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Tortora F, Guerrera V, Lettieri G, Febbraio F, Piscopo M. Prediction of Pesticide Interactions with Proteins Involved in Human Reproduction by Using a Virtual Screening Approach: A Case Study of Famoxadone Binding CRBP-III and Izumo. Int J Mol Sci 2024; 25:5790. [PMID: 38891976 PMCID: PMC11171824 DOI: 10.3390/ijms25115790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
In recent years, the awareness that pesticides can have other effects apart from generic toxicity is growing. In particular, several pieces of evidence highlight their influence on human fertility. In this study, we investigated, by a virtual screening approach, the binding between pesticides and proteins present in human gametes or associated with reproduction, in order to identify new interactions that could affect human fertility. To this aim, we prepared ligand (pesticides) and receptor (proteins) 3D structure datasets from online structural databases (such as PubChem and RCSB), and performed a virtual screening analysis using Autodock Vina. In the comparison of the predicted interactions, we found that famoxadone was predicted to bind Cellular Retinol Binding Protein-III in the retinol-binding site with a better minimum energy value of -10.4 Kcal/mol and an RMSD of 3.77 with respect to retinol (-7.1 Kcal/mol). In addition to a similar network of interactions, famoxadone binding is more stabilized by additional hydrophobic patches including L20, V29, A33, F57, L117, and L118 amino acid residues and hydrogen bonds with Y19 and K40. These results support a possible competitive effect of famoxadone on retinol binding with impacts on the ability of developing the cardiac tissue, in accordance with the literature data on zebrafish embryos. Moreover, famoxadone binds, with a minimum energy value between -8.3 and -8.0 Kcal/mol, to the IZUMO Sperm-Egg Fusion Protein, interacting with a network of polar and hydrophobic amino acid residues in the cavity between the 4HB and Ig-like domains. This binding is more stabilized by a predicted hydrogen bond with the N185 residue of the protein. A hindrance in this position can probably affect the conformational change for JUNO binding, avoiding the gamete membrane fusion to form the zygote. This work opens new interesting perspectives of study on the effects of pesticides on fertility, extending the knowledge to other typologies of interaction which can affect different steps of the reproductive process.
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Affiliation(s)
- Fabiana Tortora
- Institute of Genetics and Biophysics “Adriano Buzzati Traverso”, National Research Council (CNR), Via P. Castellino 111, 80131 Naples, Italy;
- Institute of Biochemistry and Cell Biology, National Research Council (CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Valentina Guerrera
- Institute of Biochemistry and Cell Biology, National Research Council (CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Gennaro Lettieri
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy (M.P.)
| | - Ferdinando Febbraio
- Institute of Biochemistry and Cell Biology, National Research Council (CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Marina Piscopo
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy (M.P.)
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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] [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 - Föll, Mecklenburg & Partner GmbH, Münster, Germany
| | - Geertje Lewin
- Preclinical Science - Föll, Mecklenburg & Partner GmbH, Münster, Germany
| | - Thomas Hofmann
- Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | - Markus Frericks
- Agricultural Solutions - Toxicology CP, BASF SE, Limburgerhof, Germany
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Ciallella HL, Russo DP, Sharma S, Li Y, Sloter E, Sweet L, Huang H, Zhu H. Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:5984-5998. [PMID: 35451820 PMCID: PMC9191745 DOI: 10.1021/acs.est.2c01040] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2140 toxicological high-throughput screening assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4 and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.
