Application of Multivariate Adaptive Regression Splines (MARSplines) Methodology for Screening of Dicarboxylic Acids Cocrystal Using 1D and 2D Molecular Descriptors - Publication - MOST Wiedzy

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Application of Multivariate Adaptive Regression Splines (MARSplines) Methodology for Screening of Dicarboxylic Acids Cocrystal Using 1D and 2D Molecular Descriptors

Abstract

Dicarboxylic acids (DiAs) are probably one of the most popular cocrystals formers. Due to the high hydrophilicity and non-toxicity, they are promising solubilizes of active pharmaceutical ingredients (APIs). Although DiAs appear to be highly capable of forming multicomponent crystals with various compounds, some systems reported in the literature are physical mixtures the solid state without forming stable intermolecular complex. In this study an accurate cocrystals screening model was developed based on the MARSplines (Multivariate Adaptive Regression Splines) methodology and easily computable descriptors driven simply from the SMILES codes. Additionally, the dataset was enriched with several new mixtures of sulfamethazine. As it was demonstrated, this sulfonamide can form new multicomponent crystals with oxalic, malonic and maleic acids. In the case of the latter system, a significant 10-fold solubility advantage was observed. The whole dataset comprised 608 cocrystals and 104 systems hardy miscible in the solid state, denoted as simple eutectics. The final 7-factor equation was subjected to external and internal validation procedures, which indicated its high predicting power. The reliability of the proposed approach can be illustrated by the proper classification probability of cocrystals reaching 91%. The classification quality of simple binary eutectics was found to be only slightly worse (TN%=81%).

Citations

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Authors (6)

  • Photo of  Tomasz Jeliński

    Tomasz Jeliński

  • Photo of  Julia Słabuszewska

    Julia Słabuszewska

  • Photo of  Dorota Ziółkowska

    Dorota Ziółkowska

  • Photo of  Karina Mroczyńska

    Karina Mroczyńska

  • Photo of  Piotr Cysewski

    Piotr Cysewski

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Details

Category:
Magazine publication
Type:
Magazine publication
Published in:
CRYSTAL GROWTH & DESIGN
ISSN: 1528-7483
Publication year:
2019
DOI:
Digital Object Identifier (open in new tab) 10.1021/acs.cgd.9b00318
Verified by:
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