ChemEngineering 2025, 9(3), 60; https://doi.org/10.3390/chemengineering9030060 (registering DOI) - 1 Jun 2025
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Introduction: The use of response surface designs for drug formulation is highly warranted nowadays. Such smart designs reduce the number of required experiments compared to full-factorial designs, while providing highly accurate and reliable results. Aim: This study compares the effectiveness of
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Introduction: The use of response surface designs for drug formulation is highly warranted nowadays. Such smart designs reduce the number of required experiments compared to full-factorial designs, while providing highly accurate and reliable results. Aim: This study compares the effectiveness of two of the most commonly used response surface designs—Central Composite Design (CCD) and D-optimal Design (DOD)—in modeling a polymer-based drug delivery system. The performance of the two designs was further evaluated under a challenging scenario where a central point was deliberately converted into an outlier. Methods: Both methods were assessed using ANOVA, R-squared values, and adequate precision, and were assessed through an experimental validation point. Results: Both models demonstrated statistical significance (p-value < 0.05), confirming their ability to describe the relationships between formulation variables and critical quality attributes (CQAs). CCD achieved higher R-squared and predicted R-squared values compared to DOD (0.9977 and 0.9846 vs. 0.8792 and 0.7858, respectively), rendering it as the superior approach in terms of modeling complex variables’ interactions. However, DOD proved to be more predictive as it scored a lower percentage relative error. Conclusion: The demonstrated resilience of both models, despite the introduction of an outlier, further validates their utility in real-world applications, instead of the exhaustive full-factorial design.
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