Smart Data Analytics for Risk Based Regulatory Science and Bioprocessing Decisions

项目总结/摘要 该提案旨在将数据分析解决方案应用于生物制造中遇到的挑战 运营我们将开发和验证两个制造商可以使用的流程建模工具 定量评估与其选择的制造模式相关的工艺风险。 第一个工具将协助制造商选择适当的制造模式。 第二个工具提供了一个全面的第一原理模型的生物制药 制造操作，允许用户在计算机上测试各种模型并定量 每一个选择所带来的风险。我们将通过实验验证这些工具在 在试验台设施中的批量和连续单克隆抗体生产工艺。 此外，我们将研究数据分析方法，以适当地纳入文本数据 将各种资源整合到工厂范围的运营模型中，以便制造商能够利用所有 提供相关信息，以优化其向受影响国家提供优质药品的能力， 患者最后，我们将研究使用数据分析方法，以更好地连接 通过合并外部数据源，将生产过程与临床经验数据相结合 商业产品发布后生成的数据，如不良事件、结局数据、已发表的 研究和其他文本数据。这项工作将展示数据分析如何利用 额外的数据源，为制造商基于风险的决策提供信息。此外，工具 根据这一建议开发的软件还将作为监管机构的培训工具， 提高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， 选择不合适的模式。这项工作完成后，将改善（a） 通过增加对选择制造工艺过程的了解， （B）通过保证产品质量，使制造商有能力选择合适的 （c）通过优化制造来提高安全性和效率 运营

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.