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Brigo A, Naga D, Muster W. Increasing the Value of Data Within a Large Pharmaceutical Company Through In Silico Models. Methods Mol Biol 2022; 2425:637-674. [PMID: 35188649 DOI: 10.1007/978-1-0716-1960-5_24] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The present contribution describes how in silico models and methods are applied at different stages of the drug discovery process in the pharmaceutical industry. A description of the most relevant computational methods and tools is given along with an evaluation of their performance in the assessment of potential genotoxic impurities and the prediction of off-target in vitro pharmacology. The challenges of predicting the outcome of highly complex in vivo studies are discussed followed by considerations on how novel ways to manage, store, exchange, and analyze data may advance knowledge and facilitate modeling efforts. In this context, the current status of broad data sharing initiatives, namely, eTOX and eTransafe, will be described along with related projects that could significantly reduce the use of animals in drug discovery in the future.
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Affiliation(s)
- Alessandro Brigo
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Centre Basel, Basel, Switzerland.
| | - Doha Naga
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Centre Basel, Basel, Switzerland
- Department of Pharmaceutical Chemistry, Group of Pharmacoinformatics, University of Vienna, Wien, Austria
| | - Wolfgang Muster
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Centre Basel, Basel, Switzerland
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2
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Kadioglu O, Klauck SM, Fleischer E, Shan L, Efferth T. Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation. Arch Toxicol 2021; 95:2485-2495. [PMID: 34021777 PMCID: PMC8241674 DOI: 10.1007/s00204-021-03058-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 04/21/2021] [Indexed: 12/21/2022]
Abstract
The majority of drug candidates fails the approval phase due to unwanted toxicities and side effects. Establishment of an effective toxicity prediction platform is of utmost importance, to increase the efficiency of the drug discovery process. For this purpose, we developed a toxicity prediction platform with machine-learning strategies. Cardiotoxicity prediction was performed by establishing a model with five parameters (arrhythmia, cardiac failure, heart block, hypertension, myocardial infarction) and additional toxicity predictions such as hepatotoxicity, reproductive toxicity, mutagenicity, and tumorigenicity are performed by using Data Warrior and Pro-Tox-II software. As a case study, we selected artemisinin derivatives to evaluate the platform and to provide a list of safe artemisinin derivatives. Artemisinin from Artemisia annua was described first as an anti-malarial compound and later its anticancer properties were discovered. Here, random forest feature selection algorithm was used for the establishment of cardiotoxicity models. High AUC scores above 0.830 were achieved for all five cardiotoxicity indications. Using a chemical library of 374 artemisinin derivatives as a case study, 7 compounds (deoxydihydro-artemisinin, 3-hydroxy-deoxy-dihydroartemisinin, 3-desoxy-dihydroartemisinin, dihydroartemisinin-furano acetate-d3, deoxyartemisinin, artemisinin G, artemisinin B) passed the toxicity filtering process for hepatotoxicity, mutagenicity, tumorigenicity, and reproductive toxicity in addition to cardiotoxicity. Experimental validation with the cardiomyocyte cell line AC16 supported the findings from the in silico cardiotoxicity model predictions. Transcriptomic profiling of AC16 cells upon artemisinin B treatment revealed a similar gene expression profile as that of the control compound, dexrazoxane. In vivo experiments with a Zebrafish model further substantiated the in silico and in vitro data, as only slight cardiotoxicity in picomolar range was observed. In conclusion, our machine-learning approach combined with in vitro and in vivo experimentation represents a suitable method to predict cardiotoxicity of drug candidates.
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Affiliation(s)
- Onat Kadioglu
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128, Mainz, Germany
| | - Sabine M Klauck
- Division of Cancer Genome Research, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | | | - Letian Shan
- The First Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128, Mainz, Germany.
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3
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Green DVS, Pickett S, Luscombe C, Senger S, Marcus D, Meslamani J, Brett D, Powell A, Masson J. BRADSHAW: a system for automated molecular design. J Comput Aided Mol Des 2020; 34:747-765. [PMID: 31637565 PMCID: PMC7292824 DOI: 10.1007/s10822-019-00234-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 10/05/2019] [Indexed: 12/18/2022]
Abstract
This paper introduces BRADSHAW (Biological Response Analysis and Design System using an Heterogenous, Automated Workflow), a system for automated molecular design which integrates methods for chemical structure generation, experimental design, active learning and cheminformatics tools. The simple user interface is designed to facilitate access to large scale automated design whilst minimising software development required to introduce new algorithms, a critical requirement in what is a very fast moving field. The system embodies a philosophy of automation, best practice, experimental design and the use of both traditional cheminformatics and modern machine learning algorithms.
