Machine learning in biopharmaceutical manufacturing
The biotechnology industry is expected to increase the production of new biopharmaceuticals.1 Biopharmaceuticals require high-quality standards, high initial investments for approval and introduction into the market as well as continued investment in manufacturing.2,3
In order to achieve profitable and sustainable manufacturing of biopharmaceuticals, bioprocess and bioproduct development must be planned and executed concurrently throughout the production lifecycle.4 Currently, bioprocess development is a critical bottleneck for the successful implementation of innovation obtained during bioproduct development.5 The majority of host-cell screening, initial conditions, material attributes and bioprocess parameter in-depth optimisation, as well as the identification of relationships between critical process parameters (CPP) and critical quality attributes (CQA), happens at an early stage of development during the implementation and execution of design of experiments (DoE) towards the established quality by design (QbD) paradigm.4,6
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