1
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Chung E, Russo DP, Ciallella HL, Wang YT, Wu M, Aleksunes LM, Zhu H. Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure. Environ Sci Technol 2023; 57:6573-6588. [PMID: 37040559 PMCID: PMC10134506 DOI: 10.1021/acs.est.3c00648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 06/19/2023]
Abstract
Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure-activity relationship (QSAR) models. However, conventional QSAR models have limited training data, leading to low predictivity for new compounds. We developed a data-driven modeling approach for constructing carcinogenicity-related models and used these models to identify potential new human carcinogens. To this goal, we used a probe carcinogen dataset from the US Environmental Protection Agency's Integrated Risk Information System (IRIS) to identify relevant PubChem bioassays. Responses of 25 PubChem assays were significantly relevant to carcinogenicity. Eight assays inferred carcinogenicity predictivity and were selected for QSAR model training. Using 5 machine learning algorithms and 3 types of chemical fingerprints, 15 QSAR models were developed for each PubChem assay dataset. These models showed acceptable predictivity during 5-fold cross-validation (average CCR = 0.71). Using our QSAR models, we can correctly predict and rank 342 IRIS compounds' carcinogenic potentials (PPV = 0.72). The models predicted potential new carcinogens, which were validated by a literature search. This study portends an automated technique that can be applied to prioritize potential toxicants using validated QSAR models based on extensive training sets from public data resources.
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Affiliation(s)
- Elena Chung
- Department
of Chemistry and Biochemistry, Rowan University, 201 Mullica Hill Road, Glassboro, New Jersey 08028, United States
| | - Daniel P. Russo
- Department
of Chemistry and Biochemistry, Rowan University, 201 Mullica Hill Road, Glassboro, New Jersey 08028, United States
| | - Heather L. Ciallella
- Department
of Toxicology, Cuyahoga County Medical Examiner’s
Office, 11001 Cedar Avenue, Cleveland, Ohio 44106, United States
| | - Yu-Tang Wang
- Institute
of Agro-Products Processing Science and Technology, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-Products
Processing, Ministry of Agriculture, Beijing 100193, China
| | - Min Wu
- School
of Life Science and Technology, China Pharmaceutical
University, No. 24, Tong Jia Xiang, Nanjing 210009, China
| | - Lauren M. Aleksunes
- Department
of Pharmacology and Toxicology, Rutgers
University, Ernest Mario School of Pharmacy, 170 Frelinghuysen Road, Piscataway, New Jersey 08854, United States
| | - Hao Zhu
- Department
of Chemistry and Biochemistry, Rowan University, 201 Mullica Hill Road, Glassboro, New Jersey 08028, United States
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2
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Wang T, Russo DP, Bitounis D, Demokritou P, Jia X, Huang H, Zhu H. Integrating structure annotation and machine learning approaches to develop graphene toxicity models. Carbon N Y 2023; 204:484-494. [PMID: 36845527 PMCID: PMC9957041 DOI: 10.1016/j.carbon.2022.12.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.
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Affiliation(s)
- Tong Wang
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Daniel P. Russo
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Dimitrios Bitounis
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Xuelian Jia
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, Pennsylvania, USA
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
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3
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Jia X, Wen X, Russo DP, Aleksunes LM, Zhu H. Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay. J Hazard Mater 2022; 436:129193. [PMID: 35739723 PMCID: PMC9262097 DOI: 10.1016/j.jhazmat.2022.129193] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 05/20/2023]
Abstract
Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.
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Affiliation(s)
- Xuelian Jia
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Xia Wen
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA.
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4
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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. Environ Sci Technol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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5
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Russo DP, Zhu H. High-Throughput Screening Assay Profiling for Large Chemical Databases. Methods Mol Biol 2022; 2474:125-132. [PMID: 35294761 DOI: 10.1007/978-1-0716-2213-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
High-throughput screening (HTS) techniques are increasingly being adopted by a variety of fields of toxicology. Notably, large-scale research efforts from government, industrial, and academic laboratories are screening millions of chemicals against a variety of biomolecular targets, producing an enormous amount of publicly available HTS assay data. These HTS assay data provide toxicologists important information on how chemicals interact with different biomolecular targets and provide illustrations of potential toxicity mechanisms. Open public data repositories, such as the National Institutes of Health's PubChem ( http://pubchem.ncbi.nlm.nih.gov ), were established to accept, store, and share HTS data. Through the PubChem website, users can rapidly obtain the PubChem assay results for compounds by using different chemical identifiers (including SMILES, InChIKey, IUPAC names, etc.). However, obtaining these data in a user-friendly format suitable for modeling and other informatics analysis (e.g., gathering PubChem data for hundreds or thousands of chemicals in a modeling friendly format) directly through the PubChem web portal is not feasible. This chapter aims to introduce two approaches to obtain the HTS assay results for large datasets of compounds from the PubChem portal. First, programmatic access via PubChem's PUG-REST web service using the Python programming language will be described. Second, most users, who lack programming skills, can directly obtain PubChem data for a large set of compounds by using the freely available Chemical In vitro-In vivo Profiling (CIIPro) portal ( http://www.ciipro.rutgers.edu ).
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Affiliation(s)
- Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
- Department of Chemistry, Rutgers University, Camden, NJ, USA
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
- Department of Chemistry, Rutgers University, Camden, NJ, USA.
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6
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Ciallella HL, Chung E, Russo DP, Zhu H. Automatic Quantitative Structure-Activity Relationship Modeling to Fill Data Gaps in High-Throughput Screening. Methods Mol Biol 2022; 2474:169-187. [PMID: 35294765 DOI: 10.1007/978-1-0716-2213-1_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Advances in high-throughput screening (HTS) revolutionized the environmental and health sciences data landscape. However, new compounds still need to be experimentally synthesized and tested to obtain HTS data, which will still be costly and time-consuming when a large set of new compounds need to be studied against many tests. Quantitative structure-activity relationship (QSAR) modeling is a standard method to fill data gaps for new compounds. The major challenge for many toxicologists, especially those with limited computational backgrounds, is efficiently developing optimized QSAR models for each assay with missing data for certain test compounds. This chapter aims to introduce a freely available and user-friendly QSAR modeling workflow, which trains and optimizes models using five algorithms without the need for a programming background.
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Affiliation(s)
- Heather L Ciallella
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Elena Chung
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
- Department of Chemistry, Rutgers University, Camden, NJ, USA
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
- Department of Chemistry, Rutgers University, Camden, NJ, USA.
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7
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Bassan A, Alves VM, Amberg A, Anger LT, Auerbach S, Beilke L, Bender A, Cronin MT, Cross KP, Hsieh JH, Greene N, Kemper R, Kim MT, Mumtaz M, Noeske T, Pavan M, Pletz J, Russo DP, Sabnis Y, Schaefer M, Szabo DT, Valentin JP, Wichard J, Williams D, Woolley D, Zwickl C, Myatt GJ. In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity. Comput Toxicol 2021; 20:100187. [PMID: 35340402 PMCID: PMC8955833 DOI: 10.1016/j.comtox.2021.100187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.
