Computational intelligence models to predict porosity of tablets using minimum features.

The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs), and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE) scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC) (in percentage), granule size fraction (in micrometers), and die compaction force (in kilonewtons) as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1%) and symbolic regression (NRMSE =4%) as the best-performing methods, also exhibiting reliable predictive behavior when presented with a challenging external validation data set (best achieved symbolic regression: NRMSE =3%). Symbolic regression demonstrates the transition from the black box modeling paradigm to more transparent predictive models. Predictive performance and feature selection behavior of CI models hints at the most important variables within this factor space.

Effect of roll-compaction and milling conditions on granules and tablet properties.

Dry granulation is an agglomeration process used to produce size-enlarged particles (granules), improving the handling properties of powders such as flowability. In this process, powders are compacted using a roll press to produce ribbons, which are milled in granules used further in the tableting process. The granule and tablet properties are influenced by the existence of different designs of the roll compactors, milling systems and the interaction between process parameters and raw material properties. The main objective of this work was to investigate how different roll-compaction conditions and milling process parameters impact on ribbons, granules and tablet properties, highlighting the role of the sealing system (cheek plates and rimmed roll). In this context, two common excipients differing in their mechanical behaviour (MCC and mannitol) are used. The study is based on the analysis of granule size distribution together with the characterization of loss of compactability during die compaction. Results show that the tensile strength of tablets is lower when using granules than when the raw materials are compressed. Moreover, the plastic material (MCC) is more sensitive than the brittle one (mannitol). Regarding the roll-force, it is observed that the higher the roll force, the lower the tensile strength of tablets from granulated material is. These findings are in agreement with the literature. The comparison of sealing systems shows that the rimmed-roll system leads to slightly stronger tablets than the use of cheek plates. In addition, the use of the rimmed-roll system reduces the amount of fines, in particular when high roll force is applied. Overall, it can be concluded that roll-compaction effect is predominant over the milling effect on the production of fines but less significant on the tablet properties. This study points out that the balance between a good flowability by reducing the amount of fines and appropriate tablet strength is achieved with rimmed-roll and the highest roll-force used.