(664b) Design Space Identification for Reliable Batch Blending Operation Scale-up Using Empirical Correlations in Pharmaceutical Manufacturing

Batch blending is an important step in pharmaceutical drug product manufacturing for preparing the final formulation blend that goes into the drug product dosage form. Typically, the batch process is initially explored and developed at a small (lab) scale and then scaled up to the intended commercial scale. Currently, there is a gap in the availability of rigorous scale-up criteria for batch blending. A commonly used scale-up factor for blending operation is ‘K-value’ which is a scale-independent parameter to quantify the effect of lubrication on tablet physical properties [1, 2]. Kushner [3] proposed empirical formulae, derived through experimental knowledge, to calculate the K-value.

Given the straightforward nature of the empirical formula [3], we adopted the K-value approach to scale-up a batch blending operation. We assume that K-value can be used to ensure both comparable lubrication and blend content uniformity across different scales. In this work, we use an early-stage process development case study to demonstrate the use of K-value for batch blending process scale-up. An optimization routine was formulated to design the blending process at the pilot and commercial scales based on lab scale information – acceptable K-value range – to achieve maximum operational flexibility for process scale-up. Viable design spaces were successfully identified across various bin sizes through the optimization framework and tablets generated using the blend demonstrated acceptable quality attributes. Through this work, we highlight that while elegant algorithms and workflows exist for obtaining maximal design spaces in the form of mathematical functions of control variables [4-7], a simple optimization formulation like the one discussed here can be sufficient to obtain acceptable design spaces for batch blending scale up that are easily implementable while guaranteeing tablet quality attributes.

References:

[1] Kushner, J. and H. Schlack, Commercial scale validation of a process scale-up model for lubricant blending of pharmaceutical powders. International Journal of Pharmaceutics, 2014. 475(1): p. 147-155.

[2] Nassar, J., et al., Lubrication empirical model to predict tensile strength of directly compressed powder blends. International Journal of Pharmaceutics, 2021. 592: p. 119980.

[3] Kushner, J., Incorporating Turbula mixers into a blending scale-up model for evaluating the effect of magnesium stearate on tablet tensile strength and bulk specific volume. International Journal of Pharmaceutics, 2012. 429(1): p. 1-11.

[4] Grossmann, I.E. and C.A. Floudas, Active constraint strategy for flexibility analysis in chemical processes. Computers & Chemical Engineering, 1987. 11(6): p. 675-693.

[5] Boukouvala, F., F.J. Muzzio, and M.G. Ierapetritou, Design Space of Pharmaceutical Processes Using Data-Driven-Based Methods. Journal of Pharmaceutical Innovation, 2010. 5(3): p. 119-137.

[6] Ochoa, M.P., et al., Novel flexibility index formulations for the selection of the operating range within a design space. Computers & Chemical Engineering, 2021. 149: p. 107284.

[7] Zhao, F., et al., Novel formulations of flexibility index and design centering for design space definition. Computers & Chemical Engineering, 2022. 166: p. 107969.