By Dr. Samir Kulkarni, Ph.D.
Pfizer, Groton, CT

The improved development and scaleup of Nirmatrelvir (the active 3CL protease inhibitor in PaxlovidTM) with efficient chemical synthesis led to FDA emergency use authorization in just 17 months. With an aim to decrease the environmental impact of the manufacturer, process efficiency was improved significantly during development. The focus was kept on the improved isolation and crystallization processes of the intermediate and final API isolation to achieve desired particle size material. Also, the particle size of isolated Active Pharmaceutical Ingredients (API’s) has a large impact on their downstream drug product performance. The desired targeted Particle Size Distributions (PSD’s) can be achieved by properly setting up crystallization process conditions. Accurate predictive models of PSD with varying process conditions facilitates fast exploration of process parameter space and process set up. This work details an alternative approach, which combines fundamental thermodynamic knowledge with statistical optimal design and data-driven model to create a practical workflow. The methodology is applied on the development of the final crystallization of Nirmatrelvir to deliver API with desired PSD’s. Based on the discreet assessments for the API crystallization process, parameters were identified that were statistically, practically, and/or scientifically meaningful for PSD of the final isolation process. The statistical model was then used to predict the product PSD for plant batches, on crystallizers with various configurations and scales across multiple plants and different geographic locations in the world. This hybrid model helps to provide a more robust particle size control strategy by delivering API for more than 2000 batches with very tight particle size distribution.