2016
DOI: 10.1007/s11095-016-2043-9
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Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

Abstract: Knowledge Management (KM) is a key enabler for achieving quality in a lifecycle approach for production of biopharmaceuticals. Due to the important role that it plays towards successful implementation of Quality by Design (QbD), an analysis of KM solutions is needed. This work provides a comprehensive review of the interface between KM and QbD-driven biopharmaceutical production systems as perceived by academic as well as industrial viewpoints. A comprehensive set of 356 publications addressing the application… Show more

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Cited by 16 publications
(13 citation statements)
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“…Furthermore, very large amounts of historical bioprocess data have been accumulated in the bioindustry, and successful utilization of this digital resource calls for more intelligent control systems. KBCSs have emerged and ripened during the last decade, with the aim of solving this problem (Rathore et al, 2017;Borchert et al, 2019;. The KBCS has been implemented at two levels.…”
Section: Knowledge-based Control Systemmentioning
confidence: 99%
“…Furthermore, very large amounts of historical bioprocess data have been accumulated in the bioindustry, and successful utilization of this digital resource calls for more intelligent control systems. KBCSs have emerged and ripened during the last decade, with the aim of solving this problem (Rathore et al, 2017;Borchert et al, 2019;. The KBCS has been implemented at two levels.…”
Section: Knowledge-based Control Systemmentioning
confidence: 99%
“…Often, such situations must be managed under time pressure, and decisions are made on the readily available data and best educated guesses by experts. It is an infrequent practice to rigorously include any similar data and know-how from previous activities directly into the decision process due to often severe levels of heterogeneity, which can be a result of (partially) different utilized devices, scales, and materials as well as differently structured or quantified data . A smart digital solution enabling an automatic leverage of available prior information from heterogeneous sources by reliably deducing the transferrable know-how could enable a tremendous breakthrough for supporting complex decision making in biopharma manufacturing …”
Section: The Ambassador: Transfer Learningmentioning
confidence: 99%
“…It is an infrequent practice to rigorously include any similar data and know-how from previous activities directly into the decision process due to often severe levels of heterogeneity, which can be a result of (partially) different utilized devices, scales, and materials as well as differently structured or quantified data. 37 A smart digital solution enabling an automatic leverage of available prior information from heterogeneous sources by reliably deducing the transferrable know-how could enable a tremendous breakthrough for supporting complex decision making in biopharma manufacturing. 38 The general structure of hybrid models is quite attractive to apply such transfer learning concept in small data environments, where the mechanistic backbone accounts for major generic effects while the machine learning part enables fine-tuning based on the limited available data, for instance to a specific molecule.…”
Section: The Multitalent: Hybrid Modelingmentioning
confidence: 99%
“…Three categories of intelligent automation systems, ranging from rule-based systems to dynamic AI-based systems have been identified and listed by Huysentruyt et al [ 17 ]. These categories are “rule-based static systems,” “AI-based static systems,” and “AI-based dynamic systems.” Additionally, Rathore et al [ 18 ] provided a review of the interface between Knowledge Management (KM) and Quality by Design (QbD)-driven biopharmaceutical production systems as perceived by academic as well as industrial viewpoints. It includes a comprehensive set of 356 publications addressing the applications of KM tools to QbD-related tasks, including a specific class related to intelligent process management in continuous pharmaceutical operations and intelligent decision support in pharmaceutical development.…”
Section: Introductionmentioning
confidence: 99%