Small-scale models (SSMs) are critical to biopharmaceutical process development, technology transfer, process characterization, and process validation. Demonstrating that an SSM represents the large-scale manufacturing system is also required by regulatory authorities.  

However, while many biopharmaceutical companies are trying to implement qualified SSMs, there are many hurdles to overcome when designing, executing, and analyzing SSM qualification studies. These include a lack of industry alignment and little guidance available from regulatory bodies that is specific to the process of justifying SSMs. 

This situation drove BioPhorum to publish Justification of small-scale models: an industry perspective, which gives an overview of current practices for the qualification and refinement of SSMs used when developing drug substance manufacturing processes. 

Faster, higher, stronger 

The lack of an effective and justifiable SSM (or a process for designing and conducting one) slows down technology implementation, reduces the information available to demonstrate process understanding and reduces process flexibility. It also adds to companies’ significant cost, resources and time investments if they rely on traditional full-scale qualification. 

BioPhorum’s paper addresses these risks and will help companies to use their resources more effectively, implement technologies faster and create a greater understanding of SSMs throughout the development lifecycle. Ultimately, it will also ensure patients receive products faster while maintaining manufacturing flexibility. 

The guidance allows companies the flexibility to change their SSM model design based on their internal procedures, the product being developed and the data available to support the justification. 

“This paper is a tangible tool that will give companies a real starting point of where to go,” said Valerie Pferdeort, Senior Engineer I, Cell Culture Development at Biogen. “They will need to do their own research and develop the details, but people won’t have to start in the woods anymore. They now have a sense of what has been done and what has been accepted in development activities.”

The comprehensive document discusses the applications of SSMs, special considerations for their use for viral clearance studies, quality oversight, study design approaches, execution details and statistical methods. 

To ensure the paper represents industry practice, benchmarking surveys on justifying small-scale models were conducted among BioPhorum workstream members. These surveys questioned everything from the general application of SSMs to the detailed specifics of the number of runs required to qualify a model. The survey results are included in an appendix. 

The process of creating this paper has been invaluable to the team and has already helped some to develop their own internal processes and procedures for qualifying SSMs. 

With the variety of methods and approaches currently being used by companies to justify SSMs, this paper will be an invaluable resource for the whole industry and act as a toolkit so companies can develop and implement their own internal guidance.

Justification of small-scale models: an industry perspective

3.32 MB 1 file(s)
Release Date - 5th May 2021

  QSSM     scale-up     small-scale models     technology transfer     viral clearance  

Abstract

Small-scale models (SSMs) are widely used in the biopharmaceutical industry. These models are used for process development and optimization, scale-up, technology transfer, process characterization, process validation, virus clearance studies, and resolution of deviations encountered during manufacturing throughout a product’s lifecycle. SSMs are also referred to as ‘scale-down models’ or ‘scaled-down models’. Demonstration that an SSM is representative of the large-scale manufacturing system is called ‘small-scale model qualification’ (SSMQ), which is sometimes also referred to as ‘assessment’, ‘evaluation’, or ‘verification’. The demonstration is an important task that supports process validation and is required by regulatory authorities. However, design, execution and analysis of SSMQ studies can be challenging due to the lack of clear guidance on current best practices. This white paper provides options and tools for design, execution, and data analysis of SSMQs together with illustrative case studies.