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Affiliation(s)
- Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Vinicius M. Alves
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | - Scott Auerbach
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | - Andreas Bender
- AI and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW
| | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | | | - Jui-Hua Hsieh
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Nigel Greene
- Data Science and AI, DSM, IMED Biotech Unit, AstraZeneca, Boston, USA
| | - Raymond Kemper
- Nuvalent, One Broadway, 14th floor, Cambridge, MA, 02142, USA
| | - Marlene T. Kim
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, 20993, USA
| | - Moiz Mumtaz
- Office of the Associate Director for Science (OADS), Agency for Toxic Substances and Disease, Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Tobias Noeske
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Julia Pletz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Daniel P. Russo
- Department of Chemistry, Rutgers University, Camden, NJ 08102, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Yogesh Sabnis
- UCB Biopharma SRL, Chemin du Foriest – B-1420 Braine-l’Alleud, Belgium
| | - Markus Schaefer
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | | | - Joerg Wichard
- Bayer AG, Genetic Toxicology, Müllerstr. 178, 13353 Berlin, Germany
| | - Dominic Williams
- Functional & Mechanistic Safety, Clinical Pharmacology & Safety Sciences, AstraZeneca, Darwin Building 310, Cambridge Science Park, Milton Rd, Cambridge CB4 0FZ, UK
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, USA
| | - Glenn J. Myatt
- Instem, 1393 Dublin Road, Columbus, OH 43215. USA
- Corresponding author. (G.J. Myatt)
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8
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Bassan A, Alves VM, Amberg A, Anger LT, Beilke L, Bender A, Bernal A, Cronin MT, Hsieh JH, Johnson C, Kemper R, Mumtaz M, Neilson L, Pavan M, Pointon A, Pletz J, Ruiz P, Russo DP, Sabnis Y, Sandhu R, Schaefer M, Stavitskaya L, Szabo DT, Valentin JP, Woolley D, Zwickl C, Myatt GJ. In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities. Comput Toxicol 2021; 20:100188. [PMID: 35721273 PMCID: PMC9205464 DOI: 10.1016/j.comtox.2021.100188] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.
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Affiliation(s)
- Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Vinicius M. Alves
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, United States
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lennart T. Anger
- Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, United States
| | - Andreas Bender
- AI and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United States
| | | | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Jui-Hua Hsieh
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, United States
| | | | - Raymond Kemper
- Nuvalent, One Broadway, 14th floor, Cambridge, MA 02142, United States
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, United States
| | - Louise Neilson
- Broughton Nicotine Services, Oak Tree House, West Craven Drive, Earby, Lancashire BB18 6JZ UK
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Amy Pointon
- Functional and Mechanistic Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Julia Pletz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, United States
| | - Daniel P. Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, United States
- Department of Chemistry, Rutgers University, Camden, NJ 08102, United States
| | - Yogesh Sabnis
- UCB Biopharma SRL, Chemin du Foriest, B-1420 Braine-l’Alleud, Belgium
| | - Reena Sandhu
- SafeDose Ltd., 20 Dundas Street West, Suite 921, Toronto, Ontario M5G2H1, Canada
| | - Markus Schaefer
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lidiya Stavitskaya
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, United States
| | - Glenn J. Myatt
- Instem, 1393 Dublin Road, Columbus, OH 43215, United States
- Corresponding author: (G.J. Myatt)
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9
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Mansouri K, Karmaus AL, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TEH, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, Bhattacharya S, Bastos JV, Boyd S, Brown JB, Capuzzi SJ, Chushak Y, Ciallella H, Clark AM, Consonni V, Daga PR, Ekins S, Farag S, Fedorov M, Fourches D, Gadaleta D, Gao F, Gearhart JM, Goh G, Goodman JM, Grisoni F, Grulke CM, Hartung T, Hirn M, Karpov P, Korotcov A, Lavado GJ, Lawless M, Li X, Luechtefeld T, Lunghini F, Mangiatordi GF, Marcou G, Marsh D, Martin T, Mauri A, Muratov EN, Myatt GJ, Nguyen DT, Nicolotti O, Note R, Pande P, Parks AK, Peryea T, Polash AH, Rallo R, Roncaglioni A, Rowlands C, Ruiz P, Russo DP, Sayed A, Sayre R, Sheils T, Siegel C, Silva AC, Simeonov A, Sosnin S, Southall N, Strickland J, Tang Y, Teppen B, Tetko IV, Thomas D, Tkachenko V, Todeschini R, Toma C, Tripodi I, Trisciuzzi D, Tropsha A, Varnek A, Vukovic K, Wang Z, Wang L, Waters KM, Wedlake AJ, Wijeyesakere SJ, Wilson D, Xiao Z, Yang H, Zahoranszky-Kohalmi G, Zakharov AV, Zhang FF, Zhang Z, Zhao T, Zhu H, Zorn KM, Casey W, Kleinstreuer NC. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite. Environ Health Perspect 2021; 129:109001. [PMID: 34647794 PMCID: PMC8516060 DOI: 10.1289/ehp10369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 05/21/2023]
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10
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Ciallella HL, Russo DP, Aleksunes LM, Grimm FA, Zhu H. Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach. Environ Sci Technol 2021; 55:10875-10887. [PMID: 34304572 PMCID: PMC8713073 DOI: 10.1021/acs.est.1c02656] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and β (ERα and ERβ) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERβ activations. After training, the resultant network successfully inferred critical relationships among ERα/ERβ target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERβ signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.
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Affiliation(s)
- Heather L Ciallella
- Center for Computational and Integrative Biology, Rutgers University Camden, Camden, New Jersey 08103, United States
| | - Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University Camden, Camden, New Jersey 08103, United States
- Department of Chemistry, Rutgers University Camden, Camden, New Jersey 08102, United States
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Fabian A Grimm
- ExxonMobil Biomedical Sciences, Inc., Annandale, New Jersey 08801, United States
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University Camden, Camden, New Jersey 08103, United States
- Department of Chemistry, Rutgers University Camden, Camden, New Jersey 08102, United States
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11
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Lane T, Russo DP, Zorn KM, Clark AM, Korotcov A, Tkachenko V, Reynolds RC, Perryman AL, Freundlich JS, Ekins S. Correction to "Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery". Mol Pharm 2021; 18:2833. [PMID: 34137624 DOI: 10.1021/acs.molpharmaceut.1c00428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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12
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Dodson A, Mi K, Russo DP, Scott C, Saulnier M, Snyder K, Racz R. Aggregation and analysis of secondary pharmacology data from investigational new drug submissions at the US Food and Drug Administration. J Pharmacol Toxicol Methods 2021; 111:107098. [PMID: 34229067 DOI: 10.1016/j.vascn.2021.107098] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/14/2021] [Accepted: 06/29/2021] [Indexed: 01/23/2023]
Abstract
Secondary pharmacology studies are utilized by the pharmaceutical industry as a cost-efficient tool to identify potential safety liabilities of drugs before entering Phase 1 clinical trials. These studies are recommended by the Food and Drug Administration (FDA) as a part of the Investigational New Drug (IND) application. However, despite the utility of these assays, there is little guidance on which targets should be screened and which format should be used. Here, we evaluated 226 secondary pharmacology profiles obtained from close to 90 unique sponsors. The results indicated that the most tested target in our set was the GABA benzodiazepine receptor (tested 168 times), the most hit target was adenosine 3 (hit 24 times), and the target with the highest hit percentage was the quinone reductase 2 (NQO2) receptor (hit 29% of the time). The overall results were largely consistent with those observed in previous publications. However, this study also identified the need for improvement in the submission process of secondary pharmacology studies by industry, which could enhance their utility for regulatory purpose. FDA-industry collaborative working groups will utilize this data to determine the best methods for regulatory submission of these studies and evaluate the need for a standard target panel.