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Affiliation(s)
- Darren V S Green
- Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK.
| | - Stephen Pickett
- Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - Chris Luscombe
- Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - Stefan Senger
- Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - David Marcus
- Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - Jamel Meslamani
- Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA
| | - David Brett
- Tessella Ltd, Walkern Road, Stevenage, Hertfordshire, SG1 3QP, UK
| | - Adam Powell
- Tessella Ltd, Walkern Road, Stevenage, Hertfordshire, SG1 3QP, UK
| | - Jonathan Masson
- Tessella Ltd, Walkern Road, Stevenage, Hertfordshire, SG1 3QP, UK
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4
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Bruno A, Costantino G, Sartori L, Radi M. The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization. Curr Med Chem 2019; 26:3838-3873. [PMID: 29110597 DOI: 10.2174/0929867324666171107101035] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/27/2017] [Accepted: 09/28/2017] [Indexed: 01/04/2023]
Abstract
BACKGROUND Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. METHODS In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. RESULTS A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. CONCLUSION The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.
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Affiliation(s)
- Agostino Bruno
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Gabriele Costantino
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Luca Sartori
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
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Pinches MD, Thomas R, Porter R, Camidge L, Briggs K. Curation and analysis of clinical pathology parameters and histopathologic findings from eTOXsys, a large database project (eTOX) for toxicologic studies. Regul Toxicol Pharmacol 2019; 107:104396. [PMID: 31128168 DOI: 10.1016/j.yrtph.2019.05.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/07/2019] [Accepted: 05/21/2019] [Indexed: 11/30/2022]
Abstract
Large data sharing projects amongst the pharmaceutical industry have the potential to generate new insights using data on a scale that has not been previously available. A retrospective analysis of the preclinical toxicology data collected as part of the eTOX project was conducted with the aim to provide background rates and treatment-related value analysis on both clinical pathology and histopathology datasets. Incorporated into this analysis was an extensive data consolidation task to standardise all data. Reference intervals for common clinical pathology parameters in rat and dog were generated, alongside background histopathology incidence rates in the liver, heart and kidney. Systematically applied decision thresholds allowed consistent relabelling of data points considered anomalous, and maximum fold change estimates. Relabelling of anomalous data points was conducted for the histopathology data using a Bayesian model to identify dose-dependent increases in pathologies. The results of this study allow: newly generated data to be analysed using the same methodology, rates and distributions to be used when building predictive dose-response models, and the possibility to correlate clinical pathology findings with concurrent histopathology findings. In the first half of this paper we discuss data curation, in the second half we report on the analytical methods and results.
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Affiliation(s)
- Mark D Pinches
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Robert Thomas
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Rosemary Porter
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Lucinda Camidge
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Katharine Briggs
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK.
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Jain S, Grandits M, Ecker GF. Interspecies comparison of putative ligand binding sites of human, rat and mouse P-glycoprotein. Eur J Pharm Sci 2018; 122:134-143. [PMID: 29936088 PMCID: PMC6422297 DOI: 10.1016/j.ejps.2018.06.022] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 06/18/2018] [Accepted: 06/19/2018] [Indexed: 01/16/2023]
Abstract
Prior to the clinical phases of testing, safety, efficacy and pharmacokinetic profiles of lead compounds are evaluated in animal studies. These tests are primarily performed in rodents, such as mouse and rats. In order to reduce the number of animal experiments, computational models that predict the outcome of these studies and thus aid in prioritization of preclinical candidates are heavily needed. However, although computational models for human off-target interactions with decent quality are available, they cannot easily be transferred to rodents due to lack of respective data. In this study, we assess the transferability of human P-glycoprotein activity data for development of in silico models to predict in vivo effects in rats and mouse using a structure-based approach. P-glycoprotein (P-gp) is an ATP-dependent efflux transporter that transports xenobiotic compounds such as toxins and drugs out of cells and has a broad substrate and inhibitor specificity. Being mostly expressed at barriers, it influences the bioavailability of drugs and thus contributes also to toxicity. Comparison of the binding site interaction profiles of human, rat and mouse P-gp derived from docking studies with a set of common inhibitors suggests that the inhibitors share potentially similar binding modes. These findings encourage the use of in vitro human P-gp data for predicting in vivo effects in rodents and thus contributes to the 3Rs (Replace, Reduce and Refine) of animal experiments.
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Affiliation(s)
- Sankalp Jain
- University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
| | - Melanie Grandits
- University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
| | - Gerhard F Ecker
- University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria.
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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.
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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
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8
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Steger-Hartmann T, Pognan F. Improving the Safety Assessment of Chemicals and Drug Candidates by the Integration of Bioinformatics and Chemoinformatics Data. Basic Clin Pharmacol Toxicol 2018; 123 Suppl 5:29-36. [DOI: 10.1111/bcpt.12956] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 12/27/2017] [Indexed: 12/11/2022]
Affiliation(s)
| | - Francois Pognan
- Discovery and Investigative Safety; Novartis Pharma AG; Basel Switzerland
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9
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Abstract
The present contribution describes how in silico models are applied at different stages of the drug discovery process in the pharmaceutical industry. A thorough description of the most relevant computational methods and tools is given along with an in-depth evaluation of their performance in the context of potential genotoxic impurities assessment.The challenges of predicting the outcome of highly complex studies are discussed followed by considerations on how novel ways to manage, store, share and analyze data may advance knowledge and facilitate modeling efforts.
