(T1130-06-40) Extending the Tabletability Mixture Model to Poorly Compressible APIs

Purpose: Tablet products must possess sufficient mechanical strength to endure stresses during transportation and handling.¹ Therefore, tabletability, i.e., tablet tensile strength (σ) vs. pressure (P), is a critical property in the development of a tablet product. Recently, a mixture model based on the Vreeman-Sun equation with a power mixing rule was developed to reliably predict the tabletability of mixtures from their individual components.² This model has the potential to enable more efficient, material-sparing development of high-quality tablet products (manuscript under review). However, its application is currently limited to components with attainable tabletability profiles and cannot accommodate poorly compressible active pharmaceutical ingredients (APIs). In this work, we aim to broaden the applicability of the model by developing an approach to incorporate poorly compressible APIs into the predictive framework.

Methods: Acetaminophen (APAP) and L-alanine (ALA) were selected as model APIs. Microcrystalline cellulose (MCC), hypromellose (HPMC), lactose monohydrate (LM), and crospovidone were used as excipients. Binary mixtures of API and MCC at various ratios, along with realistic formulations containing 20% API, 50% MCC or HPMC, 25% LM, and 5% crospovidone, were prepared. Cylindrical compacts of pure excipients, binary mixtures, and formulations were prepared by compression over a pressure range of 15–450 MPa. MgSt spray was applied to punch tips and die wall as an external lubricant before each compaction. Tablet tensile strength was determined from the maximum breaking force and tablet dimensions. Tabletability prediction was performed as follows. Tabletability mixture model: The three tabletability parameters (σ_max, α, β) of each individual component were obtained by fitting σ vs. P data with the Vreeman-Sun equation (1). Each of the three parameters of a given mixture (X_m) were predicted from those of individual components (X_n) based on the mixture composition (C_n) using the power law mixing rule (2):
σ=σ_maxexp(αW(-exp(-P/β-1)))               (1)
X_>m=X₁^C1×X₂^C2×…×X_n^Cn                    (2)
Approaches for predicting mixtures of poorly compressible APIs: For binary mixtures, the model uses data from the pure excipient and a single mixture of the poorly compressible API and excipient as a starting point to predict the tabletability of any other binary mixtures of the same API and excipient (Figure 1). For all formulations, the model estimates the three "hypothetical" tabletability parameters of the poorly compressible API by applying the power law mixing rule to a series of binary mixtures with MCC. These hypothetical tabletability parameters of the API were then used to predict the tabletability of any formulations containing the API. The predictive performance of the model was evaluated by comparing the experimental data with the predictive profile.

Results: Predictions based on MCC and a binary mixture containing less than 80% API showed good agreement with experimental results, indicating strong predictive performance. However, when using MCC and a mixture with 80% API loading, the model underestimated the tabletability of other binary mixtures, likely due to the percolation effect. This suggests that binary mixtures with extreme compositions should be avoided as starting points for prediction, as they may lead to significant inaccuracies. Additionally, this approach allows for the estimation of a "hypothetical" tabletability profile for the API. However, depending on the starting binary mixture, variations in predicted profiles may occur due to both predictive and experimental errors. A more consistent and reasonable “hypothetical” profile can be obtained by fitting data from pure MCC and all binary mixtures using the power law mixing rule (Figure 2). The σ_max and β parameters of the poorly compressible APIs generally followed this rule. While no clear trend was observed for the α parameter, which has been shown to have a minimal impact on the overall tabletability prediction (manuscript under review). The three parameters of the poorly compressible APIs can be extrapolated from the fitted curve. Using these extrapolated tabletability parameters of the APIs, the predictive performance of each API was evaluated in two representative formulation systems. The predicted tabletability profiles showed good agreement with experimental data, with a slight deviation observed in the APAP-HPMC-LM-crospovidone formulation (Figure 3).

Conclusion: Our results demonstrated that using tabletability data from a pure excipient and an API–excipient mixture, we can accurately predict the tabletability of other binary mixtures. However, extremely high API loadings in the binary mixture should be avoided to minimize the risk of percolation effects. By extrapolating from MCC and a series of MCC–API mixtures, “hypothetical” tabletability parameters for poorly compressible APIs can be derived, which led to good predictions in realistic formulation systems. Consequently, by enabling the prediction of tabletability for formulations containing APIs that cannot form intact tablets on their own, this approach enhances the versatility of the mixture model in tablet formulation development.

References: 1. Jain, S. Mechanical properties of powders for compaction and tableting: an overview. Pharm. Sci. Technol. Today 2, 20–31 (1999).
2. Vreeman, G. & Sun, C. C. A powder tabletability equation. Powder Technol. 408, 117709 (2022).

Acknowledgements: We thank the National Science Foundation for support through the Industry University Collaborative Research Center (IUCRC) grant IIP-2137264, Center for Integrated Materials Science and Engineering for Pharmaceutical Products (CIMSEPP).


Figure 1. Experimental tabletability data (open squares) and predictive tabletability profiles (dashed lines) of binary mixtures of MCC and APAP. The tabletability profiles of binary mixtures (dashed lines) were predicted from the pure MCC and a) 80%MCC-20%APAP, b) 60%MCC-40%APAP, c) 50%MCC-50%APAP, d) 40%MCC-60%APAP, e) 20%MCC-80%APAP (solid lines).


Figure 2. The a) σ_max, b) α, and c) β of MCC and its binary mixtures with APAP (top) and ALA (bottom). The hypothetical parameters of the pure APIs were predicted from MCC and its mixtures using the power law mixing rule.


Figure 3. The experimental tabletability data (points) and predicted tabletability profile (dashed lines) of a) APAP formulations and b) ALA formulations.