Pharma 4.0: Use of Advanced Multivariate Analytics at Industrial Scale for Process Troubleshooting, Establishing Material Control and Improving Process Robustness in Pharmaceutical Industrial setting: Case Study

IF 2.7 4区医学 Q2 PHARMACOLOGY & PHARMACY Journal of Pharmaceutical Innovation Pub Date : 2022-10-08 DOI:10.1007/s12247-022-09689-z

Vaidehi Soman, Akshay Hatewar, Adolf Miranda, Abhaykumar Nalawade, Hansraj Gocher, Ram Kumar

{"title":"Pharma 4.0: Use of Advanced Multivariate Analytics at Industrial Scale for Process Troubleshooting, Establishing Material Control and Improving Process Robustness in Pharmaceutical Industrial setting: Case Study","authors":"Vaidehi Soman, Akshay Hatewar, Adolf Miranda, Abhaykumar Nalawade, Hansraj Gocher, Ram Kumar","doi":"10.1007/s12247-022-09689-z","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>This project aimed to use advanced multivariate analytics at scale for troubleshooting and improving the process robustness of pharmaceutical products. Additionally, the project aimed to make technology more accessible and empower people to use advanced analytics on the roadmap of Industry 4.0.</p><h3>Methods </h3><p>A web-based platform based on advanced analytics was designed and implemented. Using this platform, data was accessed from the cloud database with a few clicks, analyzed and interpreted using Advanced Analytics by end users who are Process Subject Matter Experts without much background in Data Science.</p><h3>Results</h3><p>This article highlights how Advanced Analytics is used at an industrial scale in Pharmaceutical Industrial Setting for troubleshooting a process and ultimately making the processes and products more robust thus ensuring that the drug supply to the patient is uninterrupted. Three application case studies are demonstrated, where conclusions drawn from the process models combined with the subject matter expertise successfully solved problems in a pharmaceutical industrial setting.</p><h3>Conclusion</h3><p>Considering the criticality of the drug product and the complex manufacturing processes in the pharmaceutical industry, this article establishes how historical data can be leveraged at scale to get value and meaningful conclusions using multivariate models and machine learning algorithms.</p></div>","PeriodicalId":656,"journal":{"name":"Journal of Pharmaceutical Innovation","volume":"18 3","pages":"1560 - 1574"},"PeriodicalIF":2.7000,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Pharmaceutical Innovation","FirstCategoryId":"3","ListUrlMain":"https://link.springer.com/article/10.1007/s12247-022-09689-z","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose

This project aimed to use advanced multivariate analytics at scale for troubleshooting and improving the process robustness of pharmaceutical products. Additionally, the project aimed to make technology more accessible and empower people to use advanced analytics on the roadmap of Industry 4.0.

Methods

A web-based platform based on advanced analytics was designed and implemented. Using this platform, data was accessed from the cloud database with a few clicks, analyzed and interpreted using Advanced Analytics by end users who are Process Subject Matter Experts without much background in Data Science.

Results

This article highlights how Advanced Analytics is used at an industrial scale in Pharmaceutical Industrial Setting for troubleshooting a process and ultimately making the processes and products more robust thus ensuring that the drug supply to the patient is uninterrupted. Three application case studies are demonstrated, where conclusions drawn from the process models combined with the subject matter expertise successfully solved problems in a pharmaceutical industrial setting.

Conclusion

Considering the criticality of the drug product and the complex manufacturing processes in the pharmaceutical industry, this article establishes how historical data can be leveraged at scale to get value and meaningful conclusions using multivariate models and machine learning algorithms.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Pharma 4.0：在制药工业环境中使用先进的工业规模多变量分析进行工艺故障排除、建立材料控制和提高工艺稳健性：案例研究

目的本项目旨在大规模使用先进的多元分析技术，以排除故障并提高制药产品工艺的稳健性。此外，该项目还旨在使技术更易于获取，并使人们能够在工业 4.0 的路线图上使用高级分析技术。方法设计并实施了一个基于高级分析技术的网络平台。使用该平台，终端用户只需点击几下即可从云数据库中访问数据，并使用高级分析技术对数据进行分析和解释，这些终端用户都是流程主题专家，没有太多数据科学背景。本文展示了三个应用案例研究，其中从流程模型中得出的结论与主题专业知识相结合，成功解决了制药工业环境中的问题。结论考虑到制药工业中药物产品和复杂生产流程的关键性，本文介绍了如何利用多变量模型和机器学习算法大规模利用历史数据，以获得有价值和有意义的结论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Journal of Pharmaceutical Innovation PHARMACOLOGY & PHARMACY-

CiteScore

3.70

自引率

3.80%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Pharmaceutical Innovation (JPI), is an international, multidisciplinary peer-reviewed scientific journal dedicated to publishing high quality papers emphasizing innovative research and applied technologies within the pharmaceutical and biotechnology industries. JPI''s goal is to be the premier communication vehicle for the critical body of knowledge that is needed for scientific evolution and technical innovation, from R&D to market. Topics will fall under the following categories: Materials science, Product design, Process design, optimization, automation and control, Facilities; Information management, Regulatory policy and strategy, Supply chain developments , Education and professional development, Journal of Pharmaceutical Innovation publishes four issues a year.