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Affiliation(s)
- Andrew Dodson
- Division of Applied Regulatory Science, Food and Drug Administration, Silver Spring, MD 20993, United States of America; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States of America
| | - Kevin Mi
- Office of New Drugs, Food and Drug Administration, Silver Spring, MD 20993, United States of America
| | - Daniel P Russo
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States of America; Office of New Drugs, Food and Drug Administration, Silver Spring, MD 20993, United States of America
| | - Christina Scott
- Division of Applied Regulatory Science, Food and Drug Administration, Silver Spring, MD 20993, United States of America; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States of America
| | - Muriel Saulnier
- Office of New Drugs, Food and Drug Administration, Silver Spring, MD 20993, United States of America
| | - Kevin Snyder
- Office of New Drugs, Food and Drug Administration, Silver Spring, MD 20993, United States of America
| | - Rebecca Racz
- Division of Applied Regulatory Science, Food and Drug Administration, Silver Spring, MD 20993, United States of America.
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13
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Zhao L, Russo DP, Wang W, Aleksunes LM, Zhu H. Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data. Toxicol Sci 2021; 174:178-188. [PMID: 32073637 DOI: 10.1093/toxsci/kfaa005] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.
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Affiliation(s)
- Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey
| | - Wenyi Wang
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey.,Department of Chemistry, Rutgers University, Camden, New Jersey
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14
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Mansouri K, Karmaus AL, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TE, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, Bhattacharya S, Bastos JV, Boyd S, Brown J, Capuzzi SJ, Chushak Y, Ciallella H, Clark AM, Consonni V, Daga PR, Ekins S, Farag S, Fedorov M, Fourches D, Gadaleta D, Gao F, Gearhart JM, Goh G, Goodman JM, Grisoni F, Grulke CM, Hartung T, Hirn M, Karpov P, Korotcov A, Lavado GJ, Lawless M, Li X, Luechtefeld T, Lunghini F, Mangiatordi GF, Marcou G, Marsh D, Martin T, Mauri A, Muratov EN, Myatt GJ, Nguyen DT, Nicolotti O, Note R, Pande P, Parks AK, Peryea T, Polash AH, Rallo R, Roncaglioni A, Rowlands C, Ruiz P, Russo DP, Sayed A, Sayre R, Sheils T, Siegel C, Silva AC, Simeonov A, Sosnin S, Southall N, Strickland J, Tang Y, Teppen B, Tetko IV, Thomas D, Tkachenko V, Todeschini R, Toma C, Tripodi I, Trisciuzzi D, Tropsha A, Varnek A, Vukovic K, Wang Z, Wang L, Waters KM, Wedlake AJ, Wijeyesakere SJ, Wilson D, Xiao Z, Yang H, Zahoranszky-Kohalmi G, Zakharov AV, Zhang FF, Zhang Z, Zhao T, Zhu H, Zorn KM, Casey W, Kleinstreuer NC. CATMoS: Collaborative Acute Toxicity Modeling Suite. Environ Health Perspect 2021; 129:47013. [PMID: 33929906 PMCID: PMC8086800 DOI: 10.1289/ehp8495] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BACKGROUND Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.
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Affiliation(s)
- Kamel Mansouri
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA
| | - Agnes L. Karmaus
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
| | | | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Prachi Pradeep
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | | | - Timothy E.H. Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Dave Allen
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | | | - Davide Ballabio
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Shannon Bell
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Sudin Bhattacharya
- Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Joyce V. Bastos
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | - Stephen Boyd
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
| | - J.B. Brown
- Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Stephen J. Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Yaroslav Chushak
- Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA
- Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA
| | - Heather Ciallella
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Alex M. Clark
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA
| | - Viviana Consonni
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | | | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA
| | - Sherif Farag
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Maxim Fedorov
- Skoltech, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Feng Gao
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
| | - Jeffery M. Gearhart
- Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA
- Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA
| | - Garett Goh
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jonathan M. Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Francesca Grisoni
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Christopher M. Grulke
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | | | - Matthew Hirn
- Department of Computational Mathematics, Science & Engineering, Department of Mathematics, Michigan State University, East Lansing, Michigan, USA
| | - Pavel Karpov
- Institute of Structural Biology, Helmholtz Zentrum München (GmbH), Neuherberg, Germany
| | | | - Giovanna J. Lavado
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | - Xinhao Li
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Filippo Lunghini
- Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France
| | - Giuseppe F. Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Gilles Marcou
- Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France
| | - Dan Marsh
- Underwriters Laboratories, Northbrook, Illinois, USA
| | - Todd Martin
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | | | - Eugene N. Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | | | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Reine Note
- L’Oréal Research & Innovation, Aulnay-sous-Bois, France
| | - Paritosh Pande
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | | | - Tyler Peryea
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Robert Rallo
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | - Patricia Ruiz
- Office of Innovation and Analytics, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Daniel P. Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Ahmed Sayed
- Rosettastein Consulting UG, Freising, Germany
| | - Risa Sayre
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Timothy Sheils
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Charles Siegel
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Arthur C. Silva
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | - Anton Simeonov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Sergey Sosnin
- Skoltech, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Noel Southall
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Judy Strickland
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Brian Teppen
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
| | - Igor V. Tetko
- Institute of Structural Biology, Helmholtz Zentrum München (GmbH), Neuherberg, Germany
- BIGCHEM GmbH, Unterschleissheim, Germany
| | - Dennis Thomas
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | | | - Roberto Todeschini
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Cosimo Toma
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Ignacio Tripodi
- Computer Science/Interdisciplinary Quantitative Biology, University of Colorado, Boulder, Colorado, USA
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France
| | - Kristijan Vukovic
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Zhongyu Wang
- School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China
| | - Liguo Wang
- School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China
| | | | - Andrew J. Wedlake
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | | | - Dan Wilson
- The Dow Chemical Company, Midland, Michigan, USA
| | - Zijun Xiao
- School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Gergely Zahoranszky-Kohalmi
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Zhen Zhang
- Dow Agrosciences, Indianapolis, Indiana, USA
| | - Tongan Zhao
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | | | - Warren Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA
| | - Nicole C. Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA
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15
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Ciallella HL, Russo DP, Aleksunes LM, Grimm FA, Zhu H. Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches. J Transl Med 2021; 101:490-502. [PMID: 32778734 PMCID: PMC7873171 DOI: 10.1038/s41374-020-00477-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 07/19/2020] [Accepted: 07/21/2020] [Indexed: 11/23/2022] Open
Abstract
As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)populations. Traditional experimental testing regimens to identify toxicants that induce endocrine disruption can be expensive and time-consuming. Computational modeling has emerged as a promising and cost-effective alternative method for screening and prioritizing potentially endocrine-active compounds. The efficient identification of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge for computational toxicology studies. Here, we sought to apply classic machine-learning algorithms and deep-learning approaches to a panel of over 7500 compounds tested against 18 Toxicity Forecaster assays related to nuclear estrogen receptor (ERα and ERβ) activity. Three binary fingerprints (Extended Connectivity FingerPrints, Functional Connectivity FingerPrints, and Molecular ACCess System) were used as chemical descriptors in this study. Each descriptor was combined with four machine-learning and two deep- learning (normal and multitask neural networks) approaches to construct models for all 18 ER assays. The resulting model performance was evaluated using the area under the receiver- operating curve (AUC) values obtained from a fivefold cross-validation procedure. The results showed that individual models have AUC values that range from 0.56 to 0.86. External validation was conducted using two additional sets of compounds (n = 592 and n = 966) with established interactions with nuclear ER demonstrated through experimentation. An agonist, antagonist, or binding score was determined for each compound by averaging its predicted probabilities in relevant assay models as an external validation, yielding AUC values ranging from 0.63 to 0.91. The results suggest that multitask neural networks offer advantages when modeling mechanistically related endpoints. Consensus predictions based on the average values of individual models remain the best modeling strategy for computational toxicity evaluations.
