Advances in industrial biopharmaceutical batch process monitoring: Machine‐learning methods for small data problems

Tulsyan Aditya; Garvin Christopher; ündey Cenk

摘要

Abstract <section xml:id="bit26605-sec-0001" numbered="no"> >Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real‐time. The state‐of‐the‐art real‐time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry‐wide problem in BPM, referred to as the “Low‐N” problem, wherein a product has a limited production history. The current best industrial practice to address the Low‐N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low‐N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine‐learning methods. To the best of authors’ knowledge, this is the first article to provide a solution to the Low‐N problem in biopharmaceutical manufacturing using machine‐learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low‐N scenarios. </section> </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract XMLNS =“http://www.wiley.com/namespaces/wiley”type =“main”xml：lang =“en”> <title type =“main”>抽象</ title> <section XML：ID =“Bit26605-SEC-0001”编号=“否”> >生物制药制造包括多个不同的处理步骤，该步骤需要实时地同时对许多变量进行有效和有效地监控许多变量。近年来，最先进的实时多变量统计批处理监测（BPM）平台已在使用中，以确保全面监测作为互补工具，以便继续进行验证以检测弱信号。本文在BPM中解决了长期的行业范围内的问题，称为“低N”问题，其中产品具有有限的生产历史。解决低N问题的当前最佳工业实践是从多变量转换为单变量BPM，直到有足够的产品历史可用于构建和部署多元BPM平台。每次批量都在没有强大的多元BPM平台的情况下造成没有检测到可能对过程和产品性能产生影响的过程中开发的潜在弱信号的风险。在本文中，我们提出了一种通过结合硬件开发和机器学习方法的组合产生任意大量的Silico批次来解决低N问题的方法。据作者所知，这是第一篇在使用机器学习方法提供生物制药制造中的低N问题的方法。提出了来自散装药物制造的若干工业案例研究，以证明在各种低N场景下BPM的提出方法的功效。</ p> </ section> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-15017/'>《Biotechnology and Bioengineering》</a> <b style="margin: 0 2px;">|</b><span>2018年第8期</span><b style="margin: 0 2px;">|</b><span>共10页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Tulsyan Aditya&option=202" target="_blank" rel="nofollow">Tulsyan Aditya;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Garvin Christopher&option=202" target="_blank" rel="nofollow">Garvin Christopher;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=ündey Cenk&option=202" target="_blank" rel="nofollow">ündey Cenk;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Digital Integration and Predictive TechnologiesAmgen Inc. Cambridge Massachusetts;</p> <p>Digital Integration and Predictive TechnologiesAmgen Inc. West Greenwich Rhode Island;</p> <p>Digital Integration and Predictive TechnologiesAmgen Inc. Thousand Oaks California;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/180.html" title="生物工程学（生物技术）">生物工程学（生物技术）;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=biopharmaceutical manufacturing&option=203" rel="nofollow">biopharmaceutical manufacturing;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=low‐N problem&option=203" rel="nofollow">low‐N problem;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=machine‐learning&option=203" rel="nofollow">machine‐learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=real‐time multivariate process monitoring&option=203" rel="nofollow">real‐time multivariate process monitoring;</a> </p> <div class="translation"> 机译：生物制药制造;低于问题;机器学习;实时多变量过程监控; </div> </li> </ul> </div> </div> <div class="literature cardcommon" id="literaturereference" style="display:none"> <div class="similarity "> <h3 class="all_title" id="enpatent111">引文网络</h3> <div class="referencetab clearfix"> <ul id="referencedaohang"> <li dataid="referenceul">参考文献</li> <li dataid="citationul">引证文献</li> <li dataid="commonreferenceul">共引文献</li> <li dataid="commoncitationul">同被引文献</li> <li dataid="tworeferenceul">二级参考文献</li> <li dataid="twocitationul">二级引证文献</li> </ul> </div> <div class="reference_details" id="referenceList"> <ul id="referenceul"></ul> <ul id="citationul"></ul> 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