A QbD Approach to Biopharma Process Development Achieves Compliance and Optimization
For pharmaceutical and biopharma companies, adopting a Quality by Design (QbD) approach to process development makes good business sense. In addition to creating more robust formulas and well-documented processes, following a QbD approach can help ease regulatory compliance and ensure a stable process for long-term production optimization. Since its adoption over 10 years ago, QbD is increasingly viewed as the best-practice approach for process development among regulators in the biotech industry. [1]
This article is posted on our Science Snippets Blog.
QbD differs from past approaches to product development by building quality into every step of the process, rather than relying on testing to achieve quality. In essence, QbD is a statistical approach to development that focuses on process understanding and control by assessing variables that may impact quality. That means a QbD approach hinges being able to analyze a broad set of data effectively.
Quality by Design 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 [2]
The International Conference on Harmonization (ICH) Q8 guidance from 2009 describes the principles of quality by design as a systematic approach to process development. Although QbD is not mandatory, ICH guidelines provide well-supported reasons to adopt the method.
A QbD-based approach provides a high degree of assurance that a pharmaceutical manufacturing process is adjustable within a design space and therefore robust, and that it is managed with a control strategy that uses modern statistical process control methods. It enables a lifecycle approach to validation and continuous process verification.
Quality by design can’t be tested into a product but must be built from the ground up.
Benefits of a QbD approach
Using QbD makes good business sense and helps:
- Eliminate batch failures
- Minimize deviations and costly investigations
- Avoid regulatory compliance problems
- Invest in the future through deeper organizational learning
- Enable a science-based approach
- Support better development decisions
- Empower technical staff
- Build quality into the entire product lifecycle
An Approach to Quality Built on Data Analytics
In pharmaceutical development, manufacturers must be able to demonstrate product robustness and deliver the intended product quality 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. QbD and process analytical technologies (PAT) are increasingly met with support and even preference among regulators in the biotech industry. [3]
Furthermore, the nature of biotechnology operations – with a variety of procedures and steps, advanced sampling techniques, sensor technologies and analyzers – results in large complex datasets with underlying multivariate interactions. Multiple operational parameters along with raw materials that impact process performance and product quality and interact with each other can make optimizing a process precarious.
Optimization efforts aimed at improving product yield or productivity must be carefully monitored for any possible negative impact on a product’s safety and/or efficacy. That requires a tool which can deal with complexities effectively and obtain the relevant information from these interconnected data sets. In the last two decades, multivariate data analysis (MVDA) has risen as the go-to technique for achieving such understanding.
In addition, establishing an acceptable design space is a core element of QbD, making Design of Experiments (DOE) an important tool for implementing QbD in bioprocess development.
Steps for Quality by Design Process Development
A QbD development process may include steps such as:
1. Quality Target Product Profile (QTTP): Identify target product profile that illustrates the use, safety and efficacy of the product
2. Critical Quality Attributes (CQAs): Determine a target profile to use as a quantitative surrogate for aspects of clinical safety and efficacy of the product during development.
3. Risk assessments: Link raw material attributes and critical process parameters (CPP) to CQAs, and perform risk assessments.
4. Design space: Develop a design space (using design of experiments)
5. Control strategy: Design and implement a control strategy
6. Continual improvement: Manage product lifecycle including continual improvement
Figure 1: The Steps in Implementing QBD approach. Source: Implementation of Quality by Design (QbD) Principles in Regulatory Dossiers of Medicinal Products in the European Union (EU)
Using MVDA To Create Models
Establishing the process parameters and critical quality attributes can be done using MVDA. Multivariate data analysis applied to early bioprocess development data can increase understanding of cell cultivation and other processes and fulfill the risk assessment and CQA development steps for QbD.
Principal component analysis can help identify any batch deviations (even if previously unidentifiable) and establish the model for well-controlled processes.
Multivariate data analysis can be used to analyze early development data to reveal relevant information for later development and scale-up.
For example, optimizing processes for biologics (such as biosimilars) at full scale is impractical due to high costs, limited resources, and the work needed to evaluate hundreds of independent variables. A viable alternative to support process design in-line with ICH guidelines is to develop a scaled-down model (SDM) that represents the proposed large-scale commercial process, backed up by a qualification process that confirms the SDM has a predictable relationship with the full-scale process.
Several approaches can be used to qualify scale-down models, including risk analysis, tests, quality range approach and equivalence, but these are limited by individual CQA or CPP comparability and therefore cannot capture the linkage and relationships among variables. However using MVDA allows you to compare the full-scale process and SDM by projecting the multidimensional datasets into a few principle components to enable exploration of linkage between variables. MVDA has been widely used to evaluate comparability in cell culture processes. [4]
SIMCA® MVDA software can be used to generate a batch level partial least squares (PLS) model based on multiple batches of cell culture process datasets at the full scale.
The statistical process control charts of a well-controlled batch process can be used to successfully predict the performance of a small-scale culture. Here is an example:
Figure 2. The scores of PC1 over time with 3 standard deviations from full-scale marked as red dotted lines: (a) Real data from full-scale production, (b) predicted performance of the scale-down model based on a PLS batch evolution model.
Using a QbD Approach for DOE Supports ICH Q8 Compliance
Determining what the acceptable ranges for a 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.
Of course, one way for a company to know what the stability of a formula over time will be is to measure it over time. 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 Experiments 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 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 way will determine what the acceptable variations or levels in the critical quality attributes can be.
Watch the webinar: From Design of Experiments to Design Space Estimation
Using DOE as a Quality by Design Approach
Predicting formulation robustness requires a careful Design of Experiments that can 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 QbD 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® 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.
Read more about MODDE® Design of Experiments Software
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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?
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Sources
1. Horst, J.P., Turimella, S.L., Metsers, F. et al. Implementation of Quality by Design (QbD) Principles in Regulatory Dossiers of Medicinal Products in the European Union (EU) Between 2014 and 2019. Ther Innov Regul Sci 55, 583–590 (2021). https://doi.org/10.1007/s43441-020-00254-9
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. A.S. Rathore, N. Bhushan and S.Hadpe, Biotech Prog. 22 (2), pp.307-15 (March-April 2011).
4. Tsang VL, et al. Development of a scale down cell culture model using multivariate analysis as a qualification tool. Biotechnol Prog. 2014;30(1):152–60. https://doi.org/10.1002/btpr.1819
5. A. S. Rathore and R. Mhatre, Eds., Quality by Design for Biopharmaceuticals (John Wiley & Sons, Hoboken, NJ, 2009).
6. Nadpara, et al, Int. J. Pharm. Sci. Rev. Res., 17(2), 2012; no 04, 20-28 https://globalresearchonline.net/journalcontents/v17-2/04.pdf
7. Ashrani S, Goyal A, Vaishnav R. Quality by Design and Process Analytical Technology: Important Tools for Buliding Quality
in Pharmaceutical Products. Biomed J Sci &Tech Res 2(1)- 2018. BJSTR.MS.ID.000704. DOI: 10.26717/BJSTR.2018.02.000704