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Affiliation(s)
- Alessandro Brigo
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland.
| | - Wolfgang Muster
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
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10
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Briggs KA. Is preclinical data sharing the new norm? Drug Discov Today 2016; 23:499-502. [PMID: 27173642 DOI: 10.1016/j.drudis.2016.05.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Revised: 04/04/2016] [Accepted: 05/04/2016] [Indexed: 11/16/2022]
Abstract
Is preclinical data sharing the new norm? In my experience, it is certainly becoming more commonplace. However, it is not yet standard practice and remains the preserve of special projects. Here, I expound the benefits of sharing proprietary preclinical data using examples of successful initiatives. The main barriers to data sharing are then described, with suggestions for how these might be overcome. To maximise the benefits and minimise the risks involved, I suggest that organisations look to develop standard operating procedures for data sharing.
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Garcia-Serna R, Vidal D, Remez N, Mestres J. Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms. Chem Res Toxicol 2015; 28:1875-87. [PMID: 26360911 DOI: 10.1021/acs.chemrestox.5b00260] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety. The added value of such approaches is that, beyond the traditional identification of potentially labile chemical fragments for selected toxicity end points, they have the potential to provide mechanistic insights for a much larger and diverse set of safety events in a statistically sound nonsupervised manner, based on the similarity to drug classes, the interaction with secondary targets, and the interference with biological pathways. The combined identification of chemical and biological hazards enhances our ability to assess the safety risk of bioactive small molecules with higher confidence than that using structural alerts only. We are still a very long way from reliably predicting drug safety, but advances toward gaining a better understanding of the mechanisms leading to adverse outcomes represent a step forward in this direction.
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Affiliation(s)
- Ricard Garcia-Serna
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - David Vidal
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - Nikita Remez
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain.,Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Jordi Mestres
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain.,Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
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12
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Otava M, Shkedy Z, Talloen W, Verheyen GR, Kasim A. Identification of in vitro and in vivo disconnects using transcriptomic data. BMC Genomics 2015; 16:615. [PMID: 26282683 PMCID: PMC4539666 DOI: 10.1186/s12864-015-1726-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2015] [Accepted: 06/26/2015] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Integrating transcriptomic experiments within drug development is increasingly advocated for the early detection of toxicity. This is partly to reduce costs related to drug failures in the late, and expensive phases of clinical trials. Such an approach has proven useful both in the study of toxicology and carcinogenicity. However, general lack of translation of in vitro findings to in vivo systems remains one of the bottle necks in drug development. This paper proposes a method for identifying disconnected genes between in vitro and in vivo toxicogenomic rat experiments. The analytical framework is based on the joint modeling of dose-dependent in vitro and in vivo data using a fractional polynomial framework and biclustering algorithm. RESULTS Most disconnected genes identified belonged to known pathways, such as drug metabolism and oxidative stress due to reactive metabolites, bilirubin increase, glutathion depletion and phospholipidosis. We also identified compounds that were likely to induce disconnect in gene expression between in vitro and in vivo toxicogenomic rat experiments. These compounds include: sulindac and diclofenac (both linked to liver damage), naphtyl isothiocyanate (linked to hepatoxocity), indomethacin and naproxen (linked to gastrointestinal problem and damage of intestines). CONCLUSION The results confirmed that there are important discrepancies between in vitro and in vivo toxicogenomic experiments. However, the contribution of this paper is to provide a tool to identify genes that are disconnected between the two systems. Pathway analysis of disconnected genes may improve our understanding of uncertainties in the mechanism of actions of drug candidates in humans, especially concerning the early detection of toxicity.
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Affiliation(s)
- Martin Otava
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Martelarenlaan 32, Hasselt, 3500, Belgium.
| | - Ziv Shkedy
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Martelarenlaan 32, Hasselt, 3500, Belgium.
| | - Willem Talloen
- Janssen, Pharmaceutical companies of Johnson & Johnson, Turnhoutseweg 30, Beerse, 2340, Belgium.
| | | | - Adetayo Kasim
- Wolfson Research Institute for Health and Wellbeing, Durham University, University Boulevard, TS17 6BH Thornaby, Stockton-on-Tees, UK.
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Hewitt M, Ellison CM, Cronin MTD, Pastor M, Steger-Hartmann T, Munoz-Muriendas J, Pognan F, Madden JC. Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models. Adv Drug Deliv Rev 2015; 86:101-11. [PMID: 25794480 DOI: 10.1016/j.addr.2015.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 03/05/2015] [Accepted: 03/11/2015] [Indexed: 11/28/2022]
Abstract
The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project "eTOX" (electronic toxicity) and its application to the in silico models developed within the frame of this project.
