See how CrysAI™ applies deep learning to crystallization and cell imaging—automating segmentation, improving reproducibility, and scaling insight.

Bayesian optimisation reduces pharmaceutical reaction development from 1,200 to a manageable experiment subset, identifying optimal yield conditions faster using Gaussian Process Regression.

We’ve curated a list of key crystallization publications from the past 25 years, showcasing the research and breakthroughs that have defined our approach and expertise. These publications represent the culmination of decades of innovative work by APC’s crystallization experts, illustrating our ongoing impact in this field.

A scale-down and CFD modelling approach delivered a 10% crystallisation yield improvement within existing regulatory filing parameters in 12 weeks.

For the synthesis & crystallization of APIs, Mixed Suspension Mixed Product Removal Crystallizers (MSMPRCs) can help address common issues in batch production.

Resin screening using 96-well plates and robocolumns eliminated a 50% yield loss in mAb polishing chromatography and defined a commercially viable purification step.

A lack of process understanding & control leads to: inability to scale, product quality drift, & costly delays from batch failures & regulatory concerns

Four essential elements for an ATMP development plan: risk assessment, scaled-up process control, CMC decision records, and early manufacturing integration.

In this case study, scientists determine drying conditions that preserve the API physicochemical properties, while minimizing the drying time.

Learn how a good numerical modeling strategy can help scale-up of single-use bioreactors.