Utilization of AI in pharmaceutical development enables efficient management of complex data, prudent selection of tests, and rapid prediction of quality-related parameters. Yet mere speed does not ensure a model’s reliability. Implementation of the Quality by Design (QbD) concept establishes a thorough development process, clarifying what the model is used for, what results would be considered valid, and how the risk of incorrect predictions would be managed.
Let us take an example of production data collected through a two-year process at a particular stage of production. The data might be used to make predictions using a machine learning model. However, if an unexpectedly humid production week occurs or the excipient lot is changed, the conditions may change completely. Therefore, the problem is not whether a prediction is produced but whether it is valid for the decision-making process.
This is where the concept of QbD methodology is brought to the surface. QbD provides a scientific, risk-based understanding of how AI models should be applied in decision-making processes that impact product quality.
How AI is being used in pharmaceutical process optimization
Artificial intelligence applied in drug development is a broad concept that encompasses many different uses. These include all processes from initial discovery through production to post-marketing activities.
AI in drug development can be illustrated through the following examples:
- Assisting in experiment choice and selection in formulation and manufacturing process development.
- Recognizing relationships in multivariate process information.
- Aiding predictive models for the attributes of products and processes.
- Detecting deviations, tendencies, or patterns in data so that expert analysis can be requested.
In regulated pharma applications, it is crucial to pay attention to the model’s intended purpose, the relevance of the data used, the validity of the predictions, the governance policies in place, and the model’s lifecycle management, apart from the algorithm itself.
The meaning of Quality by Design in pharmaceutical procedures
Quality by Design defines drug product development as a systematic approach that starts with predetermined objectives and emphasizes understanding of the product and the process, applying modern scientific and quality risk management methods.
QbD Key Concepts Include:
- Quality Target Product Profile (QTPP): An expected summary of all the desired quality characteristics that the product should have.
- Critical Quality Attributes (CQAs): Physical, chemical, microbiological, or biological properties. These attributes must be controlled within the correct limits, distributions or ranges to assure the quality of the product.
- Critical material properties and critical process parameters: Process and input variables affecting CQAs.
- Risk assessment: A methodology to identify, evaluate and rank Quality risks.
- Control Strategy: A planned system for implementing controls that is based on an understanding of the product and the manufacturing process.
AI and Quality by Design
In the context of AI, QbD will not replace AI, nor will AI replace QbD. The former provides rationale for development and an environment for QA, while the latter is used for data-heavy analysis in that framework.
This method renders the AI-driven pharmaceutical process optimization considerably more structured. Instead of asking the model to “optimize the process,” a person can pose a more specific question.
What regulatory changes mean for the future
Regulatory authorities are starting to pay attention to whether AI-generated output can be trusted for its intended purpose.
Draft FDA guidance issued in January 2025, titled Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, is a non-binding recommendation.
It presents a seven-step, risk-based model framework to define and assess model credibility for a context of use.
The steps suggested are:
- Define the question of interest.
- Define the model’s context of use.
- Examine a model’s risks.
- Design the credibility assessment.
- Execute the plan.
- Write down results and deviations.
- Determine whether the model can be used for the required purpose.
For teams in the pharmaceutical production field, the biggest takeaway is simple. The reliability of any model should be evaluated based on the choice it helps to make and the implications of a potential mistake.
Conclusion
The role of AI in pharmaceutical manufacturing is not just faster analysis. Its real worth lies in enabling better scientific and quality decisions within a clear scope.
Quality by Design gives you that scope. As AI tools improve, that discipline will continue to matter: know what you want the model to do, know when the evidence ends, and ensure the process is safe when the answer is unknown.
Learn more about how Spoclearn’s Quality by Design training course can provide a deeper understanding of these principles.
FAQs
1. How does AI contribute to Quality by Design?
AI can assist with various data-oriented QbD tasks, including pattern recognition in experimental data, selecting experiments to conduct, performing multivariate analyses, and interpreting data while maintaining scientific reasoning to identify CQAs.
2. Does AI have the capability to establish a design space?
AI has the potential to interpret data related to the design space either alongside designed experiments or in conjunction with the built-up mechanistic understanding; however, establishing a design space requires the applicant to submit adequate evidence and for it to be verified by the health authorities.
3. Is QbD certification necessary for AI in pharmaceutical applications?
There is no strict requirement for anyone working in pharmaceutical development using artificial intelligence (AI) tools to hold a QbD certification, but this can provide individuals with the required terminology and theories related to pharmaceutical development, risk management, understanding processes, and regulations.