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Thomas Hankemeier - One of the best experts on this subject based on the ideXlab platform.
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Understanding and Classifying Metabolite Space and Metabolite-Likeness
PloS one, 2011Co-Authors: Julio E. Peironcely, Andreas Bender, Theo H. Reijmers, Leon Coulier, Thomas HankemeierAbstract:While the entirety of ‘Chemical Space’ is huge (and assumed to contain between 1063 and 10200 ‘small molecules’), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous Metabolites, defined as ‘naturally occurring’ products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human Metabolites in two ways. Firstly, in order to understand Metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of Metabolites and non-Metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing Metabolites from non-Metabolites, by assigning a ‘Metabolite-likeness’ score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of Metabolite-likeness, the one being more ‘synthetic’ and the other being more ‘Metabolite-like’. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying Metabolites, as well as to understanding Metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble Metabolites, and in our work particularly for assessing the Metabolite-likeness of candidate molecules during Metabolite identification in the metabolomics field.
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Expanding and understanding Metabolite space
Journal of Cheminformatics, 2010Co-Authors: Julio E. Peironcely, Andreas Bender, Miguel Rojas-chertó, Theo H. Reijmers, Leon Coulier, Thomas HankemeierAbstract:In metabolomics the identity and role of low mass molecules called Metabolites that are produced in cell metabolic processes are investigated. These make them valuable indicators of the phenotype of a biological system. The 'Metabolite Space' is the total chemical universe of Metabolites present in all compartments and in all states from any organism. These molecules exhibit common features that form what can be called 'Metabolite likeness'. Here, we focus on the human Metabolite space, including both endogenous and exogenous (such as drug) Metabolites. In order to analyze the 'Metabolite Space', we collected data from the Human Metabolome Database (HMDB) [1] which is a comprehensive database for human Metabolites containing over 7000 compounds that were identified in several human biofluids and tissues. As there still remain many compounds to be identified that lay outside the boundaries of this known space, exploring this unknown region is crucial to evaluate 'Metabolite likeness'. In order to expand 'Metabolite Space' in our approach we employed the Retrosynthetic Combinatorial Analysis Procedure (RECAP) [2] to generate new molecules that possess features similar to those present in Metabolites, however in other (but still likely) rearrangements. We studied how discernible these new molecules are from real Metabolites and, hence, whether synthetic organic chemistry reactions are indeed able to expand the known universe of Metabolites. We further studied the new chemistry present in the expanded Metabolite space by looking at Murcko assemblies [3], ring systems and other chemical properties. The new Metabolite space is compared to other small molecules, such as those obtained from the ZINC database, that are not Metabolites. By combining all the above analyses we expect to characterize better the Metabolite space, and furthermore, to predict the Metabolite-likeness of a molecule and to understand its immanent properties.
Julio E. Peironcely - One of the best experts on this subject based on the ideXlab platform.
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Understanding and Classifying Metabolite Space and Metabolite-Likeness
PloS one, 2011Co-Authors: Julio E. Peironcely, Andreas Bender, Theo H. Reijmers, Leon Coulier, Thomas HankemeierAbstract:While the entirety of ‘Chemical Space’ is huge (and assumed to contain between 1063 and 10200 ‘small molecules’), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous Metabolites, defined as ‘naturally occurring’ products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human Metabolites in two ways. Firstly, in order to understand Metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of Metabolites and non-Metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing Metabolites from non-Metabolites, by assigning a ‘Metabolite-likeness’ score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of Metabolite-likeness, the one being more ‘synthetic’ and the other being more ‘Metabolite-like’. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying Metabolites, as well as to understanding Metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble Metabolites, and in our work particularly for assessing the Metabolite-likeness of candidate molecules during Metabolite identification in the metabolomics field.