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Affiliation(s)
- Mark Hewitt
- School of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, City Campus, Wulfruna Street, WV1 1SB, England, United Kingdom; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, United Kingdom.
| | - Claire M Ellison
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, United Kingdom.
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, United Kingdom.
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader 88, E-08003 Barcelona, Spain.
| | - Thomas Steger-Hartmann
- Bayer HealthCare, Bayer Pharma AG, Investigational Toxicology, Müllerstraße 178, 13352 Berlin, Germany.
| | - Jordi Munoz-Muriendas
- Chemical Sciences, Computational Chemistry, GlaxoSmithKline, Stevenage, SG1 2NY, England, United Kingdom.
| | - Francois Pognan
- Biochemical & Cellular Toxicology, Discovery Investigative Safety - PreClinical Safety, Novartis Pharma AG, Werk Klybeck, Postfach, CH-4002 Basel, Switzerland.
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, United Kingdom.
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14
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Sanz F, Carrió P, López O, Capoferri L, Kooi DP, Vermeulen NPE, Geerke DP, Montanari F, Ecker GF, Schwab CH, Kleinöder T, Magdziarz T, Pastor M. Integrative Modeling Strategies for Predicting Drug Toxicities at the eTOX Project. Mol Inform 2015; 34:477-84. [DOI: 10.1002/minf.201400193] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Accepted: 04/01/2015] [Indexed: 11/11/2022]
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Sahota T, Danhof M, Della Pasqua O. The impact of composite AUC estimates on the prediction of systemic exposure in toxicology experiments. J Pharmacokinet Pharmacodyn 2015; 42:251-61. [PMID: 25868863 PMCID: PMC4432106 DOI: 10.1007/s10928-015-9413-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2014] [Accepted: 03/20/2015] [Indexed: 12/18/2022]
Abstract
Current toxicity protocols relate measures of systemic exposure (i.e. AUC, Cmax) as obtained by non-compartmental analysis to observed toxicity. A complicating factor in this practice is the potential bias in the estimates defining safe drug exposure. Moreover, it prevents the assessment of variability. The objective of the current investigation was therefore (a) to demonstrate the feasibility of applying nonlinear mixed effects modelling for the evaluation of toxicokinetics and (b) to assess the bias and accuracy in summary measures of systemic exposure for each method. Here, simulation scenarios were evaluated, which mimic toxicology protocols in rodents. To ensure differences in pharmacokinetic properties are accounted for, hypothetical drugs with varying disposition properties were considered. Data analysis was performed using non-compartmental methods and nonlinear mixed effects modelling. Exposure levels were expressed as area under the concentration versus time curve (AUC), peak concentrations (Cmax) and time above a predefined threshold (TAT). Results were then compared with the reference values to assess the bias and precision of parameter estimates. Higher accuracy and precision were observed for model-based estimates (i.e. AUC, Cmax and TAT), irrespective of group or treatment duration, as compared with non-compartmental analysis. Despite the focus of guidelines on establishing safety thresholds for the evaluation of new molecules in humans, current methods neglect uncertainty, lack of precision and bias in parameter estimates. The use of nonlinear mixed effects modelling for the analysis of toxicokinetics provides insight into variability and should be considered for predicting safe exposure in humans.
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Affiliation(s)
- Tarjinder Sahota
- Division of Pharmacology, Leiden Academic Centre for Drug Research, University of Leiden, Leiden, The Netherlands
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Carrió P, López O, Sanz F, Pastor M. eTOXlab, an open source modeling framework for implementing predictive models in production environments. J Cheminform 2015; 7:8. [PMID: 25774224 PMCID: PMC4358905 DOI: 10.1186/s13321-015-0058-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2014] [Accepted: 02/24/2015] [Indexed: 11/10/2022] Open
Abstract
Background Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. Results We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. Conclusions The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by eTOXlab (web services, VM, object-oriented programming) provide an elegant solution to common practical issues; the system can be installed easily in heterogeneous environments and integrates well with other software. Moreover, the system provides a simple and safe solution for building models with confidential structures that can be shared without disclosing sensitive information. Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0058-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pau Carrió
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader 88, E-08003 Barcelona, Spain
| | - Oriol López
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader 88, E-08003 Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader 88, E-08003 Barcelona, Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader 88, E-08003 Barcelona, Spain
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Kasturi J, Brown AP, Brown P, Madhavan S, Prabakar L, Wally JL. Interconnectivity of Disparate Nonclinical Data Silos for Drug Discovery and Development. Ther Innov Regul Sci 2014; 48:498-506. [DOI: 10.1177/2168479014531421] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Briggs K, Barber C, Cases M, Marc P, Steger-Hartmann T. Value of shared preclinical safety studies - The eTOX database. Toxicol Rep 2014; 2:210-221. [PMID: 28962354 PMCID: PMC5598263 DOI: 10.1016/j.toxrep.2014.12.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Revised: 12/05/2014] [Accepted: 12/09/2014] [Indexed: 11/26/2022] Open
Abstract
First analysis of the eTOX database for 1214 drugs or drug candidates. Shared data mainly from short term <20 days preclinical studies in rat via oral route. Identified the most frequent treatment related findings. Evaluated predictivity of clinical chemistry biomarkers. Present a first use case of the database during early drug development.
