Advancing continuous biomanufacturing of monoclonal antibodies using an experimentally validated modeling platform

项目摘要 由于对生物制品的需求不断增加，人们需要不断地在科学和商业方面取得进展 在上游细胞培养和下游纯化两个步骤中进行制造。我们的目标是提供一个 由计算框架补充的实验基础设施，以研究持续的 制造单抗(MAbbs)。建模方法的基础和验证将使用 具体的实验证据，以增强我们对过程的理解并改进模型性能和 设计阶段的利用率。拟议的创新源于以下目标： 作为本项目的成果交付使用，即：1)建立灌流生物反应器的多尺度模型 捕捉操作参数和细胞系特性对关键质量属性(CQA)的影响 通过实验结果验证；2)开发优化连续层析操作的方法， 包括主要捕获和抛光步骤，在一系列工艺条件下，以实现最佳清除 与工艺和产品相关的杂质；以及3)开发预测模型，使能够确定 考虑产品的上下游装置直接耦合的最佳操作条件 质量属性。 为了实现过程控制，这是先进制造的最终目标，我们将探索设计 确定关键工艺参数(CPP)、关键材料属性之间的关系 (CMA)和目标CQA。我们将为所有重要的单元操作开发预测模型，并进行验证 通过实验确定了设计空间，并对实验数据进行了统计分析 用于识别所有关键参数/属性的数据。经过验证的模型随后将用作虚拟工具来执行 对厂内下游操作的风险评估，如扩大/启动/关闭和比较过程 运营场景。在结合已开发的战略方面，我们设想与 连续上游设施，以实现全自动连续生物制造生产线的探索和 优化流程互动。这条概念验证线将用于清楚地量化风险和 基于绩效的指标。

Project Summary Due to increased demand for biologics, there is an ongoing need to scientifically and commercially advance manufacturing in both upstream cell culture and downstream purification steps. Our goal is to provide an experimental infrastructure complemented by a computational framework to investigate the continuous manufacturing of monoclonal antibodies (mAbs). The modeling approach will be based and validated using specific experimental evidence to enhance our process understanding and improve model performance and utilization during the design phase. The proposed innovations stem from the following objectives that will be delivered as the outcome of this project, namely, 1) To develop a multiscale model for perfusion bioreactor capturing the effects of operating parameters and cell line characteristics on critical quality attributes (CQAs) validated by experimental results; 2) To develop methods for optimizing continuous chromatographic operations, including primary capture and polishing steps, under a range of process conditions for optimal clearance of process- and product-related impurities; and 3) To develop predictive models that will enable determination of optimal operating conditions with direct coupling of upstream and downstream units accounting for product quality attributes. To enable process control, which is the ultimate target of advanced manufacturing, we will explore the design space and identify the relationships between critical process parameters (CPPs), critical material attributes (CMAs), and targeted CQAs. We will develop predictive models for all the important unit operations, validated by experiments, that can be used to determine the design space along with statistical analysis of experimental data to identify all critical parameters/attributes. The validated models will then be used as a virtual tool to perform risk assessment for in-plant downstream operations such as scale-up/start-up/shutdown and compare process operating scenarios. In terms of combining the developed strategies, we envision the integration with a continuous upstream facility to achieve a fully automated continuous biomanufacturing line exploring and optimizing the process interactions. This proof-of-concept line will be used to clearly quantify risk and performance-based metrics.