Timely drug discovery and toxicology approaches have seen a rise in strategies which use data as ... more Timely drug discovery and toxicology approaches have seen a rise in strategies which use data as a basis for decisions at various stages. Such approaches include (automated) data integration and curation efforts, predictive machine learning approaches, as well as structure-based molecular design strategies that make use of the wealth of publicly available data sources and data types. In this talk, various computational workflows which have been developed in my lab for addressing research questions related to toxicology will be presented. In one project, ligand- and structure-based methods have been combined in an effective data-driven manner to decipher the molecular basis of ligand recognition and selectivity for hepatic Organic Anion Transporting Polypeptides (OATPs). In the framework of this successful project, novel highly potent inhibitors of these SLC uptake transporters have been identified by an AI-driven virtual screening approach. At the other end of the spectrum, we are u...
Additional file 1. The list of descriptor names, instructions on how to run the python script, th... more Additional file 1. The list of descriptor names, instructions on how to run the python script, the distribution plots for the important descriptors, the heat map of activities for the dense dataset, the structure of the over-represented scaffolds in the sparse dataset, a 2D representation of a PCA run on Morgan fingerprints (ECFP-like) for both dense and sparse datasets, and the structures of the 9 misclassified compounds.
Background The human ATP binding cassette transporters Breast Cancer Resistance Protein (BCRP) an... more Background The human ATP binding cassette transporters Breast Cancer Resistance Protein (BCRP) and Multidrug Resistance Protein 1 (P-gp) are co-expressed in many tissues and barriers, especially at the bloodâ brain barrier and at the hepatocyte canalicular membrane. Understanding their interplay in affecting the pharmacokinetics of drugs is of prime interest. In silico tools to predict inhibition and substrate profiles towards BCRP and P-gp might serve as early filters in the drug discovery and development process. However, to build such models, pharmacological data must be collected for both targets, which is a tedious task, often involving manual and poorly reproducible steps. Results Compounds with inhibitory activity measured against BCRP and/or P-gp were retrieved by combining Open Data and manually curated data from literature using a KNIME workflow. After determination of compound overlap, machine learning approaches were used to establish multi-label classification models fo...
This read-across case study characterises thirteen, structurally similar carboxylic acids demonst... more This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.
Correction for 'From linked open data to molecular interaction: studying selectivity trends f... more Correction for 'From linked open data to molecular interaction: studying selectivity trends for ligands of the human serotonin and dopamine transporter’ by Barbara Zdrazil et al., Med. Chem. Commun., 2016, 7, 1819–1831.
Transport proteins represent an eminent class of drug targets and ADMET (absorption, distribution... more Transport proteins represent an eminent class of drug targets and ADMET (absorption, distribution, metabolism, excretion, toxicity) associated genes. There exists a large number of distinct activity assays for transport proteins, depending on not only the measurement needed (e.g. transport activity, strength of ligand–protein interaction), but also due to heterogeneous assay setups used by different research groups. Efforts to systematically organize this (divergent) bioassay data have large potential impact in Public-Private partnership and conventional commercial drug discovery. In this short review, we highlight some of the frequently used high-throughput assays for transport proteins, and we discuss emerging assay ontologies and their application to this field. Focusing on human P-glycoprotein (Multidrug resistance protein 1; gene name: ABCB1, MDR1), we exemplify how annotation of bioassay data per target class could improve and add to existing ontologies, and we propose to include an additional layer of metadata supporting data fusion across different bioassays.
Within the last decade open data concepts has been gaining increasing interest in the area of dru... more Within the last decade open data concepts has been gaining increasing interest in the area of drug discovery. With the launch of ChEMBL and PubChem, an enormous amount of bioactivity data was made easily accessible to the public domain. In addition, platforms that semantically integrate those data, such as the Open PHACTS Discovery Platform, permit querying across different domains of open life science data beyond the concept of ligand-target-pharmacology. However, most public databases are compiled from literature sources and are thus heterogeneous in their coverage. In addition, assay descriptions are not uniform and most often lack relevant information in the primary literature and, consequently, in databases. This raises the question how useful large public data sources are for deriving computational models. In this perspective, we highlight selected open-source initiatives and outline the possibilities and also the limitations when exploiting this huge amount of bioactivity data.
Journal of Enzyme Inhibition and Medicinal Chemistry, 2010
The human polymerase α (pol α) is a promising target for the therapy of cancer e.g. of the skin. ... more The human polymerase α (pol α) is a promising target for the therapy of cancer e.g. of the skin. The authors recently built a homology model of the active site of human DNA pol α. This 3D model was now used for molecular modelling studies with eight novel analogues of 2-butylanilino-dATP, which is a highly selective nucleoside inhibitor of mammalian pol α. Our results suggest that a higher hydrophobicity of a carbohydrate side chain (pointing into a spacious hydrophobic cavity) may enhance the strength of the interaction with the target protein. Moreover, acyclic acyclovir-like derivatives outperformed those with a sugar-moiety, indicating that structural flexibility and higher conformational adaptability has a positive effect on the receptor affinity. Cytotoxicity tests confirmed our theoretical findings. Besides, one of our most promising compounds in the molecular modelling studies revealed high selectivity for the SCC-25 cell line derived from squamous cell carcinoma in man.
Drug Discovery is a lengthy and costly process and has faced a period of declining productivity w... more Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to inspect target innovation trends in drug discovery. The study highlights protein families and classes that have received more attention and those that have just emerged in the scientific literature, thus highlighting novel opportunities for drug intervention. In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of diseases, targets associated with cancer-related pathologies, intellectual disabili...
