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Mastrolorito F, Togo MV, Gambacorta N, Trisciuzzi D, Giannuzzi V, Bonifazi F, Liantonio A, Imbrici P, De Luca A, Altomare CD, Ciriaco F, Amoroso N, Nicolotti O. TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity. Chem Res Toxicol 2024; 37:323-339. [PMID: 38200616 DOI: 10.1021/acs.chemrestox.3c00310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.
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Affiliation(s)
- Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Viviana Giannuzzi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Fedele Bonifazi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Antonella Liantonio
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Paola Imbrici
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Annamaria De Luca
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
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Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine. Int J Mol Sci 2022; 24:ijms24010004. [PMID: 36613446 PMCID: PMC9819745 DOI: 10.3390/ijms24010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/16/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.
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Luconi M, Sogorb MA, Markert UR, Benfenati E, May T, Wolbank S, Roncaglioni A, Schmidt A, Straccia M, Tait S. Human-Based New Approach Methodologies in Developmental Toxicity Testing: A Step Ahead from the State of the Art with a Feto-Placental Organ-on-Chip Platform. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15828. [PMID: 36497907 PMCID: PMC9737555 DOI: 10.3390/ijerph192315828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Developmental toxicity testing urgently requires the implementation of human-relevant new approach methodologies (NAMs) that better recapitulate the peculiar nature of human physiology during pregnancy, especially the placenta and the maternal/fetal interface, which represent a key stage for human lifelong health. Fit-for-purpose NAMs for the placental-fetal interface are desirable to improve the biological knowledge of environmental exposure at the molecular level and to reduce the high cost, time and ethical impact of animal studies. This article reviews the state of the art on the available in vitro (placental, fetal and amniotic cell-based systems) and in silico NAMs of human relevance for developmental toxicity testing purposes; in addition, we considered available Adverse Outcome Pathways related to developmental toxicity. The OECD TG 414 for the identification and assessment of deleterious effects of prenatal exposure to chemicals on developing organisms will be discussed to delineate the regulatory context and to better debate what is missing and needed in the context of the Developmental Origins of Health and Disease hypothesis to significantly improve this sector. Starting from this analysis, the development of a novel human feto-placental organ-on-chip platform will be introduced as an innovative future alternative tool for developmental toxicity testing, considering possible implementation and validation strategies to overcome the limitation of the current animal studies and NAMs available in regulatory toxicology and in the biomedical field.
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Affiliation(s)
- Michaela Luconi
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy
- I.N.B.B. (Istituto Nazionale Biostrutture e Biosistemi), Viale Medaglie d’Oro 305, 00136 Rome, Italy
| | - Miguel A. Sogorb
- Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, 03202 Elche, Spain
| | - Udo R. Markert
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Tobias May
- InSCREENeX GmbH, Inhoffenstr. 7, 38124 Braunschweig, Germany
| | - Susanne Wolbank
- Ludwig Boltzmann Institut for Traumatology, The Research Center in Cooperation with AUVA, Austrian Cluster for Tissue Regeneration, Donaueschingenstrasse 13, 1200 Vienna, Austria
| | - Alessandra Roncaglioni
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Astrid Schmidt
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - Marco Straccia
- FRESCI by Science&Strategy SL, C/Roure Monjo 33, Vacarisses, 08233 Barcelona, Spain
| | - Sabrina Tait
- Centre for Gender-Specific Medicine, Istituto Superiore di Sanità, 00161 Rome, Italy
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Weyrich A, Joel M, Lewin G, Hofmann T, Frericks M. Review of the state of science and evaluation of currently available in silico prediction models for reproductive and developmental toxicity: A case study on pesticides. Birth Defects Res 2022; 114:812-842. [PMID: 35748219 PMCID: PMC9545887 DOI: 10.1002/bdr2.2062] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/10/2022] [Accepted: 05/28/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND In silico methods for toxicity prediction have increased significantly in recent years due to the 3Rs principle. This also applies to predicting reproductive toxicology, which is one of the most critical factors in pesticide approval. The widely used quantitative structure-activity relationship (QSAR) models use experimental toxicity data to create a model that relates experimentally observed toxicity to molecular structures to predict toxicity. Aim of the study was to evaluate the available prediction models for developmental and reproductive toxicity regarding their strengths and weaknesses in a pesticide database. METHODS The reproductive toxicity of 315 pesticides, which have a GHS classification by ECHA, was compared with the prediction of different in silico models: VEGA, OECD (Q)SAR Toolbox, Leadscope Model Applier, and CASE Ultra by MultiCASE. RESULTS In all models, a large proportion (up to 77%) of all pesticides were outside the chemical space of the model. Analysis of the prediction of remaining pesticides revealed a balanced accuracy of the models between 0.48 and 0.66. CONCLUSION Overall, predictions were only meaningful in rare cases and therefore always require evaluation by an expert. The critical factors were the underlying data and determination of molecular similarity, which offer great potential for improvement.
