Innovations in Pharmaceutical Manufacturing Proceedings of a Workshop—in Brief (2020) / Chapter Skim
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Innovations in Pharmaceutical Manufacturing: Proceedings of a Workshop - in Brief
Innovations in Pharmaceutical Manufacturing: Proceedings of a Workshop - in Brief
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Almost 2 decades ago, CDER launched an initiative called Pharmaceutical Quality for the 21st Century with the goal of achieving an agile, flexible pharmaceutical manufacturing sector that reliably produces high-quality drugs without the need for extensive regulatory oversight. She noted that advanced manufacturing has moved from the laboratory feasibility stage to commercial applications, that innovative technologies are in the pipeline, and that proposals are being submitted to the CDER Emerging Technologies Team from all types of companies throughout the pharmaceutical sector.
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DRUG PRODUCT MANUFACTURING Daniel Blackwood, a research fellow in the Drug Product Design Group of Pharmaceutical Sciences–Small Molecule at Pfizer, began the session on drug product manufacturing by describing activities focused on continuous manufacturing in his company. He noted that interest in continuous manufacturing arose several decades ago as the industry began to prepare for patent expiry of its blockbuster, high-volume medicines.
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He said that his company wants to create flexible manufacturing with new technology that has the ability to produce small product batches. To achieve that goal, Merck will rely on portable manufacturing units, robotics to improve compliance, data analytics and information technology integration, and continuous manufacturing.
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exploit data analytics and machine learning. He noted that there are many applications of the strategies to pharmaceutical manufacturing and described one that uses an automated molecular synthesizer to produce, purify, and characterize a product by using flowsheet models, process intensification, optimized plug-and-play fluidic modules, and feedback control (Coley et al.
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He provided several examples of models that could be used given specific data characteristics. In closing, Braatz predicted that there will be advances in methods that combine data analytics and machine learning with first-principles models, increased use of tensorial data streams, and a broadening of the scope of data analytics and machine learning in pharmaceutical applications.
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She noted mass spectrometry as an option but favored other spectroscopic techniques, such as Raman, given their lower cost and their ability to provide a unique fingerprint with no sample pretreatment. In closing, she emphasized the need to make the business case for investing in advanced process control and hoped that enhancing models with advanced sensor data to optimize feed control and ultimately product quality would be more common in the next 5–10 years.
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Discussion Seongkyu Yoon, a professor in the Francis College of Engineering at the University of Massachusetts Lowell, and Saly RomeroTorres, senior manager of Advanced Data Analytics at Biogen, moderated a discussion with the speakers and audience. Romero-Torres opened by asking the speakers to elaborate on the business process for successfully implementing innovative technologies.
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Tom noted that complex molecules, such as oligonucleotides and peptides, involve new regulatory challenges, including various issues associated with impurities and use of analytical methods different from those used to characterize traditional small molecules. As a final note on route invention, Tom mentioned co-processed APIs and the question, from a regulatory standpoint, of whether to treat them as drug substances or as drug-product intermediates.6 Regarding process invention, Tom acknowledged that innovations have been focused on continuous manufacturing and noted several challenges, including process-control complexities, lack of first-principle understanding, development of real-time feedback loops and predictive modeling, and uncertainties of regulatory requirements.
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Günter Jagschies, principal consultant at Gemini BioProcessing and formerly with GE Healthcare Life Sciences, discussed manufacturing of biologics and innovations on the horizon. He began by listing several challenges for the biopharmaceutical industry and said that addressing them will require a focus on increasing facility output and process yield, creating a flexible system to produce a variety of drugs on various scales, simplifying operations, decreasing infrastructure cost, improving drug quality, and streamlining regulatory compliance.
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He noted that there is often a disconnect between academic research and industry needs and emphasized that more sustained funding in fields that are relevant to industry might facilitate solutions to drug shortages. He agreed with Thommes that the pharmaceutical industry could learn from other industries and highlighted continuous manufacturing and process intensification that have been achieved in other industries as possibly valuable examples from which to learn.
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Several audience members debated issues associated with data analytics and control. Braatz noted that digital twins constitute a technology that the committee will likely discuss in its report and encouraged the committee to define the term because there is much confusion about what it means.
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2019. Bayesian probabilistic modeling in pharmaceutical process development.
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