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Chemotyping the distribution of vitamin D metabolites in human serum.

ABSTRACT: Most studies examining the relationships between vitamin D and disease or health focus on the main 25-hydroxyvitamin D3 (25(OH)D3) metabolite, thus potentially overlooking contributions and dynamic effects of other vitamin D metabolites, the crucial roles of several of which have been previously demonstrated. The ideal assay would determine all relevant high and low-abundant vitamin D species simultaneously. We describe a sensitive quantitative assay for determining the chemotypes of vitamin D metabolites from serum after derivatisation and ultra-high performance liquid chromatography-electrospray ionisation-tandem mass spectrometry (UHPLC-ESI-MS/MS). We performed a validation according to the 'FDA Guidance for Industry Bioanalytical Method Validation'. The proof-of-concept of the method was then demonstrated by following the metabolite concentrations in patients with chronic liver diseases (CLD) during the course of a vitamin D supplementation study. The new quantitative profiling assay provided highly sensitive, precise and accurate chemotypes of the vitamin D metabolic process rather than the usually determined 25(OH)D3 concentrations.

SUBMITTER: Muller MJ

PROVIDER: S-EPMC4750036 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Chemotyping the distribution of vitamin D metabolites in human serum.

Müller Miriam J MJ Stokes Caroline S CS Lammert Frank F Volmer Dietrich A DA

Scientific reports 20160211

Most studies examining the relationships between vitamin D and disease or health focus on the main 25-hydroxyvitamin D3 (25(OH)D3) metabolite, thus potentially overlooking contributions and dynamic effects of other vitamin D metabolites, the crucial roles of several of which have been previously demonstrated. The ideal assay would determine all relevant high and low-abundant vitamin D species simultaneously. We describe a sensitive quantitative assay for determining the chemotypes of vitamin D m ...[more]

PMID: 26864540

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Project description:BackgroundThe current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.ResultsWe have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.ConclusionThe property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.

Dataset Information

Chemotyping the distribution of vitamin D metabolites in human serum.

Publications

Chemotyping the distribution of vitamin D metabolites in human serum.

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