362894 A Computational Approach to Predict Lyophilizaiton Performance and Monitor Process Robustness for Biologics

Thursday, November 20, 2014: 4:55 PM
202 (Hilton Atlanta)
Xiaodong Chen1, Vikram Sadineni1, Mita Maity1, Venkatramana Rao1 and Matthew Enterline2, (1)Drug Product Science & Technology, Bristol-Myers Squibb, New Brunswick, NJ, (2)Department of Chemical Engineering, University of Delaware, Newark, DE

Lyophilization of injectable pharmaceutical products is an approach commonly undertaken to formulate drugs that are chemically or physically unstable to be commercialized as ready to use solutions. Transferring the process parameters with appropriate design space from laboratory scale lyophilizer to the commercial scale without a loss in product quality and process robustness is one of the important aspects of commercializing a lyophilized product. This process is often accomplished by costly engineering runs or through an iterative process at the commercial scale. Here, we are highlighting an experimental process followed by a computational approach to predict commercial process parameters for the primary drying phase of lyophilization.

The objective of this research is to characterize the primary drying process using a combination of laboratory scale lyophilization experiments and finite element method (FEM) based computational modeling approach. Heat and mass transfer coefficients are determined either by the manometric temperature measurement (MTM) based measurement or gravimetric method based sublimation experiments. This data will be used as inputs for the FEM based software called PASSAGE ®, which computes primary drying duration for critical primary drying process parameters such as shelf temperature and chamber pressure. Since, the heat and mass transfer coefficients will vary at different lyophilization scales. These parameters are especially difficult to measure at commercial scale with the available tools and without significant usage of expensive drug substance. We present an alternative approach to use appropriate factors while scaling-up from laboratory scale to commercial scale. As a result, one can predict commercial scale primary drying duration based on these parameters. Additionally, the computational model is validated by comparing predicted product temperature and primary drying duration with experimental studies at lab-scale. Finally, three applications were presented to demonstrate the capability of the model, the first one is a scale-up model to predict primary drying duration at commercial scale, the second one is a computational model to identify a proven acceptable range for the critical primary drying process parameters and the last one is focused on predicting product temperature due to accidental drifts in process parameters during the primary drying stage of the process. The approach presented here provides a process for robust lyophilization scale-up and because of the simple and minimalistic nature of the approach, it will be less capital intensive. This study also facilitates the implementation of Quality by Design (QbD) concept to biologics manufacturing.

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