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Affiliation(s)
| | - Madeleine Joel
- Preclinical Science – FöllMecklenburg & Partner GmbHMünsterGermany
| | - Geertje Lewin
- Preclinical Science – FöllMecklenburg & Partner GmbHMünsterGermany
| | - Thomas Hofmann
- Experimental Toxicology and EcologyBASF SELudwigshafenGermany
| | - Markus Frericks
- Agricultural Solutions – Toxicology CPBASF SELimburgerhofGermany
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Cvitanović Tomaš T, Urlep Ž, Moškon M, Mraz M, Rozman D. LiverSex Computational Model: Sexual Aspects in Hepatic Metabolism and Abnormalities. Front Physiol 2018; 9:360. [PMID: 29706895 PMCID: PMC5907313 DOI: 10.3389/fphys.2018.00360] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/22/2018] [Indexed: 12/12/2022] Open
Abstract
The liver is to date the best example of a sexually dimorphic non-reproductive organ. Over 1,000 genes are differentially expressed between sexes indicating that female and male livers are two metabolically distinct organs. The spectrum of liver diseases is broad and is usually prevalent in one or the other sex, with different contributing genetic and environmental factors. It is thus difficult to predict individual's disease outcomes and treatment options. Systems approaches including mathematical modeling can aid importantly in understanding the multifactorial liver disease etiology leading toward tailored diagnostics, prognostics and therapy. The currently established computational models of hepatic metabolism that have proven to be essential for understanding of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) are limited to the description of gender-independent response or reflect solely the response of the males. Herein we present LiverSex, the first sex-based multi-tissue and multi-level liver metabolic computational model. The model was constructed based on in silico liver model SteatoNet and the object-oriented modeling. The crucial factor in adaptation of liver metabolism to the sex is the inclusion of estrogen and androgen receptor responses to respective hormones and the link to sex-differences in growth hormone release. The model was extensively validated on literature data and experimental data obtained from wild type C57BL/6 mice fed with regular chow and western diet. These experimental results show extensive sex-dependent changes and could not be reproduced in silico with the uniform model SteatoNet. LiverSex represents the first large-scale liver metabolic model, which allows a detailed insight into the sex-dependent complex liver pathologies, and how the genetic and environmental factors interact with the sex in disease appearance and progression. We used the model to identify the most important sex-dependent metabolic pathways, which are involved in accumulation of triglycerides representing initial steps of NAFLD. We identified PGC1A, PPARα, FXR, and LXR as regulatory factors that could become important in sex-dependent personalized treatment of NAFLD.
