(778b) Modeling the Effects of Material Properties on Tablet Compaction a Case Study for Development

Interest in pharmaceutical continuous manufacturing (PCM) is rapidly growing. As it has been extensively reported PCM can deliver enormous advantages, including faster product development using less material, smaller equipment, superior process control, optimized performance, and more reliable quality performance [1-5]. However, to achieve these advantages in full, we need to develop accurate models of the process able of accounting for effects of material properties. Such models can be used to support faster and more economical process design as well as better process and quality control. As the pharmaceutical industry modernizes its manufacturing practices and incorporates more efficient processing approaches, it is important to reevaluate which process design elements affect product quality and the means to study these systems. The purpose of this presentation is to provide insight on a methodology to correlate and link the effect of raw material properties to equipment and process performance from using both data-driven and semi-empirical models. Such methodology aims at providing modeling tools to predict process performance in silico, minimizing the amount of experiments and/or narrowing the experimental scope. The presentation will include a case study where this protocol was applied to the characterization of tablet compression.

Lubricated blends of three (3) commonly used in the pharmaceutical industry: Microcrystalline Cellulose (Avicel) 101, Microcrystalline Cellulose (Avicel) 301, and Milled Lactose. Blends where made as formulations with varying levels of the lubricant, Magnesium Stereate, ranging from 0.25-1.5%. Material characterization was performed for all twelve (12) blends. The characterization included compressibility, permeability, density, particle size, and cohesion measurements. The characterization was part of the development of a reduce size Raw Material Properties Database (RMPD), which was later used to develop correlations between semi-empirical unit operation model parameters and material properties. Using Principal Component Analysis, the raw material properties were collapsed to two (2) major principle components, in order to understand the operating material space. All blends were then compressed using a two by three (2x3) experimental design, varying the fill depth and tablet thickness of the tablets produced, respectively. Tablet properties (e.g., weight, hardness, thickness, and compression force) were collected for all tablets and collected in a results matrix.

Results from the PCA analysis showed that raw material properties were slightly affected by the addition of the lubricant to the three (3) blends of Microcrystalline Cellulose (Avicel) 101, Microcrystalline Cellulose (Avicel) 301, and Milled Lactose. From the results matrix, we regressed the constant model coefficients for the Kuentz Equation, which is a model relating tablet hardness to changes in porosity. Two coefficients (i.e., the maximum tensile strength and the critical relative density) were regressed for each formulation’s tablet compression profile. We evaluated several empirical models to correlate the regressed coefficient values to the original blend material properties from the RMPD. Models evaluated included Regression PCA (R-PCA), Partial Least Square (PLS), and simple linear regressions. From the analysis, we found that each of the coefficients regressed related to a material property from the RMPD. The models will be presented in this presentation. The empirical model between blend material properties and the regressed constant values for the Kuentz equations showed less than 8% error for the formulations. The parity plots indicated a coefficient of variability (R-Squared) over 95% for both empirical model regressions along with p-values under 0.05 for all coefficients in the regression.

The results from this study indicate that correlations between material properties and semi-empirical equation regression constants are sufficiently accurate. This proof of concept of developing correlations between raw material properties and unit operation models can aid in process development, especially in the area of design space characterization and robustness analysis. Overall, we have shown with this approach that it is possible to develop these correlations and have predictive capabilities of process performance based on material property values.

References:

1. Plumb, K., Continuous Processing in the Pharmaceutical Industry: Changing the Mind Set. Chemical Engineering Research and Design, 2005. 83(6): p. 730-738.

2. Ooi, S.M., et al., Continuous processing and the applications of online tools in pharmaceutical product manufacture: developments and examples. Therapeutic Delivery, 2013. 4(4): p. 463-470.

3. Rantanen, J. and J. Khinast, The Future of Pharmaceutical Manufacturing Sciences. Journal of Pharmaceutical Sciences, 2015: p. n/a-n/a.

4. Lee, S., et al., Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production. Journal of Pharmaceutical Innovation, 2015. 10(3): p. 191-199.

5. Mascia, S., et al., End-to-End Continuous Manufacturing of Pharmaceuticals: Integrated Synthesis, Purification, and Final Dosage Formation. Angewandte Chemie International Edition, 2013. 52(47): p. 12359-12363.