." Veena went on to axcella (http://www.axcellahealth.com/about-us/veena-warikoo), Konstantin went on to Codiak BioSciences, Mahsa went on to Amgen, and Rahul went on to Alexion Pharmaceuticals.
BioPharm: Are there any pharma companies close to mastering end-to-end continuous processing for biologics? If so, who, and what product is under investigation?
Braatz: End-to-end continuous processing technology for biologics is too new to be ‘mastered’ by anyone yet, but Genzyme demonstrated feasibility and promise in the aforementioned paper. The paper serves as a strong starting point for companies interested in future development of the technology, which is needed for its application to future products.
BioPharm: How do plant design changes influence material attributes during continuous processing?
Braatz: Material attributes are more tightly controlled in a continuous process, and yield can be improved for those biologics that have a propensity to degrade during manufacturing. If designing appropriately, continuous processing reduces biologic degradation by reducing the amount of time that the biologic spends in the manufacturing process.
BioPharm: Could a mathematical model of a plant for continuous manufacturing of a small-molecule drug be replicated for a large-molecule drug?
Braatz: The process operations in large-molecule (also referred to as biologic) manufacturing have very small overlap with the process operations in small-molecule manufacturing, so very few of the individual process models are transferable from a small-molecule drug to a large-molecule drug. The overall approach and strategies for the generation of mathematical models of the individual processes and the overall plant, as well as the systematic design of control strategies to ensure product quality, are the same.
BioPharm: Is it possible that there could be too many measurements to test quality in an end-to-end bioprocessing loop (i.e., there are so many measurement points that it disrupts the process flow)?
Braatz: I have never encountered a measurement device that did not have value at some point during process development or manufacturing, even when not used in the control strategy. In particular, extra measurements are valuable for the fast detection of faulty behavior in a manufacturing system. A simple example is that a redundant measurement device is useful in detecting whether a measurement device needs maintenance or otherwise becomes faulty.
There are practical reasons to limit the number of measurements, especially at a manufacturing site. Some measurement devices are so expensive that their use in manufacturing would significantly increase the cost of making the drug while providing minimal increased assurance of product quality. Also, many advanced measurement devices, especially recently developed, are not reliable enough to be effective in a manufacturing environment. Substantial R&D would be needed to make the sensor reliable enough for manufacturing operations. Based on these considerations, an effective strategy is to heavily instrument measurements during process research and development to build understanding and mathematical models, which are then used to determine which subset of the measurements are sufficient to ensure that the product CQAs are achieved in a manufacturing facility.
BioPharm: Have you seen instances where the quality of the starting materials has dictated the CQAs at other points along a processing line?
Braatz: I have seen many instances where the quality of the starting materials has dictated the CQAs at other points along a pharmaceutical manufacturing line. I have also seen many instances when analyzing biologic data collected for manufacturing processes at MIT and at companies in which a seemingly small shift in the operation of the bioreactor had a very strong effect on the quality attributes of material leaving the chromatography train. For this reason, it is important to have traceability of material as it moves through manufacturing, irrespective of where the process is batch, continuous, or semi-continuous.
A well-known example where CQAs can be affected by the quality and/or characteristics of the starting materials is by variations in biologically defined media fed to the bioreactor. This observation has driven interest in the use of chemically derived media, in which all constituents are precisely defined.