A first analysis of a database of shared preclinical safety data for 1214 small molecule drugs and drug candidates extracted from 3970 reports donated by thirteen pharmaceutical companies for the eTOX project (www.etoxproject.eu) is presented. Species, duration of exposure and administration route data were analysed to assess if large enough subsets of homogenous data are available for building in silico predictive models. Prevalence of treatment related effects for the different types of findings recorded were analysed. The eTOX ontology was used to determine the most common treatment-related clinical chemistry and histopathology findings reported in the database. The data were then mined to evaluate sensitivity of established in vivo biomarkers for liver toxicity risk assessment. The value of the database to inform other drug development projects during early drug development is illustrated by a case study.
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Key Words
- ALP, alkaline phosphatase
- ALT, alanine aminotransferase
- AST, aspartate aminotransferase
- Biomarkers
- CDISC, Clinical Data Interchange Standards Consortium
- CRO, contract research organisation
- DILI, drug induced liver injury
- Data mining
- Data sharing
- EFPIA, European Federation of Pharmaceutical Industries and Associations
- FN, false negative
- FP, false positive
- GLP, good laboratory practice
- ICH, International Conference on Harmonisation
- IMI, Innovative Medicines Initiative
- INHAND, International Harmonization of Nomenclature and Diagnostic Criteria
- IT, information technology
- MCC, Matthews correlation coefficient
- OECD, Organisation for Economic Co-operation and Development
- Ontology
- PDF, Portable Document Format
- PDF/A, ISO-standardized version of PDF specialized for the digital preservation of electronic documents.
- QA, quality assurance
- SEND, Standard for Exchange of Nonclinical Data
- SME, small-to-medium enterprise
- TN, true negative
- TP, true positive
- Toxicology
- ULN, upper limit of normal
- eTOX, integrating bioinformatics and chemoinformatics approaches for the development of expert systems allowing the in silico prediction of toxicities
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Affiliation(s)
- Katharine Briggs
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, United Kingdom
| | - Chris Barber
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, United Kingdom
| | - Montserrat Cases
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
| | - Philippe Marc
- PreClinical Safety, Novartis Institute for Biomedical Research, Klybeckstrasse 141, CH-4057 Basel, Switzerland
| | - Thomas Steger-Hartmann
- Bayer Pharma AG, Bayer HealthCare, Investigational Toxicology, Müllerstrasse 178, D-13353 Berlin, Germany
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Hendrickx DM, Boyles RR, Kleinjans JCS, Dearry A. Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26-27 June 2013, Research Triangle Park, NC, USA. Arch Toxicol 2014; 88:2323-32. [PMID: 25326818 PMCID: PMC4247478 DOI: 10.1007/s00204-014-1387-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 10/08/2014] [Indexed: 10/25/2022]
Abstract
A joint US-EU workshop on enhancing data sharing and exchange in toxicogenomics was held at the National Institute for Environmental Health Sciences. Currently, efficient reuse of data is hampered by problems related to public data availability, data quality, database interoperability (the ability to exchange information), standardization and sustainability. At the workshop, experts from universities and research institutes presented databases, studies, organizations and tools that attempt to deal with these problems. Furthermore, a case study showing that combining toxicogenomics data from multiple resources leads to more accurate predictions in risk assessment was presented. All participants agreed that there is a need for a web portal describing the diverse, heterogeneous data resources relevant for toxicogenomics research. Furthermore, there was agreement that linking more data resources would improve toxicogenomics data analysis. To outline a roadmap to enhance interoperability between data resources, the participants recommend collecting user stories from the toxicogenomics research community on barriers in data sharing and exchange currently hampering answering to certain research questions. These user stories may guide the prioritization of steps to be taken for enhancing integration of toxicogenomics databases.
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Affiliation(s)
- Diana M Hendrickx
- Department of Toxicogenomics, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands,
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The eTOX data-sharing project to advance in silico drug-induced toxicity prediction. Int J Mol Sci 2014; 15:21136-54. [PMID: 25405742 PMCID: PMC4264217 DOI: 10.3390/ijms151121136] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Accepted: 10/20/2014] [Indexed: 11/16/2022] Open
Abstract
The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.
