Using a Quality by Design (QbD) approach for DOE supports ICH Q8 compliance
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.
Predicting formulation robustness for a pharmaceutical product requires a careful design of experiments that hold up under statistical analysis.
Determining what the acceptable ranges for the formula composition are, and which attributes are critical to quality, can be challenging. Added to this, is a need to bring products to the market quickly. Rather than waiting for years to evaluate formulations under various conditions until end of shelf life, pharmaceutical companies need to predict how the formula will perform in the future based on data from a limited period in the past.
It can be challenging for pharmaceutical companies is to prove the stability of a formulation over time. And it’s even more challenging to predict what happens with the stability in the future.
Can measuring past stability predict the future?
Of course, one way for a company to know what the stability of a formula over time will be is to measure it. But that means a product can’t be brought to market until a lot of time has passed. This isn’t a good scenario for a business that needs a rapid go-to-market strategy. So is there a way that measurements from a short period of time can be used to predict future stability of the formulation over a longer period of time?
In short, yes. If the right sort of design of experiment is developed to study the factors that actually have some impact on formulation stability, then data analysis using multiple linear regression (MLR) can be applied to create statistically accurate predictions.
One important consideration is choosing the correct factors to study. How can you be sure which factors actually will have an impact on the stability over time and thus affect shelf-life?
For example, in an antibody formulation, various formulation factors may be measured in order to determine what the critical quality attributes are. These include: pH of the solution, concentration of the antibody in the specimen, and buffer concentration. However, these can be affected by input parameter variations (formulation) or environmental factors, so understanding the relationships is essential.
Using a Quality by Design (QbD) approach to develop the testing process and to choose the critical quality attributes that will be measured can help reduce waste, meet compliance criteria and get to market faster. The design of experiments created in this will way determine what the acceptable variations or levels in the critical quality attributes can be.
Quality by Design (QbD) is “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.”
- International Conference on Harmonization (ICH) guidelines Q8 and Q8(R2)
Using a Quality by Design approach
Predicting formulation robustness requires a careful design of experiments that hold up under statistical analysis. Formulation robustness studies can also help you to select a commercial formulation that is sufficiently robust within the acceptable ranges around the label claim to meet the shelf life stability requirements. (These are typically 24-36 months for pharmaceutical products and at least 18 months at refrigerated conditions for biopharmaceutical drug products).
A reliable quality-by-design process helps create a stability or robustness testing framework that meets the ICH-Q8 standards for assessing the robustness of a formulation and can predict the critical quality attributes at end of shelf life using a few months of historical data.
A number of important steps are involved, but we can condense them down into three main points.
Step 1: Choose the right measurement factors
Ensure that the factors selected to study can be used to predict an acceptable formulation parameter range where all the values for the assessed quality attributes will be inside the specified limits.
For example, this cube represents a visualization of the volume within which parameters influencing the quality attributes will be investigated.
Step 2: Design a statistically valid study.
Consider how the factors being investigated fit into a full factorial design. For pharma companies, for example, 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
Step 3. Analyze the data using multiple linear regression.
One important way to produce a valid testing model is to use a tool that makes design of experiments easier. For example, MODDE, high-quality DOE solution from Sartorius Stedim Data Analytics, 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 affect on drug product shelf life.
Read more about Design of Experiments.
View a case example
Want to know more about how MODDE can help scientists set statically reliable robustness studies that meet International Conference on Harmonization Q8 (ICHQ8) Quality by Design standards?
Download the Growth Story featuring a Hoffmann-Roche case study.