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Affiliation(s)
- Tanja Cvitanović Tomaš
- Faculty of Medicine, Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, University of Ljubljana, Ljubljana, Slovenia
| | - Žiga Urlep
- Faculty of Medicine, Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Faculty of Medicine, Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, University of Ljubljana, Ljubljana, Slovenia
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Teixidó E, Krupp E, Amberg A, Czich A, Scholz S. Species-specific developmental toxicity in rats and rabbits: Generation of a reference compound list for development of alternative testing approaches. Reprod Toxicol 2018; 76:93-102. [PMID: 29409988 DOI: 10.1016/j.reprotox.2018.01.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Revised: 12/05/2017] [Accepted: 01/26/2018] [Indexed: 11/19/2022]
Abstract
For regulatory information requirements, developmental toxicity testing is often conducted in two mammalian species. In order to provide a set of reference compounds that could be used to explore alternative approaches to supersede testing in a second species, a retrospective data analysis was conducted. The aim was to identify compounds for which species sensitivity differences between rats and rabbits are not caused by maternal toxicity or toxicokinetic differences. A total of 330 compounds were analysed and classified according to their species-specific differences. A lack of concordance between rat and rabbit was observed in 24% of the compounds, of which 10% were found to be selective developmental toxicants in one of the species. In contrast to previously published analyses the presented comparison is based entirely on publically data allowing validating and comparing alternative approaches for developmental toxicity testing. Furthermore, this list could be useful to identify mechanisms leading to species differences.
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Affiliation(s)
- E Teixidó
- Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318, Leipzig, Germany.
| | - E Krupp
- Sanofi-Aventis Deutschland GmbH, Preclinical Safety, Industriepark Hoechst, D-65926, Frankfurt am Main, Germany
| | - A Amberg
- Sanofi-Aventis Deutschland GmbH, Preclinical Safety, Industriepark Hoechst, D-65926, Frankfurt am Main, Germany
| | - A Czich
- Sanofi-Aventis Deutschland GmbH, Preclinical Safety, Industriepark Hoechst, D-65926, Frankfurt am Main, Germany
| | - S Scholz
- Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318, Leipzig, Germany
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Howard J, Piacentino J, MacMahon K, Schulte P. Using systematic review in occupational safety and health. Am J Ind Med 2017; 60:921-929. [PMID: 28944489 DOI: 10.1002/ajim.22771] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2017] [Indexed: 12/15/2022]
Abstract
Evaluation of scientific evidence is critical in developing recommendations to reduce risk. Healthcare was the first scientific field to employ a systematic review approach for synthesizing research findings to support evidence-based decision-making and it is still the largest producer and consumer of systematic reviews. Systematic reviews in the field of occupational safety and health are being conducted, but more widespread use and adoption would strengthen assessments. In 2016, NIOSH asked RAND to develop a framework for applying the traditional systematic review elements to the field of occupational safety and health. This paper describes how essential systematic review elements can be adapted for use in occupational systematic reviews to enhance their scientific quality, objectivity, transparency, reliability, utility, and acceptability.
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Affiliation(s)
- John Howard
- National Institute for Occupational Safety and Health, Washington, District of Columbia
| | - John Piacentino
- National Institute for Occupational Safety and Health, Washington, District of Columbia
| | - Kathleen MacMahon
- National Institute for Occupational Safety and Health, Washington, District of Columbia
| | - Paul Schulte
- National Institute for Occupational Safety and Health, Washington, District of Columbia
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8
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Integrating in silico models to enhance predictivity for developmental toxicity. Toxicology 2016; 370:127-137. [DOI: 10.1016/j.tox.2016.09.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 09/08/2016] [Accepted: 09/27/2016] [Indexed: 11/17/2022]
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Marzo M, Roncaglioni A, Kulkarni S, Barton-Maclaren TS, Benfenati E. In Silico Model for Developmental Toxicity: How to Use QSAR Models and Interpret Their Results. Methods Mol Biol 2016; 1425:139-61. [PMID: 27311466 DOI: 10.1007/978-1-4939-3609-0_8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
Abstract
Modeling developmental toxicity has been a challenge for (Q)SAR model developers due to the complexity of the endpoint. Recently, some new in silico methods have been developed introducing the possibility to evaluate the integration of existing methods by taking advantage of various modeling perspectives. It is important that the model user is aware of the underlying basis of the different models in general, as well as the considerations and assumptions relative to the specific predictions that are obtained from these different models for the same chemical. The evaluation on the predictions needs to be done on a case-by-case basis, checking the analogs (possibly using structural, physicochemical, and toxicological information); for this purpose, the assessment of the applicability domain of the models provides further confidence in the model prediction. In this chapter, we present some examples illustrating an approach to combine human-based rules and statistical methods to support the prediction of developmental toxicity; we also discuss assumptions and uncertainties of the methodology.
