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Thyroid disruptions by xenobiotics have been associated with a broad spectrum of severe adverse human health effects, such as impaired brain development and metabolic syndrome. Ingestion of indoor dust and contact with industrial chemicals are two significant human exposure routes of thyroid hormone disrupting chemicals (THDCs), raising serious concerns for human health. However, it is a laborious and costly process to identify THDCs using conventional experimental methods, due to the number of chemicals in commerce and the varieties of potential disruption mechanisms. In this thesis, we are aimed at in silico identification of novel THDCs targeting transthyretin (TTR) and thyroid hormone receptor (THR) among dust contaminants and commonly used industrial chemicals. In vitro assays were used to validate the in silico prediction results. Co-crystallization and molecular dynamics (MD) simulations were applied to reveal binding modes of THDCs at the studied biological targets and to explain their intermolecular recognition. The main findings presented in this thesis are: 1. Over 144 environmental pollutants have been confirmed as TTR-binders in vitro and these cover a wide range of environmental pollutants and show distinct chemical profiles including a large group of halogenated aromatic compounds and a second group of per- and polyfluoroalkyl substances. (Paper I) 2. In total 485 organic contaminants have been reported to be detected in household dust. The developed QSAR classification model predicted 7.6% of these dust contaminants and 53.1% of their metabolites as potential TTR-binders, which emphasizes the importance of metabolic bioactivation. After in vitro validation, four novel TTR binders with IC50 ≤ 10 µM were identified, i.e. perfluoroheptanesulfonic acid, 2,4,2',4'-tetrahydroxybenzophenone (BP2), 2,4,5-trichlorophenoxyacetic acid, and 3,5,6-trichloro-2-pyridinol. (Paper II) 3. The development of a robust structure-based virtual screening (VS) protocol resu

Thyroid disruptions by xenobiotics have been associated with a broad spectrum of severe adverse human health effects, such as impaired brain development and metabolic syndrome. Ingestion of indoor dust and contact with industrial chemicals are two significant human exposure routes of thyroid hormone disrupting chemicals (THDCs), raising serious concerns for human health. However, it is a laborious and costly process to identify THDCs using conventional experimental methods, due to the number of chemicals in commerce and the varieties of potential disruption mechanisms. In this thesis, we are aimed at in silico identification of novel THDCs targeting transthyretin (TTR) and thyroid hormone receptor (THR) among dust contaminants and commonly used industrial chemicals. In vitro assays were used to validate the in silico prediction results. Co-crystallization and molecular dynamics (MD) simulations were applied to reveal binding modes of THDCs at the studied biological targets and to explain their intermolecular recognition. The main findings presented in this thesis are: 1. Over 144 environmental pollutants have been confirmed as TTR-binders in vitro and these cover a wide range of environmental pollutants and show distinct chemical profiles including a large group of halogenated aromatic compounds and a second group of per- and polyfluoroalkyl substances. (Paper I) 2. In total 485 organic contaminants have been reported to be detected in household dust. The developed QSAR classification model predicted 7.6% of these dust contaminants and 53.1% of their metabolites as potential TTR-binders, which emphasizes the importance of metabolic bioactivation. After in vitro validation, four novel TTR binders with IC50 ≤ 10 µM were identified, i.e. perfluoroheptanesulfonic acid, 2,4,2',4'-tetrahydroxybenzophenone (BP2), 2,4,5-trichlorophenoxyacetic acid, and 3,5,6-trichloro-2-pyridinol. (Paper II) 3. The development of a robust structure-based virtual screening (VS) protocol resu

Thyroid disruptions by xenobiotics have been associated with a broad spectrum of severe adverse human health effects, such as impaired brain development and metabolic syndrome. Ingestion of indoor dust and contact with industrial chemicals are two significant human exposure routes of thyroid hormone disrupting chemicals (THDCs), raising serious concerns for human health. However, it is a laborious and costly process to identify THDCs using conventional experimental methods, due to the number of chemicals in commerce and the varieties of potential disruption mechanisms. In this thesis, we are aimed at in silico identification of novel THDCs targeting transthyretin (TTR) and thyroid hormone receptor (THR) among dust contaminants and commonly used industrial chemicals. In vitro assays were used to validate the in silico prediction results. Co-crystallization and molecular dynamics (MD) simulations were applied to reveal binding modes of THDCs at the studied biological targets and to explain their intermolecular recognition. The main findings presented in this thesis are: 1. Over 144 environmental pollutants have been confirmed as TTR-binders in vitro and these cover a wide range of environmental pollutants and show distinct chemical profiles including a large group of halogenated aromatic compounds and a second group of per- and polyfluoroalkyl substances. (Paper I) 2. In total 485 organic contaminants have been reported to be detected in household dust. The developed QSAR classification model predicted 7.6% of these dust contaminants and 53.1% of their metabolites as potential TTR-binders, which emphasizes the importance of metabolic bioactivation. After in vitro validation, four novel TTR binders with IC50 ≤ 10 µM were identified, i.e. perfluoroheptanesulfonic acid, 2,4,2',4'-tetrahydroxybenzophenone (BP2), 2,4,5-trichlorophenoxyacetic acid, and 3,5,6-trichloro-2-pyridinol. (Paper II) 3. The development of a robust structure-based virtual screening (VS) protocol resu

