QSAR Modeling and Prediction of Triptan Binding Affinities

QSAR Modeling and Prediction of Triptan Binding Affinities

Lucas Alland, Solomon H. Jacobson
DOI: 10.4018/IJQSPR.2021040102
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Abstract

The purpose of this study was to use quantitative structure-activity relationships (QSARs) to identify new triptan molecules that selectively bind to h 5-HT1B and h 5-HT1D to a greater extent than to the similar h 5-HT1A receptor in order to identify novel compounds that could lead to an alternative and potentially superior migraine relief drug. Due to its possibility in migraine abortive properties, binding affinities to h 5-HT1F were also modeled. Binding affinities for 12 different triptan drugs and structurally similar substances were compiled from the literature, and descriptors were generated for those and other potential drug candidates using a variety of programs. The most significant descriptors were identified using a stepwise model, and the final QSARs were built for each activity with those descriptors, and a neural network. QSARs for all four activities were validated using a holdback method and were all found to be highly accurate. With these QSARs, activities of novel compounds similar to triptan drugs were predicted and three potential drug candidates were suggested.
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Methods

Descriptor Generation and Selection

The binding affinities of 12 different triptan migraine relief drugs and structurally similar substances to four serotonin receptors, 1B, 1D, h 5-HT1A 1A, and 1F, were identified from published literature (John et al., 1999; Ramadan, Skljarevski, Phebus, & Johnson, 2003). Binding affinities for all four receptors were found for 9 of the compounds. For the other 3 molecules, data were only available for the 1B, 1D, and 1F receptors. Using a variety of programs, specifically, Spartan (Wavefunction Inc., 2020) using parametric method 3 (PM3), PhysChem (ACD/Labs, 2020), EPI Suite (EPA Office of Pollution Prevention Toxics, 2012), and E-Dragon (Tetko et al., 2005; Virtual Computational Chemistry Laboratory, 2005), 1708 descriptors were calculated. All descriptors with less than a 1% standard deviation were eliminated as they were not indicative of the activity differences between the chemicals, leaving 1381 descriptors deemed significant. All molecular descriptors that were calculated can be found in the Supporting Information. These descriptors were entered into SAS JMP 15 (SAS Institute Inc., 2020). Using this program, a stepwise model for each activity was generated. The most significant descriptors by F Ratio before (initial) and after (final) building the model were ranked by highest F Ratio. A selection of this information is presented in Tables 2 and 3.

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