Exputec GmbH, Mariahilferstr. 147/2/2D, Vienna, Austria.
Institute of Chemical Environmental and Bioscience Engineering, Research Area Biochemical Engineering, Vienna University of Technology, Gumpendorferstr. 1a, Vienna, Austria; CD Laboratory on Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna University of Technology, Gumpendorferstr. 1a, Vienna, Austria.
Drug Discov Today. 2019 Sep;24(9):1795-1805. doi: 10.1016/j.drudis.2019.06.005. Epub 2019 Jun 14.
Multiple obstacles are driving the digital transformation of the biopharmaceutical industry. Novel digital techniques, often marketed as 'Pharma 4.0', are thought to solve some long-existing obstacles in the biopharma life cycle. Pharma 4.0 concepts, such as cyberphysical systems and dark factories, require data science tools as technological core components. Here, we review current data science applications at various stages of the bioprocess life cycle, including their scopes and data sources. We are convinced that the scope and usefulness of these tools are currently limited by technical and nontechnical problems experienced during their development and deployment. We suggest that the establishment of DevOps mind- and toolsets could improve this situation and would be essential cornerstones in the further development of Pharma 4.0 systems.
多种障碍正在推动生物制药行业的数字化转型。新型数字技术,通常被称为“制药 4.0”,被认为可以解决生物制药生命周期中的一些长期存在的障碍。制药 4.0 概念,如信息物理系统和暗工厂,需要数据科学工具作为技术核心组件。在这里,我们回顾了生物工艺生命周期各个阶段的当前数据科学应用,包括它们的范围和数据来源。我们坚信,这些工具的范围和有用性目前受到其开发和部署过程中遇到的技术和非技术问题的限制。我们建议,建立 DevOps 思维和工具集可以改善这种情况,并且是制药 4.0 系统进一步发展的重要基石。