The biopharmaceutical classification system (BCS) is now well established and utilized for the development and biowaivers of immediate oral dosage forms. The prediction of BCS class can be carried out using multilabel classification. Unlike single label classification, multilabel classification methods predict more than one class label at the same time. This paper compares two multilabel methods, binary relevance and classifier chain, for provisional BCS class prediction. Large data sets of permeability and solubility of drug and drug-like compounds were obtained from the literature and were used to build models using decision trees. The separate permeability and solubility models were validated, and a BCS validation set of 127 compounds where both permeability and solubility were known was used to compare the two aforementioned multilabel classification methods for provisional BCS class prediction. Overall, the results indicate that the classifier chain method, which takes into account label interactions, performed better compared to the binary relevance method. This work offers a comparison of multilabel methods and shows the potential of the classifier chain multilabel method for improved biological property predictions for use in drug discovery and development.
87 - 102
BCS, classification, in silico, multilabel, oral absorption, permeability, solubility, Administration, Oral, Algorithms, Biopharmaceutics, Caco-2 Cells, Chemistry, Pharmaceutical, Computer Simulation, Drug Discovery, Humans, Imaging, Three-Dimensional, Models, Theoretical, Permeability, Regression Analysis, Reproducibility of Results, Solubility