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Simon TW, Simons SS, Preston RJ, Boobis AR, Cohen SM, Doerrer NG, Fenner-Crisp PA, McMullin TS, McQueen CA, Rowlands JC. The use of mode of action information in risk assessment: Quantitative key events/dose-response framework for modeling the dose-response for key events. Crit Rev Toxicol 2014; 44 Suppl 3:17-43. [DOI: 10.3109/10408444.2014.931925] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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22
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Carrió P, Pinto M, Ecker G, Sanz F, Pastor M. Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions. J Chem Inf Model 2014; 54:1500-11. [PMID: 24821140 DOI: 10.1021/ci500172z] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
We report a novel method called ADAN (Applicability Domain ANalysis) for assessing the reliability of drug property predictions obtained by in silico methods. The assessment provided by ADAN is based on the comparison of the query compound with the training set, using six diverse similarity criteria. For every criterion, the query compound is considered out of range when the similarity value obtained is larger than the 95th percentile of the values obtained for the training set. The final outcome is a number in the range of 0-6 that expresses the number of unmet similarity criteria and allows classifying the query compound within seven reliability categories. Such categories can be further exploited to assign simpler reliability classes using a traffic light schema, to assign approximate confidence intervals or to mark the predictions as unreliable. The entire methodology has been validated simulating realistic conditions, where query compounds are structurally diverse from those in the training set. The validation exercise involved the construction of more than 1000 models. These models were built using a combination of training set, molecular descriptors, and modeling methods representative of the real predictive tasks performed in the eTOX project (a project whose objective is to predict in vivo toxicological end points in drug development). Validation results confirm the robustness of the proposed assessment methodology, which compares favorably with other classical methods based solely on the structural similarity of the compounds. ADAN characteristics make the method well-suited for estimate the quality of drug predictions obtained in extremely unfavorable conditions, like the prediction of drug toxicity end points.
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Affiliation(s)
- Pau Carrió
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute) , Dr. Aiguader, 88, E-08003 Barcelona, Spain
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23
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Khan SR, Baghdasarian A, Fahlman RP, Michail K, Siraki AG. Current status and future prospects of toxicogenomics in drug discovery. Drug Discov Today 2014; 19:562-78. [DOI: 10.1016/j.drudis.2013.11.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Revised: 09/27/2013] [Accepted: 11/01/2013] [Indexed: 01/03/2023]
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24
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Klepsch F, Vasanthanathan P, Ecker GF. Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors. J Chem Inf Model 2014; 54:218-29. [PMID: 24050383 PMCID: PMC3904775 DOI: 10.1021/ci400289j] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The ABC transporter P-glycoprotein (P-gp) actively transports a wide range of drugs and toxins out of cells, and is therefore related to multidrug resistance and the ADME profile of therapeutics. Thus, development of predictive in silico models for the identification of P-gp inhibitors is of great interest in the field of drug discovery and development. So far in silico P-gp inhibitor prediction was dominated by ligand-based approaches because of the lack of high-quality structural information about P-gp. The present study aims at comparing the P-gp inhibitor/noninhibitor classification performance obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random fores,t and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and noninhibitors, correctly predicting 73/75% of the external test set compounds. Classification based on the docking experiments using the scoring function ChemScore resulted in the correct prediction of 61% of the external test set. This demonstrates that ligand-based models currently remain the methods of choice for accurately predicting P-gp inhibitors. However, structure-based classification offers information about possible drug/protein interactions, which helps in understanding the molecular basis of ligand-transporter interaction and could therefore also support lead optimization.
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Affiliation(s)
- Freya Klepsch
- University of Vienna , Department of Medicinal Chemistry, Althanstraße 14, 1090 Vienna, Austria
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25
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Urban L, Maciejewski M, Lounkine E, Whitebread S, Jenkins JL, Hamon J, Fekete A, Muller PY. Translation of off-target effects: prediction of ADRs by integrated experimental and computational approach. Toxicol Res (Camb) 2014. [DOI: 10.1039/c4tx00077c] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Adverse drug reactions (ADRs) are associated with most drugs, often discovered late in drug development and sometimes only during extended course of clinical use.
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Affiliation(s)
- Laszlo Urban
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Mateusz Maciejewski
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Eugen Lounkine
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Steven Whitebread
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Jeremy L. Jenkins
- Developmental and Molecular Pathways
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Jacques Hamon
- Basel Screening Operations
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Basel, Switzerland
| | - Alexander Fekete
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
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26
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Johnson DE. Fusion of nonclinical and clinical data to predict human drug safety. Expert Rev Clin Pharmacol 2013; 6:185-95. [PMID: 23473595 DOI: 10.1586/ecp.13.3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Adverse drug reactions continue to be a major cause of morbidity in both patients receiving therapeutics and in drug R&D programs. Predicting and possibly eliminating these adverse events remains a high priority in industry, government agencies and healthcare systems. With small molecule candidates, the fusion of nonclinical and clinical data is essential in establishing an overall system that creates a true translational science approach. Several new advances are taking place that attempt to create a 'patient context' mechanism early in drug research and development and ultimately into the marketplace. This 'life-cycle' approach has as its core the development of human-oriented, nonclinical end points and the incorporation of clinical knowledge at the drug design stage. The next 5 years should witness an explosion of what the author views as druggable and safe chemical space, pharmacosafety molecular targets and the most important aspect, an understanding of unique susceptibilities in patients developing adverse drug reactions. Our current knowledge of clinical safety relies completely on pharmacovigilance data from approved and marketed drugs, with a few exceptions of drugs failing in clinical trials. Massive data repositories now and soon to be available via cloud computing should stimulate a major effort in expanding our view of clinical drug safety and its incorporation into early drug research and development.
