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

CMMI-EPSRC：药品的首次成功制造 (RiFTMaP)

• 批准号: 2140452
• 负责人: Zoltan Nagy
• 金额: $ 76.67万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140452&HistoricalAwards=false
• 关键词: CMMI; EPSRC; Right; First; Time

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.

这项研究是在NSF工程局资助的 - 乌克里工程和物理科学研究委员会领先机构机会（Eng-EPSRC），NSF20-510。当前的Covid-19危机危机强调了英国和美国拥有强大，智能的药品制造基地的需求。美国FDA通过实现较低的资本成本，较小的占地面积和高效的设施来确定连续的药物制造是对这些挑战的高度希望解决方案，可以通过地理位置分配，通过降低对外国供应商的依赖，并可以根据需求生产多种产品，从而提高国家安全性，从而最大程度的风险对质量产生多种产品。该项目的目的是将来自英国和美国四所大学的流程系统和制药工程专家组成的高度跨学科的团队组合在一起，目的是开发一种新颖的方法，以实现最右边的智能制造，以实现：（1）减少新产品市场的时间； （2）减少废物和增加的弹性； （3）降低制造成本。该项目的一个独特因素是能够在三个连续制造实验平台上验证最先进的模型，控制和优化程序，该平台设计用于制造药品平板电脑，一个在谢菲尔德大学（英国）和普渡大学的两个。该项目将促进国际合作，并为在新兴的高级药品制造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流程系统工程框架，从而使范式从批处理转向连续的药物制造，该框架可以实现直接智能制造的整体智能制造框架。该项目的研究目标是（1）使用基于自适应，混合模型开发和验证的新型框架创建动态，预测性药物过程和产品模型； （2）创建一个通用框架，以最佳地合成药品制造过程，并结合了对不同制造路线的基于风险的评估，以赋予实时控制和灵活操作的固有强大设计； （3）创建实时过程管理和层次质量界限框架，以实现预测性维护策略和高级容忍度控制方法； （4）开发坚固的硬和软传感器，以实现实时产品释放并增加制造系统的鲁棒性； （5）使用Sheffield和Purdue的集成药物连续制造线验证新的系统工程方法和工具。该项目的结果将是一种框架和计算工具，用于使用实时过程管理系统和灵活的实时发布测试框架的最佳设计设计，所有这些奖项均经过试验级的验证。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的功能和广泛的影响来评估Criteria criteria criteria criteria criteria。

项目成果

A Machine Learning-assisted Hybrid Model to Predict Ribbon Solid Fraction, Granule Size Distribution and Throughput in a Dry Granulation Process

机器学习辅助混合模型，用于预测干法制粒过程中的带状固体分数、颗粒尺寸分布和吞吐量

• 发表时间: 2023
• 期刊: Computer aided chemical engineering
• 作者: Yan-Shu Huang, David Sixon

A Hierarchical Approach to Monitoring Control Performance and Plant-Model Mismatch

监控控制性能和对象模型不匹配的分层方法

• DOI: 10.1016/b978-0-323-95879-0.50182-x
• 发表时间: 2022
• 期刊: Computer aided chemical engineering
• 作者: Sheriff, M. Ziyan;Huang, Yan-Shu;Bachawala, Sunidhi;Gonzelez, Marcial;Nagy, Zoltan K.;Reklaitis, Gintaras V.
• 通讯作者: Reklaitis, Gintaras V.

A Comprehensive Framework for the Modular Development of Condition Monitoring Systems for a Continuous Dry Granulation Line

连续干法制粒线状态监测系统模块化开发的综合框架

• DOI: 10.1016/b978-0-323-85159-6.50257-8
• 发表时间: 2022
• 期刊: Computer aided chemical engineering
• 作者: Lagare, Rexonni B.;Sheriff, M. Ziyan;Gonzalez, Marcial;Nagy, Zoltan K.;Reklaitis, Gintaras V.
• 通讯作者: Reklaitis, Gintaras V.

Development of mechanistic reduced order models (ROMs) for glidant and lubricant effects in continuous manufacturing of pharmaceutical solid-dosage forms

开发药物固体剂型连续生产中助流剂和润滑剂效应的机械降阶模型 (ROM)

• DOI: 10.1016/b978-0-323-95879-0.50189-2
• 发表时间: 2022
• 期刊: Computer aided chemical engineering
• 作者: Bachawala, Sunidhi;Gonzalez, Marcial
• 通讯作者: Gonzalez, Marcial

Development of a Virtual Sensor for Real-Time Prediction of Granule Flow Properties

开发实时预测颗粒流动特性的虚拟传感器

• DOI: 10.1016/b978-0-323-95879-0.50181-8
• 发表时间: 2022
• 期刊: Computer aided chemical engineering
• 作者: Lagare, Rexonni B.;da Conceicao, Mariana Araujo;Rosario, Ariana Camille;Young, Katherine Leigh;Huang, Yan-Shu;Sheriff, M. Ziyan;Clementson, Clairmont;Mort, Paul;Nagy, Zoltan K.
• 通讯作者: Nagy, Zoltan K.