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

Bayesian Optimization provides a more efficient alternative to traditional Design of Experiments (DoE) for process development, particularly when navigating multiple factors and levels. This case study highlights an innovative approach involving the exploration of areas characterized by high uncertainty and high potential yield. This focus enabled the identification of optimal operating ranges for critical factors, ultimately maximizing reaction yield.

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.

Using data-rich experimentation and mixing models, which leverage CFD, a right-first scale up of the new process was achieved within the yield improvements translating directly back to the plant.

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

In this study, the final purification step of a mAb was resulting in significant yield loss (up to 50%). Learn how experts redefined this problematic process.

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

Tech transfer of Advanced Therapy Medicinal Products (ATMPs) presents additional challenges beyond those faced when transferring more traditional biologics.

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.