Data-driven insights into the properties of liquisolid systems based on machine learning algorithms

Abstract

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®, Fujicalin®, and Syloid®) 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.