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Affiliation(s)
- Heather L. Ciallella
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08103, USA
| | - Daniel P. Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08103, USA
- Department of Chemistry, Rutgers University, Camden, NJ, 08102, USA
| | - Swati Sharma
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08103, USA
| | - Yafan Li
- The Lubrizol Corporation, Wickliffe, OH, 44092, USA
| | - Eddie Sloter
- The Lubrizol Corporation, Wickliffe, OH, 44092, USA
| | - Len Sweet
- The Lubrizol Corporation, Wickliffe, OH, 44092, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08103, USA
- Department of Chemistry, Rutgers University, Camden, NJ, 08102, USA
- Corresponding Author333 Hao Zhu, 201 South Broadway, Joint Health Sciences Center, Rutgers University, Camden, New Jersey 08103; Telephone: (856) 225-6781;
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Zhang H, Shen C, Liu RZ, Mao J, Liu CT, Mu B. Developing novel in silico prediction models for assessing chemical reproductive toxicity using the naïve Bayes classifier method. J Appl Toxicol 2020; 40:1198-1209. [PMID: 32207182 DOI: 10.1002/jat.3975] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 02/18/2020] [Accepted: 02/29/2020] [Indexed: 02/05/2023]
Abstract
Assessment of reproductive toxicity is one of the important safety considerations in drug development. Thus, in the present research, the naïve Bayes (NB)-classifier method was applied to develop binary classification models. Six important molecular descriptors for reproductive toxicity were selected by the genetic algorithm. Then, 110 classification models were developed using six molecular descriptors and10 types of fingerprints with 11 different maximum diameters. Among these established models, the model based on six molecular descriptors and the SciTegic extended-connectivity fingerprints with 20 maximum diameters (LCFC_20) displayed the best prediction performance for reproductive toxicity (NB-1), which gave a 0.884 receiver operating characteristic (ROC) score and 91.8% overall prediction accuracy for the Training Set, and produced a 0.888 ROC score and 83.0% overall accuracy for the external Test Set I. In addition, for the external rat multi-generation reproductive toxicity dataset (Test Set II), the NB-1 model generated a 0.806 ROC score and 85.1% concordance. The generated prediction results indicated that the NB-1 model could give robust and reliable predictions for a reproductive toxicity potential of chemicals. Thus, the established model could be applied to filter early-stage molecules for potential reproductive adverse effects. In addition, six important molecular descriptors and new structural alerts for reproductive toxicity were identified, which could help medicinal chemists rationally guide the optimization of lead compounds and select chemicals with the best prospects of being safe and effective.
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Affiliation(s)
- Hui Zhang
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, China.,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China
| | - Chen Shen
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Ru-Zhuo Liu
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Jun Mao
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Chun-Tao Liu
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Bo Mu
- Basic Medical College of North Sichuan Medical College, Nanchong, Sichuan, China.,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China
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6
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Rusyn I, Greene N. The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives. Toxicol Sci 2019; 161:276-284. [PMID: 29378069 DOI: 10.1093/toxsci/kfx196] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The field of experimental toxicology is rapidly advancing by incorporating novel techniques and methods that provide a much more granular view into the mechanisms of potential adverse effects of chemical exposures on human health. The data from various in vitro assays and computational models are useful not only for increasing confidence in hazard and risk decisions, but also are enabling better, faster and cheaper assessment of a greater number of compounds, mixtures, and complex products. This is of special value to the field of green chemistry where design of new materials or alternative uses of existing ones is driven, at least in part, by considerations of safety. This article reviews the state of the science and decision-making in scenarios when little to no data may be available to draw conclusions about which choice in green chemistry is "safer." It is clear that there is no "one size fits all" solution and multiple data streams need to be weighed in making a decision. Moreover, the overall level of familiarity of the decision-makers and scientists alike with new assessment methodologies, their validity, value and limitations is evolving. Thus, while the "impact" of the new developments in toxicology on the field of green chemistry is great already, it is premature to conclude that the data from new assessment methodologies have been widely accepted yet.
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Affiliation(s)
- Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843
| | - Nigel Greene
- Predictive Compound Safety and ADME, AstraZeneca Pharmaceuticals LP, Waltham, Massachusetts 02451
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7
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Jiang C, Yang H, Di P, Li W, Tang Y, Liu G. In silico prediction of chemical reproductive toxicity using machine learning. J Appl Toxicol 2019; 39:844-854. [DOI: 10.1002/jat.3772] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 12/05/2018] [Accepted: 12/15/2018] [Indexed: 12/30/2022]
Affiliation(s)
- Changsheng Jiang
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
| | - Peiwen Di
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
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8
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Gramatica P, Papa E, Sangion A. QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2018; 20:38-47. [PMID: 29226926 DOI: 10.1039/c7em00519a] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.
