CMMI-EPSRC: Right First Time Manufacture of Pharmaceuticals (RiFTMaP)

Purdue University, West Lafayette IN

Investigators

Abstract

This research was funded under the NSF Directorate for Engineering - UKRI Engineering and Physical Sciences Research Council Lead Agency Opportunity (ENG-EPSRC), NSF 20-510. The current COVID-19 crisis has highlighted the need for the UK and USA to have a strong, smart pharmaceutical manufacturing base. The US FDA has identified continuous pharmaceutical manufacturing as a highly promising solution to these challenges by enabling lower capital cost, smaller footprint and highly efficient facilities, which can be distributed geographically, improve national security by reducing dependency on foreign suppliers and can produce multiple products on demand with minimum risk to quality. The aim of this project is to bring together a highly interdisciplinary team of experts in process systems and pharmaceutical engineering from four universities in the UK and USA, with the objective to develop a novel approach for the right-first-time smart manufacturing of pharmaceuticals to achieve: (1) reduced time to market of new products; (2) reduced waste and increased resilience; and (3) reduced cost of manufacture. A unique element of this project is the ability to validate the state of the art models, control and optimization procedures on three continuous manufacturing experimental platforms designed for the manufacturing of pharmaceutical tablets, one at the University of Sheffield (UK) and two at Purdue University. The project will foster international collaboration and contribute to the highly qualified workforce and technology infrastructure needed to remain competitive in the emerging advanced pharmaceutical manufacturing domain. This project will enable the paradigm shift from batch to continuous pharmaceutical manufacturing by providing a holistic process systems engineering framework that enables right-first-time smart manufacturing. The research objectives of the project are to (1) create dynamic, predictive pharmaceutical process and product models using a novel risk-based framework for adaptive, hybrid model development and validation; (2) create a general framework for the optimal synthesis of pharmaceutical manufacturing processes, incorporating a risk-based evaluation of different manufacturing routes to give inherently robust design for real-time control and flexible operation; (3) create real-time process management and hierarchical Quality-by-Control frameworks for predictive maintenance strategies and advanced fault-tolerant control approaches; (4) develop robust hard and soft sensors to enable real-time product release and increase the robustness of the manufacturing system; and (5) validate the new systems engineering methodologies and tools using integrated, drug product continuous manufacturing lines at Sheffield and Purdue. The outcome of this project will be a framework and computational tools for optimal design of pharmaceutical processes with a real-time process management system and a flexible real-time release testing framework, all verified at pilot scale. 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.