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Affiliation(s)
- Heather L Ciallella
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA
| | - Fabian A Grimm
- ExxonMobil Biomedical Sciences, Inc., Annandale, NJ, USA
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
- Department of Chemistry, Rutgers University, Camden, NJ, USA.
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16
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Jia X, Ciallella HL, Russo DP, Zhao L, James MH, Zhu H. Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids. ACS Sustain Chem Eng 2021; 9:3909-3919. [PMID: 34239782 PMCID: PMC8259887 DOI: 10.1021/acssuschemeng.0c09139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow proven to be applicable by screening new analgesic opioids. To this end, a large opioid data set was used as the probe to automatically obtain bioassay data from the PubChem portal. There were 114 PubChem bioassays selected to build quantitative structure-activity relationship (QSAR) models based on the testing results across the probe compounds. The compounds tested in each bioassay were used to develop 12 models using the combination of three machine learning approaches and four types of chemical descriptors. The model performance was evaluated by the coefficient of determination (R 2) obtained from 5-fold cross-validation. In total, 49 models developed for 14 bioassays were selected based on the criteria and were identified to be mainly associated with binding affinities to different opioid receptors. The models for these 14 bioassays were further used to fill data gaps in the probe opioids data set and to predict general drug compounds in the DrugBank data set. This study provides a universal modeling strategy that can take advantage of large public data sets for computer-aided drug design (CADD).
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Affiliation(s)
- Xuelian Jia
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States
| | - Heather L Ciallella
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States
| | - Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States
| | - Morgan H James
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University and Rutgers Biomedical Health Sciences, Piscataway, New Jersey 08854, United States; Brain Health Institute, Rutgers University and Rutgers Biomedical and Health Sciences, Piscataway, New Jersey 08854, United States
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States; Department of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
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17
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Russo DP, Tham T, Bardash Y, Kraus D. The effect of race in head and neck cancer: A meta-analysis controlling for socioeconomic status. Am J Otolaryngol 2020; 41:102624. [PMID: 32663732 DOI: 10.1016/j.amjoto.2020.102624] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 06/18/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To investigate the association between race and ethnicity and prognosis in head and neck cancers (HNC), while controlling for socioeconomic status (SES). MATERIALS AND METHODS Medline, Scopus, EMBASE, and the Cochrane Library were used to identify studies for inclusion, from database inception till March 5th 2019. Studies that analyzed the role of race and ethnicity in overall survival (OS) for malignancies of the head and neck were included in this study. For inclusion, the study needed to report a multivariate analysis controlling for some proxy of SES (for example household income or employment status). Pooled estimates were generated using a random effects model. Subgroup analysis by tumor sub-site, meta-regression, and sensitivity analyses were also performed. RevMan 5.3, Meta Essentials, and OpenMeta[Analyst] were used for statistical analysis. RESULTS Ten studies from 2004 to 2019 with a total of 108,990 patients were included for analysis in this study. After controlling for SES, tumor stage, and treatment variables, blacks were found to have a poorer survival compared to whites (HR = 1.27, 95%CI: 1.18-1.36, p < 0.00001). Subgroup analysis by sub-site and sensitivity analysis agreed with the primary result. No differences in survival across sub-sites were observed. Meta-regression did not identify any factors associated with the pooled estimate. CONCLUSIONS In HNC, blacks have poorer OS compared to whites even after controlling for socioeconomic factors.
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Affiliation(s)
- Daniel P Russo
- Department of Otolaryngology, Head and Neck Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, United States of America; New York Head & Neck Institute, Otolaryngology - Head and Neck Surgery, United States of America.
| | - Tristan Tham
- Department of Otolaryngology, Head and Neck Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, United States of America; New York Head & Neck Institute, Otolaryngology - Head and Neck Surgery, United States of America
| | - Yonatan Bardash
- Department of Otolaryngology, Head and Neck Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, United States of America; New York Head & Neck Institute, Otolaryngology - Head and Neck Surgery, United States of America
| | - Dennis Kraus
- Department of Otolaryngology, Head and Neck Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, United States of America; New York Head & Neck Institute, Otolaryngology - Head and Neck Surgery, United States of America
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18
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Wang YT, Russo DP, Liu C, Zhou Q, Zhu H, Zhang YH. Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches. J Agric Food Chem 2020; 68:12132-12140. [PMID: 32915574 DOI: 10.1021/acs.jafc.0c04624] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Food-derived angiotensin I-converting enzyme (ACE) inhibitory peptides could potentially be used as safe supportive therapeutic products for high blood pressure. Theoretical approaches are promising methods with the advantage through exploring the relationships between peptide structures and their bioactivities. In this study, peptides with ACE inhibitory activity were collected and curated. Quantitative structure-activity relationship (QSAR) models were developed by using the combination of various machine learning approaches and chemical descriptors. The resultant models have revealed several structure features accounting for the ACE inhibitions. 14 new dipeptides predicted to lower blood pressure by inhibiting ACE were selected. Molecular docking indicated that these dipeptides formed hydrogen bonds with ACE. Five of these dipeptides were synthesized for experimental testing. The QSAR models developed were proofed to design and propose novel ACE inhibitory peptides. Machine learning algorithms and properly selected chemical descriptors can be promising modeling approaches for rational design of natural functional food components.
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Affiliation(s)
- Yu-Tang Wang
- Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin 150030, PR China
- Department of Food Science, Northeast Agricultural University, Harbin 150030, PR China
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Chang Liu
- Department of Food Science, Northeast Agricultural University, Harbin 150030, PR China
| | - Qian Zhou
- Department of Food Science, Northeast Agricultural University, Harbin 150030, PR China
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
- Department of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
| | - Ying-Hua Zhang
- Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin 150030, PR China
- Department of Food Science, Northeast Agricultural University, Harbin 150030, PR China
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19
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Russo DP, Yan X, Shende S, Huang H, Yan B, Zhu H. Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties. Anal Chem 2020; 92:13971-13979. [PMID: 32970421 DOI: 10.1021/acs.analchem.0c02878] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This "end-to-end" deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities.
