Coupling high‐throughput and modeling approaches to streamline early‐stage process development for biologics

工作流程 过程开发 计算机科学 过程(计算) 时间轴 过程建模 吞吐量 开发(拓扑) 工艺工程 工艺验证 比例(比率) 系统工程 工艺优化 验证和确认 数据库 工程类 无线 考古 操作系统 数学分析 物理 历史 环境工程 电信 量子力学 数学 运营管理
作者
John P. Welsh,Scott H. Altern,Jessica Y. Lyall,Sean Burgess,Michael Rauscher,Abraham M. Lenhoff,Steven M. Cramer,Chris Williams
出处
期刊:Biotechnology Progress [American Chemical Society]
标识
DOI:10.1002/btpr.3523
摘要

Abstract Platforms have long been implemented for downstream process development of monoclonal antibodies (mAbs) to streamline development and reduce timelines. These platforms are also increasingly being used for other complex biologics modalities. While development has traditionally been conducted at the lab bench scale in a sequential manner, automated miniaturized and parallelized approaches like RoboColumns and resin plates have also been implemented for chromatographic screening. Additionally, mechanistic modeling for chromatographic separations has also seen increased use for development applications. In this manuscript, we propose a workflow with elements of both high‐throughput screening and modeling that provides a streamlined roadmap for early process development. The workflow utilizes automated resin plate screens to both narrow screening conditions and calibrate binding isotherm parameters. Mechanistic models are then used to characterize a robust range of conditions suitable for an early manufacturing process. Miniaturized RoboColumns then confirm the process space, thus completing the development without the use of any traditional lab‐scale columns. Case studies demonstrate the utility of this workflow for both cation‐exchange (CEX) and multimodal cation‐exchange (MMCEX) processes. Process parameter sensitivities across process ranges for the models are compared with typical design‐of‐experiment (DOE) statistical models. The models are able to predict the mAb product as well as aggregate impurities. This workflow provides a practical method to enable increased process understanding while also reducing timeline and material requirements for development.
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