期刊
ANALYTICA CHIMICA ACTA
卷 1004, 期 -, 页码 32-39出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2017.11.070
关键词
On-line HPLC; Automated sample-prep; Unattended reaction monitoring; Process analytical technology; Microfluidics; Instrument development
资金
- David Holdych and Graham Marshall of GlobalFIA Inc.
- Calvin Becker and Bradley Greiner of AbbVie Inc.
- Daniel W. Armstrong of University of Texas at Arlington
- AbbVie Experiential Internship Program
- AbbVie
In-process sampling and analysis is an important aspect of monitoring kinetic profiles and impurity formation or rejection, both in development and during commercial manufacturing. In pharmaceutical process development, the technology of choice for a substantial portion of this analysis is high-performance liquid chromatography (HPLC). Traditionally, the sample extraction and preparation for reaction characterization have been performed manually. This can be time consuming, laborious, and impractical for long processes. Depending on the complexity of the sample preparation, there can be variability introduced by different analysts, and in some cases, the integrity of the sample can be compromised during handling. While there are commercial instruments available for on-line monitoring with HPLC, they lack capabilities in many key areas. Some do not provide integration of the sampling and analysis, while others afford limited flexibility in sample preparation. The current offerings provide a limited number of unit operations available for sample processing and no option for workflow customizability. This work describes development of a microfluidic automated program (MAP) which fully automates the sample extraction, manipulation, and on-line LC analysis. The flexible system is controlled using an intuitive Microsoft Excel based user interface. The autonomous system is capable of unattended reaction monitoring that allows flexible unit operations and workflow customization to enable complex operations and on-line sample preparation. The automated system is shown to offer advantages over manual approaches in key areas while providing consistent and reproducible in-process data. (c) 2017 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据