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Expanding and understanding Metabolite space
Journal of Cheminformatics, 2010Co-Authors: Julio E. Peironcely, Andreas Bender, Miguel Rojas-chertó, Theo H. Reijmers, Leon Coulier, Thomas HankemeierAbstract:In metabolomics the identity and role of low mass molecules called Metabolites that are produced in cell metabolic processes are investigated. These make them valuable indicators of the phenotype of a biological system. The 'Metabolite Space' is the total chemical universe of Metabolites present in all compartments and in all states from any organism. These molecules exhibit common features that form what can be called 'Metabolite likeness'. Here, we focus on the human Metabolite space, including both endogenous and exogenous (such as drug) Metabolites. In order to analyze the 'Metabolite Space', we collected data from the Human Metabolome Database (HMDB) [1] which is a comprehensive database for human Metabolites containing over 7000 compounds that were identified in several human biofluids and tissues. As there still remain many compounds to be identified that lay outside the boundaries of this known space, exploring this unknown region is crucial to evaluate 'Metabolite likeness'. In order to expand 'Metabolite Space' in our approach we employed the Retrosynthetic Combinatorial Analysis Procedure (RECAP) [2] to generate new molecules that possess features similar to those present in Metabolites, however in other (but still likely) rearrangements. We studied how discernible these new molecules are from real Metabolites and, hence, whether synthetic organic chemistry reactions are indeed able to expand the known universe of Metabolites. We further studied the new chemistry present in the expanded Metabolite space by looking at Murcko assemblies [3], ring systems and other chemical properties. The new Metabolite space is compared to other small molecules, such as those obtained from the ZINC database, that are not Metabolites. By combining all the above analyses we expect to characterize better the Metabolite space, and furthermore, to predict the Metabolite-likeness of a molecule and to understand its immanent properties.
Johänning Janina - One of the best experts on this subject based on the ideXlab platform.
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Metabolismus, Pharmakodynamik und klinische Relevanz der östrogenen TamoxifenMetaboliten bei der endokrinen Therapie des Mammakarzinoms
Universität Tübingen, 2020Co-Authors: Johänning JaninaAbstract:Dissertation ist gesperrt bis 11. Dezember 2020 !Tamoxifen ist die Standardtherapie für Hormonrezeptor-positiven Brustkrebs bei prämenopausalen Frauen und eine Alternative zur Therapie mit Aromataseinhibitoren bei postmenopausalen Frauen. Dabei wird Tamoxifen von Cytochrom P450 (CYP-) Enzymen zu über 60 Metaboliten verstoffwechselt, welche zum Teil anti-östrogene wie auch östrogene Eigenschaften aufweisen. Die anti-östrogenen Metaboliten 4-Hydroxytamoxifen (4-OH-Tamoxifen) und N-Desmethyl-4-hydroxytamoxifen (Endoxifen) haben eine 100-fach stärkere Affinität zum Östrogenrezeptor (ER) und sind hauptsächlich für den antiproliferativen Effekt im Brustgewebe verantwortlich. Zu den östrogenen Metaboliten gehören Tamoxifen Bisphenol (Bisphenol) und beide Isomere von Metabolit E, welche in ER-positiven Brustkrebs-Zellen die Expression von Östrogen-regulierten Genen induzieren und somit die Zellproliferation beeinflussen. Darüber hinaus wurden diese Metaboliten in Xenotransplantaten eines Mausmodells für die Tamoxifenresistenz sowie in Mammakarzinomgewebe von Patientinnen mit klinischer Resistenz