1. Construction of Discrimination Models of Cationic Drugs for Phospholipidosis Induction Potential by Using Interaction Data with Immobilized Artificial Membrane as Well as Physicochemical Properties.
- Author
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Iwakuma, Yoshie and Kuroda, Yukihiro
- Subjects
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FISHER discriminant analysis , *ARTIFICIAL membranes , *IONIC interactions , *SUPPORT vector machines , *HYDROPHOBIC interactions - Abstract
Accurate prediction of the phospholipidosis–induction risk of drugs at early stages is important in drug development. So far, discrimination models for predicting the induction risk of cationic drugs have been proposed, but it is still challenging to accurately predict the risk of cationic drugs with intermediate hydrophobicity (log P). In this study, we introduced a parameter (Δlog k 40) reflecting not only hydrophobic interaction but also interactions with the polar headgroup between cationic drugs and phospholipids, obtained with liquid chromatography using an immobilized artificial membrane column. The parameter was used along with other physicochemical properties as features to construct discrimination models. Linear discriminant analysis, the modified Mahalanobis discriminant analysis, support vector machine, and random forest were employed for model construction. The results showed that all discrimination models exhibited good predictive performance, with the modified Mahalanobis discriminant analysis and random forest providing the best results for cationic drugs, suggesting that the usefulness of the parameter reflecting complex interactions between cationic drugs and immobilized artificial membrane for constructing discrimination models to predict the induction risk. Furthermore, by applying the parameter as a feature in constructing discrimination models, we demonstrated an improvement in the predictive performance for drugs with intermediate hydrophobicity. [Display omitted] • Models to discriminate phospholipidosis induction risk of cationic drugs were made. • A feature representing interactions with immobilized artificial membrane are used. • The feature reflects both hydrophobic and ionic interactions with membrane. • A model allowed accurate prediction of drugs with intermediate hydrophobicity. • Considering complex interactions of drugs and membranes is useful for prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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