A preprint reports needing 2.5 to 7.1 times fewer designs to engineer a production strain with machine learning. We read it in full plus 40 other primary sources. The saving is in simulated designs: no strain was built. What machine learning has done with real lab data, and where the models break.
We read 46 primary sources on AI in bioprocessing. In GMP today: narrow soft sensors and multivariate monitoring. Closed-loop control, predictive twins and language models are lab, development or claims, and the EU Annex 22 on AI is still a draft.
We read 40 primary sources on bioreactor scale-up. No criterion can be held with the others; in cell culture the trouble is usually CO2 and the sparger, in microbial fermentation oxygen and heat, and kLa correlations do not travel between scales.
We read 43 primary sources. Raman tracks glucose well and titre poorly, capacitance counts cells until they stop growing and swell, models break when the cell line, scale or probe changes, and no regulator requires any of it.
We read 46 primary sources. ATF and TFF use the same hollow fibre and differ in the pump. Both hold back product as the filter ages, the head-to-head studies disagree, and what fouls the fibre is particles the size of its pores.
We read 40 primary sources. Perfusion makes several times more per litre per day and, in three models, needs five to eight times less bioreactor volume, but in the cost models read in full it only wins at small scale.
We read 38 primary sources on CHO perfusion. Two flows deserve most of the attention on cost: the medium you buy and the product the bleed throws away.
Daily briefs from company releases, papers and regulators
Bioprocess 4.0Brief
over 99%
Reduction in host cell DNA in the monoclonal antibody culture fluid after flocculation with the best of five cationic polymers tested (Biotechnology Progress, 2 Nov 2026)
A BOKU University study screened five cationic flocculants on mAb cell culture fluid. The best one cut host cell DNA by over 99% and total host cell proteins by up to 40%.