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Affiliation(s)
- Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, 201 S Broadway, Camden, New Jersey 08103, United States
| | - Xiliang Yan
- Center for Computational and Integrative Biology, Rutgers University, 201 S Broadway, Camden, New Jersey 08103, United States.,Institute of Environmental Research at Greater Bay, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Sunil Shende
- Center for Computational and Integrative Biology, Rutgers University, 201 S Broadway, Camden, New Jersey 08103, United States.,Department of Computer Science, Rutgers University, 227 Penn Street, Camden, New Jersey 08102, United States
| | | | - Bing Yan
- Institute of Environmental Research at Greater Bay, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China.,School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, 201 S Broadway, Camden, New Jersey 08103, United States.,Department of Chemistry, Rutgers University, 315 Penn Street, Camden, New Jersey 08102, United States
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20
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Zorn KM, Foil DH, Lane TR, Russo DP, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction. Environ Sci Technol 2020; 54:12202-12213. [PMID: 32857505 PMCID: PMC8194504 DOI: 10.1021/acs.est.0c03982] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.
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Affiliation(s)
- Kimberley M Zorn
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H Foil
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey 08102, United States
| | - Wendy Hillwalker
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - David J Feifarek
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Frank Jones
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - William D Klaren
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Ashley M Brinkman
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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21
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Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP, Klein JJ, Hickey AJ, Clark AM. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 2019; 18:435-441. [PMID: 31000803 PMCID: PMC6594828 DOI: 10.1038/s41563-019-0338-z] [Citation(s) in RCA: 219] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 03/07/2019] [Indexed: 05/20/2023]
Abstract
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA.
| | - Ana C Puhl
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
| | | | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
| | - Daniel P Russo
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA
| | | | - Anthony J Hickey
- RTI International, Research Triangle Park, NC, USA
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alex M Clark
- Molecular Materials Informatics, Inc., Montreal, Quebec, Canada
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22
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Russo DP, Strickland J, Karmaus AL, Wang W, Shende S, Hartung T, Aleksunes LM, Zhu H. Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across. Environ Health Perspect 2019; 127:47001. [PMID: 30933541 PMCID: PMC6785238 DOI: 10.1289/ehp3614] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
BACKGROUND Low-cost, high-throughput in vitro bioassays have potential as alternatives to animal models for toxicity testing. However, incorporating in vitro bioassays into chemical toxicity evaluations such as read-across requires significant data curation and analysis based on knowledge of relevant toxicity mechanisms, lowering the enthusiasm of using the massive amount of unstructured public data. OBJECTIVE We aimed to develop a computational method to automatically extract useful bioassay data from a public repository (i.e., PubChem) and assess its ability to predict animal toxicity using a novel bioprofile-based read-across approach. METHODS A training database containing 7,385 compounds with diverse rat acute oral toxicity data was searched against PubChem to establish in vitro bioprofiles. Using a novel subspace clustering algorithm, bioassay groups that may inform on relevant toxicity mechanisms underlying acute oral toxicity were identified. These bioassays groups were used to predict animal acute oral toxicity using read-across through a cross-validation process. Finally, an external test set of over 600 new compounds was used to validate the resulting model predictivity. RESULTS Several bioassay clusters showed high predictivity for acute oral toxicity (positive prediction rates range from 62-100%) through cross-validation. After incorporating individual clusters into an ensemble model, chemical toxicants in the external test set were evaluated for putative acute toxicity (positive prediction rate equal to 76%). Additionally, chemical fragment -in vitro-in vivo relationships were identified to illustrate new animal toxicity mechanisms. CONCLUSIONS The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points. https://doi.org/10.1289/EHP3614.
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Affiliation(s)
- Daniel P. Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Judy Strickland
- Integrated Laboratory Systems (ILS), Research Triangle Park, North Carolina, USA
| | - Agnes L. Karmaus
- Integrated Laboratory Systems (ILS), Research Triangle Park, North Carolina, USA
| | - Wenyi Wang
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Sunil Shende
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
- Department of Computer Science, Rutgers University, Camden, New Jersey, USA
| | - Thomas Hartung
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, Maryland, USA
- University of Konstanz, CAAT-Europe, Konstanz, Germany
| | - Lauren M. Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey, USA
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
- Department of Chemistry, Rutgers University, Camden, New Jersey, USA
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23
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Abstract
The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reverse transcriptase inhibitors have become susceptible to drug resistant strains of HIV, and hence, alternatives are urgently needed. We have recently pioneered the use of Bayesian machine learning to generate models with public data to identify new compounds for testing against different disease targets. The current study has used the NIAID ChemDB HIV, Opportunistic Infection and Tuberculosis Therapeutics Database for machine learning studies. We curated and cleaned data from HIV-1 wild-type cell-based and reverse transcriptase (RT) DNA polymerase inhibition assays. Compounds from this database with ≤1 μM HIV-1 RT DNA polymerase activity inhibition and cell-based HIV-1 inhibition are correlated (Pearson r = 0.44, n = 1137, p < 0.0001). Models were trained using multiple machine learning approaches (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, support vector classification, k-Nearest Neighbors, and deep neural networks as well as consensus approaches) and then their predictive abilities were compared. Our comparison of different machine learning methods demonstrated that support vector classification, deep learning, and a consensus were generally comparable and not significantly different from each other using 5-fold cross validation and using 24 training and test set combinations. This study demonstrates findings in line with our previous studies for various targets that training and testing with multiple data sets does not demonstrate a significant difference between support vector machine and deep neural networks.
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Affiliation(s)
- Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc. , Main Campus Drive, Lab 3510 , Raleigh , North Carolina 27606 , United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc. , Main Campus Drive, Lab 3510 , Raleigh , North Carolina 27606 , United States
| | - Daniel P Russo
- Collaborations Pharmaceuticals, Inc. , Main Campus Drive, Lab 3510 , Raleigh , North Carolina 27606 , United States.,The Rutgers Center for Computational and Integrative Biology , Camden , New Jersey 08102 , United States
| | - Alex M Clark
- Molecular Materials Informatics, Inc. , 2234 Duvernay Street , Montreal , Quebec H3J2Y3 , Canada
| | - Vadim Makarov
- Bach Institute of Biochemistry , Research Center of Biotechnology of the Russian Academy of Sciences , Leninsky Prospekt 33-2 , Moscow 119071 , Russia
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc. , Main Campus Drive, Lab 3510 , Raleigh , North Carolina 27606 , United States
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24
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Wang W, Yan X, Zhao L, Russo DP, Wang S, Liu Y, Sedykh A, Zhao X, Yan B, Zhu H. Universal nanohydrophobicity predictions using virtual nanoparticle library. J Cheminform 2019; 11:6. [PMID: 30659400 PMCID: PMC6689884 DOI: 10.1186/s13321-019-0329-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 01/10/2019] [Indexed: 12/12/2022] Open
Abstract
To facilitate the development of new nanomaterials, especially nanomedicines, a novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs). The core of this study was to develop a large virtual gold nanoparticle (vGNP) library with computational nanostructure simulations. Based on the vGNP library, a nanohydrophobicity model was developed and then validated against externally synthesized and tested GNPs. This approach and resulted model is an efficient and effective universal tool to visualize and predict critical physicochemical properties of new nanomaterials before synthesis, guiding nanomaterial design.
