Artificial intelligence and machine learning (AI–ML) offer vast potential in optimal design,
monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption …

The biopharmaceutical market has experienced a tremendous growth in the last years.
However, this growth should be balanced considering the difficulty in bioproduct …

In addition to activity, successful biological drugs must exhibit a series of suitable
developability properties, which depend on both protein sequence and buffer composition …

Owing to the advancement in the technologies, the vision of smart manufacturing is not
implausible. Development of sophisticated measuring tools, modelling approaches …

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 …

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 …

Multi-column periodic counter-current chromatography (PCC) has attracted wide attention
for the primary capture for the purpose of achieving continuous biomanufacturing …

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 …

Oligonucleotides (ONs) are acquiring clinical relevance and their demand is expected to
grow. However, the ON production capacity is currently limited by high manufacturing costs …

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 …