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Affiliation(s)
- Marco Marzo
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri", Milano, Italy.
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri", Milano, Italy
| | - Sunil Kulkarni
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, Canada
| | | | - Emilio Benfenati
- Mario Negri Institute for Pharmacological Research, IRCCS, Milan, Italy
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Saiakhov R, Chakravarti S, Klopman G. Effectiveness of CASE Ultra Expert System in Evaluating Adverse Effects of Drugs. Mol Inform 2013; 32:87-97. [DOI: 10.1002/minf.201200081] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Accepted: 11/20/2012] [Indexed: 11/10/2022]
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Abstract
Developmental toxicity may be estimated using commercial and noncommercial software that is already available in the market and/or literature, or models may be built from scratch using both commercial and noncommercial software packages. In this chapter, commonly available software programs that can predict the developmental toxicity of chemicals are described. In addition, a method for developing qualitative structure-activity relationship (SAR) models to predict the developmental toxicity of chemicals qualitatively (yes/no prediction) and quantitative structure-activity relationship (QSAR) models to predict quantitative estimates (e.g., LOAEL) of developmental toxicants is also described in this chapter. Additional information described in this chapter include methods to predict physicochemical properties of chemicals that can be used as descriptor variables in the model building process, statistical methods that be used to build QSAR models as well as methods to validate the models that are developed. Most of the methods described in this chapter can be used to develop models for health endpoints other than developmental toxicity as well.
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Chakravarti SK, Saiakhov RD, Klopman G. Optimizing Predictive Performance of CASE Ultra Expert System Models Using the Applicability Domains of Individual Toxicity Alerts. J Chem Inf Model 2012; 52:2609-18. [DOI: 10.1021/ci300111r] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Suman K. Chakravarti
- Multicase Inc., 23811 Chagrin
Boulevard, Suite 305, Beachwood, Ohio 44122, United States
| | - Roustem D. Saiakhov
- Multicase Inc., 23811 Chagrin
Boulevard, Suite 305, Beachwood, Ohio 44122, United States
| | - Gilles Klopman
- Multicase Inc., 23811 Chagrin
Boulevard, Suite 305, Beachwood, Ohio 44122, United States
- Case Western Reserve University,
10900 Euclid Avenue, Cleveland, Ohio 44106, United States
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Kruhlak NL, Benz RD, Zhou H, Colatsky TJ. (Q)SAR Modeling and Safety Assessment in Regulatory Review. Clin Pharmacol Ther 2012; 91:529-34. [DOI: 10.1038/clpt.2011.300] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Piparo EL, Worth A, Manibusan M, Yang C, Schilter B, Mazzatorta P, Jacobs MN, Steinkellner H, Mohimont L. Use of computational tools in the field of food safety. Regul Toxicol Pharmacol 2011; 60:354-62. [DOI: 10.1016/j.yrtph.2011.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2011] [Revised: 05/04/2011] [Accepted: 05/05/2011] [Indexed: 10/18/2022]
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Grabowski T, Jaroszewski JJ, Piotrowski W. Correlations between no observed effect level and selected parameters of the chemical structure for veterinary drugs. Toxicol In Vitro 2010; 24:953-9. [DOI: 10.1016/j.tiv.2010.01.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2009] [Revised: 12/18/2009] [Accepted: 01/11/2010] [Indexed: 11/27/2022]
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Frid AA, Matthews EJ. Prediction of drug-related cardiac adverse effects in humans-B: Use of QSAR programs for early detection of drug-induced cardiac toxicities. Regul Toxicol Pharmacol 2010; 56:276-89. [DOI: 10.1016/j.yrtph.2009.11.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2009] [Revised: 09/29/2009] [Accepted: 11/06/2009] [Indexed: 10/20/2022]
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17
