Purification of active pharmaceutical ingredients (APIs) is the vital most important process in the pharmaceutical manufacturing industries. The most important chemical process in these in the pharmaceutical manufacturing is crystallization followed by filtration, drying, and milling. APIs are purified by crystallization and these crystals are spherical in shape. As there are stringent requirements of precise crystal size in the industry, the accurate prediction of the crystal sizes upon all the above mentioned processes is crucial. In this work we present the modeling with Population Balance Method (PBM) as a novel approach for prediction of Particle Size Distribution (PSD) of the active pharmaceutical ingredients (APIs) crystals. As the process of simulation is stochastic in nature, it is likely to captures the variability in
the real manufacturing process and gives more accurate predictions. Accurately predicting this distribution through the stages of crystallization, filtration, drying, and milling is crucial for successful scale-up and uninterrupted operations. The precision in the prediction of particle size distribution (PSD) of the APIs, helps in adhering to the stringent regulations in the pharmaceutical industry. Our method offers a
significant benefit through its initial calibration at the laboratory level, requiring only small amounts of the expensive API and reliable process analytical tools. Once calibrated, this protocol can be effectively applied at pilot or industrial scales with confidence. Importantly, our protocol shows excellent precision in predicting crystal size during a 200-fold scale-up across crystallization, filtration, drying, and milling
processes. These advancements ensure more streamlined scale-up procedures and greater operational reliability in pharmaceutical manufacturing.