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Affiliation(s)
- Wenyi Wang
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Xiliang Yan
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA.,School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China
| | - Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Shenqing Wang
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China
| | - Yin Liu
- Research Center for Eco-Environmental Science, Chinese Academy of Sciences, Beijing, 100085, China
| | - Alexander Sedykh
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA.,Sciome, Research Triangle Park, NC, 27709, USA
| | - Xiaoli Zhao
- Department of Physiological Sciences, Eastern Virginia Medical School, Norfolk, VA, 23507, USA
| | - Bing Yan
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China.,School of Environment, Jinan University, Guangzhou, 510632, China
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA. .,Department of Chemistry, Rutgers University, 315 Penn St., Camden, NJ, 08102, USA. .,College of Life Science and Bio-Engineering, Beijing University of Technology, Beijing, 100124, China.
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25
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Abstract
Many chemicals that disrupt endocrine function have been linked to a variety of adverse biological outcomes. However, screening for endocrine disruption using in vitro or in vivo approaches is costly and time-consuming. Computational methods, e.g., quantitative structure-activity relationship models, have become more reliable due to bigger training sets, increased computing power, and advanced machine learning algorithms, such as multilayered artificial neural networks. Machine learning models can be used to predict compounds for endocrine disrupting capabilities, such as binding to the estrogen receptor (ER), and allow for prioritization and further testing. In this work, an exhaustive comparison of multiple machine learning algorithms, chemical spaces, and evaluation metrics for ER binding was performed on public data sets curated using in-house cheminformatics software (Assay Central). Chemical features utilized in modeling consisted of binary fingerprints (ECFP6, FCFP6, ToxPrint, or MACCS keys) and continuous molecular descriptors from RDKit. Each feature set was subjected to classic machine learning algorithms (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, Support Vector Machine) and Deep Neural Networks (DNN). Models were evaluated using a variety of metrics: recall, precision, F1-score, accuracy, area under the receiver operating characteristic curve, Cohen's Kappa, and Matthews correlation coefficient. For predicting compounds within the training set, DNN has an accuracy higher than that of other methods; however, in 5-fold cross validation and external test set predictions, DNN and most classic machine learning models perform similarly regardless of the data set or molecular descriptors used. We have also used the rank normalized scores as a performance-criteria for each machine learning method, and Random Forest performed best on the validation set when ranked by metric or by data sets. These results suggest classic machine learning algorithms may be sufficient to develop high quality predictive models of ER activity.
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Affiliation(s)
- Daniel P. Russo
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
- first author
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
- first author
| | - Alex M. Clark
- Molecular Materials Informatics, Inc., Montreal, Quebec, Canada
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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26
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Lane T, Russo DP, Zorn KM, Clark AM, Korotcov A, Tkachenko V, Reynolds RC, Perryman AL, Freundlich JS, Ekins AS. Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery. Mol Pharm 2018; 15:4346-4360. [PMID: 29672063 PMCID: PMC6167198 DOI: 10.1021/acs.molpharmaceut.8b00083] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.
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Affiliation(s)
- Thomas Lane
- Collaborations Pharmaceuticals, Inc., Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Daniel P. Russo
- Collaborations Pharmaceuticals, Inc., Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
| | - Alex M. Clark
- Molecular Materials Informatics, Inc., 1900 St. Jacques #302, Montreal H3J 2S1, Quebec, Canada
| | - Alexandru Korotcov
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Valery Tkachenko
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Robert C. Reynolds
- Department of Medicine, Division of Hematology and Oncology, University of Alabama at Birmingham, NP 2540 J, 1720 2Avenue South, Birmingham, AL 35294-3300, USA
| | - Alexander L. Perryman
- Department of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, USA
| | - Joel S. Freundlich
- Department of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, USA
- Division of Infectious Diseases, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University–New Jersey Medical School, Newark, New Jersey 07103, USA
| | - and Sean Ekins
- Collaborations Pharmaceuticals, Inc., Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
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27
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Russo DP, Kim MT, Wang W, Pinolini D, Shende S, Strickland J, Hartung T, Zhu H. CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data. Bioinformatics 2018; 33:464-466. [PMID: 28172359 DOI: 10.1093/bioinformatics/btw640] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 09/29/2016] [Accepted: 10/05/2016] [Indexed: 11/14/2022] Open
Abstract
Summary We have developed a public Chemical In vitro–In vivo Profiling (CIIPro) portal, which can automatically extract in vitro biological data from public resources (i.e. PubChem) for user-supplied compounds. For compounds with in vivo target activity data (e.g. animal toxicity testing results), the integrated cheminformatics algorithm will optimize the extracted biological data using in vitro–in vivo correlations. The resulting in vitro biological data for target compounds can be used for read-across risk assessment of target compounds. Additionally, the CIIPro portal can identify the most similar compounds based on their optimized bioprofiles. The CIIPro portal provides new powerful assessment capabilities to the scientific community and can be easily integrated with other cheminformatics tools. Availability and Implementation ciipro.rutgers.edu. Contact danrusso@scarletmail.rutgers.edu or hao.zhu99@rutgers.edu
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Affiliation(s)
- Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA
| | - Marlene T Kim
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.,Department of Chemistry, Rutgers University, Camden, NJ, USA
| | - Wenyi Wang
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA
| | - Daniel Pinolini
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA
| | - Sunil Shende
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.,Department of Computer Science, Rutgers University, Camden, NJ, USA
| | | | - Thomas Hartung
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.,University of Konstanz, CAAT-Europe, Konstanz, Germany
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.,Department of Chemistry, Rutgers University, Camden, NJ, USA
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28
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Wang W, Sedykh A, Sun H, Zhao L, Russo DP, Zhou H, Yan B, Zhu H. Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. ACS Nano 2017; 11:12641-12649. [PMID: 29149552 PMCID: PMC5772766 DOI: 10.1021/acsnano.7b07093] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.
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Affiliation(s)
- Wenyi Wang
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Alexander Sedykh
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
- Sciome, Research Triangle Park, North Carolina 27709, United States
| | - Hainan Sun
- School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
| | - Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Daniel P. Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Hongyu Zhou
- School of Environment, Jinan University, Guangzhou 510632, China
| | - Bing Yan
- School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
- Corresponding Authors. (B. Yan): . (H. Zhu):
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
- Department of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
- Corresponding Authors. (B. Yan): . (H. Zhu):
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29
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Abstract
Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.
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Affiliation(s)
- Alexandru Korotcov
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Valery Tkachenko
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Daniel P Russo
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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30
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Xiang J, Zhang Z, Mu Y, Xu X, Guo S, Liu Y, Russo DP, Zhu H, Yan B, Bai X. Discovery of Novel Tricyclic Thiazepine Derivatives as Anti-Drug-Resistant Cancer Agents by Combining Diversity-Oriented Synthesis and Converging Screening Approach. ACS Comb Sci 2016; 18:230-5. [PMID: 27082930 DOI: 10.1021/acscombsci.6b00010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
An efficient discovery strategy by combining diversity-oriented synthesis and converging cellular screening is described. By a three-round screening process, we identified novel tricyclic pyrido[2,3-b][1,4]benzothiazepines showing potent inhibitory activity against paclitaxel-resistant cell line H460TaxR (EC50 < 1.0 μM), which exhibits much less toxicity toward normal cells (EC50 > 100 μM against normal human fibroblasts). The most active hits also exhibited drug-like properties suitable for further preclinical research. This redeployment of antidepressing compounds for anticancer applications provides promising future prospects for treating drug-resistant tumors with fewer side effects.