identifiziert. Sowohl die Metabolismuswege zu Metabolit E und Bisphenol als auch der Einfluss dieser Metaboliten auf den Therapieverlauf sind bisher weitestgehend unerforscht. Das Ziel dieser Arbeit war es demnach die Enzyme, welche für die Bildung von Bisphenol und Metabolit E verantwortlich sind, zu identifizieren und die pharmakodynamischen Effekte auf den ER und Auswirkungen auf den Fremdstoffmetabolismus in vitro zu untersuchen. Darüber hinaus wurden die Plasmaspiegel der östrogenen Metaboliten in Blutproben von prä- und postmenopausalen Frauen unter Tamoxifentherapie quantifiziert, deren Abhängigkeit von genetischen Varianten der beteiligten CYP-Enzyme analysiert sowie der Einfluss auf den Therapieverlauf untersucht. Mit Hilfe von humanen Lebermikrosomen konnte gezeigt werden, dass Metabolit E wie auch Bisphenol in NADPH-abhängigen mikrosomalen Reaktionen gebildet werden. Weitere Analysen mit Supersomes™ zeigten, dass Metabolit E hauptsächlich durch die Enzyme CYP2C19, 3A, 2D6 und 1A2 aus Tamoxifen, N-Desmethyltamoxifen (DM-Tamoxifen) und N-Didesmethyltamoxifen (DDM-Tamoxifen) metabolisiert wird. Kinetische Untersuchungen ergaben die höchste Clearance für Tamoxifen mit 0,112 µl mg-1 min-1. Für die Bildung von Bisphenol aus den anti-östrogenen TamoxifenMetaboliten 4-OH-Tamoxifen, Endoxifen und N-Didesmethyl-4-hydroxytamoxifen (Norendoxifen) konnte kein spezifisches CYP-Isoenzym identifiziert werden. Im Gegensatz dazu zeigte die stereospezifische Hydroxylierung von Metabolit E zu Bisphenol durch CYP2C19 ((Z)-Isomer) und CYP2B6 ((E)-Isomer) eine bis zu 45-fach höhere Clearance verglichen mit den anti-östrogenen TamoxifenMetaboliten. Bezüglich des Phase II-Metabolismus zeigten die UGT-Enzyme 1A8 und 2B7 höchste Aktivitäten für die Glucuronidierung von Metabolit E und Bisphenol. In beiden prä- und postmenopausalen Patientenkohorten war die genetische Variante CYP2C19*2 mit einer erniedrigten metabolischen Ratio (MR) von Bisphenol zu (Z)-Metabolit E assoziiert. Darüber hinaus führte das CYP3A4*22 Allel zu signifikant niedrigeren MRs von (Z)-Metabolit E zu Tamoxifen bzw. zu DM-Tamoxifen. Die stärkste östrogene Wirkung in einem ER-abhängigen Luciferase-Reportergen-Assay in MCF7-Zellen wurde für (E)-Metabolit E beobachtet. Darüber hinaus aktivierten alle drei östrogenen Metaboliten in Transkriptomanalysen von MCF7-Zellen in gleicher Weise wie Östradiol die Expression von Genen, die mit Zellzyklus, Transkription, DNA-Replikation, Mitose, Proliferation und Nukleotidmetabolismus assoziiert sind. Es konnte gezeigt werden, dass Bisphenol im Gegensatz zu den anti-östrogenen TamoxifenMetaboliten die Aktivität aller untersuchten CYP-Enzyme (CYP1A2, 2B6, 2C8, 2C9, 2C19, 3A4) stark induziert, wobei niedrigste EC50-Werte für CYP2C19 (EC50=30,5 nM) gefunden wurden. Die in vitro beobachteten EC50-Konzentrationen waren allerdings höher als die gemessenen Patientenplasmaspiegel und setzen eine Gewebeanreicherung für eine biologische Wirkung in vivo voraus. In prämenopausalen Frauen konnte das Verhältnis von (E)-Metabolit E zu Tamoxifen als Risikofaktor für eine kürzere rezidivfreie Zeit (time to relapse; Hazard Ratio [HR] 2,813; 95 % Konfidenzintervall [KI] 1,372-5,767; p=0,005) bzw. ein kürzeres rezidivfreies Überleben (HR 2,022; 95 % KI 1,079-3,791; p=0,028) assoziiert werden. Dies impliziert, dass (E)-Metabolit E als stärkster Agonist am ER einen negativen Einfluss auf das Therapieansprechen von Tamoxifen hat und dass hohe Konzentrationen dieses Metaboliten ein möglicher Risikofaktor für das Therapieversagen von Tamoxifen sind. Die vorliegende Dissertation schafft die Basis für zukünftige Arbeiten pharmakogenetische