Flowsheet Modeling and Analysis Tools for Solid Base Pharmaceutical Products Manufacturing

项目总结 可靠地制造能够保持一致性能的产品需要了解复杂的 从分子到宏观不同尺度上发生的相互作用现象。此外，有效的 可靠的制造需要了解用于使产品相互作用的顺序步骤 并对成品的性能做出贡献。这可能是一项艰巨的任务，因为 制造过程也会受到多种原材料及其混合物的性质的影响 作为环境变量和人为错误，并由多个相互作用的处理步骤组成。因此， 制造过程很少被优化，往往缺乏健壮性，容易出现意想不到的故障。 目前，制药行业的这些问题很大程度上是临时解决的，严重依赖于 试探法。幸运的是，该行业以及食品和药物管理局(FDA)已经认识到 药品生产现代化的需要，FDA发起了一项倡议，以增强 通过设计质量(QBD)和过程分析技术(PAT)工具理解过程。这个 这些努力的主要目标包括发展对广泛的 进程；协调进程和设备；开发在线执行的技术 加工过程中关键材料性能的测量.实时控制和性能 最优化；最大限度地减少对工艺设计空间的经验试验和评估的需要。 预测模型是实现这些目标的极其有用的工具。预测性模型可用于 设计、解决问题和优化流程，评估和降低与不同类型的 扰动，无论是由于材料属性的变化、工艺的可变性，还是人为错误。建模 片剂的制造过程需要对物理现象有全面的了解 起始颗粒的性质与机械特性之间的关联 最终产品的价值。这项提案将侧重于发展这一框架。特别是，以下任务具有 已确定：(A)为固体的连续制药生产开发流程图建模工具 以药物产品为基础；(B)在总体框架内开发和整合敏感性分析工具 需要执行系统风险评估和故障模式分析，以确定稳健性 一个过程的过程。在本阶段中，我们还将演示流程图模拟工具在 制定有效的控制策略；以及(C)利用 实验设备和我们中试工厂以及我们的工业合作伙伴提供的数据。 最终目标是向FDA交付一个通用的建模工具，可以用来模拟、监控、控制 并优化固体药物产品的制药制造工艺。我们建议结束 与该机构合作，使开发的工具符合他们的期望，并最大限度地提高其可用性。

Project summary Reliable manufacturing of products capable of consistent performance requires the understanding of complex interacting phenomena occurring at different scales from the molecular to the macro scale. Moreover, effective and reliable manufacturing requires understanding how the sequential steps used to make the product interact with one another and contribute to the performance of the finished product. This can be a daunting task, since the manufacturing process can be affected by the properties of multiple raw materials and their blends, as well as environmental variables and human error and consist of a number of interacting processing steps. Thus, manufacturing processes are rarely optimized, and often lack robustness and are prone to unexpected failures. At present, these problems in the pharmaceutical industry are addressed largely ad hoc, relying heavily on heuristics. Fortunately, the industry as well as the Food and Drug Administration (FDA) have recognized the need for modernizing pharmaceutical manufacturing and the FDA has launched an initiative for enhancing process understanding through Quality by Design (QbD) and Process Analytical Technology (PAT) tools. The major goals of these efforts include the development of scientific mechanistic understanding of a wide range of processes; harmonization of processes and equipment; development of technologies to perform online measurements of critical material properties during processing; performance of real-time control and optimization; minimization of the need for empirical experimentation and evaluation of process design space. Predictive models are extremely useful tools for achieving these goals. A predictive model can be used to design, trouble shoot, and optimize a process, and assess and mitigate risks associated with different types of perturbations, whether due to changes in material properties, process variability, or human error. Modeling of the tablet manufacturing process requires a comprehensive understanding of the physical phenomena underlying the correlation between the properties of the starting particulates and the mechanical characteristics of the end product. This proposal will focus on developing this framework. In particular the following tasks have been identified: (a) to develop flowsheet modeling tools for continuous pharmaceutical manufacturing of solid based drug products; (b) to develop and integrate within the overall framework the sensitivity analysis tools required to perform systematic risk assessment and failure mode analysis needed to determine the robustness of a process. In this phase we will also demonstrate the use of the flowsheet simulation tools in the development of efficient control strategies; and (c) to validate the models and analysis tools using the experimental facility and the data in our pilot plant and from our industrial partners. The ultimate target is to deliver to FDA a generic modeling tool that can be used to simulate, monitor, control and optimize pharmaceutical manufacturing processes for solid based drug products. We propose a close collaboration with the agency so that the developed tools meet their expectations and maximize their usability.