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Affiliation(s)
- Jinbao Xiang
- The
Center for Combinatorial Chemistry and Drug Discovery, The School
of Pharmaceutical Sciences, and The College of Chemistry, Jilin University, Changchun, Jilin 130021, P. R. China
| | - Zhuoqi Zhang
- The
Center for Combinatorial Chemistry and Drug Discovery, The School
of Pharmaceutical Sciences, and The College of Chemistry, Jilin University, Changchun, Jilin 130021, P. R. China
| | - Yan Mu
- School
of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong 250100, P. R. China
| | - Xianxiu Xu
- The
Center for Combinatorial Chemistry and Drug Discovery, The School
of Pharmaceutical Sciences, and The College of Chemistry, Jilin University, Changchun, Jilin 130021, P. R. China
| | - Sigen Guo
- The
Center for Combinatorial Chemistry and Drug Discovery, The School
of Pharmaceutical Sciences, and The College of Chemistry, Jilin University, Changchun, Jilin 130021, P. R. China
| | - Yongjin Liu
- School
of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong 250100, P. R. China
| | - Daniel P. Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
- Department
of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
| | - Bing Yan
- School
of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong 250100, P. R. China
| | - Xu Bai
- The
Center for Combinatorial Chemistry and Drug Discovery, The School
of Pharmaceutical Sciences, and The College of Chemistry, Jilin University, Changchun, Jilin 130021, P. R. China
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31
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Mu Y, Liu Y, Xiang J, Zhang Q, Zhai S, Russo DP, Zhu H, Bai X, Yan B. From fighting depression to conquering tumors: a novel tricyclic thiazepine compound as a tubulin polymerization inhibitor. Cell Death Dis 2016; 7:e2143. [PMID: 26986511 PMCID: PMC4823954 DOI: 10.1038/cddis.2016.53] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2015] [Revised: 01/17/2016] [Accepted: 02/17/2016] [Indexed: 12/17/2022]
Abstract
A novel tricyclic thiazepine derivative, 6-(p-tolyl)benzo[f] pyrido[2,3-b][1,4] thiazepine 11,11-dioxide (TBPT), exhibits potent inhibitory effects in two non-small-cell lung cancer cell lines, H460 and its drug-resistant variant, H460TaxR, while exhibiting much less toxic effects on normal human fibroblasts. After five injections of TBPT at a dose of 60 mg/kg, it inhibits H460TaxR tumor growth in xenografted mouse models by 66.7% without causing observable toxicity to normal tissues. Based on gene perturbation data and a series of investigations, we reveal that TBPT is not a P-glycoprotein substrate and it inhibits microtubule formation by targeting tubulin, thereby causing cell cycle arrest at the G2/M stage and eventually inducing apoptosis. This redeployment of anti-depressant compound scaffold for anticancer applications provides a promising future for conquering drug-resistant tumors with fewer side effects.
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Affiliation(s)
- Y Mu
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong, China
| | - Y Liu
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong, China
| | - J Xiang
- The Center for Combinatorial Chemistry and Drug Discovery of Jilin University, The School of Pharmaceutical Sciences and The College of Chemistry, Jilin University, Changchun, Jilin, China
| | - Q Zhang
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong, China
| | - S Zhai
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong, China
| | - D P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA
| | - H Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.,Department of Chemistry, Rutgers University, Camden, NJ, USA
| | - X Bai
- The Center for Combinatorial Chemistry and Drug Discovery of Jilin University, The School of Pharmaceutical Sciences and The College of Chemistry, Jilin University, Changchun, Jilin, China
| | - B Yan
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong, China
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32
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Ball N, Cronin MTD, Shen J, Blackburn K, Booth ED, Bouhifd M, Donley E, Egnash L, Hastings C, Juberg DR, Kleensang A, Kleinstreuer N, Kroese ED, Lee AC, Luechtefeld T, Maertens A, Marty S, Naciff JM, Palmer J, Pamies D, Penman M, Richarz AN, Russo DP, Stuard SB, Patlewicz G, van Ravenzwaay B, Wu S, Zhu H, Hartung T. Toward Good Read-Across Practice (GRAP) guidance. ALTEX 2016; 33:149-66. [PMID: 26863606 PMCID: PMC5581000 DOI: 10.14573/altex.1601251] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 02/11/2016] [Indexed: 12/04/2022]
Abstract
Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislation such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document presents the state of the art, summarizes insights learned from reviewing ECHA published decisions regarding the relative successes/pitfalls surrounding read-across under REACH, and compiles the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data, and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.
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Affiliation(s)
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Jie Shen
- Research Institute for Fragrance Materials, Inc. Woodcliff Lake, NJ, USA
| | | | - Ewan D Booth
- Syngenta Ltd, Jealott's Hill International Research Centre, Bracknell, Berkshire, UK
| | - Mounir Bouhifd
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | | | - Laura Egnash
- Stemina Biomarker Discovery Inc., Madison, WI, USA
| | - Charles Hastings
- BASF SE, Ludwigshafen am Rhein, Germany, and Research Triangle Park, NC, USA
| | | | - Andre Kleensang
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - E Dinant Kroese
- Risk Analysis for Products in Development, TNO Zeist, The Netherlands
| | - Adam C Lee
- DuPont Haskell Global Centers for Health and Environmental Sciences, Newark, DE, USA
| | - Thomas Luechtefeld
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Alexandra Maertens
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Sue Marty
- The Dow Chemical Company, Midland, MI, USA
| | | | | | - David Pamies
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | | | - Andrea-Nicole Richarz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Daniel P Russo
- Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | | | - Grace Patlewicz
- US EPA/ORD, National Center for Computational Toxicology, Research Triangle Park, NC, USA
| | | | - Shengde Wu
- The Procter and Gamble Co., Cincinatti, OH, USA
| | - Hao Zhu
- Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Thomas Hartung
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.,University of Konstanz, CAAT-Europe, Konstanz, Germany
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33
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Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Analysis of public oral toxicity data from REACH registrations 2008-2014. ALTEX 2016; 33:111-22. [PMID: 26863198 PMCID: PMC5461469 DOI: 10.14573/altex.1510054] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 01/25/2016] [Indexed: 11/23/2022]
Abstract
The European Chemicals Agency, ECHA, made available a total of 13,832 oral toxicity studies for 8,568 substances up to December 2014. 75% of studies were from the retired OECD Test Guideline 401 (11% TG 420, 11% TG 423 and 1.5% TG 425). Concordance across guidelines, evaluated by comparing LD50 values ≥ 2000 or < 2000 mg/kg body weight from chemicals tested multiple times between different guidelines, was at least 75% and for their own repetition more than 90%. In 2009, Bulgheroni et al. created a simple model for predicting acute oral toxicity using no observed adverse effect levels (NOAEL) from 28-day repeated dose toxicity studies in rats. This was reproduced here for 1,625 substances. In 2014, Taylor et al. suggested no added value of the 90-day repeated dose oral toxicity test given the availability of a low 28-day study with some constraints. We confirm that the 28-day NOAEL is predictive (albeit imperfectly) of 90-day NOAELs, however, the suggested constraints did not affect predictivity. 1,059 substances with acute oral toxicity data (268 positives, 791 negatives, all Klimisch score 1) were used for modeling: The Chemical Development Kit was used to generate 27 molecular descriptors and a similarity-informed multilayer perceptron showing 71% sensitivity and 72% specificity. Additionally, the k-nearest neighbors (KNN) algorithm indicated that similarity-based approaches alone may be poor predictors of acute oral toxicity, but can be used to inform the multilayer perceptron model, where this was the feature with highest information value.
