Application of the Gradient boosted tree approach for thin film classification based on disintegration time

Abstract

Thin films are polymeric strips that disintegrate in the oral cavity and consist of a film-forming agent and an active pharmaceutical ingredient (API). Generally, thin films disintegrate within seconds, but their composition can be modified to allow slower disintegration and release of the loaded API, depending on the properties of the film. Research into various aspects of oral thin films is progressing rapidly, but thin films are also being discussed in the context of a broader range of other dosage forms, such as carrier for multiparticulates or nano-based dosage forms and for the fixed-dose combinations (Turković et al., 2022). Large amounts of data are being generated over the years, so integrating machine learning algorithms can be beneficial to gain more in-depth knowledge about the thin film properties and interactions between film constituents. Gradient boosted tree is one of machine learning tools that perform regression or classification by combining the outputs from individual decision trees. This work is aimed to explore the possibility of integrating a machine learning approach in evaluation of experimental data obtained by films characterization. Potential application of Gradient boosted trees for thin films characterization based on their disintegration properties as film critical quality attribute was investigated.