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Affiliation(s)
- Dale E Johnson
- University of Michigan and University of California, Berkeley Morgan Hall, Berkeley, CA 94720-3104, USA.
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27
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Péry ARR, Schüürmann G, Ciffroy P, Faust M, Backhaus T, Aicher L, Mombelli E, Tebby C, Cronin MTD, Tissot S, Andres S, Brignon JM, Frewer L, Georgiou S, Mattas K, Vergnaud JC, Peijnenburg W, Capri E, Marchis A, Wilks MF. Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2013; 456-457:307-316. [PMID: 23624004 DOI: 10.1016/j.scitotenv.2013.03.099] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Revised: 03/29/2013] [Accepted: 03/29/2013] [Indexed: 06/02/2023]
Abstract
For more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA.
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Affiliation(s)
- A R R Péry
- INERIS, Parc Alata, BP2, 60550 Verneuil-en-Halatte, France.
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Roncaglioni A, Toropov AA, Toropova AP, Benfenati E. In silico methods to predict drug toxicity. Curr Opin Pharmacol 2013; 13:802-6. [PMID: 23797035 DOI: 10.1016/j.coph.2013.06.001] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2013] [Revised: 05/28/2013] [Accepted: 06/02/2013] [Indexed: 02/07/2023]
Abstract
This review describes in silico methods to characterize the toxicity of pharmaceuticals, including tools which predict toxicity endpoints such as genotoxicity or organ-specific models, tools addressing ADME processes, and methods focusing on protein-ligand docking binding. These in silico tools are rapidly evolving. Nowadays, the interest has shifted from classical studies to support toxicity screening of candidates, toward the use of in silico methods to support the expert. These methods, previously considered useful only to provide a rough, initial estimation, currently have attracted interest as they can assist the expert in investigating toxic potential. They provide the expert with safety perspectives and insights within a weight-of-evidence strategy. This represents a shift of the general philosophy of in silico methodology, and it is likely to further evolve especially exploiting links with system biology.
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Affiliation(s)
- Alessandra Roncaglioni
- IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy
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Pellegatti M. Dogs and monkeys in preclinical drug development: the challenge of reducing and replacing. Expert Opin Drug Metab Toxicol 2013; 9:1171-80. [PMID: 23705836 DOI: 10.1517/17425255.2013.804061] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Animal experimentation is a very contentious issue affecting reputation of drug industry. There are several reasons to forecast an increase in the number of dogs and monkeys used in safety and pharmacokinetic studies. This increase may trigger a strong reaction of the public opinion. There have been many proposals and initiatives to change the present approach to safety and metabolic studies. Tests based on new technologies, in vitro cell assays, stem cells, imaging, and computational systems, have the potential to anticipate effects in humans. Unfortunately, all these efforts and ideas have not changed standard approaches and regulatory expectations. AREAS COVERED This review looks at opportunities to reduce the number of dogs and monkeys currently used in pharmaceutical research. It also discusses present efforts and approaches, their strengths and potentials and the reasons why they may not fulfill expectations. EXPERT OPINION Unless the pharmaceutical industry gets more involved, an alternative paradigm of preclinical drug development is unlikely to be established in the foreseeable future. One can imagine a scenario where the political pressure against the use of dogs and monkeys in biomedical research becomes irresistible while alternative methods are not yet established. To avoid this situation, the pharmaceutical industry should take the lead and preclinical scientists at all levels need to influence decision makers and help develop new innovative approaches in drug safety evaluation.