Biomarker zur Vorhersage der (E)-Metabolit E Plasmakonzentration zu identifizieren und damit einen Beitrag für eine personalisierte Tamoxifentherapie bei Brustkrebs zu leisten.Tamoxifen is the standard therapy for hormone receptor positive breast cancer in premenopausal women and the major option to aromatase inhibitors in postmenopausal women. It is extensively metabolized and bioactivated by enzymes of the cytochrome P450 (CYP) family to more than 60 compounds with anti-estrogenic and estrogenic properties. The anti-estrogenic Metabolites 4-hydroxytamoxifen (4-OH-tamoxifen) and N-desmethyl-4-hydroxytamoxifen (endoxifen) have a more than 100-fold higher affinity to the estrogen receptor (ER) than tamoxifen itself and are therefore believed to mediate the anti-proliferative effect of the drug. The estrogenic Metabolites Tamoxifen bisphenol (bisphenol) and both isomers of Metabolite E were shown to induce the expression of certain estrogen-regulated mRNA and therefore impact cell proliferation in ER positive breast cancer cell lines. In addition, these Metabolites were identified in a xenograft mouse model of tamoxifen resistance as well as in tumour tissue of tamoxifen treated breast cancer patients with clinical resistance. However, the metabolic pathways leading to Bisphenol and Metabolite E as well as their impact on tamoxifen therapy are still unknown. Hence, the purpose of this work was to identify the enzymes involved in the formation of the estrogenic tamoxifen Metabolites and to analyse the pharmacodynamic effects regarding their ER-activating potency and the xenobiotic metabolism in vitro. Furthermore, the plasma concentrations of Metabolite E and bisphenol were quantified in blood samples of pre- and postmenopausal women under tamoxifen therapy and the dependency on genetic variants of the involved CYP enzymes as well as to clinical outcome were analyzed. Human liver microsome experiments showed that both Metabolite E and bisphenol are formed by NADPH-dependent microsomal reactions. In particular, it could be shown that Metabolite E formation is mediated by CYP2C19, 3A, 2D6 and 1A2 from tamoxifen, N-desmethyl tamoxifen (DM-tamoxifen) and N-didesmethyl tamoxifen (DDM-tamoxifen) with tamoxifen having the highest metabolic clearance of 0.112 µl mg-1 min-1. Regarding bisphenol formation from the anti-estrogenic tamoxifen Metabolite precursors 4-OH-tamoxifen, endoxifen and N-didesmethyl-4-hydroxy tamoxifen (norendoxifen), no specific CYP-isoenzyme could be identified. In contrast, the stereospecific hydroxylation of Metabolite E to bisphenol was catalysed by CYP2C19 ((Z)-isomer) and CYP2B6 ((E)-isomer) with an up to 45-fold higher metabolic clearance compared to anti-estrogenic tamoxifen Metabolites. With regard to phase II metabolism, UGT1A8 and 2B7 were identified as main enzymes for the glucuronidation of Metabolite E and bisphenol. In both pre- and postmenopausal breast cancer patients, CYP2C19*2 variants significantly lowered the metabolic ratio (MR) of bisphenol to (Z)-Metabolite E. Furthermore CYP3A4*22 significantly decreased the MR of (Z)-Metabolite E to both tamoxifen and DM-tamoxifen, respectively. The estrogenic effects on the ER based on an ER-dependent luciferase-reportergene-assay in MCF7-cells was strongest for (E)-Metabolite E. Moreover, whole transcriptome analysis in MCF7-cells showed that all three estrogenic Metabolites displayed almost identical activated pathways associated with cell cycle, transcription, DNA-replication, mitosis, proliferation and nucleotid metabolism when compared to estradiol. Regarding the pharmacodynamic effects on the xenobiotic metabolism, bisphenol