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Affiliation(s)
- Paola Gramatica
- QSAR Research Unit on Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences (DiSTA), University of Insubria, Varese, Italy.
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9
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Gozalbes R, Vicente de Julián-Ortiz J. Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010101] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REACH legislation promotes the use of QSAR in order to reduce the huge amount of animal testing needed to demonstrate the safety of new chemical entities subjected to registration, provided they meet specific conditions to ensure their quality and predictive power. In this review, the authors summarize the state of art of in silico methods for regulatory purposes, with especial emphasis on QSAR models.
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10
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Current nonclinical testing paradigms in support of safe clinical trials: An IQ Consortium DruSafe perspective. Regul Toxicol Pharmacol 2017; 87 Suppl 3:S1-S15. [PMID: 28483710 DOI: 10.1016/j.yrtph.2017.05.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/03/2017] [Accepted: 05/04/2017] [Indexed: 12/18/2022]
Abstract
The transition from nonclinical to First-in-Human (FIH) testing is one of the most challenging steps in drug development. In response to serious outcomes in a recent Phase 1 trial (sponsored by Bial), IQ Consortium/DruSafe member companies reviewed their nonclinical approach to progress small molecules safely to FIH trials. As a common practice, safety evaluation begins with target selection and continues through iterative in silico and in vitro screening to identify molecules with increased probability of acceptable in vivo safety profiles. High attrition routinely occurs during this phase. In vivo exploratory and pivotal FIH-enabling toxicity studies are then conducted to identify molecules with a favorable benefit-risk profile for humans. The recent serious incident has reemphasized the importance of nonclinical testing plans that are customized to the target, the molecule, and the intended clinical plan. Despite the challenges and inherent risks of transitioning from nonclinical to clinical testing, Phase 1 studies have a remarkably good safety record. Given the rapid scientific evolution of safety evaluation, testing paradigms and regulatory guidance must evolve with emerging science. The authors posit that the practices described herein, together with science-based risk assessment and management, support safe FIH trials while advancing development of important new medicines.
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11
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Elliott EG, Ettinger AS, Leaderer BP, Bracken MB, Deziel NC. A systematic evaluation of chemicals in hydraulic-fracturing fluids and wastewater for reproductive and developmental toxicity. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2017; 27:90-99. [PMID: 26732376 DOI: 10.1038/jes.2015.81] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2015] [Revised: 09/24/2015] [Accepted: 09/25/2015] [Indexed: 05/17/2023]
Abstract
Hydraulic-fracturing fluids and wastewater from unconventional oil and natural gas development contain hundreds of substances with the potential to contaminate drinking water. Challenges to conducting well-designed human exposure and health studies include limited information about likely etiologic agents. We systematically evaluated 1021 chemicals identified in hydraulic-fracturing fluids (n=925), wastewater (n=132), or both (n=36) for potential reproductive and developmental toxicity to triage those with potential for human health impact. We searched the REPROTOX database using Chemical Abstract Service registry numbers for chemicals with available data and evaluated the evidence for adverse reproductive and developmental effects. Next, we determined which chemicals linked to reproductive or developmental toxicity had water quality standards or guidelines. Toxicity information was lacking for 781 (76%) chemicals. Of the remaining 240 substances, evidence suggested reproductive toxicity for 103 (43%), developmental toxicity for 95 (40%), and both for 41 (17%). Of these 157 chemicals, 67 had or were proposed for a federal water quality standard or guideline. Our systematic screening approach identified a list of 67 hydraulic fracturing-related candidate analytes based on known or suspected toxicity. Incorporation of data on potency, physicochemical properties, and environmental concentrations could further prioritize these substances for future drinking water exposure assessments or reproductive and developmental health studies.
