1
|
Segovia-Zafra A, Di Zeo-Sánchez DE, López-Gómez C, Pérez-Valdés Z, García-Fuentes E, Andrade RJ, Lucena MI, Villanueva-Paz M. Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction. Acta Pharm Sin B 2021; 11:3685-3726. [PMID: 35024301 PMCID: PMC8727925 DOI: 10.1016/j.apsb.2021.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/07/2021] [Accepted: 11/10/2021] [Indexed: 02/08/2023] Open
Abstract
Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical human in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.
Collapse
Affiliation(s)
- Antonio Segovia-Zafra
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid 28029, Spain
| | - Daniel E. Di Zeo-Sánchez
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
| | - Carlos López-Gómez
- Unidad de Gestión Clínica de Aparato Digestivo, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga 29010, Spain
| | - Zeus Pérez-Valdés
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
| | - Eduardo García-Fuentes
- Unidad de Gestión Clínica de Aparato Digestivo, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga 29010, Spain
| | - Raúl J. Andrade
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid 28029, Spain
| | - M. Isabel Lucena
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid 28029, Spain
- Platform ISCIII de Ensayos Clínicos, UICEC-IBIMA, Málaga 29071, Spain
| | - Marina Villanueva-Paz
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
| |
Collapse
|
2
|
Pognan F, Steger-Hartmann T, Díaz C, Blomberg N, Bringezu F, Briggs K, Callegaro G, Capella-Gutierrez S, Centeno E, Corvi J, Drew P, Drewe WC, Fernández JM, Furlong LI, Guney E, Kors JA, Mayer MA, Pastor M, Piñero J, Ramírez-Anguita JM, Ronzano F, Rowell P, Saüch-Pitarch J, Valencia A, van de Water B, van der Lei J, van Mulligen E, Sanz F. The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives. Pharmaceuticals (Basel) 2021; 14:ph14030237. [PMID: 33800393 PMCID: PMC7999019 DOI: 10.3390/ph14030237] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/25/2021] [Accepted: 02/27/2021] [Indexed: 12/19/2022] Open
Abstract
eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.
Collapse
Affiliation(s)
- François Pognan
- Preclinical Safety/Translational Medicine, Novartis, 4057 Basel, Switzerland;
| | | | - Carlos Díaz
- Synapse Research Managers SL, 28006 Madrid, Spain;
| | | | - Frank Bringezu
- Chemical & Preclinical Safety, Merck Healthcare KGaA, 64293 Darmstadt, Germany;
| | | | - Giulia Callegaro
- Leiden Academic Centre for Drug Research (LACDR), Leiden University, 2300 RA Leiden, The Netherlands; (G.C.); (B.v.d.W.)
| | | | - Emilio Centeno
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Javier Corvi
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; (S.C.-G.); (J.C.); (J.M.F.); (A.V.)
| | | | | | - José M. Fernández
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; (S.C.-G.); (J.C.); (J.M.F.); (A.V.)
| | - Laura I. Furlong
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
- MedBioinformatics Solutions SL, 08018 Barcelona, Spain
| | - Emre Guney
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Jan A. Kors
- Department of Medical Informatics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (J.A.K.); (J.v.d.L.); (E.v.M.)
| | - Miguel Angel Mayer
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Manuel Pastor
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Janet Piñero
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Juan Manuel Ramírez-Anguita
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Francesco Ronzano
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Philip Rowell
- Lhasa Limited, Leeds LS11 5PS, UK; (K.B.); (W.C.D.); (P.R.)
| | - Josep Saüch-Pitarch
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; (S.C.-G.); (J.C.); (J.M.F.); (A.V.)
- Catalan Institution for Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
| | - Bob van de Water
- Leiden Academic Centre for Drug Research (LACDR), Leiden University, 2300 RA Leiden, The Netherlands; (G.C.); (B.v.d.W.)
| | - Johan van der Lei
- Department of Medical Informatics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (J.A.K.); (J.v.d.L.); (E.v.M.)
| | - Erik van Mulligen
- Department of Medical Informatics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (J.A.K.); (J.v.d.L.); (E.v.M.)