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Dekant W, Melching-Kollmuß S, Kalberlah F. Toxicity assessment strategies, data requirements, and risk assessment approaches to derive health based guidance values for non-relevant metabolites of plant protection products. Regul Toxicol Pharmacol 2010; 56:135-42. [DOI: 10.1016/j.yrtph.2009.10.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2009] [Revised: 09/28/2009] [Accepted: 10/22/2009] [Indexed: 10/20/2022]
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18
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Valerio LG, Yang C, Arvidson KB, Kruhlak NL. A structural feature-based computational approach for toxicology predictions. Expert Opin Drug Metab Toxicol 2010; 6:505-18. [DOI: 10.1517/17425250903499286] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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20
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Yang C, Valerio LG, Arvidson KB. Computational Toxicology Approaches at the US Food and Drug Administration. Altern Lab Anim 2009; 37:523-31. [DOI: 10.1177/026119290903700509] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
For over a decade, the United States Food and Drug Administration (US FDA) has been engaged in the applied research, development, and evaluation of computational toxicology methods used to support the safety evaluation of a diverse set of regulated products. The basis for evaluating computational toxicology methods is multi-factorial, including the potential for increased efficiency, reduction in the numbers of animals used, lower costs, and the need to explore emerging technologies that support the goals of the US FDA's Critical Path Initiative (e.g. to make decision support information available early in the drug review process). The US FDA's efforts have been facilitated by agency-approved data-sharing agreements between government and commercial software developers. This commentary review describes former and current scientific initiatives at the agency, in the area of computational toxicology methods. In particular, toxicology-based QSAR models, ToxML databases and knowledgebases will be addressed. Notably, many of the computational toxicology tools available are commercial products — however, several are emerging as non-commercial products, which are freely-available to the public, and which will facilitate the understanding of how these programs work and avoid the “black box” paradigm. Through productive collaborations, the US FDA Center for Drug Evaluation and Research, and the Center for Food Safety and Applied Nutrition, have worked together to evaluate, develop and apply these methods to chemical toxicity endpoints of regulatory interest.
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Affiliation(s)
- Chihae Yang
- Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA
| | - Luis G. Valerio
- Office of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Kirk B. Arvidson
- Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA
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Knudsen TB, Martin MT, Kavlock RJ, Judson RS, Dix DJ, Singh AV. Profiling the activity of environmental chemicals in prenatal developmental toxicity studies using the U.S. EPA's ToxRefDB. Reprod Toxicol 2009; 28:209-19. [DOI: 10.1016/j.reprotox.2009.03.016] [Citation(s) in RCA: 99] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2009] [Revised: 03/31/2009] [Accepted: 03/31/2009] [Indexed: 11/30/2022]
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Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities. Regul Toxicol Pharmacol 2009; 54:23-42. [DOI: 10.1016/j.yrtph.2009.01.009] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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23
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Matthews EJ, Kruhlak NL, Benz RD, Contrera JF, Marchant CA, Yang C. Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action–Based Predictions of Chemical Carcinogenesis in Rodents. Toxicol Mech Methods 2008; 18:189-206. [DOI: 10.1080/15376510701857379] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Matthews EJ, Kruhlak NL, Daniel Benz R, Ivanov J, Klopman G, Contrera JF. A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals. Regul Toxicol Pharmacol 2007; 47:136-55. [DOI: 10.1016/j.yrtph.2006.10.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2006] [Indexed: 11/28/2022]
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