Crystallization Process Design
Three world-class crystallization researchers present how to deliver significant improvements in crystallization process design.
Many important medicines and advanced materials are composed of microscopic particles. Tight control of particle size and shape is needed to ensure these products are manufactured easily and are fit for their intended use. In order to do this challenging particle formation, separation and processing steps must be designed in the laboratory and implemented in manufacturing. The resources and references below describe how industry leaders are approaching this difficult task.
Crystallization in Process Chemistry
This white paper discusses how to apply simple PAT tools to crystallization in process chemistry.
FBRM Method of Measurement
Use FBRM (Focused Beam Reflectance Measurement) Method of Measurement to track particle size, shape, and count.
Monitor Tablet & Granule Disintegration
The role of in-process particle measurement to complement traditional API dissolution studies is presented. The use of particle size and count measurement illustrates the potential to support scientist's efforts to develop process understanding for solid oral dosage form development.
Liquid-Liquid Phase Separation
This presentation describes a strategy employed to design and develop robust, scalable crystallization processes that avoids Liquid-Liquid Phase Separation (LLPS) or oiling out.
Improving Crystallization and Precipitation
This webinar introduces case studies and highlights best practices used to overcome crystallization and precipitation challenges. The focus will be on scientific journal articles and conference presentations that have been presented over the last 20 years in the field of crystallization optimization. This important body of work, covering a range of industries, will be distilled into a summary that provides useful guidelines for understanding and optimizing the challenging unit operations of crystallization and precipitation.
Particle Design Challenges
- Very few "first principles" methods are available to support effective particle design
- Information obtained at small scale for particle systems does not often reliably predict large scale behavior
- Process parameter interactions for particle systems are often non-linear, leading to design problems of extreme complexity
- Particles change when sampled and can be challenging to handle safely making characterization difficult
- Individual processing steps change particles in a unique way (e.g. filtration, drying, conveying, and formulation)
- Obtaining useful data for particle systems that can be applied to predictive models is a significant challenge
