Novel Method for Constructing a Large-Scale Design Space in Lubrication Process by Using Bayesian Estimation Based on the Reliability of a Scale-Up Rule

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A reliable large-scale design space was constructed by integrating the reliability of a scale-up rule into the Bayesian estimation without enforcing a large-scale design of experiments (DoE). A small-scale DoE was conducted using various Froude numbers (X1) and blending times (X2) in the lubricant blending process for theophylline tablets. The response surfaces, design space, and their reliability of the compression rate of the powder mixture (Y1), tablet hardness (Y2), and dissolution rate (Y3) on a small scale were calculated using multivariate spline interpolation, a bootstrap resampling technique, and self-organizing map clustering. A constant Froude number was applied as a scale-up rule. Experiments were conducted at four different small scales with the same Froude number and blending time in order to determine the discrepancies in the response variables between the scales so as to indicate the reliability of the scale-up rule. Three experiments under an optimal condition and two experiments under other conditions were performed on a large scale. The response surfaces on the small scale were corrected to those on the large scale by Bayesian estimation using the large-scale results and the reliability of the scale-up rule. Large-scale experiments performed under three additional sets of conditions showed that the corrected design space was more reliable than the small-scale design space even when there was some discrepancy in the pharmaceutical quality between the manufacturing scales. This approach is useful for setting up a design space in pharmaceutical development when a DoE cannot be performed at a commercial large manufacturing scale.

Novel Method for Constructing a Large-Scale Design Space in Lubrication Process by Using Bayesian Estimation Based on the Reliability of a Scale-Up Rule

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