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Affiliation(s)
- Elise G Elliott
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA
- Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale University, New Haven, CT, USA
| | - Adrienne S Ettinger
- Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale University, New Haven, CT, USA
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Brian P Leaderer
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA
- Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale University, New Haven, CT, USA
| | - Michael B Bracken
- Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale University, New Haven, CT, USA
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Nicole C Deziel
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA
- Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale University, New Haven, CT, USA
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12
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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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13
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Basant N, Gupta S, Singh KP. QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes. Toxicol Res (Camb) 2016; 5:1029-1038. [PMID: 30090410 PMCID: PMC6062388 DOI: 10.1039/c6tx00083e] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 04/07/2016] [Indexed: 01/08/2023] Open
Abstract
The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) QSAR models were developed using a novel dataset composed of the toxicity endpoints for 334 chemicals. Relevant structural features of chemicals responsible for toxicity potential were identified and used in QSAR modeling. The generalization and prediction abilities of the constructed QSAR models were evaluated by internal and external validation procedures and by deriving several stringent statistical criteria parameters. In the test set, the two models (DTF and DTB) yielded R2 of 0.856 and 0.945, between the experimental and predicted endpoint toxicity values. The models were also evaluated for predictive use through the most recent criteria based on root mean squared error (RMSE) and mean absolute error (MAE). The values of various statistical validation coefficients derived for the test data were above their respective threshold limits and thus put a high confidence in this analysis. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals and can be useful tools for screening new chemicals for safety assessment.
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Affiliation(s)
| | - Shikha Gupta
- Environmental Chemistry Division , CSIR-Indian Institute of Toxicology Research , Post Box 80 , Mahatma Gandhi Marg , Lucknow-226 001 , India . ;
| | - Kunwar P Singh
- Environmental Chemistry Division , CSIR-Indian Institute of Toxicology Research , Post Box 80 , Mahatma Gandhi Marg , Lucknow-226 001 , India . ;
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Gould J, Callis CM, Dolan DG, Stanard B, Weideman PA. Special endpoint and product specific considerations in pharmaceutical acceptable daily exposure derivation. Regul Toxicol Pharmacol 2016; 79 Suppl 1:S79-93. [PMID: 27233924 DOI: 10.1016/j.yrtph.2016.05.022] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 05/19/2016] [Indexed: 12/12/2022]
Abstract
Recently, a guideline has been published by the European Medicines Agency (EMA) on setting safe limits, permitted daily exposures (PDE) [also called acceptable daily exposures (ADE)], for medicines manufactured in multi-product facilities. The ADE provides a safe exposure limit for inadvertent exposure of a drug due to cross-contamination in manufacturing. The ADE determination encompasses a standard risk assessment, requiring an understanding of the toxicological and pharmacological effects, the mechanism of action, drug compound class, and the dose-response as well as the pharmacokinetic properties of the compound. While the ADE concept has broad application in pharmaceutical safety there are also nuances and specific challenges associated with some toxicological endpoints or drug product categories. In this manuscript we discuss considerations for setting ADEs when the following specific adverse health endpoints may constitute the critical effect: genotoxicity, developmental and reproductive toxicity (DART), and immune system modulation (immunostimulation or immunosuppression), and for specific drug classes, including antibody drug conjugates (ADCs), emerging medicinal therapeutic compounds, and compounds with limited datasets. These are challenging toxicological scenarios that require a careful evaluation of all of the available information in order to establish a health-based safe level.
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Mangiatordi GF, Alberga D, Altomare CD, Carotti A, Catto M, Cellamare S, Gadaleta D, Lattanzi G, Leonetti F, Pisani L, Stefanachi A, Trisciuzzi D, Nicolotti O. Mind the Gap! A Journey towards Computational Toxicology. Mol Inform 2016; 35:294-308. [PMID: 27546034 DOI: 10.1002/minf.201501017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 03/23/2016] [Indexed: 11/11/2022]
Abstract
Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end-users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.
