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HPLC Gradient Retention of Tryptophan and its Metabolites on Three Stationary Phases in Context of Lipophilicity Assessment
This paper is a continuation of lipophilicity research on 14 compounds (tryptophan, kynurenine pathway products, auxin pathway products, serotonin pathway products, tryptamine, as well as two synthetic auxin analogs): indole-2-acetic acid sodium salt (IAA), serotonin, 5-hydroxy-L-tryptophan, tryptam...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774878/ https://www.ncbi.nlm.nih.gov/pubmed/33107556 http://dx.doi.org/10.1093/chromsci/bmaa074 |
Sumario: | This paper is a continuation of lipophilicity research on 14 compounds (tryptophan, kynurenine pathway products, auxin pathway products, serotonin pathway products, tryptamine, as well as two synthetic auxin analogs): indole-2-acetic acid sodium salt (IAA), serotonin, 5-hydroxy-L-tryptophan, tryptamine, L-tryptophan, L-kynurenine (KYN), kynurenic acid (KYA), 3-hydroxy-DL-kynurenine, naphtyl-1-acetamide, indole-3-propionic acid (IPA), naphthalene-1-acetic acid (NAA), indole-3-butyric acid (IBA), indole-3-pyruvic acid (IPV), as well as melatonin. They were chromatographed in high performance liquid chromatography gradient conditions on tree stationary phases (C18, CN, DIOL) using three modifiers on each phase (methanol, acetonitrile and acetone). The resulting retention data was correlated with computational lipophilicity indices. Six compounds were proven to be ionized in neutral pH physiological conditions (IAA, KYA, IPA, NAA, IBA and IPV) and they were rechromatographed with acidic mobile phase to enhance the resulting dataset. It can be concluded that the retention times are highly correlated with lipophilicity regardless of used modifier and column and the main differentiating trend can be only connected to presence of naphthalene or indole ring. The principal component analysis, additive linear modeling, as well as multiplicative trilinear parallel factor analysis (PARAFAC) modeling helped to understand the internal structure of the obtained results. |
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