SDM vs Commercial scale: Introduction, Limitation and Goals

Commercial Scale Objective: Demonstrate that a process is well understood and can be adequately controlled to maintain high quality.

Limitation: Full scale commercial process is expensive to run; slow, difficult and expensive to change; requires large volumes.

A single commercial scale batch can have a volume upwards of 20kL and cost millions of dollars.

SDM Goal:

Develop scaled down representations of unit operations that are predictive of key features of the large-scale process.

Confirm the scaling principles and scale predictions with qualification studies.

Perform most experimental work on the small-scale system to infer properties of the large-scale process.

Develop scaled down representations of unit operations that are predictive of key features of the large-scale process.

Scale down representation:

Smaller equipment and/or volumes (e.g., 250mL bioreactor).

Supported by an engineering scale down model (SDM) that links large and small scales per engineering scaling principles.

Is predictive of commercial scale at target on key features of interest.

Is expected to be predictive of commercial scale off-target based on the SDM, prior experience, expert knowledge, simulation, computer modeling, literature, data, e.g., Data predicting large scale on-target performance Data across multiple scales that confirms the SDM

Important Notes:

The SDM is MORE than the engineering scaling equations.

A SDM may be partial – not every aspect of full scale is modeled .

Some unit operations do not need to be scaled down (e.g., vial thaw).

Regulatory framework: Small scale model:

ICH Q11 step 4 version guideline “Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities), May 2012” states as follow. Small-scale models can be developed and used to support process development studies. The development of a model should account for scale effects and be representative of the proposed commercial process. A scientifically justified model can enable a prediction of quality and can be used to support the extrapolation of operating conditions across multiple scales and equipment.

EMA guideline (on process validation for the manufacturing of biotechnology derived active substance, November 2016) described small scale model as,  A small-scale model must be designed and executed, and ultimately justified, as an appropriate representation of the manufacturing process.  When used, small scale models should be described and their relevance for the commercial scale should be justified, in terms of objective, design, inputs and outputs. When validation studies are highly dependent on the small-scale model studies, it may be necessary to demonstrate that when operating under the same conditions using representative input materials, the outputs resulting from the commercial scale process match those of the small-scale model. Any difference in operating conditions, inputs or outputs should be appropriately justified.

Whereas US FDA guideline (Process validation guidance for Industry, January 2011) described small scale model as,  “It is important to understand the degree to which models represent the commercial process, including any differences that might exist, as this may have an impact on the relevance of information derived from the models.”

SDM Qualification Approach

Graphtal’s offering for Bioprocess Scale Down Model Qualification : 

Graphtal offers a range of advanced analytics solutions that specifically aid in the scale-down model qualification process for bioprocesses, particularly in monoclonal antibody (mAbs) development.

Graphtal support for scale down model validation through data analysis, statistical support, and setting equivalence margins for both scale-down and full-scale processes using TOST (Two One-Sided Tests).

Graphtal helps to use MVDA to integrate data, identify patterns, compare and qualify the scale-down model for improved accuracy and performance.

Graphtal customizes sample size calculations as per your available manufacturing batch number available for analysis and calculate right EAC or practical significant difference to ensure statistically valid results.