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

该研究由NSF工程理事会- UKRI工程和物理科学研究理事会牵头机构机会（ENG-EPSRC）NSF 20- 510资助。当前的COVID-19危机凸显了英国和美国需要拥有强大的智能制药基地。美国FDA已将连续制药确定为应对这些挑战的非常有前途的解决方案，其实现了更低的资本成本、更小的占地面积和高效的设施，这些设施可以在地理上分布，通过减少对外国供应商的依赖来提高国家安全，并可以按需生产多种产品，同时将质量风险降至最低。该项目的目的是汇集来自英国和美国四所大学的过程系统和制药工程专家组成的高度跨学科团队，旨在开发一种新的方法，用于首次正确的智能制药，以实现：（1）缩短新产品的上市时间;（2）减少浪费并提高弹性;（3）降低制造成本。该项目的一个独特之处在于能够在三个连续生产实验平台上验证最先进的模型、控制和优化程序，这三个实验平台是为生产药片而设计的，一个在谢菲尔德大学（英国），两个在普渡大学。该项目将促进国际合作，并为在新兴的先进制药领域保持竞争力所需的高素质劳动力和技术基础设施做出贡献。该项目将通过提供一个整体的过程系统工程框架，实现从批量到连续的制药制造模式的转变，从而实现正确的第一次智能制造。该项目的研究目标是：（1）使用一种新的基于风险的框架，建立动态的、可预测的制药工艺和产品模型，用于自适应的、混合模型的开发和验证;（2）建立一个通用框架，用于优化合成制药工艺，结合对不同生产路线的基于风险的评估，为实时控制和灵活操作提供固有的鲁棒设计;（3）为预测性维护策略和先进的容错控制方法创建实时过程管理和分级质量控制框架;（4）开发鲁棒的硬传感器和软传感器，以实现实时产品发布并增加制造系统的鲁棒性;和（5）使用谢菲尔德和普渡的集成药品连续生产线验证新的系统工程方法和工具。该项目的成果将是一个框架和计算工具，用于制药过程的优化设计，包括实时过程管理系统和灵活的实时释放测试框架，所有这些都在试验规模上得到验证。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.