strongly induced the activity of all tested CYP-enzymes (CYP1A2, 2B6, 2C8, 2C9, 2C19, 3A4) with lowest EC50-values for CYP2C19 (EC50=30,5 nM). However, given that the observed EC50-concentrations in vitro are higher than those found in patient plasma a biological impact would require a tissue enrichment of estrogenic tamoxifen Metabolites in vivo. In premenopausal women the ratio of (E)-Metabolite E to tamoxifen was identified as a risk factor of reduced time to relapse (hazard ratio [HR] 2.813; 95 % confidence interval [CI]: 1.372-5.767; p=0.005) and relapse-free survival (HR 2.022; 95 % CI: 1.079-3.791; p=0.028). This suggests that (E)-Metabolite E due to its property as the strongest ER-agonist among the investigated estrogenic Metabolites, negatively influences the response to tamoxifen therapy and is a risk factor for therapeutic failure in the clinic. This dissertation provides a basis for future works to identify of pharmacogenetic biomarkers as predictors for (E)-Metabolite E plasma concentration which contributes to further personalise tamoxifen treatment of hormone receptor positive breast cancer patients
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Metabolismus, Pharmakodynamik und klinische Relevanz der östrogenen TamoxifenMetaboliten bei der endokrinen Therapie des Mammakarzinoms
Universität Tübingen, 2020Co-Authors: Johänning JaninaAbstract:Tamoxifen ist die Standardtherapie für Hormonrezeptor-positiven Brustkrebs bei prämenopausalen Frauen und eine Alternative zur Therapie mit Aromataseinhibitoren bei postmenopausalen Frauen. Dabei wird Tamoxifen von Cytochrom P450 (CYP-) Enzymen zu über 60 Metaboliten verstoffwechselt, welche zum Teil anti-östrogene wie auch östrogene Eigenschaften aufweisen. Die anti-östrogenen Metaboliten 4-Hydroxytamoxifen (4-OH-Tamoxifen) und N-Desmethyl-4-hydroxytamoxifen (Endoxifen) haben eine 100-fach stärkere Affinität zum Östrogenrezeptor (ER) und sind hauptsächlich für den antiproliferativen Effekt im Brustgewebe verantwortlich. Zu den östrogenen Metaboliten gehören Tamoxifen Bisphenol (Bisphenol) und beide Isomere von Metabolit E, welche in ER-positiven Brustkrebs-Zellen die Expression von Östrogen-regulierten Genen induzieren und somit die Zellproliferation beeinflussen. Darüber hinaus wurden diese Metaboliten in Xenotransplantaten eines Mausmodells für die Tamoxifenresistenz sowie in Mammakarzinomgewebe von Patientinnen mit klinischer Resistenz identifiziert. Sowohl die Metabolismuswege zu Metabolit E und Bisphenol als auch der Einfluss dieser Metaboliten auf den Therapieverlauf sind bisher weitestgehend unerforscht. Das Ziel dieser Arbeit war es demnach die Enzyme, welche für die Bildung von Bisphenol und Metabolit E verantwortlich sind, zu identifizieren und die pharmakodynamischen Effekte auf den ER und Auswirkungen auf den Fremdstoffmetabolismus in vitro zu untersuchen. Darüber hinaus wurden die Plasmaspiegel der östrogenen Metaboliten in Blutproben von prä- und postmenopausalen Frauen unter Tamoxifentherapie quantifiziert, deren Abhängigkeit von genetischen Varianten der beteiligten CYP-Enzyme analysiert sowie der Einfluss auf den Therapieverlauf untersucht. Mit Hilfe von humanen Lebermikrosomen konnte gezeigt werden, dass Metabolit E wie auch Bisphenol in NADPH-abhängigen mikrosomalen Reaktionen gebildet werden. Weitere Analysen mit Supersomes™ zeigten, dass Metabolit E hauptsächlich durch die Enzyme CYP2C19, 3A, 2D6 und 1A2 aus Tamoxifen, N-Desmethyltamoxifen (DM-Tamoxifen) und N-Didesmethyltamoxifen (DDM-Tamoxifen) metabolisiert wird. Kinetische Untersuchungen ergaben die höchste Clearance für Tamoxifen mit 0,112 µl mg-1 min-1. Für die