Applications of Machine Learning in Biopharmaceutical Process
  Development and Manufacturing: Current Trends, Challenges, and Opportunities
Thanh Tung Khuat Robert Bassett Ellen Otte Alistair Grevis-James Bogdan Gabrys
While machine learning (ML) has made significant contributions to the
biopharmaceutical field, its applications are still in the early stages in
terms of providing direct support for quality-by-design based development and
manufacturing of biopharmaceuticals, hindering the enormous potential for
bioprocesses automation from their development to manufacturing. However, the
adoption of ML-based models instead of conventional multivariate data analysis
methods is significantly increasing due to the accumulation of large-scale
production data. This trend is primarily driven by the real-time monitoring of
process variables and quality attributes of biopharmaceutical products through
the implementation of advanced process analytical technologies. Given the
complexity and multidimensionality of a bioproduct design, bioprocess
development, and product manufacturing data, ML-based approaches are
increasingly being employed to achieve accurate, flexible, and high-performing
predictive models to address the problems of analytics, monitoring, and control
within the biopharma field. This paper aims to provide a comprehensive review
of the current applications of ML solutions in a bioproduct design, monitoring,
control, and optimisation of upstream, downstream, and product formulation
processes. Finally, this paper thoroughly discusses the main challenges related
to the bioprocesses themselves, process data, and the use of machine learning
models in biopharmaceutical process development and manufacturing. Moreover, it
offers further insights into the adoption of innovative machine learning
methods and novel trends in the development of new digital biopharma solutions.