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Affiliation(s)
- Giuseppe Felice Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Domenico Alberga
- Dipartimento Interateneo di Fisica 'M.Merlin', Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Angelo Carotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Marco Catto
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Saverio Cellamare
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Domenico Gadaleta
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Gianluca Lattanzi
- Dipartimento Interateneo di Fisica 'M.Merlin', Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Leonardo Pisani
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Angela Stefanachi
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy.
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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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17
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Abstract
Computational approaches offer the attraction of being both fast and cheap to run being able to process thousands of chemical structures in a few minutes. As with all new technology, there is a tendency for these approaches to be hyped up and claims of reliability and performance may be exaggerated. So just how good are these computational methods?
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Affiliation(s)
- Nigel Greene
- Worldwide Medicinal Chemistry
- Pfizer Inc. Groton
- CT 06340, USA
| | - William Pennie
- Drug Safety Research and Evaluation
- Takeda Pharmaceuticals International Inc
- Cambridge, USA
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18
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Satpathy R, Konkimalla VB, Ratha J. Application of bioinformatics tools and databases in microbial dehalogenation research: A review. APPL BIOCHEM MICRO+ 2014. [DOI: 10.1134/s0003683815010147] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Ko R, Low Dog T, Gorecki DKJ, Cantilena LR, Costello RB, Evans WJ, Hardy ML, Jordan SA, Maughan RJ, Rankin JW, Smith-Ryan AE, Valerio LG, Jones D, Deuster P, Giancaspro GI, Sarma ND. Evidence-based evaluation of potential benefits and safety of beta-alanine supplementation for military personnel. Nutr Rev 2014; 72:217-25. [PMID: 24697258 DOI: 10.1111/nure.12087] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
This Department of Defense-sponsored evidence-based review evaluates the safety and putative outcomes of enhancement of athletic performance or improved recovery from exhaustion in studies involving beta-alanine alone or in combination with other ingredients. Beta-alanine intervention studies and review articles were collected from 13 databases, and safety information was collected from adverse event reporting portals. Due to the lack of systematic studies involving military populations, all the available literature was assessed with a subgroup analysis of studies on athletes to determine if beta-alanine would be suitable for the military. Available literature provided only limited evidence concerning the benefits of beta-alanine use, and a majority of the studies were not designed to address safety. Overall, the strength of evidence in terms of the potential for risk of bias in the quality of the available literature, consistency, directness, and precision did not support the use of beta-alanine by military personnel. The strength of evidence for a causal relation between beta-alanine and paresthesia was moderate.
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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
Use of predictive technologies is an important aspect of many efforts in today's research, development, and regulatory landscapes. Computational methods as predictive tools for supporting drug safety assessments is of widespread interest as the field of in silico assessments rapidly changes with emerging technologies and the large amount of existing data available for modeling. There are challenges associated with application of in silico analyses for drug toxicity predictions and need for strategies and harmonization to enable an acceptable in silico evaluation for prediction of specific toxicity assay outcomes. This chapter will provide an overview focused on computational tools using structure-activity relationships and will highlight initiatives for use of computational assessments and realistic applications for predictive modeling in evaluating potential toxicities of drug-related molecules.
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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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24
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Valerio LG, Choudhuri S. Chemoinformatics and chemical genomics: potential utility of in silico methods. J Appl Toxicol 2012; 32:880-9. [PMID: 22886396 DOI: 10.1002/jat.2804] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 06/26/2012] [Accepted: 06/27/2012] [Indexed: 12/24/2022]
Abstract
Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.
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Affiliation(s)
- Luis G Valerio
- Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration, White Oak 51, Room 4128, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA.
