Literature DB >> 23351040

How do metabolites differ from their parent molecules and how are they excreted?

Johannes Kirchmair1, Andrew Howlett, Julio E Peironcely, Daniel S Murrell, Mark J Williamson, Samuel E Adams, Thomas Hankemeier, Leo van Buren, Guus Duchateau, Werner Klaffke, Robert C Glen.   

Abstract

Understanding which physicochemical properties, or property distributions, are favorable for successful design and development of drugs, nutritional supplements, cosmetics, and agrochemicals is of great importance. In this study we have analyzed molecules from three distinct chemical spaces (i) approved drugs, (ii) human metabolites, and (iii) traditional Chinese medicine (TCM) to investigate four aspects determining the disposition of small organic molecules. First, we examined the physicochemical properties of these three classes of molecules and identified characteristic features resulting from their distinctive biological functions. For example, human metabolites and TCM molecules can be larger and more hydrophobic than drugs, which makes them less likely to cross membranes. We then quantified the shifts in physicochemical property space induced by metabolism from a holistic perspective by analyzing a data set of several thousand experimentally observed metabolic trees. Results show how the metabolic system aims to retain nutrients/micronutrients while facilitating a rapid elimination of xenobiotics. In the third part we compared these global shifts with the contributions made by individual metabolic reactions. For better resolution, all reactions were classified into phase I and phase II biotransformations. Interestingly, not all metabolic reactions lead to more hydrophilic molecules. We were able to identify biotransformations leading to an increase of logP by more than one log unit, which could be used for the design of drugs with enhanced efficacy. The study closes with the analysis of the physicochemical properties of metabolites found in the bile, faeces, and urine. Metabolites in the bile can be large and are often negatively charged. Molecules with molecular weight >500 Da are rarely found in the urine, and most of these large molecules are charged phase II conjugates.

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Year:  2013        PMID: 23351040     DOI: 10.1021/ci300487z

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  8 in total

1.  In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics.

Authors:  Lochana C Menikarachchi; Dennis W Hill; Mai A Hamdalla; Ion I Mandoiu; David F Grant
Journal:  J Chem Inf Model       Date:  2013-09-12       Impact factor: 4.956

Review 2.  Predicting drug metabolism: experiment and/or computation?

Authors:  Johannes Kirchmair; Andreas H Göller; Dieter Lang; Jens Kunze; Bernard Testa; Ian D Wilson; Robert C Glen; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2015-04-24       Impact factor: 84.694

3.  The Therapeutic Potential of 2-{[4-(2-methoxyphenyl)piperazin-1-yl]alkyl}-1H-benzo[d]imidazoles as Ligands for Alpha1-Adrenergic Receptor - Comparative In Silico and In Vitro Study.

Authors:  Jelena Z Penjišević; Vladimir B Šukalović; Deana B Andrić; Relja Suručić; Sladjana V Kostić-Rajačić
Journal:  Appl Biochem Biotechnol       Date:  2022-05-04       Impact factor: 3.094

4.  Cheminformatics Research at the Unilever Centre for Molecular Science Informatics Cambridge.

Authors:  Julian E Fuchs; Andreas Bender; Robert C Glen
Journal:  Mol Inform       Date:  2015-03-10       Impact factor: 3.353

Review 5.  Computational methods and tools to predict cytochrome P450 metabolism for drug discovery.

Authors:  Jonathan D Tyzack; Johannes Kirchmair
Journal:  Chem Biol Drug Des       Date:  2019-01-15       Impact factor: 2.817

6.  An electron transfer competent structural ensemble of membrane-bound cytochrome P450 1A1 and cytochrome P450 oxidoreductase.

Authors:  Goutam Mukherjee; Prajwal P Nandekar; Rebecca C Wade
Journal:  Commun Biol       Date:  2021-01-08

7.  Deep Learning Based Drug Metabolites Prediction.

Authors:  Disha Wang; Wenjun Liu; Zihao Shen; Lei Jiang; Jie Wang; Shiliang Li; Honglin Li
Journal:  Front Pharmacol       Date:  2020-01-30       Impact factor: 5.810

8.  GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics.

Authors:  Christina de Bruyn Kops; Martin Šícho; Angelica Mazzolari; Johannes Kirchmair
Journal:  Chem Res Toxicol       Date:  2020-08-26       Impact factor: 3.739

  8 in total

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