In this age of technology, the vision of manufacturing industries built of smart factories is not
a farfetched future. As a prerequisite for Industry 4.0, industrial sectors are moving towards …

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

To overcome pandemics, such as COVID-19, vaccines are urgently needed at very high
volumes. Here we assess the techno-economic feasibility of producing RNA vaccines for the …

Highlights•Mathematical models as tools to establish quantitative links between bioprocess
CPPs and KPIs.•Review of the advantages and limitations of mechanistic and statistical …

Highlights•Challenges the biopharmaceutical industry is facing at the moment.•Summary of
data science challenges due to biopharma particularities.•Review of today's data science …

The recent increase in digitalization of industrial systems has resulted in a boost in data
availability in the industrial environment. This has favored the adoption of machine learning …

In the dynamic landscape of industrial evolution, Industry 4.0 (I4. 0) presents opportunities to
revolutionise products, processes, and production. It is now clear that enabling technologies …

Integrated continuous manufacturing is entering the biopharmaceutical industry. The main
drivers range from improved economics, manufacturing flexibility, and more consistent …

Due to the lack of complete understanding of metabolic networks and reaction pathways,
establishing a universal mechanistic model for mammalian cell culture processes remains a …

Bioreactors of various forms have been widely used in environmental protection, healthcare,
industrial biotechnology, and space exploration. Robust demand in the field stimulated the …