Liquisolid systems (LS) represent a formulation approach where liquid drug or its dispersion is transformed into a powder with good flowability and compactibility, leading to enhanced drug dissolution and bioavailability. Many research groups have focused on the preparation and investigation of LS, leading to a higher need for comprehensive evaluation of factors impacting LS characteristics. The aim of this work was to investigate the applicability of machine learning algorithms in the LS evaluation, using data mined from published literature, and provide an insight into critical factors governing the liquisolid system performance. The dataset was prepared using publication search engines and relevant keywords, with a total of 425 formulations included in the database. The database focused on preparation methods, formulation parameters, and liquisolid system characteristics. Subsequently, critical properties of the liquisolid system, i.e. flowability, compact hardness, and drug dissolution, were analyzed using machine learning algorithms, including Gradient Boosting, Adaptive Boosting and Random Forest. In addition to conventional preparation methods and excipients, novel technologies (fluid bed preparation, extrusion/spheronization) and materials (Neusilin (R), Fujicalin (R), and Syloid (R)) enhanced the properties of liquisolid systems. The analysis revealed that formulation factors, such as carrier and coating agent type and content, liquid phase load, model drug type and content, as well as preparation method, significantly influenced liquisolid system characteristics. The models developed exhibited high prediction accuracy when applied on test data (higher than 80 %). This indicates that the machine learning models may provide an insight into the critical attributes affecting the LS performance and may be used as a valuable tool in the development and optimization of these samples.

An understanding of material properties and processing effects on solid dosage forms performance is required within the Quality-by-design approach to pharmaceutical development. Several research groups have developed mathematical approaches aiming to facilitate the selection of formulation composition and the manufacturing technology. These approaches are based on material particulate, bulk and compression-related properties. This paper provides theoretical assumptions and a critical review of different mathematical approaches for processability characterization of powders and multiparticulate units. Mathematical approaches have mainly been developed for directly compressible materials, but sometimes other manufacturing technologies, such as roller compaction and wet granulation, are also considered. The obtained compact tensile strength has been implemented in the majority of approaches, as an important characteristic describing compact mechanical properties. Flowability should be also evaluated, since it affects sample processability. Additionally, particle size and shape, material density and compressibility, compactibility and tabletability profiles have been also distinguished as relevant properties for solid dosage form development. The application of mathematical approaches may contribute to the mechanistic understanding of critical material attributes and facilitate dosage form development and optimization. However, it is essential to select the appropriate one, based on the intended dosage form characteristics, in order to ensure that all relevant powder/multiparticulate units characteristics are implemented and critically evaluated.