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Bienfait B, Ertl P. JSME: a free molecule editor in JavaScript. J Cheminform 2013; 5:24. [PMID: 23694746 PMCID: PMC3662632 DOI: 10.1186/1758-2946-5-24] [Citation(s) in RCA: 172] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Accepted: 05/16/2013] [Indexed: 12/02/2022] Open
Abstract
Background A molecule editor, i.e. a program facilitating graphical input and interactive editing of molecules, is an indispensable part of every cheminformatics or molecular processing system. Today, when a web browser has become the universal scientific user interface, a tool to edit molecules directly within the web browser is essential. One of the most popular tools for molecular structure input on the web is the JME applet. Since its release nearly 15 years ago, however the web environment has changed and Java applets are facing increasing implementation hurdles due to their maintenance and support requirements, as well as security issues. This prompted us to update the JME editor and port it to a modern Internet programming language - JavaScript. Summary The actual molecule editing Java code of the JME editor was translated into JavaScript with help of the Google Web Toolkit compiler and a custom library that emulates a subset of the GUI features of the Java runtime environment. In this process, the editor was enhanced by additional functionalities including a substituent menu, copy/paste, drag and drop and undo/redo capabilities and an integrated help. In addition to desktop computers, the editor supports molecule editing on touch devices, including iPhone, iPad and Android phones and tablets. In analogy to JME the new editor is named JSME. This new molecule editor is compact, easy to use and easy to incorporate into web pages. Conclusions A free molecule editor written in JavaScript was developed and is released under the terms of permissive BSD license. The editor is compatible with JME, has practically the same user interface as well as the web application programming interface. The JSME editor is available for download from the project web page http://peter-ertl.com/jsme/
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Affiliation(s)
- Bruno Bienfait
- Molecular Networks GmbH, Henkestrasse 91, Erlangen, D-91052, Germany
| | - Peter Ertl
- Novartis Institutes for BioMedical Research, Novartis Campus, Basel, CH-4056, Switzerland
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Cases M, Pastor M, Sanz F. The eTOX Library of Public Resources for in Silico Toxicity Prediction. Mol Inform 2013; 32:24-35. [DOI: 10.1002/minf.201200099] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2012] [Accepted: 11/20/2012] [Indexed: 12/29/2022]
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Muthas D, Boyer S, Hasselgren C. A critical assessment of modeling safety-related drug attrition. MEDCHEMCOMM 2013. [DOI: 10.1039/c3md00072a] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Henderson VC, Kimmelman J, Fergusson D, Grimshaw JM, Hackam DG. Threats to validity in the design and conduct of preclinical efficacy studies: a systematic review of guidelines for in vivo animal experiments. PLoS Med 2013; 10:e1001489. [PMID: 23935460 PMCID: PMC3720257 DOI: 10.1371/journal.pmed.1001489] [Citation(s) in RCA: 193] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2013] [Accepted: 06/13/2013] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND The vast majority of medical interventions introduced into clinical development prove unsafe or ineffective. One prominent explanation for the dismal success rate is flawed preclinical research. We conducted a systematic review of preclinical research guidelines and organized recommendations according to the type of validity threat (internal, construct, or external) or programmatic research activity they primarily address. METHODS AND FINDINGS We searched MEDLINE, Google Scholar, Google, and the EQUATOR Network website for all preclinical guideline documents published up to April 9, 2013 that addressed the design and conduct of in vivo animal experiments aimed at supporting clinical translation. To be eligible, documents had to provide guidance on the design or execution of preclinical animal experiments and represent the aggregated consensus of four or more investigators. Data from included guidelines were independently extracted by two individuals for discrete recommendations on the design and implementation of preclinical efficacy studies. These recommendations were then organized according to the type of validity threat they addressed. A total of 2,029 citations were identified through our search strategy. From these, we identified 26 guidelines that met our eligibility criteria--most of which were directed at neurological or cerebrovascular drug development. Together, these guidelines offered 55 different recommendations. Some of the most common recommendations included performance of a power calculation to determine sample size, randomized treatment allocation, and characterization of disease phenotype in the animal model prior to experimentation. CONCLUSIONS By identifying the most recurrent recommendations among preclinical guidelines, we provide a starting point for developing preclinical guidelines in other disease domains. We also provide a basis for the study and evaluation of preclinical research practice. Please see later in the article for the Editors' Summary.
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Affiliation(s)
- Valerie C. Henderson
- Studies of Translation, Ethics and Medicine (STREAM) Group, Biomedical Ethics Unit, Department of Social Studies of Medicine, McGill University, Montréal, Québec, Canada
| | - Jonathan Kimmelman
- Studies of Translation, Ethics and Medicine (STREAM) Group, Biomedical Ethics Unit, Department of Social Studies of Medicine, McGill University, Montréal, Québec, Canada
- * E-mail:
| | - Dean Fergusson
- Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Jeremy M. Grimshaw
- Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Dan G. Hackam
- Division of Clinical Pharmacology, Department of Medicine, University of Western Ontario, London, Ontario, Canada
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Laverty H, Gunn M, Goldman M. Improving R&D productivity of pharmaceutical companies through public-private partnership: experiences from the Innovative Medicines Initiative. Expert Rev Pharmacoecon Outcomes Res 2012; 12:545-8. [PMID: 23136843 DOI: 10.1586/erp.12.59] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Salunke S, Giacoia G, Tuleu C. The STEP (Safety and Toxicity of Excipients for Paediatrics) database. Part 1—A need assessment study. Int J Pharm 2012; 435:101-11. [DOI: 10.1016/j.ijpharm.2012.05.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Revised: 05/01/2012] [Accepted: 05/03/2012] [Indexed: 11/25/2022]
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Moggs J, Moulin P, Pognan F, Brees D, Leonard M, Busch S, Cordier A, Heard DJ, Kammüller M, Merz M, Bouchard P, Chibout SD. Investigative safety science as a competitive advantage for Pharma. Expert Opin Drug Metab Toxicol 2012; 8:1071-82. [DOI: 10.1517/17425255.2012.693914] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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