Thyroid disruptions by xenobiotics have been associated with a broad spectrum of severe adverse human health effects, such as impaired brain development and metabolic syndrome. Ingestion of indoor dust and contact with industrial chemicals are two significant human exposure routes of thyroid hormone disrupting chemicals (THDCs), raising serious concerns for human health. However, it is a laborious and costly process to identify THDCs using conventional experimental methods, due to the number of chemicals in commerce and the varieties of potential disruption mechanisms. In this thesis, we are aimed at in silico identification of novel THDCs targeting transthyretin (TTR) and thyroid hormone receptor (THR) among dust contaminants and commonly used industrial chemicals. In vitro assays were used to validate the in silico prediction results. Co-crystallization and molecular dynamics (MD) simulations were applied to reveal binding modes of THDCs at the studied biological targets and to explain their intermolecular recognition. The main findings presented in this thesis are: 1. Over 144 environmental pollutants have been confirmed as TTR-binders in vitro and these cover a wide range of environmental pollutants and show distinct chemical profiles including a large group of halogenated aromatic compounds and a second group of per- and polyfluoroalkyl substances. (Paper I) 2. In total 485 organic contaminants have been reported to be detected in household dust. The developed QSAR classification model predicted 7.6% of these dust contaminants and 53.1% of their metabolites as potential TTR-binders, which emphasizes the importance of metabolic bioactivation. After in vitro validation, four novel TTR binders with IC50 ≤ 10 µM were identified, i.e. perfluoroheptanesulfonic acid, 2,4,2',4'-tetrahydroxybenzophenone (BP2), 2,4,5-trichlorophenoxyacetic acid, and 3,5,6-trichloro-2-pyridinol. (Paper II) 3. The development of a robust structure-based virtual screening (VS) protocol resu

Thyroid disruptions by xenobiotics have been associated with a broad spectrum of severe adverse human health effects, such as impaired brain development and metabolic syndrome. Ingestion of indoor dust and contact with industrial chemicals are two significant human exposure routes of thyroid hormone disrupting chemicals (THDCs), raising serious concerns for human health. However, it is a laborious and costly process to identify THDCs using conventional experimental methods, due to the number of chemicals in commerce and the varieties of potential disruption mechanisms. In this thesis, we are aimed at in silico identification of novel THDCs targeting transthyretin (TTR) and thyroid hormone receptor (THR) among dust contaminants and commonly used industrial chemicals. In vitro assays were used to validate the in silico prediction results. Co-crystallization and molecular dynamics (MD) simulations were applied to reveal binding modes of THDCs at the studied biological targets and to explain their intermolecular recognition. The main findings presented in this thesis are: 1. Over 144 environmental pollutants have been confirmed as TTR-binders in vitro and these cover a wide range of environmental pollutants and show distinct chemical profiles including a large group of halogenated aromatic compounds and a second group of per- and polyfluoroalkyl substances. (Paper I) 2. In total 485 organic contaminants have been reported to be detected in household dust. The developed QSAR classification model predicted 7.6% of these dust contaminants and 53.1% of their metabolites as potential TTR-binders, which emphasizes the importance of metabolic bioactivation. After in vitro validation, four novel TTR binders with IC50 ≤ 10 µM were identified, i.e. perfluoroheptanesulfonic acid, 2,4,2',4'-tetrahydroxybenzophenone (BP2), 2,4,5-trichlorophenoxyacetic acid, and 3,5,6-trichloro-2-pyridinol. (Paper II) 3. The development of a robust structure-based virtual screening (VS) protocol resu