| | - Ferran Sanz
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
- Correspondence:
| |
Collapse
|
3
|
Pastor M, Quintana J, Sanz F. Development of an Infrastructure for the Prediction of Biological Endpoints in Industrial Environments. Lessons Learned at the eTOX Project. Front Pharmacol 2018; 9:1147. [PMID: 30364191 PMCID: PMC6193068 DOI: 10.3389/fphar.2018.01147] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 09/21/2018] [Indexed: 11/13/2022] Open
Abstract
In silico methods are increasingly being used for assessing the chemical safety of substances, as a part of integrated approaches involving in vitro and in vivo experiments. A paradigmatic example of these strategies is the eTOX project http://www.etoxproject.eu, funded by the European Innovative Medicines Initiative (IMI), which aimed at producing high quality predictions of in vivo toxicity of drug candidates and resulted in generating about 200 models for diverse endpoints of toxicological interest. In an industry-oriented project like eTOX, apart from the predictive quality, the models need to meet other quality parameters related to the procedures for their generation and their intended use. For example, when the models are used for predicting the properties of drug candidates, the prediction system must guarantee the complete confidentiality of the compound structures. The interface of the system must be designed to provide non-expert users all the information required to choose the models and appropriately interpret the results. Moreover, procedures like installation, maintenance, documentation, validation and versioning, which are common in software development, must be also implemented for the models and for the prediction platform in which they are implemented. In this article we describe our experience in the eTOX project and the lessons learned after 7 years of close collaboration between industrial and academic partners. We believe that some of the solutions found and the tools developed could be useful for supporting similar initiatives in the future.
Collapse
Affiliation(s)
| | | | - Ferran Sanz
- *Correspondence: Manuel Pastor, Ferran Sanz,
| |
Collapse
|
4
|
Romero L, Cano J, Gomis-Tena J, Trenor B, Sanz F, Pastor M, Saiz J. In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk. J Chem Inf Model 2018; 58:867-878. [PMID: 29547274 DOI: 10.1021/acs.jcim.7b00440] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies. For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval. However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs. The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk. The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue. Multiple channel-drug interactions and state-of-the-art human ventricular action potential models ( O'Hara , T. , PLos Comput. Biol. 2011 , 7 , e1002061 ) were used in our simulations. Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current ( IKs and IKr, respectively) and the L-type calcium current ( ICaL) at different levels. The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties. On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC). Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds. Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC50 based test.
Collapse
Affiliation(s)
- Lucia Romero
- Centro de Investigación e Innovación en Bioingeniería (CI2B) , Universitat Politècnica de València , camino de Vera, s/n , 46022 Valencia , Spain
| | - Jordi Cano
- Centro de Investigación e Innovación en Bioingeniería (CI2B) , Universitat Politècnica de València , camino de Vera, s/n , 46022 Valencia , Spain
| | - Julio Gomis-Tena
- Centro de Investigación e Innovación en Bioingeniería (CI2B) , Universitat Politècnica de València , camino de Vera, s/n , 46022 Valencia , Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (CI2B) , Universitat Politècnica de València , camino de Vera, s/n , 46022 Valencia , Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Department of Experimental and Health Sciences , Universitat Pompeu Fabra , Carrer del Dr. Aiguader 88 , 08002 Barcelona , Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Department of Experimental and Health Sciences , Universitat Pompeu Fabra , Carrer del Dr. Aiguader 88 , 08002 Barcelona , Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (CI2B) , Universitat Politècnica de València , camino de Vera, s/n , 46022 Valencia , Spain
| |
Collapse
|
5
|
López-Massaguer O, Pinto-Gil K, Sanz F, Amberg A, Anger LT, Stolte M, Ravagli C, Marc P, Pastor M. Generating Modeling Data From Repeat-Dose Toxicity Reports. Toxicol Sci 2018; 162:287-300. [PMID: 29155963 PMCID: PMC5837688 DOI: 10.1093/toxsci/kfx254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Over the past decades, pharmaceutical companies have conducted a large number of high-quality in vivo repeat-dose toxicity (RDT) studies for regulatory purposes. As part of the eTOX project, a high number of these studies have been compiled and integrated into a database. This valuable resource can be queried directly, but it can be further exploited to build predictive models. As the studies were originally conducted to investigate the properties of individual compounds, the experimental conditions across the studies are highly heterogeneous. Consequently, the original data required normalization/standardization, filtering, categorization and integration to make possible any data analysis (such as building predictive models). Additionally, the primary objectives of the RDT studies were to identify toxicological findings, most of which do not directly translate to in vivo endpoints. This article describes a method to extract datasets containing comparable toxicological properties for a series of compounds amenable for building predictive models. The proposed strategy starts with the normalization of the terms used within the original reports. Then, comparable datasets are extracted from the database by applying filters based on the experimental conditions. Finally, carefully selected profiles of toxicological findings are mapped to endpoints of interest, generating QSAR-like tables. In this work, we describe in detail the strategy and tools used for carrying out these transformations and illustrate its application in a data sample extracted from the eTOX database. The suitability of the resulting tables for developing hazard-predicting models was investigated by building proof-of-concept models for in vivo liver endpoints.
