Smart Data Analytics for Risk Based Regulatory Science and Bioprocessing Decisions

项目摘要/摘要 该提案旨在将数据分析解决方案应用于生物制造过程中遇到的挑战 行动。我们将开发并验证制造商可以使用的两个流程建模工具 定量评估与他们选择的制造模式相关的工艺风险。 第一个工具将帮助制造商选择适当的制造模式。 第二个工具提供了生物制药的综合第一原理模型 一种制造操作，允许用户在计算机中测试各种型号并进行量化 在每一次选择中产生的风险。我们将在实验中验证这些工具的性能 在试验台设施中批量和连续地制造单抗的方法。 此外，我们还将研究数据分析方法，以便适当地合并文本数据 工厂范围内的运营模式，以便制造商能够利用所有 向他们提供相关信息，以优化他们向 病人。最后，我们将研究数据分析方法的使用，以便更好地将 结合外部数据源对临床体验数据的制作过程 在商业产品发布后生成，如不良事件、结果数据，发布 研究，以及其他文本数据。这项工作将演示数据分析如何利用 其他数据来源，为制造商基于风险的决策提供信息。此外，这些工具 根据这项提议开发的工具还将作为监管机构的培训工具，谁可以使用它们 增加他们自己对分析工具如何用于开发 制造模式和控制策略影响产品质量和产生的风险 选择了不合适的模特。这项工作完成后，将改善(A)监管 通过增加对选择制造工艺的过程的理解来实现工艺 型号，(B)通过确保制造商有技能选择适当的产品质量 应用工具；(C)通过优化制造实现安全和效率 行动。

Project Summary/Abstract This proposal aims to apply data analytic solutions to challenges encountered in biomanufacturing operations. We will develop and validate two process modeling tools that manufacturers can use to quantitatively assess the process risks associated with their choice of manufacturing model. The first tool will assist manufacturers in their selection of the appropriate manufacturing model. The second tool provides a comprehensive first-principles model of a biopharmaceutical manufacturing operation which allows the user to test in silico a variety of models and quantitate the risk incurred in each choice. We will experimentally validate performance of these tools in a batch and continuous monoclonal antibody manufacturing process in a testbed facility. Additionally, we will investigate data analytic methods to appropriately incorporate textual data sources into plant-wide operational models so that manufacturers are able to utilize all of the relevant information available to them to optimize their ability to supply quality medicines to patients. Finally, we will investigate the use of data analytic methods to better connect the manufacturing process to clinical experiential data by incorporation of external data sources generated after commercial product launch, such as adverse events, outcomes data, published research, and other textual data. This work will demonstrate how data analytics can leverage additional data sources to inform manufacturers’ risk-based decision making. In addition, the tools developed under this proposal will also function as training tools for regulators, who can use them to increase their own understanding of how analytical tools used in development of the manufacturing model and control strategy affect product quality and the risk incurred through selection of an inappropriate model. At its completion this work will improve (a) the regulatory process by increasing understanding around the process of choosing manufacturing process models, (b) product quality by ensuring manufacturers have the skills to choose the appropriate tools for their application, and (c) safety and efficiency through optimization of manufacturing operations.