Modelling the Evolution of Pore Structure during the Disintegration of Pharmaceutical Tablets.

Pharmaceutical tablet disintegration is a critical process for dissolving and enabling the absorption of the drug substance into the blood stream. The tablet disintegration process consists of multiple connected and interdependent mechanisms: liquid penetration, swelling, dissolution, and break-up. One key dependence is the dynamic change of the pore space in a tablet caused by the swelling of particles while the tablet takes up liquid. This study analysed the changes in the pore structure during disintegration by coupling the discrete element method (DEM) with a single-particle swelling model and experimental liquid penetration data from terahertz-pulsed imaging (TPI). The coupled model is demonstrated and validated for pure microcrystalline cellulose (MCC) tablets across three porosities (10, 15, and 22%) and MCC with three different concentrations of croscarmellose sodium (CCS) (2, 5, and 8% w/w). The model was validated using experimental tablet swelling from TPI. The model captured the difference in the swelling behaviour of tablets with different porosities and formulations well. Both the experimental and modelling results showed that the swelling was lowest (i.e., time to reach the maximum normalised swelling capacity) for tablets with the highest CCS concentration, cCCS = 8%. The simulations revealed that this was caused by the closure of the pores in both the wetted volume and dry volume of the tablet. The closure of the pores hinders the liquid from accessing other particles and slows down the overall swelling process. This study provides new insights into the changes in the pore space during disintegration, which is crucial to better understand the impact of porosity and formulations on the performance of tablets.

Exploring the performance-controlling tablet disintegration mechanisms for direct compression formulations.

The design and manufacture of tablets is a challenging process due to the complex interrelationships between raw material properties, the manufacturing settings and the tablet properties. An important factor in formulation and process design is the fact that raw material and tablet properties drive the disintegration and dissolution performance of the final drug product. This study aimed to identify the mechanisms which control tablet disintegration for 16 different immediate-release placebo formulations based on raw material and tablet properties. Each formulation consisted of two fillers (47% each), one disintegrant and a lubricant. Tablets were manufactured by direct compression using four different combinations of the fillers microcrystalline cellulose (MCC), mannitol, lactose and dibasic calcium phosphate anhydrous (DCPA). The disintegration mechanism was primarily driven by the filler combination, where MCC/lactose tablets were identified as wettability controlled, MCC/mannitol tablets as dissolution controlled and DCPA-based tablets (MCC/DCPA and lactose/DCPA) as swelling controlled. A change of 2% in porosity for the wettability controlled tablets (MCC/lactose) caused a significant acceleration of the disintegration process (77% reduction of disintegration time), whereas for swelling controlled tablets (MCC/DCPA) the same porosity change did not considerably impact the disintegration process (3% change in disintegration time). By classifying these formulations, critical formulation and manufacturing properties can be identified to allow tablet performance to be optimised.

Quantification of swelling characteristics of pharmaceutical particles.

Particle swelling is a crucial component in the disintegration of a pharmaceutical tablet. The swelling of particles in a tablet creates stress inside the tablet and thereby pushes apart adjoining particles, eventually causing the tablet to break-up. This work focused on quantifying the swelling of single particles to identify the swelling-limited mechanisms in a particle, i.e. diffusion- or absorption capacity-limited. This was studied for three different disintegrants (sodium starch glycolate/SSG, croscarmellose sodium/CCS, and low-substituted hydroxypropyl cellulose/L-HPC) and five grades of microcrystalline cellulose (MCC) using an optical microscope coupled with a bespoke flow cell and utilising a single particle swelling model. Fundamental swelling characteristics, such as diffusion coefficient, maximum liquid absorption ratio and swelling capacity (maximum swelling of a particle) were determined for each material. The results clearly highlighted the different swelling behaviour for the various materials, where CCS has the highest diffusion coefficient with 739.70 µm2/s and SSG has the highest maximum absorption ratio of 10.04 g/g. For the disintegrants, the swelling performance of SSG is diffusion-limited, whereas it is absorption capacity-limited for CCS. L-HPC is both diffusion- and absorption capacity-limited. This work also reveals an anisotropic, particle facet dependant, swelling behaviour, which is particularly strong for the liquid uptake ability of two MCC grades (PH101 and PH102) and for the absorption capacity of CCS. Having a better understanding of swelling characteristics of single particles will contribute to improving the rational design of a formulation for oral solid dosage forms.

A predictive integrated framework based on the radial basis function for the modelling of the flow of pharmaceutical powders.

This study presents a modelling framework to predict the flowability of various commonly used pharmaceutical powders and their blends. The flowability models were trained and validated on 86 samples including single components and binary mixtures. Two modelling paradigms based on artificial intelligence (AI) namely, a radial basis function (RBF) and an integrated network were employed to model the flowability represented by the flow function coefficient (FFC) and the bulk density (RHOB). Both approaches were utilized to map the input parameters (i.e. particle size, shape descriptors and material type) to the flow properties. The input parameters of the blends were determined from the particle size, shape and material type properties of the single components. The results clearly indicated that the integrated network outperformed the single RBF network in terms of the predictive performance and the generalization capabilities. For the integrated network, the coefficient of determination of the testing data set (not used for training the model) for FFC was R2=0.93, reflecting an acceptable predictive power of this model. Since the flowability of the blends can be predicted from single component size and shape descriptors, the integrated network can assist formulators in selecting excipients and their blend concentrations to improve flowability with minimal experimental effort and material resulting in the (i) minimization of the time required, (ii) exploration and examination of the design space, and (iii) minimization of material waste.