Film coating as a method to enhance the preparation of tablets from dimenhydrinate crystals

Functional in situ formed deep eutectic solvents improving mechanical properties of powders by enhancing interfacial interactions

As novel green solvents, deep eutectic solvent (DES) with distinct liquid properties has gained increasing interest in pharmaceutical fields. In this study, DES was firstly utilized for improving powder mechanical properties and tabletability of drugs, and the interfacial interaction mechanism was explored. Honokiol (HON), a natural bioactive compound, was used as model drug, and two novel HON-based DESs were synthesized with choline chloride (ChCl) and l-menthol (Men), respectively. The extensive non-covalent interactions were account for DES formation according to FTIR, ¹H NMR and DFT calculation. PLM, DSC and solid-liquid phase diagram revealed that DES successfully in situ formed in HON powders, and the introduction of trace amount DES (99:1 w/w for HON-ChCl, 98:2 w/w for HON-Men) significantly improve mechanical properties of HON. Surface energy analysis and molecular simulation revealed that the introduced DES promoted the formation of solid-liquid interfaces and generation of polar interactions, which increase interparticulate interactions, thus better tabletability. Compared to nonionic HON-Men DES, ionic HON-ChCl DES exhibited better improvement effect, since their more hydrogen-bonding interactions and higher viscosity promote stronger interfacial interactions and adhesion effect. The current study provides a brand-new green strategy for improving powder mechanical properties and fills in the blank of DES application in pharmaceutical industry.

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales

The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R²= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.

Application of machine learning to a material library for modeling of relationships between material properties and tablet properties

This study investigates the usefulness of machine learning for modeling complex relationships in a material library. We tested 81 types of active pharmaceutical ingredients (APIs) and their tablets to construct the library, which included the following variables: 20 types of API material properties, one type of process parameter (three levels of compression pressure), and two types of tablet properties (tensile strength (TS) and disintegration time (DT)). The machine learning algorithms boosted tree (BT) and random forest (RF) were applied to analysis of our material library to model the relationships between input variables (material properties and compression pressure) and output variables (TS and DT). The calculated BT and RF models achieved higher performance statistics compared with a conventional modeling method (i.e., partial least squares regression), and revealed the material properties that strongly influence TS and DT. For TS, true density, the tenth percentile of the cumulative percentage size distribution, loss on drying, and compression pressure were of high relative importance. For DT, total surface energy, water absorption rate, polar surface energy, and hygroscopicity had significant effects. Thus, we demonstrate that BT and RF can be used to model complex relationships and clarify important material properties in a material library.

Effect of lubricant on spreading of coating liquid on surface of tablets containing pancreatin

The objective of this study was to evaluate the spreading of the coating liquid on different tablets containing pancreatin and microcrystalline cellulose. The effects of the ratio of the components, the presence of magnesium stearate and the blending circumstances were investigated. The contact angle of the liquids on the different tablets did not change linearly. For the mixture containing 50% pancreatin, the deviation of the measured value from the predicted one was more than 25%. This deterioration was also detected for mixtures containing 1% lubricant, but the extent was lower and was not modified by change of the mixing circumstances. This phenomenon was explained by the special microstructure of the surface of the tablet. This was predicted from the spreading coefficient, calculated from the surface free energy. The enrichment of pancreatin on the surface was preferred in binary mixtures. The spreading of magnesium stearate was most preferred for the powder mixture, and thus prediction of the properties of the tablet was easier for these mixtures. The extent of the effect of this excipient on the surface properties was very wide-ranging. The change in the spreading of the coating liquid was significant; however, the change in the work of friction was negligible.