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Affiliation(s)
- Thomas Luechtefeld
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Daniel P Russo
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA
| | | | - Hao Zhu
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.,Department of Chemistry, Rutgers University at Camden, NJ, USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.,CAAT-Europe, University of Konstanz, Konstanz, Germany
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34
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Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Analysis of publically available skin sensitization data from REACH registrations 2008-2014. ALTEX 2016; 33:135-48. [PMID: 26863411 PMCID: PMC5546098 DOI: 10.14573/altex.1510055] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 01/26/2016] [Indexed: 01/13/2023]
Abstract
The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization. A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found. A chemical similarity map was produced using PubChem’s 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel’s module recognition algorithm revealing wide module-dependent variation. Approximately 31% of mapped chemicals are Michael’s acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value. A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity. This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction.
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Affiliation(s)
- Thomas Luechtefeld
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Daniel P Russo
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA
| | | | - Hao Zhu
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.,Department of Chemistry, Rutgers University at Camden, NJ, USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.,CAAT-Europe, University of Konstanz, Konstanz, Germany
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35
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Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008-2014. ALTEX 2016; 33:95-109. [PMID: 26863090 PMCID: PMC5408747 DOI: 10.14573/altex.1510052] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 01/29/2016] [Indexed: 12/21/2022]
Abstract
The European Chemicals Agency (ECHA) warehouses the largest public dataset of in vivo and in vitro toxicity tests. In December 2014 this data was converted into a structured, machine readable and searchable database using natural language processing. It contains data for 9,801 unique substances, 3,609 unique study descriptions and 816,048 study documents. This allows exploring toxicological data on a scale far larger than previously possible. Substance similarity analysis was used to determine clustering of substances for hazards by mapping to PubChem. Similarity was measured using PubChem 2D conformational substructure fingerprints, which were compared via the Tanimoto metric. Following K-Core filtration, the Blondel et al. (2008) module recognition algorithm was used to identify chemical modules showing clusters of substances in use within the chemical universe. The Global Harmonized System of Classification and Labelling provides a valuable information source for hazard analysis. The most prevalent hazards are H317 “May cause an allergic skin reaction” with 20% and H318 “Causes serious eye damage” with 17% positive substances. Such prevalences obtained for all hazards here are key for the design of integrated testing strategies. The data allowed estimation of animal use. The database covers about 20% of substances in the high-throughput biological assay database Tox21 (1,737 substances) and has a 917 substance overlap with the Comparative Toxicogenomics Database (~7% of CTD). The biological data available in these datasets combined with ECHA in vivo endpoints have enormous modeling potential. A case is made that REACH should systematically open regulatory data for research purposes.
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Affiliation(s)
- Thomas Luechtefeld
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Daniel P Russo
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA
| | | | - Hao Zhu
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.,Department of Chemistry, Rutgers University at Camden, NJ, USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.,CAAT-Europe, University of Konstanz, Konstanz, Germany
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36
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Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data. ALTEX 2016; 33:123-34. [PMID: 26863293 PMCID: PMC5461467 DOI: 10.14573/altex.1510053] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 02/05/2016] [Indexed: 12/22/2022]
Abstract
Public data from ECHA online dossiers on 9,801 substances encompassing 326,749 experimental key studies and additional information on classification and labeling were made computable. Eye irritation hazard, for which the rabbit Draize eye test still represents the reference method, was analyzed. Dossiers contained 9,782 Draize eye studies on 3,420 unique substances, indicating frequent retesting of substances. This allowed assessment of the test's reproducibility test based on all substances tested more than once. There was a 10% chance of a non-irritant evaluation given after a prior severe-irritant result as given by UN GHS classification criteria. The most reproducible outcomes were the results negative (94% reproducible) and severe eye irritant (73% reproducible). To evaluate whether other GHS categorizations predict eye irritation we built a dataset of 5,629 substances (1,931 'irritant' and 3,698 'non-irritant'). The two best decision trees with up to three other GHS classifications resulted in balanced accuracies of 68% and 73%, i.e., in the rank order of the Draize rabbit eye test itself, but both use inhalation toxicity data ("May cause respiratory irritation"), which is not typically available. Next, a dataset of 929 substances with at least one Draize study was mapped to PubChem to compute chemical similarity using 2D conformational fingerprints and Tanimoto similarity. Using a minimum similarity of 0.7 and simple classification by the closest chemical neighbor resulted in balanced accuracy from 73% over 737 substances to 100% at a threshold of 0.975 over 41 substances. This represents a strong support of read-across and (Q)SAR approaches in this area.
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Affiliation(s)
- Thomas Luechtefeld
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Daniel P Russo
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA
| | | | - Hao Zhu
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.,Department of Chemistry, Rutgers University at Camden, NJ, USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.,CAAT-Europe, University of Konstanz, Konstanz, Germany
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37
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Abstract
The progress of high-throughput screening (HTS) techniques is changing the chemical data landscape by producing massive biological data from tested compounds. Public data repositories (e.g., PubChem) receive HTS data provided by various institutes and this data pool is being updated on a daily basis. The goal of these data sharing efforts is to let users quickly obtain the biological data of target compounds. Without a universal chemical identifier, the repositories (e.g., PubChem) provide users various methods to query and retrieve chemical properties and biological data by several different chemical identifiers (e.g., SMILES, InChIKey, and IUPAC name). The major challenge for most users, especially computational modelers, is obtaining the biological data for a large dataset of compounds (e.g., thousands of drug molecules) instead of a single compound. This chapter aims to introduce the steps to access the public data repositories for target compounds with specific emphasis on the automatic data downloading for large datasets.
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Affiliation(s)
- Daniel P Russo
- Department of Chemistry, Rutgers Center for Computational and Integrative Biology, Rutgers University, 315 Penn Street, Camden, NJ, 08102, USA
| | - Hao Zhu
- Department of Chemistry, Rutgers Center for Computational and Integrative Biology, Rutgers University, 315 Penn Street, Camden, NJ, 08102, USA.
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38
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Abstract
BACKGROUND Angiosarcoma (AS) is an uncommon tumor that rarely develops after external beam radiation therapy (EBRT). Thirty-six cases have been reported in the literature. METHODS The authors present two additional cases. Each of these patients received breast-conserving treatment for breast carcinoma that consisted of tylectomy and EBRT. In each case, AS developed in the field of prior irradiation. RESULTS Currently, seven cases of AS after radiation therapy for breast-conserving treatment of breast carcinoma have been reported. The average time interval between the administration of radiation therapy and the development of AS is 8.6 years. CONCLUSIONS This complication is rare and should not influence the decision to offer breast-conserving therapy to patients. However, patients should be informed that AS may develop as a result of radiation therapy.
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Affiliation(s)
- S Edeiken
- Jersey Shore Medical Center, Neptune, New Jersey
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Russo DP, Harris JH. Electrooptic modulation in a thin film waveguide. Appl Opt 1971; 10:163453. [PMID: 20111394 DOI: 10.1364/ao.10.2786_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
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