Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities

Khuat, Thanh Tung; Bassett, Robert; Otte, Ellen; Grevis-James, Alistair; Gabrys, Bogdan

doi:10.1016/j.compchemeng.2024.108585

Cited by 13 publications

References 414 publications

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Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

El Salamouni,

Cater,

Spenkelink

et al. 2024

FEBS Open Bio

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Nanobodies, the smallest functional antibody fragment derived from camelid heavy‐chain‐only antibodies, have emerged as powerful tools for diverse biomedical applications. In this comprehensive review, we discuss the structural characteristics, functional properties, and computational approaches driving the design and optimisation of synthetic nanobodies. We explore their unique antigen‐binding domains, highlighting the critical role of complementarity‐determining regions in target recognition and specificity. This review further underscores the advantages of nanobodies over conventional antibodies from a biosynthesis perspective, including their small size, stability, and solubility, which make them ideal candidates for economical antigen capture in diagnostics, therapeutics, and biosensing. We discuss the recent advancements in computational methods for nanobody modelling, epitope prediction, and affinity maturation, shedding light on their intricate antigen‐binding mechanisms and conformational dynamics. Finally, we examine a direct example of how computational design strategies were implemented for improving a nanobody‐based immunosensor, known as a Quenchbody. Through combining experimental findings and computational insights, this review elucidates the transformative impact of nanobodies in biotechnology and biomedical research, offering a roadmap for future advancements and applications in healthcare and diagnostics.

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Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

El Salamouni,

Cater,

Spenkelink

et al. 2024

FEBS Open Bio

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Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process

Agarwal,

McCready,

et al. 2024

Biotechnology Progress

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The bio‐pharmaceutical industry heavily relies on mammalian cells for the production of bio‐therapeutic proteins. The complexity of implementing and high cost‐of‐goods of these processes are currently limiting more widespread patient access. This is driving efforts to enhance cell culture productivity and cost reduction. Upstream process intensification (PI), using perfusion approaches in the seed train and/or the main bioreactor, has shown substantial promise to enhance productivity. However, developing optimal process conditions for perfusion‐based processes remain challenging due to resource and time constraints. Model‐based optimization offers a solution by systematically screening process parameters like temperature, pH, and culture media to find the optimum conditions in silico. To our knowledge, this is the first experimentally validated model to explain the perfusion dynamics under different operating conditions and scales for process optimization. The hybrid model accurately describes Chinese hamster ovary (CHO) cell culture growth dynamics and a neural network model explains the production of mAb, allowing for optimization of media exchange rates. Results from six perfusion runs in Ambr® 250 demonstrated high accuracy, confirming the model's utility. Further, the implementation of dynamic media exchange rate schedule determined through model‐based optimization resulted in 50% increase in volumetric productivity. Additionally, two 5 L‐scale experiments validated the model's reliable extrapolation capabilities to large bioreactors. This approach could reduce the number of wet lab experiments needed for culture process optimization, offering a promising avenue for improving productivity, cost‐of‐goods in bio‐pharmaceutical manufacturing, in turn improving patient access to pivotal medicine.

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Development of a Cloud-Based Tablet Dispensing and Packaging Machine Monitoring and Control System in Pharmacies

Wang,

Chen

2024

Lecture Notes in Mechanical Engineering

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Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities

Cited by 13 publications

References 414 publications

Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process

Development of a Cloud-Based Tablet Dispensing and Packaging Machine Monitoring and Control System in Pharmacies

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