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

新冠肺炎疫情造成的基本药物供应中断再次证实，基于少数集中生产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。该项目建议通过重新设计制药生产生态系统来解决这些不足，使药品生产更接近需求点--患者--采用先进的生产方法，包括连续加工和高度自动化，以确保产品质量，并引入根据患者特征生产个性化剂量的能力。具体地说，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路径，基于数学模型的连续过程设计，以及基于模型的方法来控制和管理制造有效成分和实际剂量的过程。制造过程规模的缩小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，使用建立在临床数据基础上的数学模型，结合相关患者的特征，可以确定最适合单个患者的特定剂量。该项目将开发的复杂自动化将使这种剂量的生产数量足以满足个别患者的需求，有效地提供按需药房能力。该项目将产生一个综合框架，以创建一个弹性的、分布式的制药生产生态系统，以最佳地满足个性化的患者需求。该项目采取多学科方法，支持代表性不足的群体更广泛地参与科学、技术和经济研究。参与活动的扩大将为制药过程工程教育教材提供丰富的资源。项目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路线提供信息，并随后使用两种代表性药物伊马替尼和赖诺普利对这一方法进行演示；(2)创建和演示用于分布式药物制造的模块化微型工厂的稳健数字设计和最佳实时操作的一般策略；(3)开发基于组合第一原理和贝叶斯数学模型的通用、有效的个性化药物治疗方案评估策略，所用数据来自临床文献；(4)设计和实施用于过程/模型验证的微型工厂试验台，它集成了药物合成和个性化配方能力，配备了非侵入性的过程分析工具，并具有实时过程管理和控制系统；以及(5)开发和演示了药品供应链优化设计和运营的一般战略，其中药品在地理上分布的微型工厂网络中生产。通过这些目标涵盖的研究活动，该项目将展示与现有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带来的巨大经济和环境效益、显著的浪费最小化、更好的风险管理和更高的灵活性，以适应市场动态和短缺。研究成果还将作为教学和研究项目纳入本科生和研究生课程。试验台和由此产生的案例研究将在访问日期间用于外联活动，并通过一个网站激励K-12学生从事STEM职业，同时注意通过与少数群体和代表性不足的群体接触来实现多样性。这项研究将通过出版物和在会议和工业访问期间的演讲来传播。通过这些途径，该项目将为在新兴的先进制药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。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.