Since the approval of the first therapeutic monoclonal antibody in 1986, monoclonal antibody has become an important class of drugs within the biopharmaceutical industry, with indications and superior efficacy across multiple therapeutic areas, such as oncology and immunology. Although there has been great advance in this field, there are still challenges that hinder or delay the development and approval of new antibodies.
For example, we have seen issues in manufacturing, such as quality, process inconsistency and large manufacturing cost, which can be attributed to production failure, delay in approval and drug shortage. Recently, the development of new technologies, such as Process Analytical Tools (PCT), and the use of statistical tools, such as quality by design (QbD), Design of Experiment (DoE) and Statistical Process Control (SPC), has enabled us to identify critical process parameters and attributes, and monitor manufacturing performance.
However, these methods might not be reliable or comprehensive enough to accurately describe the relationship between critical process parameters and attributes, or still lack the ability to forecast manufacturing performance. In this work, by utilizing multiple modeling approaches, we have developed a systematic framework to optimize and control monoclonal antibody manufacturing process.
In our first study, we leverage DoE-PCA approach to unambiguously identify critical process parameters to improve process yield and cost of goods, followed by the use of Monte Carlo simulation to validate the impact of parameters on these attributes. In our second study, we use a Bayesian approach to predict product quality for future manufacturing batches, and hence mitigation strategies can be put in place if the data suggest a potential deviation. Finally, we use neural network model to accurately characterize the impurity reduction of each purification step, and ultimately use this model to develop acceptance criteria for the feed based on the predetermined process specifications. Overall, the work in this thesis demonstrates that the framework is powerful and more reliable for process optimization, monitoring and control.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8GX5TJP |
| Date | January 2018 |
| Creators | Li, Ying Fei |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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