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From Traditional Statistics to Adaptive Multivariate Models: Exploring the Role of AI in Modern Pharmaceutical
Mario Stassen1, Matt Schmucki2, Francisco Valero3
1Department of Life Sciences Innovation, Stassen Pharmaconsult BV, Aerdenhout, The Netherlands. mmhstassen@gmail.com.
PDA Journal of Pharmaceutical Science and Technology
|July 31, 2026
Summary
Traditional statistical process control (SPC) methods struggle with modern pharmaceutical data. Advanced AI-based models offer improved process understanding and quality by addressing limitations of classical approaches.
Area of Science:
- Pharmaceutical Manufacturing
- Process Analytical Technology
- Data Science
Background:
- Modern pharmaceutical manufacturing generates high-frequency, high-dimensional, and dynamic data.
- Classical statistical process control (SPC) methods (e.g., Shewhart, Nelson's rules) have limitations due to violated assumptions of independence, stationarity, and linearity.
- These limitations lead to inconsistent diagnostics and high false-alarm rates in bioprocesses.
Purpose of the Study:
- To examine the limitations of classical statistical methods in pharmaceutical manufacturing.
- To explore the potential of artificial intelligence (AI)-based models for analyzing complex bioprocess data.
- To propose a framework for regulatory acceptance of advanced modeling approaches.
Main Methods:
- Review of classical statistical process control (SPC) limitations.
- Analysis of the multivariate and dynamic nature of pharmaceutical processes.
- Examination of fixed deterministic and probabilistic AI-based models for process analytics.
Main Results:
- Classical SPC methods are constrained by data assumptions frequently violated in modern bioprocessing.
- AI-based models offer a potential solution to analytical gaps in pharmaceutical data analysis.
- A structured framework is proposed for evaluating and accepting advanced modeling techniques.
Conclusions:
- Advanced, model-based approaches are necessary to overcome limitations of traditional SPC in pharmaceutical manufacturing.
- AI-based models can enhance process understanding, product quality, and manufacturing efficiency.
- Evidence-based regulatory acceptance of advanced modeling is crucial for industry advancement.
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