EFRI DCheM: Digital design of a network of distributed modular and agile manufacturing systems with optimal supply chain for personalized medical treatments

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The disruptions in the supply of essential medicines caused by the COVID-19 pandemic have reconfirmed that global pharmaceutical supply chains based on production in a small number of centralized manufacturing sites, using traditional large-batch manufacturing methods that produce one-size-fits-all dosages, are inherently unreliable and inefficient. This project proposes to address these deficiencies by re-engineering the pharmaceutical manufacturing ecosystem to bring production of medicines closer to the point of demand—the patient—employing advanced manufacturing methods including continuous processing and high levels of automation to assure product quality and to introduce the capability of producing dosages that are personalized to the characteristics of the patient. Specifically, we will develop the technology necessary to achieve these capabilities and demonstrate them using two representative generic drugs, one for the treatment of cancer and another for treating high blood pressure. These technologies include high throughput screening to identity efficient flow chemistry pathways, mathematical model-based design of continuous processes, and model-based approaches for the control and management of the processes for making both the active ingredient and the actual dosage. The down-sized scale of the manufacturing processes will enable distributed dosage production to serve regional markets and thus substantially shorten the supply chain. Moreover, using mathematical models built on clinical data combined with relevant patient characteristics, the specific dosage best suited for an individual patient can be determined. The sophisticated automation to be developed in this project will enable production of this dosage in amounts sufficient to meet the needs of that individual patient, effectively providing pharmacy-on-demand capabilities. This project will produce an integrated framework for creating a resilient, distributed pharmaceutical manufacturing ecosystem that will optimally meet individualized patient needs. The project takes a multi-disciplinary approach and supports broader participation of underrepresented groups in STEM research. The broadening participation activities will result in a rich resource for pharmaceutical process engineering education course materials. The project goals will be achieved by executing five specific aims: (1) development of high-throughput experiments and machine learning techniques for informing the discovery of new routes for continuous chemical synthesis and subsequent demonstration of this approach using two representative drugs, Imatinib and Lisinopril; (2) creation and demonstration of a general strategy for robust digital design and optimal real-time operation of modular mini-plants for distributed drug manufacturing; (3) development of a general, effective strategy for estimation of individualized drug treatment regimens based on combined first-principles and Bayesian mathematical models implemented with data collected from the clinical literature; (4) design and implementation of a mini-plant testbed for process/model validation, which integrates drug synthesis and personalized formulation capabilities, is equipped with non-invasive process analytical tools, and features real-time process management and control systems; and (5) development and demonstration of a general strategy for the optimal design and operation of pharmaceutical supply chains, wherein drug products are produced in geographically distributed networks of mini-plants. Through the research activities encompassed by these aims, the project will demonstrate the substantial economic and environmental benefits, significant waste-minimization, better risk management, and higher agility to adapt to market dynamics and shortages offered by these new distributed manufacturing and supply chain configurations when compared to the existing centralized supply chain infrastructure. The research findings also will be incorporated into undergraduate and graduate curricula both in teaching and as research projects. The test bed and resulting case studies will be used in outreach activities during visitation days, and through a website, motivating K-12 students towards STEM careers, with attention to diversity by engaging with minorities and underrepresented groups. The research will be disseminated through publications and presentations at conferences and during industrial visits. Through these avenues, the project will contribute highly qualified workforce members and to the technology infrastructure needed to remain competitive in the emerging advanced pharmaceutical manufacturing domain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.