Bildung von Bisphenol aus den anti-östrogenen TamoxifenMetaboliten 4-OH-Tamoxifen, Endoxifen und N-Didesmethyl-4-hydroxytamoxifen (Norendoxifen) konnte kein spezifisches CYP-Isoenzym identifiziert werden. Im Gegensatz dazu zeigte die stereospezifische Hydroxylierung von Metabolit E zu Bisphenol durch CYP2C19 ((Z)-Isomer) und CYP2B6 ((E)-Isomer) eine bis zu 45-fach höhere Clearance verglichen mit den anti-östrogenen TamoxifenMetaboliten. Bezüglich des Phase II-Metabolismus zeigten die UGT-Enzyme 1A8 und 2B7 höchste Aktivitäten für die Glucuronidierung von Metabolit E und Bisphenol. In beiden prä- und postmenopausalen Patientenkohorten war die genetische Variante CYP2C19*2 mit einer erniedrigten metabolischen Ratio (MR) von Bisphenol zu (Z)-Metabolit E assoziiert. Darüber hinaus führte das CYP3A4*22 Allel zu signifikant niedrigeren MRs von (Z)-Metabolit E zu Tamoxifen bzw. zu DM-Tamoxifen. Die stärkste östrogene Wirkung in einem ER-abhängigen Luciferase-Reportergen-Assay in MCF7-Zellen wurde für (E)-Metabolit E beobachtet. Darüber hinaus aktivierten alle drei östrogenen Metaboliten in Transkriptomanalysen von MCF7-Zellen in gleicher Weise wie Östradiol die Expression von Genen, die mit Zellzyklus, Transkription, DNA-Replikation, Mitose, Proliferation und Nukleotidmetabolismus assoziiert sind. Es konnte gezeigt werden, dass Bisphenol im Gegensatz zu den anti-östrogenen TamoxifenMetaboliten die Aktivität aller untersuchten CYP-Enzyme (CYP1A2, 2B6, 2C8, 2C9, 2C19, 3A4) stark induziert, wobei niedrigste EC50-Werte für CYP2C19 (EC50=30,5 nM) gefunden wurden. Die in vitro beobachteten EC50-Konzentrationen waren allerdings höher als die gemessenen Patientenplasmaspiegel und setzen eine Gewebeanreicherung für eine biologische Wirkung in vivo voraus. In prämenopausalen Frauen konnte das Verhältnis von (E)-Metabolit E zu Tamoxifen als Risikofaktor für eine kürzere rezidivfreie Zeit (time to relapse; Hazard Ratio [HR] 2,813; 95 % Konfidenzintervall [KI] 1,372-5,767; p=0,005) bzw. ein kürzeres rezidivfreies Überleben (HR 2,022; 95 % KI 1,079-3,791; p=0,028) assoziiert werden. Dies impliziert, dass (E)-Metabolit E als stärkster Agonist am ER einen negativen Einfluss auf das Therapieansprechen von Tamoxifen hat und dass hohe Konzentrationen dieses Metaboliten ein möglicher Risikofaktor für das Therapieversagen von Tamoxifen sind. Die vorliegende Dissertation schafft die Basis für zukünftige Arbeiten pharmakogenetische Biomarker zur Vorhersage der (E)-Metabolit E Plasmakonzentration zu identifizieren und damit einen Beitrag für eine personalisierte Tamoxifentherapie bei Brustkrebs zu leisten.Tamoxifen is the standard therapy for hormone receptor positive breast cancer in premenopausal women and the major option to aromatase inhibitors in postmenopausal women. It is extensively metabolized and bioactivated by enzymes of the cytochrome P450 (CYP) family to more than 60 compounds with anti-estrogenic and estrogenic properties. The anti-estrogenic Metabolites 4-hydroxytamoxifen (4-OH-tamoxifen) and N-desmethyl-4-hydroxytamoxifen (endoxifen) have a more than 100-fold higher affinity to the estrogen receptor (ER) than tamoxifen itself and are therefore believed to mediate the anti-proliferative effect of the drug. The estrogenic Metabolites Tamoxifen bisphenol (bisphenol) and both isomers of Metabolite E were shown to induce the expression of certain estrogen-regulated mRNA and therefore impact cell proliferation in ER positive breast cancer cell lines. In addition, these Metabolites were identified in a xenograft mouse model of tamoxifen resistance as well as in tumour tissue of tamoxifen treated breast cancer patients with