Sokolov Michael
DataHow, c/o ETH Zurich, Vladimir-Prelog-Weg 1, Zurich, 8093, Switzerland.
Ind Eng Chem Res. 2020 Sep 8;59(40):17587-17592. doi: 10.1021/acs.iecr.0c02994. eCollection 2020 Oct 7.
In 2020, the Covid-19 pandemic resulted in a worldwide challenge without an evident solution. Many persons and authorities involved befriended the value of available data and established expertise to make decisions under time pressure. This omnipresent example is used to illustrate the decision-making procedure in biopharmaceutical manufacturing. This commentary addresses important challenges and opportunities to support risk management in biomanufacturing through a process-centered digitalization approach combining two vital worlds-formalized engineering fundamentals and data empowerment through customized machine learning. With many enabling technologies already available and first success stories reported, it will depend on the interaction of different groups of stakeholders how and when the huge potential of the discussed technologies will be broadly and systematically realized.
2020年，新冠疫情给全球带来了挑战，且尚无明显的解决方案。许多相关人员和当局认可现有数据的价值，并凭借专业知识在时间紧迫的情况下做出决策。这个普遍存在的例子被用来阐释生物制药生产中的决策过程。本评论探讨了重要的挑战和机遇，通过以流程为中心的数字化方法支持生物制造中的风险管理，该方法结合了两个重要领域——形式化的工程基础和通过定制机器学习实现的数据赋能。由于已经有许多使能技术可用，并且也有了首批成功案例报道，所讨论技术的巨大潜力将如何以及何时得到广泛而系统的实现，将取决于不同利益相关者群体之间的互动。
