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

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

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

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

Computational solvent screening narrows 70 candidates to under 10 in 2–3 days, with solubility predictions within 20% of experiment—applied to API crystal morphology design.

Predicting the percolation threshold, using statistical models or experimentation, is an evidence-based approach that can get solid dosage forms designed faster.

Computational fluid dynamics virtual experiments let pharma and biotech teams solve process challenges before entering the lab, cutting development time and resource use.