Lyophilization is a key technology in the manufacturing of stable drug products for biopharmaceuticals by gently removing water at low temperature and pressure conditions. The complexity of the lyophilization process poses challenges when it comes to achieving consistent product quality and process efficiency. This webinar presents a comprehensive overview of a lyophilization model including a robustness analysis, highlighting the significance of predictive models and robustness assessment methodologies in optimizing process parameters and ensuring product quality.

The presented lyophilization model simulates the primary drying phase, the longest and most critical phase. We will present the model structure, validation experiments and use cases. The model integrates thermodynamic principles, heat and mass transfer equations, and material properties to predict critical process parameters such as product temperature, ice sublimation rate and probabilities of certain failure such as collapse, choked-flow or meltback. By providing insights into the process dynamics and performance under different operating conditions, these models facilitate process optimization, scale-up, and technology transfer. The tool enables in silico robustness analysis which plays a vital role in assessing the sensitivity of lyophilization processes to variations in process parameters, equipment characteristics, and material attributes. Thus, the acceptable ranges of chamber pressure and shelf temperature can be identified to ensure product quality and consistency.

In conclusion, lyophilization modelling and robustness analysis offer valuable tools for optimizing process parameters, minimizing variability, and ensuring product quality and consistency in freeze-drying operations. Continued research and innovation in this field are essential to address emerging challenges and advance the state-of-the-art in lyophilization technology.