Quantitative structure-activity relationship (QSAR) models have gained popularity in the pharmaceutical industry due to their potential to substantially decrease drug development costs by reducing expensive laboratory and clinical tests. QSAR modeling consists of two fundamental steps, namely, descriptor discovery and model building. Descriptor discovery methods are either based on chemical domain knowledge or purely data-driven. The former, chemoinformatics-based, and the latter, substructures-based, methods for QSAR modeling, have been developed quite independently. As a consequence, evaluations involving both types of descriptor discovery method are rarely seen. In this study, a comparative analysis of chemoinformatics-based and substructure-based approaches is presented. Two chemoinformatics-based approaches; ECFI and SELMA, are compared to five approaches for substructure discovery; CP, graphSig, MFI, MoFa and SUBDUE, using 18 QSAR datasets. The empirical investigation shows that one of the chemo-informatics-based approaches, ECFI, results in significantly more accurate models compared to all other methods, when used on their own. Results from combining descriptor sets are also presented, showing that the addition of ECFI descriptors to any other descriptor set leads to improved predictive performance for that set, while the use of ECFI descriptors in many cases also can be improved by adding descriptors generated by the other methods.

Quantitative structure-activity relationship (QSAR) models have gained popularity in the pharmaceutical industry due to their potential to substantially decrease drug development costs by reducing expensive laboratory and clinical tests. QSAR modeling consists of two fundamental steps, namely, descriptor discovery and model building. Descriptor discovery methods are either based on chemical domain knowledge or purely data-driven. The former, chemoinformatics-based, and the latter, substructures-based, methods for QSAR modeling, have been developed quite independently. As a consequence, evaluations involving both types of descriptor discovery method are rarely seen. In this study, a comparative analysis of chemoinformatics-based and substructure-based approaches is presented. Two chemoinformatics-based approaches; ECFI and SELMA, are compared to five approaches for substructure discovery; CP, graphSig, MFI, MoFa and SUBDUE, using 18 QSAR datasets. The empirical investigation shows that one of the chemo-informatics-based approaches, ECFI, results in significantly more accurate models compared to all other methods, when used on their own. Results from combining descriptor sets are also presented, showing that the addition of ECFI descriptors to any other descriptor set leads to improved predictive performance for that set, while the use of ECFI descriptors in many cases also can be improved by adding descriptors generated by the other methods., QC 20180123

Quantitative structure-activity relationship (QSAR) models have gained popularity in the pharmaceutical industry due to their potential to substantially decrease drug development costs by reducing expensive laboratory and clinical tests. QSAR modeling consists of two fundamental steps, namely, descriptor discovery and model building. Descriptor discovery methods are either based on chemical domain knowledge or purely data-driven. The former, chemoinformatics-based, and the latter, substructures-based, methods for QSAR modeling, have been developed quite independently. As a consequence, evaluations involving both types of descriptor discovery method are rarely seen. In this study, a comparative analysis of chemoinformatics-based and substructure-based approaches is presented. Two chemoinformatics-based approaches; ECFI and SELMA, are compared to five approaches for substructure discovery; CP, graphSig, MFI, MoFa and SUBDUE, using 18 QSAR datasets. The empirical investigation shows that one of the chemo-informatics-based approaches, ECFI, results in significantly more accurate models compared to all other methods, when used on their own. Results from combining descriptor sets are also presented, showing that the addition of ECFI descriptors to any other descriptor set leads to improved predictive performance for that set, while the use of ECFI descriptors in many cases also can be improved by adding descriptors generated by the other methods., AuthorCount:3

Quantitative structure-activity relationship (QSAR) models have gained popularity in the pharmaceutical industry due to their potential to substantially decrease drug development costs by reducing expensive laboratory and clinical tests. QSAR modeling consists of two fundamental steps, namely, descriptor discovery and model building. Descriptor discovery methods are either based on chemical domain knowledge or purely data-driven. The former, chemoinformatics-based, and the latter, substructures-based, methods for QSAR modeling, have been developed quite independently. As a consequence, evaluations involving both types of descriptor discovery method are rarely seen. In this study, a comparative analysis of chemoinformatics-based and substructure-based approaches is presented. Two chemoinformatics-based approaches; ECFI and SELMA, are compared to five approaches for substructure discovery; CP, graphSig, MFI, MoFa and SUBDUE, using 18 QSAR datasets. The empirical investigation shows that one of the chemo-informatics-based approaches, ECFI, results in significantly more accurate models compared to all other methods, when used on their own. Results from combining descriptor sets are also presented, showing that the addition of ECFI descriptors to any other descriptor set leads to improved predictive performance for that set, while the use of ECFI descriptors in many cases also can be improved by adding descriptors generated by the other methods., Forskningsfinansiär: Swedish Foundation for Strategic Research through the project High-Performance Data Mining for Drug Effect Detection at Stockholm University, Sweden, Grant Number: IIS11-0053