Timely drug discovery and toxicology approaches have seen a rise in strategies which use data as ... more Timely drug discovery and toxicology approaches have seen a rise in strategies which use data as a basis for decisions at various stages. Such approaches include (automated) data integration and curation efforts, predictive machine learning approaches, as well as structure-based molecular design strategies that make use of the wealth of publicly available data sources and data types. In this talk, various computational workflows which have been developed in my lab for addressing research questions related to toxicology will be presented. In one project, ligand- and structure-based methods have been combined in an effective data-driven manner to decipher the molecular basis of ligand recognition and selectivity for hepatic Organic Anion Transporting Polypeptides (OATPs). In the framework of this successful project, novel highly potent inhibitors of these SLC uptake transporters have been identified by an AI-driven virtual screening approach. At the other end of the spectrum, we are u...
Additional file 1. The list of descriptor names, instructions on how to run the python script, th... more Additional file 1. The list of descriptor names, instructions on how to run the python script, the distribution plots for the important descriptors, the heat map of activities for the dense dataset, the structure of the over-represented scaffolds in the sparse dataset, a 2D representation of a PCA run on Morgan fingerprints (ECFP-like) for both dense and sparse datasets, and the structures of the 9 misclassified compounds.
Background The human ATP binding cassette transporters Breast Cancer Resistance Protein (BCRP) an... more Background The human ATP binding cassette transporters Breast Cancer Resistance Protein (BCRP) and Multidrug Resistance Protein 1 (P-gp) are co-expressed in many tissues and barriers, especially at the bloodâ brain barrier and at the hepatocyte canalicular membrane. Understanding their interplay in affecting the pharmacokinetics of drugs is of prime interest. In silico tools to predict inhibition and substrate profiles towards BCRP and P-gp might serve as early filters in the drug discovery and development process. However, to build such models, pharmacological data must be collected for both targets, which is a tedious task, often involving manual and poorly reproducible steps. Results Compounds with inhibitory activity measured against BCRP and/or P-gp were retrieved by combining Open Data and manually curated data from literature using a KNIME workflow. After determination of compound overlap, machine learning approaches were used to establish multi-label classification models fo...
This read-across case study characterises thirteen, structurally similar carboxylic acids demonst... more This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.
Correction for 'From linked open data to molecular interaction: studying selectivity trends f... more Correction for 'From linked open data to molecular interaction: studying selectivity trends for ligands of the human serotonin and dopamine transporter’ by Barbara Zdrazil et al., Med. Chem. Commun., 2016, 7, 1819–1831.
Transport proteins represent an eminent class of drug targets and ADMET (absorption, distribution... more Transport proteins represent an eminent class of drug targets and ADMET (absorption, distribution, metabolism, excretion, toxicity) associated genes. There exists a large number of distinct activity assays for transport proteins, depending on not only the measurement needed (e.g. transport activity, strength of ligand–protein interaction), but also due to heterogeneous assay setups used by different research groups. Efforts to systematically organize this (divergent) bioassay data have large potential impact in Public-Private partnership and conventional commercial drug discovery. In this short review, we highlight some of the frequently used high-throughput assays for transport proteins, and we discuss emerging assay ontologies and their application to this field. Focusing on human P-glycoprotein (Multidrug resistance protein 1; gene name: ABCB1, MDR1), we exemplify how annotation of bioassay data per target class could improve and add to existing ontologies, and we propose to include an additional layer of metadata supporting data fusion across different bioassays.
Within the last decade open data concepts has been gaining increasing interest in the area of dru... more Within the last decade open data concepts has been gaining increasing interest in the area of drug discovery. With the launch of ChEMBL and PubChem, an enormous amount of bioactivity data was made easily accessible to the public domain. In addition, platforms that semantically integrate those data, such as the Open PHACTS Discovery Platform, permit querying across different domains of open life science data beyond the concept of ligand-target-pharmacology. However, most public databases are compiled from literature sources and are thus heterogeneous in their coverage. In addition, assay descriptions are not uniform and most often lack relevant information in the primary literature and, consequently, in databases. This raises the question how useful large public data sources are for deriving computational models. In this perspective, we highlight selected open-source initiatives and outline the possibilities and also the limitations when exploiting this huge amount of bioactivity data.
Journal of Enzyme Inhibition and Medicinal Chemistry, 2010
The human polymerase α (pol α) is a promising target for the therapy of cancer e.g. of the skin. ... more The human polymerase α (pol α) is a promising target for the therapy of cancer e.g. of the skin. The authors recently built a homology model of the active site of human DNA pol α. This 3D model was now used for molecular modelling studies with eight novel analogues of 2-butylanilino-dATP, which is a highly selective nucleoside inhibitor of mammalian pol α. Our results suggest that a higher hydrophobicity of a carbohydrate side chain (pointing into a spacious hydrophobic cavity) may enhance the strength of the interaction with the target protein. Moreover, acyclic acyclovir-like derivatives outperformed those with a sugar-moiety, indicating that structural flexibility and higher conformational adaptability has a positive effect on the receptor affinity. Cytotoxicity tests confirmed our theoretical findings. Besides, one of our most promising compounds in the molecular modelling studies revealed high selectivity for the SCC-25 cell line derived from squamous cell carcinoma in man.
Drug Discovery is a lengthy and costly process and has faced a period of declining productivity w... more Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to inspect target innovation trends in drug discovery. The study highlights protein families and classes that have received more attention and those that have just emerged in the scientific literature, thus highlighting novel opportunities for drug intervention. In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of diseases, targets associated with cancer-related pathologies, intellectual disabili...
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