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Thomas RS, Black MB, Li L, Healy E, Chu TM, Bao W, Andersen ME, Wolfinger RD. A comprehensive statistical analysis of predicting in vivo hazard using high-throughput in vitro screening. Toxicol Sci 2012; 128:398-417. [PMID: 22543276 DOI: 10.1093/toxsci/kfs159] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Over the past 5 years, increased attention has been focused on using high-throughput in vitro screening for identifying chemical hazards and prioritizing chemicals for additional in vivo testing. The U.S. Environmental Protection Agency's ToxCast program has generated a significant amount of high-throughput screening data allowing a broad-based assessment of the utility of these assays for predicting in vivo responses. In this study, a comprehensive cross-validation model comparison was performed to evaluate the predictive performance of the more than 600 in vitro assays from the ToxCast phase I screening effort across 60 in vivo endpoints using 84 different statistical classification methods. The predictive performance of the in vitro assays was compared and combined with that from chemical structure descriptors. With the exception of chronic in vivo cholinesterase inhibition, the overall predictive power of both the in vitro assays and the chemical descriptors was relatively low. The predictive power of the in vitro assays was not significantly different from that of the chemical descriptors and aggregating the assays based on genes reduced predictive performance. Prefiltering the in vitro assay data outside the cross-validation loop, as done in some previous studies, significantly biased estimates of model performance. The results suggest that the current ToxCast phase I assays and chemicals have limited applicability for predicting in vivo chemical hazards using standard statistical classification methods. However, if viewed as a survey of potential molecular initiating events and interpreted as risk factors for toxicity, the assays may still be useful for chemical prioritization.
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Affiliation(s)
- Russell S Thomas
- The Hamner Institutes for Health Sciences Research Triangle Park, North Carolina 27709, USA.
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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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Martin MT, Knudsen TB, Reif DM, Houck KA, Judson RS, Kavlock RJ, Dix DJ. Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening1. Biol Reprod 2011; 85:327-39. [DOI: 10.1095/biolreprod.111.090977] [Citation(s) in RCA: 119] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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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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29
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Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse. Mol Divers 2010; 15:467-76. [DOI: 10.1007/s11030-010-9268-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2010] [Accepted: 08/05/2010] [Indexed: 10/19/2022]
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Cassano A, Manganaro A, Martin T, Young D, Piclin N, Pintore M, Bigoni D, Benfenati E. CAESAR models for developmental toxicity. Chem Cent J 2010; 4 Suppl 1:S4. [PMID: 20678183 PMCID: PMC2913331 DOI: 10.1186/1752-153x-4-s1-s4] [Citation(s) in RCA: 1134] [Impact Index Per Article: 81.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Background The new REACH legislation requires assessment of a large number of chemicals in the European market for several endpoints. Developmental toxicity is one of the most difficult endpoints to assess, on account of the complexity, length and costs of experiments. Following the encouragement of QSAR (in silico) methods provided in the REACH itself, the CAESAR project has developed several models. Results Two QSAR models for developmental toxicity have been developed, using different statistical/mathematical methods. Both models performed well. The first makes a classification based on a random forest algorithm, while the second is based on an adaptive fuzzy partition algorithm. The first model has been implemented and inserted into the CAESAR on-line application, which is java-based software that allows everyone to freely use the models. Conclusions The CAESAR QSAR models have been developed with the aim to minimize false negatives in order to make them more usable for REACH. The CAESAR on-line application ensures that both industry and regulators can easily access and use the developmental toxicity model (as well as the models for the other four endpoints).
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Affiliation(s)
- Antonio Cassano
- Laboratory of Chemistry and Environmental Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
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31
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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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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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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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35
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Saiakhov RD, Klopman G. MultiCASE Expert Systems and the REACH Initiative. Toxicol Mech Methods 2008; 18:159-75. [DOI: 10.1080/15376510701857460] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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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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Kruhlak NL, Choi SS, Contrera JF, Weaver JL, Willard JM, Hastings KL, Sancilio LF. Development of a Phospholipidosis Database and Predictive Quantitative Structure-Activity Relationship (QSAR) Models. Toxicol Mech Methods 2008; 18:217-27. [DOI: 10.1080/15376510701857262] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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