clinical resistance. However, the metabolic pathways leading to Bisphenol and Metabolite E as well as their impact on tamoxifen therapy are still unknown. Hence, the purpose of this work was to identify the enzymes involved in the formation of the estrogenic tamoxifen Metabolites and to analyse the pharmacodynamic effects regarding their ER-activating potency and the xenobiotic metabolism in vitro. Furthermore, the plasma concentrations of Metabolite E and bisphenol were quantified in blood samples of pre- and postmenopausal women under tamoxifen therapy and the dependency on genetic variants of the involved CYP enzymes as well as to clinical outcome were analyzed. Human liver microsome experiments showed that both Metabolite E and bisphenol are formed by NADPH-dependent microsomal reactions. In particular, it could be shown that Metabolite E formation is mediated by CYP2C19, 3A, 2D6 and 1A2 from tamoxifen, N-desmethyl tamoxifen (DM-tamoxifen) and N-didesmethyl tamoxifen (DDM-tamoxifen) with tamoxifen having the highest metabolic clearance of 0.112 µl mg-1 min-1. Regarding bisphenol formation from the anti-estrogenic tamoxifen Metabolite precursors 4-OH-tamoxifen, endoxifen and N-didesmethyl-4-hydroxy tamoxifen (norendoxifen), no specific CYP-isoenzyme could be identified. In contrast, the stereospecific hydroxylation of Metabolite E to bisphenol was catalysed by CYP2C19 ((Z)-isomer) and CYP2B6 ((E)-isomer) with an up to 45-fold higher metabolic clearance compared to anti-estrogenic tamoxifen Metabolites. With regard to phase II metabolism, UGT1A8 and 2B7 were identified as main enzymes for the glucuronidation of Metabolite E and bisphenol. In both pre- and postmenopausal breast cancer patients, CYP2C19*2 variants significantly lowered the metabolic ratio (MR) of bisphenol to (Z)-Metabolite E. Furthermore CYP3A4*22 significantly decreased the MR of (Z)-Metabolite E to both tamoxifen and DM-tamoxifen, respectively. The estrogenic effects on the ER based on an ER-dependent luciferase-reportergene-assay in MCF7-cells was strongest for (E)-Metabolite E. Moreover, whole transcriptome analysis in MCF7-cells showed that all three estrogenic Metabolites displayed almost identical activated pathways associated with cell cycle, transcription, DNA-replication, mitosis, proliferation and nucleotid metabolism when compared to estradiol. Regarding the pharmacodynamic effects on the xenobiotic metabolism, bisphenol strongly induced the activity of all tested CYP-enzymes (CYP1A2, 2B6, 2C8, 2C9, 2C19, 3A4) with lowest EC50-values for CYP2C19 (EC50=30,5 nM). However, given that the observed EC50-concentrations in vitro are higher than those found in patient plasma a biological impact would require a tissue enrichment of estrogenic tamoxifen Metabolites in vivo. In premenopausal women the ratio of (E)-Metabolite E to tamoxifen was identified as a risk factor of reduced time to relapse (hazard ratio [HR] 2.813; 95 % confidence interval [CI]: 1.372-5.767; p=0.005) and relapse-free survival (HR 2.022; 95 % CI: 1.079-3.791; p=0.028). This suggests that (E)-Metabolite E due to its property as the strongest ER-agonist among the investigated estrogenic Metabolites, negatively influences the response to tamoxifen therapy and is a risk factor for therapeutic failure in the clinic. This dissertation provides a basis for future works to identify of pharmacogenetic biomarkers as predictors for (E)-Metabolite E plasma concentration which contributes to further personalise tamoxifen treatment of hormone receptor positive breast cancer patients
Ian Fairweather - One of the best experts on this subject based on the ideXlab platform.
