Artificial intelligence and machine learning applications in biopharmaceutical manufacturing
Artificial intelligence and machine learning (AI–ML) offer vast potential in optimal design,
monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption …
monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption …
Integration and digitalization in the manufacturing of therapeutic proteins
The biopharmaceutical market has experienced a tremendous growth in the last years.
However, this growth should be balanced considering the difficulty in bioproduct …
However, this growth should be balanced considering the difficulty in bioproduct …
Design of biopharmaceutical formulations accelerated by machine learning
In addition to activity, successful biological drugs must exhibit a series of suitable
developability properties, which depend on both protein sequence and buffer composition …
developability properties, which depend on both protein sequence and buffer composition …
[HTML][HTML] Digitization in bioprocessing: The role of soft sensors in monitoring and control of downstream processing for production of biotherapeutic products
Owing to the advancement in the technologies, the vision of smart manufacturing is not
implausible. Development of sophisticated measuring tools, modelling approaches …
implausible. Development of sophisticated measuring tools, modelling approaches …
Hybrid modeling—a key enabler towards realizing digital twins in biopharma?
Digital twins (DTs) represent a vividly emerging technology in the manufacturing industry
strongly motivated by the goals of industry 4.0. It strives for smart factories with completely …
strongly motivated by the goals of industry 4.0. It strives for smart factories with completely …
Hybrid models based on machine learning and an increasing degree of process knowledge: Application to capture chromatographic step
In process engineering, two paradigms of modeling approaches exist: the mechanistic and
the data-driven approaches with the former being completely based on knowledge while the …
the data-driven approaches with the former being completely based on knowledge while the …
[HTML][HTML] Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach
Hybrid modeling in bioprocess engineering has emerged as a promising approach to
strengthen process system engineering applications. However, understanding evolution of …
strengthen process system engineering applications. However, understanding evolution of …
A novel framework of surrogate-based feasibility analysis for establishing design space of twin-column continuous chromatography
Multi-column periodic counter-current chromatography (PCC) has attracted wide attention
for the primary capture for the purpose of achieving continuous biomanufacturing …
for the primary capture for the purpose of achieving continuous biomanufacturing …
Hybrid modeling for biopharmaceutical processes: advantages, opportunities, and implementation
Process models are mathematical formulations (essentially a set of equations) that try to
represent the real system/process in a digital or virtual form. These are derived either based …
represent the real system/process in a digital or virtual form. These are derived either based …
[HTML][HTML] Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities
TT Khuat, R Bassett, E Otte, A Grevis-James… - Computers & Chemical …, 2024 - Elsevier
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 …
field, its applications are still in the early stages in terms of providing direct support for quality …