High-throughput, computer assisted, specific MetID. A revolution for drug discovery

Zamora, Ismael; Fontaine, Fabien; Serra, Blanca; Plasencia, Guillem

doi:10.1016/j.ddtec.2012.10.015

Cited by 42 publications

(46 citation statements)

References 17 publications

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“…Another novel method, developed in the framework of the Human Cytochrome P450 Consortium Initiative 4 , is based on automatic structure elucidation (MassMetaSite). This method is capable of predicting both phase 1 and phase 2 reactions in biomatrices; it also reads experimental data to compare predictions with experiments to automatically elucidate structures, rate of formation, pathways, phenotyping, and kinetic analysis (Zamora et al, 2013). …”

Section: Modeling Of Cypsmentioning

confidence: 99%

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

et al. 2015

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The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

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Section: Modeling Of Cypsmentioning

confidence: 99%

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

et al. 2015

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“…After 0-and 60-min exposure, samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and then processed with MetaSite and WebMetabase (Molecular Discovery, Ltd.; www.moldiscovery.com/) (31). In addition, carbazeran, cinchonine, and famciclovir, well-known AOX substrates, together with a representative example of each scaffold, were also tested in the presence of 2,6-dichlorophenolindophenol (DCPIP), a selective inhibitor of AOX to ensure the reliability of our method (SI Appendix, Figs.…”

Section: Evaluation Of Aox-mediated Metabolism By In Vitro Assays On mentioning

confidence: 99%

Structure–metabolism relationships in human- AOX: Chemical insights from a large database of aza-aromatic and amide compounds

Lepri

Ceccarelli

Milani

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Aldehyde oxidase (AOX) is a metabolic enzyme catalyzing the oxidation of aldehyde and aza-aromatic compounds and the hydrolysis of amides, moieties frequently shared by the majority of drugs. Despite its key role in human metabolism, to date only fragmentary information about the chemical features responsible for AOX susceptibility are reported and only "very local" structure-metabolism relationships based on a small number of similar compounds have been developed. This study reports a more comprehensive coverage of the chemical space of structures with a high risk of AOX phase I metabolism in humans. More than 270 compounds were studied to identify the site of metabolism and the metabolite(s). Both electronic [supported by density functional theory (DFT) calculations] and exposure effects were considered when rationalizing the structure-metabolism relationship.aldehyde oxidase | amide hydrolysis | site of metabolism | variously decorated heterocycles | structure-metabolism relationship I n recent years, the cytochromes P450 (CYP450)-mediated metabolism has been intensively studied. As a consequence, the development of new and more efficient in silico and in vitro screening systems contributed to decrease the discontinuation rates of new drugs in clinical studies by 10% in recent years (1). In particular, several models for in silico prediction of human CYP450-mediated metabolism have been reported so far (2-4) and, more recently, for human FMO3 (5). Nevertheless, a deeper understanding of non-CYP drug enzyme metabolism is advisable to further improve screening and models efficacy (6). Among all non-CYP metabolic enzymes (7), emerging importance has been attributed to human aldehyde oxidase (hAOX), a cytosolic drugmetabolizing enzyme expressed in human liver that, similarly to CYPs, contributes to new chemical entities' oxidation, but acting in the absence of NADPH cofactor. Drugs that are substrates for AOX often exhibit high metabolic clearance, resulting in low exposure and hence in decreased drug efficacy in humans. In particular, AOX catalyzes the oxidation of a wide range of azaaromatic scaffolds at the unsubstituted carbon in ortho to the nitrogen (SI Appendix, Fig. S1 A and B), usually the most electron deficient (8). Several marketed drugs are well-known substrates of AOX (e.g., methotrexate, famciclovir, and zaleplon) (9), and several compounds have failed due to undetected AOX oxidation (e. g., BIBX1382, RO-1, FK3453, and carbazeran) (10-14). The role of AOX in drug development has become more relevant in the last few years as a result of organic synthesis strategies designed to reduce cytochromes P450-related metabolism, based on chemical modifications and decorations that have subsequently increased the drug reactivity toward AOX (11,15). In addition, AOX was recently found to be responsible for the rapid hydrolysis of an amide bond in GDC-0834 (SI Appendix, Fig. S1C), a potent inhibitor of Bruton's tyrosine kinase (16). Although the development of structure-metabolism relationships (SMRs) for hAOX ...

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“…The maximum metabolite count limit was set to 30 within the retention time range of 0.5 to 6 min. Mass‐MetaSite is a software tool to process MS spectra and to rationalize fragment ions for metabolite identification . Here, the new subroutine of Mass‐MetaSite (GSH mode) was used.…”

Section: Methodsmentioning

confidence: 99%

“…Mass-MetaSite is a software tool to process MS spectra and to rationalize fragment ions for metabolite identification. [25,26] Here, the new subroutine of Mass-MetaSite (GSH mode) was used. In this mode the software automatically prompts the analysis of the fragment spectra for neutral losses and for GSH fragment ions as defined and provided by the user.…”

Section: Data Processingmentioning

confidence: 99%

Post‐acquisition analysis of untargeted accurate mass quadrupole time‐of‐flight MS^E data for multiple collision‐induced neutral losses and fragment ions of glutathione conjugates

Brink

Fontaine

Marschmann

et al. 2014

Rapid Comm Mass Spectrometry

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A software-aided approach was developed to reliably retrieve GSH adduct formation data out of untargeted complex full scan QTOFMS(E) data in a fast and efficient way. The present approach to detect and analyze multiple collision-induced neutral losses and fragment ions of glutathione conjugates in untargeted MS(E) data might be applicable to higher throughput to assess reactive metabolite formation in drug discovery.

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High-throughput, computer assisted, specific MetID. A revolution for drug discovery

Cited by 42 publications

References 17 publications

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

Structure–metabolism relationships in human- AOX: Chemical insights from a large database of aza-aromatic and amide compounds

Post‐acquisition analysis of untargeted accurate mass quadrupole time‐of‐flight MS^E data for multiple collision‐induced neutral losses and fragment ions of glutathione conjugates

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High-throughput, computer assisted, specific MetID. A revolution for drug discovery

Cited by 42 publications

References 17 publications

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

Structure–metabolism relationships in human- AOX: Chemical insights from a large database of aza-aromatic and amide compounds

Post‐acquisition analysis of untargeted accurate mass quadrupole time‐of‐flight MSE data for multiple collision‐induced neutral losses and fragment ions of glutathione conjugates

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About

Post‐acquisition analysis of untargeted accurate mass quadrupole time‐of‐flight MS^E data for multiple collision‐induced neutral losses and fragment ions of glutathione conjugates