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The comparative metabolism of triclabendazole sulphoxide by triclabendazole-susceptible and triclabendazole-resistant Fasciola hepatica
Parasitology Research, 2004Co-Authors: Mark W. Robinson, Jill Lawson, Alan Trudgett, Elizabeth M. Hoey, Ian FairweatherAbstract:Benzimidazole anthelmintics are widely used against nematode, cestode and trematode parasites. The drugs undergo several enzyme-mediated reactions within the host animal that produce a number of Metabolites. Although it has been shown that certain helminths, including Fasciola hepatica , can metabolise albendazole, nothing is known regarding the ability of the liver fluke to metabolise triclabendazole, which is the major flukicidal compound currently on the market. In the current study, adult triclabendazole-susceptible flukes were treated with triclabendazole sulphoxide in vitro, and the metabolism of the drug was monitored by high performance liquid chromatography. The data show that F. hepatica can metabolise triclabendazole sulphoxide into its relatively inert sulphone Metabolite. Parallel experiments using triclabendazole-resistant flukes showed that the conversion of triclabendazole sulphoxide to triclabendazole sulphone was on average 20.29% greater in the resistant flukes compared with the susceptible flukes. The results are discussed with regard to the mechanism of triclabendazole resistance in F. hepatica .
Leon Coulier - One of the best experts on this subject based on the ideXlab platform.
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Understanding and Classifying Metabolite Space and Metabolite-Likeness
PloS one, 2011Co-Authors: Julio E. Peironcely, Andreas Bender, Theo H. Reijmers, Leon Coulier, Thomas HankemeierAbstract:While the entirety of ‘Chemical Space’ is huge (and assumed to contain between 1063 and 10200 ‘small molecules’), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous Metabolites, defined as ‘naturally occurring’ products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human Metabolites in two ways. Firstly, in order to understand Metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of Metabolites and non-Metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing Metabolites from non-Metabolites, by assigning a ‘Metabolite-likeness’ score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of Metabolite-likeness, the one being more ‘synthetic’ and the other being more ‘Metabolite-like’. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying Metabolites, as well as to understanding Metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble Metabolites, and in our work particularly for assessing the Metabolite-likeness of candidate molecules during Metabolite identification in the metabolomics field.
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Expanding and understanding Metabolite space
Journal of Cheminformatics, 2010Co-Authors: Julio E. Peironcely, Andreas Bender, Miguel Rojas-chertó, Theo H. Reijmers, Leon Coulier, Thomas HankemeierAbstract:In metabolomics the identity and role of low mass molecules called Metabolites that are produced in cell metabolic processes are investigated. These make them valuable indicators of the phenotype of a biological system. The 'Metabolite Space' is the total chemical universe of Metabolites present in all compartments and in all states from any organism. These molecules exhibit common features that form what can be called 'Metabolite likeness'. Here, we focus on the human Metabolite space, including both endogenous and exogenous (such as drug) Metabolites. In order to analyze the 'Metabolite Space', we collected data from the Human Metabolome Database (HMDB) [1] which is a comprehensive database for human Metabolites containing over 7000 compounds that were identified in several human biofluids and tissues. As there still remain many compounds to be identified that lay outside the boundaries of this known space, exploring this unknown region is crucial to evaluate 'Metabolite likeness'. In order to expand 'Metabolite Space' in our approach we employed the Retrosynthetic Combinatorial Analysis Procedure (RECAP) [2] to generate new molecules that possess features similar to those present in Metabolites, however in other (but still likely) rearrangements. We studied how discernible these new molecules are from real Metabolites and, hence, whether synthetic organic chemistry reactions are indeed able to expand the known universe of Metabolites. We further studied the new chemistry present in the expanded Metabolite space by looking at Murcko assemblies [3], ring systems and other chemical properties. The new Metabolite space is compared to other small molecules, such as those obtained from the ZINC database, that are not Metabolites. By combining all the above analyses we expect to characterize better the Metabolite space, and furthermore, to predict the Metabolite-likeness of a molecule and to understand its immanent properties.