Collapse
Affiliation(s)
- Oriol López-Massaguer
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Kevin Pinto-Gil
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | | | - Lennart T Anger
- Sanofi, Preclinical Safety, 65926 Frankfurt am Main, Germany
| | - Manuela Stolte
- Sanofi, Preclinical Safety, 65926 Frankfurt am Main, Germany
| | - Carlo Ravagli
- Translational Medicine, Novartis Institute for Biomedical Research, CH-4002 Basel, Switzerland
| | - Philippe Marc
- Translational Medicine, Novartis Institute for Biomedical Research, CH-4002 Basel, Switzerland
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Universitat Pompeu Fabra, 08003 Barcelona, Spain
| |
Collapse
|
6
|
Hevener KE. Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation. Methods Mol Biol 2018; 1800:275-285. [PMID: 29934898 DOI: 10.1007/978-1-4939-7899-1_13] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process, is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This chapter focuses on the application of computational molecular filters, applied either prescreening or postscreening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.
Collapse
Affiliation(s)
- Kirk E Hevener
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA.
| |
Collapse
|
7
|
López-Massaguer O, Sanz F, Pastor M. An automated tool for obtaining QSAR-ready series of compounds using semantic web technologies. Bioinformatics 2017; 34:131-133. [DOI: 10.1093/bioinformatics/btx566] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 09/06/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Oriol López-Massaguer
- Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| |
Collapse
|
8
|
Tetko IV, Maran U, Tropsha A. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development. Mol Inform 2016; 36. [PMID: 27778468 DOI: 10.1002/minf.201600082] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 10/03/2016] [Indexed: 01/08/2023]
Abstract
Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.
Collapse
Affiliation(s)
- Igor V Tetko
- Institute of Structural Biology, Helmholtz Zentrum München -, German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-, 85764, Neuherberg, Germany.,BigChem GmbH, Ingolstädter Landstraße 1, b. 60w, D-, 85764, Neuherberg, Germany
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu, 50411, Estonia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Butlerov Institute of Chemistry, Kazan Federal University, Kremlyovskaya St. 18, 420008, Kazan, Russia
| |
Collapse
|
9
|
Schwarz T, Montanari F, Cseke A, Wlcek K, Visvader L, Palme S, Chiba P, Kuchler K, Urban E, Ecker GF. Subtle Structural Differences Trigger Inhibitory Activity of Propafenone Analogues at the Two Polyspecific ABC Transporters: P-Glycoprotein (P-gp) and Breast Cancer Resistance Protein (BCRP). ChemMedChem 2016; 11:1380-94. [PMID: 26970257 PMCID: PMC4949556 DOI: 10.1002/cmdc.201500592] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 02/03/2016] [Indexed: 12/18/2022]
Abstract
The transmembrane ABC transporters P‐glycoprotein (P‐gp) and breast cancer resistance protein (BCRP) are widely recognized for their role in cancer multidrug resistance and absorption and distribution of compounds. Furthermore, they are linked to drug–drug interactions and toxicity. Nevertheless, due to their polyspecificity, a molecular understanding of the ligand‐transporter interaction, which allows designing of both selective and dual inhibitors, is still in its infancy. This study comprises a combined approach of synthesis, in silico prediction, and in vitro testing to identify molecular features triggering transporter selectivity. Synthesis and testing of a series of 15 propafenone analogues with varied rigidity and basicity of substituents provide first trends for selective and dual inhibitors. Results indicate that both the flexibility of the substituent at the nitrogen atom, as well as the basicity of the nitrogen atom, trigger transporter selectivity. Furthermore, inhibitory activity of compounds at P‐gp seems to be much more influenced by logP than those at BCRP. Exploiting these differences further should thus allow designing specific inhibitors for these two polyspecific ABC‐transporters.
Collapse
Affiliation(s)
- Theresa Schwarz
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Floriane Montanari
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Anna Cseke
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Katrin Wlcek
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Lene Visvader
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Sarah Palme
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Peter Chiba
- Department of Medicinal Chemistry, Medical University Vienna, Währingerstraße 10, 1090, Vienna, Austria
| | - Karl Kuchler
- Department of Medical Biochemistry, Max F. Perutz Laboratories, Medical University Vienna, Dr. Bohr-Gasse 9/2, 1030, Vienna, Austria
| | - Ernst Urban
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Gerhard F Ecker
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria.
| |
Collapse
|
10
|
Carrió P, Sanz F, Pastor M. Toward a unifying strategy for the structure-based prediction of toxicological endpoints. Arch Toxicol 2015; 90:2445-60. [PMID: 26553148 DOI: 10.1007/s00204-015-1618-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 10/19/2015] [Indexed: 01/13/2023]
Abstract
Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.
Collapse
Affiliation(s)
- Pau Carrió
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003, Barcelona, Spain.
| |
Collapse
|