Smart Manufacturing of Pharmaceutics by Discrete Particle Modeling

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The pharmaceutical sector is identified as important to Canada's high-tech economy and as a key element of Ontario's Innovation Agenda (published December 2015) which focuses on strategic areas to build employment. This sector relies heavily upon fundamental research to support its products, and is keenly in need of persons completing post-graduate studies, trained to implement their innovations. This proposal centres on pharmaceutical manufacturing' which has received increasing attention since 2004 when federal regulatory bodies, recognizing a decline in interest to manufacture drugs, recommended the industry transition from their batch unit operations to continuous processes for the sake of lower costs without jeopardizing drug quality. Solid oral dosage forms, like tablets, are still the most popular class of drugs sold and as a result, their manufacture will be the earliest adopter of continuous technologies, though very cautiously at first till the process is understood. For the majority of tableted pharmaceuticals, agglomeration of powdered ingredients into granules is the first step to final drug production. Twin-screw granulation is the newest continuous technology in the industry and is generally considered the most productive based on giving the highest consistency for the highest throughput rate. Our research group was one of three early global innovators in establishing the technologies behind twin-screw granulation, and has work closely with most major pharmaceutical companies to understand their concerns. Under the enclosed proposal we intend to once again shift our focus, away from the now increasingly busy research space of formulation development, into the sparsely covered knowledge areas of modeling the new technology. Using our last eight years of accumulated process knowledge for validation, this proposal seeks to develop a readily adaptable representation of the microscale mechanics of twin-screw granulation and then, in the short term, use that model to explain the fundamentals of the process as ingredients and configurations differ. The long term goal of the outlined research program is to combine new sensor technologies that we will devise using machine vision and acoustics, with our model of the process to devise a smart manufacturing strategy that not only identifies quality control issues but explains the causes for such deviations. The outlined five-year program will train at least six HQP in granulation, discrete particle simulations, acoustic and optical sensors, and characterization of particulate systems, giving them highly sought skills for an industry increasingly employing chemical engineers to implement its new continuous technologies.