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.

由Covid-19大流行引起的必需药物供应的破坏已经重新确认，使用传统的大批量制造方法，基于少数集中式制造地点的生产，全球药品供应链，这些供应链使用传统的大批量制造方法来生产单一尺寸适合所有剂量，这是本质上不可靠且不可靠的。该项目的建议是通过重新设计药物制造生态系统来解决这些缺陷，以使药物的生产更接近需求点（患者），从而将先进的制造方法雇用，包括连续处理和高水平的自动化，以确保产品质量和生产具有个性化剂量的能力，以使患者具有个性化的能力。具体而言，我们将开发实现这些能力所需的技术，并使用两种代表性的通用药物，一种用于治疗癌症，另一种用于治疗高血压。这些技术包括高吞吐量筛选到身份有效流动化学途径，基于数学模型的连续过程的设计以及基于模型的方法来控制和管理过程，以使主动性和实际剂量同时进行。制造工艺的尺寸尺寸尺寸的规模将使分布式剂量生产能够为区域市场服务，从而大大缩短供应链。此外，使用基于临床数据与相关患者特征的数学模型，可以确定最适合单个患者的特定剂量。该项目中要开发的复杂自动化将使该剂量能够以足以满足该患者的需求的量，从而有效地提供需求的药房。该项目将产生一个集成的框架，以创建一个有弹性的，分布式的药物制造生态系统，该生态系统将最佳地满足个性化的患者需求。项目采用多学科的方法，并支持代表性不足的群体在STEM研究中的更广泛参与。扩大参与活动将为药品工程教育课程材料提供丰富的资源。将通过执行五个具体目标来实现项目目标：（1）开发高通量实验和机器学习技术，以告知发现连续化学合成的新例程，并随后使用两种代表性药物Imatinib和Lisinopril进行了这种方法； （2）创建和演示一般策略，用于强大的数字设计以及用于分布式药物制造的模块化迷你植物的最佳实时操作； （3）制定基于从临床文献收集的数据实施的基于联合第一原理和贝叶斯数学模型的共同估算个性化药物治疗方案的一般有效策略； （4）设计和实施经过过程/模型验证测试的小型植物，该过程/模型验证集成了药物合成和个性化公式功能，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； （5）制定和演示药物供应链最佳设计和运行的一般策略，其中在小型植物的地理分布网络中生产药品。通过这些目标所包含的研究活动，该项目将证明与现有的集中式供应链基础设施相比，相比，这些新的分布式制造和供应链配置相比，这些新的分布式制造和供应链配置提供了这些新的分布式制造和供应链配置所提供的市场动态和短缺。研究结果还将在教学和研究项目中纳入本科和研究生课程。测试床和由此产生的案例研究将在访问期间以及通过网站进行外展活动中使用，从而激发K-12学生朝着STEM职业发展，并通过与少数族裔和代表性不足的群体互动来关注多样性。该研究将通过会议和工业访问期间的出版物和演讲进行传播。通过这些途径，该项目将贡献高素质的劳动力成员，并为在新兴的高级制药制造业领域保持竞争力所需的技术基础设施。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来获得的支持。

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