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 认为连续药品制造是应对这些挑战的一个非常有前途的解决方案，它可以降低资本成本、缩小占地面积并提高设施效率，这些设施可以按地理位置分布，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工艺系统和制药工程专家组成的高度跨学科团队，目标是开发一种一次性成功的药品智能制造的新方法，以实现：（1）缩短新产品的上市时间； (2) 减少浪费并提高复原力； (3)降低制造成本。该项目的独特之处在于能够在三个专为制造药片而设计的连续制造实验平台上验证最先进的模型、控制和优化程序，其中一个位于英国谢菲尔德大学，两个位于普渡大学。该项目将促进国际合作，并为在新兴的先进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。该项目将通过提供能够实现一次成功的智能制造的整体流程系统工程框架，实现从批量到连续药品制造的范式转变。该项目的研究目标是（1）使用基于风险的新型框架来创建动态的、预测性的制药工艺和产品模型，以进行适应性、混合模型的开发和验证； (2) 创建药品生产工艺优化综合的总体框架，纳入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稳健设计； (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层质量控制框架； (4) 开发强大的硬传感器和软传感器，以实现实时产品发布并提高制造系统的稳健性； (5) 使用谢菲尔德和普渡大学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用于优化制药工艺设计的框架和计算工具，具有实时工艺管理系统和灵活的实时释放测试框架，所有这些都经过试点规模的验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

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.