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

EFRI DCheM：分布式模块化和敏捷制造系统网络的数字化设计，具有个性化医疗的最佳供应链

• 批准号: 2132142
• 负责人: Zoltan Nagy
• 金额: $ 199.97万
• 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: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132142&HistoricalAwards=false
• 关键词: EFRI; DCheM; Digital; design; network

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.

COVID-19疫情导致的基本药物供应中断再次证实，基于少数集中生产基地生产的全球药品供应链，使用传统的大批量生产方法生产一刀切的剂量，本质上是不可靠和低效的。该项目提出通过重新设计药品制造生态系统来解决这些缺陷，使药品生产更接近患者的需求点，采用先进的制造方法，包括连续加工和高水平的自动化，以确保产品质量，并引入生产个性化剂量的能力。具体而言，我们将开发实现这些能力所需的技术，并使用两种具有代表性的仿制药进行演示，一种用于治疗癌症，另一种用于治疗高血压。这些技术包括高通量筛选，以确定有效的流动化学途径，基于数学模型的连续工艺设计，以及基于模型的方法，用于控制和管理制备活性成分和实际剂量的工艺。制造过程的缩小规模将使分布式剂量生产能够服务于区域市场，从而大大缩短供应链。此外，使用基于临床数据结合相关患者特征建立的数学模型，可以确定最适合个体患者的具体剂量。该项目将开发的先进自动化技术将使该剂量的生产量足以满足个别患者的需求，有效地提供按需配药的能力。该项目将产生一个集成框架，用于创建一个弹性的分布式制药生态系统，以最佳地满足个性化的患者需求。该项目采用多学科方法，支持代表性不足的群体更广泛地参与STEM研究。扩大参与活动将导致制药工艺工程教育课程材料的丰富资源。该项目的目标将通过执行五个具体目标来实现：（1）开发高通量实验和机器学习技术，为发现连续化学合成的新途径提供信息，并随后使用两种代表性药物伊马替尼和赖诺普利演示这种方法;（2）创建和演示用于分布式药物制造的模块化小型工厂的稳健数字设计和最佳实时操作的一般策略;（3）基于结合第一性原理和贝叶斯数学模型，利用从临床文献中收集的数据，开发用于估计个体化药物治疗方案的通用有效策略;（4）设计和实施用于工艺/模型验证的小型工厂试验台，该试验台整合了药物合成和个性化配制能力，配备了非侵入性过程分析工具，并具有实时过程管理和控制系统;以及（5）开发和演示药品供应链优化设计和运营的一般策略，其中药品在地理上分布的小型工厂网络中生产。通过这些目标所包含的研究活动，该项目将展示与现有的集中式供应链基础设施相比，这些新的分布式制造和供应链配置所带来的巨大经济和环境效益，显着的浪费最小化，更好的风险管理以及更高的灵活性，以适应市场动态和短缺。研究结果也将纳入本科和研究生课程的教学和研究项目。测试平台和由此产生的案例研究将在访问日期间用于外联活动，并通过网站激励K-12学生走向STEM职业，通过与少数民族和代表性不足的群体接触来关注多样性。将通过出版物和在会议上以及在工业访问期间的介绍传播这项研究。通过这些途径，该项目将贡献高素质的劳动力成员和技术基础设施，需要保持在新兴的先进制药领域的竞争力。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Innovative process for manufacturing pharmaceutical mini-tablets using 3D printing

使用 3D 打印制造药物迷你片剂的创新工艺

• 发表时间: 2023
• 期刊: Computer aided chemical engineering
• 作者: Sundarkumar, V;Wang, W;Nagy, Z.K.;Reklaitis, G.V.
• 通讯作者: Reklaitis, G.V.

Hybrid, Interpretable Machine Learning for Thermodynamic Property Estimation using Grammar2vec for Molecular Representation

• DOI: 10.1016/j.fluid.2022.113531
• 发表时间: 2022-06
• 期刊: Fluid Phase Equilibria
• 影响因子: 2.6
• 作者: Vipul Mann;Karoline Brito;R. Gani;V. Venkatasubramanian

Machine learning enabled integrated formulation and process design framework for a pharmaceutical 3D printing platform

机器学习为制药 3D 打印平台提供集成配方和工艺设计框架

• DOI: 10.1002/aic.17990
• 发表时间: 2022
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Sundarkumar, Varun;Nagy, Zoltan K.;Reklaitis, Gintaras V.
• 通讯作者: Reklaitis, Gintaras V.

Group contribution-based property modeling for chemical product design: A perspective in the AI era

• DOI: 10.1016/j.fluid.2023.113734
• 发表时间: 2023
• 期刊: Fluid Phase Equilibria
• 影响因子: 2.6
• 作者: Vipul Mann;R. Gani;V. Venkatasubramanian