Creating a Design of Experiments Study to Predict Formula Robustness

In pharmaceutical development, manufacturers must be able to demonstrate product robustness and deliver the intended quality of the product within allowable ranges for the claimed shelf-life period. Both international and country-specific regulatory agencies, such as the FDA, pay close attention to these claims. In order to hold up under regulatory scrutiny and to create accurate labels, pharmaceutical companies must be able to prove the stability of the formula over time. 

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The goal of QbD in pharmaceutical development, including formulation and manufacturing process development, is to consistently deliver the intended quality of the product within allowable ranges.¹

A critical step in drug development is final formulation development. All pharmaceutical products are formulated to a specific dosage to be effectively delivered to patients. A good formulation must be stable – during both manufacturing and for its product shelf life.

For a company to successfully create a robust formula, it’s necessary to take steps to know which factors in the formula composition are critical to quality and what the acceptable ranges of variation are. In other words, to create a product design space that is as robust or broad as possible while still ensuring the product produces the desired (and specified results) under the stated shelf-life conditions.  Using the right tools can make this process more accurate, as well as faster, and less resource-intensive. 

Using Historical Data to Predict Future Shelf-Life

One way to calculate the shelf-life of a formula is to evaluate samples of the product over time. But using this approach to calculating shelf-life means delaying the market launch of a product until the shelf-life testing period has ended. That’s not an approach that supports a rapid go-to-market business goal. An alternative approach, then, is to take measurements from a shorter period of time, perhaps months instead of years, and use data analytics to predict the future stability of the formulation.

This approach requires using Design of Experiments (DOE) to study the factors that have the most impact on formulation stability and using multivariate data analysis to create statistically accurate predictions. 

For example, when creating an antibody product, determining which factors may affect the critical quality attributes is an essential part of the process. The factors that may affect the formula robustness could include pH of the solution, amount of antibody used, and buffer concentration. However, changes in the formulation, time, or environmental factors could affect the interactions, so understanding the relationship between these elements and the process is important.

Using DOE as a Quality by Design (QbD) Approach 

A reliable Quality by Design (QbD) process helps create a stability or robustness testing framework that meets regulatory standards for assessing a formulation. It also helps predict the critical quality attributes at end of shelf life using a few months of historical data.

In 2008 and 2009, the International Conference on Harmonization (ICH) guidelines Q8 and Q8(R2) established an approach to quality by design in drug formulations. ICH defines quality by design as “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management .”²

“Critical formulation attributes and process parameters are generally identified through an assessment of the extent to which their variation can have an impact on the quality of the drug product. ”– ICH Guidelines

DOE is an accepted tool in the pharma literature1 to develop a formulation map, or design space, that allows scientists to choose the optimal formulation conditions. Formulation robustness studies using DOE can help identify:

• Critical quality attributes: to understand which ingredients must be tightly controlled to maintain final drug product stability and efficacy
• Limits in variations: to understand the levels of formulation components that have maximal, minimal, or no effect on final drug product stability and efficacy
• Final product stability: to understand the impact of process deviations 
• Interactions: to understand how various formula components affect each other

Using a QbD approach can help reduce waste, meet compliance criteria and get the product to market faster. DOE helps create a QbD approach based on statistical analysis that can hold up under regulatory scrutiny. This includes defining normal operating ranges (NORs) or proven acceptable ranges (PARs) as part of the process development.

Steps to Using DOE for QbD

Creating a Design of Experiment using a QbD approach involves several key steps. These can be summarized as:

1. Select the Right Factors for Measurement 
   
   The factors selected to study must be the right ones in order to predict the acceptable formulation range. This ensures the critical quality attributes remain inside the specified limits.
2. Design a Statistically Valid Study
   
   Robustness studies must be able to prove that specific critical quality attributes stay within the acceptable ranges for the entire shelf-life period. In addition:
   The study must result in a regression model that is statistically significant
   The study must provide output parameters (quality attributes) that are within predefined limits
3.  Analyze the Data Using Multiple Linear Regression

One important way to produce a valid testing model is to use a tool that makes the Design of Experiments easier. For example, MODDE^® DOE software can help companies set up multivariate formulation robustness studies that demonstrate the acceptable ranges of quality for a target composition, define the allowable edges of the composition range, and predict the stability requirements needed to reach the end of shelf life.
Using software such as MODDE^® allows scientists, no matter what their level of statistical expertise, to develop models that provide statistically significant results and can reliably identify the parameters that may have an effect on drug product shelf life.

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References:

1. C. Wurth et al., “Quality by Design Approaches to Formulation Robustness- An Antibody Case Study,”Journal of Pharmaceutical Sciences (2016) 1e9 
2. ICH Q8 (R2). Pharmaceutical development, ICH Harmonized Tripartite Guidelines. International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. 2009.
3. D. Awotwe-Otoo, et al, “ Quality by design: Impact of formulation variables and their interactions on quality attributes of a lyophilized monoclonal antibody,” International Journal of Pharmaceutics (2012), p 167-175. ISSN 0378-5173,