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Guo W, Liu J, Dong F, Hong H. Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, TOXICOLOGY AND CARCINOGENESIS 2024:1-28. [PMID: 39228157 DOI: 10.1080/26896583.2024.2396731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effective, suffer from being costly and time-consuming. In response to this challenge, new alternative methodologies, notably machine learning and deep learning techniques, have attracted attention for their potential in developing carcinogenicity prediction models. This article reviews the progress in predicting carcinogenicity using various machine learning and deep learning algorithms. A comparative analysis on these developed models reveals that support vector machine, random forest, and ensemble learning are commonly preferred for their robustness and effectiveness in predicting chemical carcinogenicity. Conversely, models based on deep learning algorithms, such as feedforward neural network, convolutional neural network, graph convolutional neural network, capsule neural network, and hybrid neural networks, exhibit promising capabilities but are limited by the size of available carcinogenicity datasets. This review provides a comprehensive analysis of current machine learning and deep learning models for carcinogenicity prediction, underscoring the importance of high-quality and large datasets. These observations are anticipated to catalyze future advancements in developing effective and generalizable machine learning and deep learning models for predicting chemical carcinogenicity.
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Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR
| | - Jie Liu
- National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR
| | - Fan Dong
- National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR
| | - Huixiao Hong
- National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR
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2
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Abou Hajal A, Al Meslamani AZ. Overcoming barriers to machine learning applications in toxicity prediction. Expert Opin Drug Metab Toxicol 2024; 20:549-553. [PMID: 38088128 DOI: 10.1080/17425255.2023.2294939] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/11/2023] [Indexed: 07/25/2024]
Affiliation(s)
- Abdallah Abou Hajal
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
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3
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Amorim AM, Piochi LF, Gaspar AT, Preto A, Rosário-Ferreira N, Moreira IS. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. Chem Res Toxicol 2024; 37:827-849. [PMID: 38758610 PMCID: PMC11187637 DOI: 10.1021/acs.chemrestox.3c00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.
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Affiliation(s)
- Ana M.
B. Amorim
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD
Programme in Biosciences, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PURR.AI,
Rua Pedro Nunes, IPN Incubadora, Ed C, 3030-199 Coimbra, Portugal
| | - Luiz F. Piochi
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Ana T. Gaspar
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - António
J. Preto
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD Programme
in Experimental Biology and Biomedicine, Institute for Interdisciplinary
Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Nícia Rosário-Ferreira
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Irina S. Moreira
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
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Zhang H, Yi H, Hao Y, Zhao L, Pan W, Xue Q, Liu X, Fu J, Zhang A. Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133092. [PMID: 38039812 DOI: 10.1016/j.jhazmat.2023.133092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/07/2023] [Accepted: 11/23/2023] [Indexed: 12/03/2023]
Abstract
Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUCROC) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity.
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Affiliation(s)
- Huazhou Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China
| | - Hang Yi
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China
| | - Yuxing Hao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Lu Zhao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Wenxiao Pan
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Qiao Xue
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Xian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China.
| | - Jianjie Fu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China; Institute of Environment and Health, Jianghan University, Wuhan 430056, PR China
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China; Institute of Environment and Health, Jianghan University, Wuhan 430056, PR China.
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5
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Kaplan BLF, Hoberman AM, Slikker W, Smith MA, Corsini E, Knudsen TB, Marty MS, Sobrian SK, Fitzpatrick SC, Ratner MH, Mendrick DL. Protecting Human and Animal Health: The Road from Animal Models to New Approach Methods. Pharmacol Rev 2024; 76:251-266. [PMID: 38351072 PMCID: PMC10877708 DOI: 10.1124/pharmrev.123.000967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/18/2023] [Accepted: 12/01/2023] [Indexed: 02/16/2024] Open
Abstract
Animals and animal models have been invaluable for our current understanding of human and animal biology, including physiology, pharmacology, biochemistry, and disease pathology. However, there are increasing concerns with continued use of animals in basic biomedical, pharmacological, and regulatory research to provide safety assessments for drugs and chemicals. There are concerns that animals do not provide sufficient information on toxicity and/or efficacy to protect the target population, so scientists are utilizing the principles of replacement, reduction, and refinement (the 3Rs) and increasing the development and application of new approach methods (NAMs). NAMs are any technology, methodology, approach, or assay used to understand the effects and mechanisms of drugs or chemicals, with specific focus on applying the 3Rs. Although progress has been made in several areas with NAMs, complete replacement of animal models with NAMs is not yet attainable. The road to NAMs requires additional development, increased use, and, for regulatory decision making, usually formal validation. Moreover, it is likely that replacement of animal models with NAMs will require multiple assays to ensure sufficient biologic coverage. The purpose of this manuscript is to provide a balanced view of the current state of the use of animal models and NAMs as approaches to development, safety, efficacy, and toxicity testing of drugs and chemicals. Animals do not provide all needed information nor do NAMs, but each can elucidate key pieces of the puzzle of human and animal biology and contribute to the goal of protecting human and animal health. SIGNIFICANCE STATEMENT: Data from traditional animal studies have predominantly been used to inform human health safety and efficacy. Although it is unlikely that all animal studies will be able to be replaced, with the continued advancement in new approach methods (NAMs), it is possible that sometime in the future, NAMs will likely be an important component by which the discovery, efficacy, and toxicity testing of drugs and chemicals is conducted and regulatory decisions are made.
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Affiliation(s)
- Barbara L F Kaplan
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - Alan M Hoberman
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - William Slikker
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - Mary Alice Smith
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - Emanuela Corsini
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - Thomas B Knudsen
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - M Sue Marty
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - Sonya K Sobrian
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - Suzanne C Fitzpatrick
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - Marcia H Ratner
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
| | - Donna L Mendrick
- Center for Environmental Health Sciences, Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi (B.L.F.K.); Charles River Laboratories, Inc., Horsham, Pennsylvania (A.M.H.); Retired, National Center for Toxicological Research, Jefferson, Arkansas (W.S.); University of Georgia, Athens, Georgia (M.A.S.); Department of Pharmacological and Biomolecular Sciences, 'Rodolfo Paoletti' Università degli Studi di Milano, Milan, Italy (E.C.); US Environmental Protection Agency, Research Triangle Park, North Carolina (T.B.K.); Dow, Inc., Midland, Michigan (M.S.M.); Howard University College of Medicine, Washington DC (S.K.S.); Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland (S.C.F.); Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (M.H.R.); and National Center for Toxicological Research, US Food and Drug Administration, Silver Spring, Maryland (D.L.M.)
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6
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Li T, Liu Z, Thakkar S, Roberts R, Tong W. DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application. Regul Toxicol Pharmacol 2023; 144:105486. [PMID: 37633327 DOI: 10.1016/j.yrtph.2023.105486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/14/2023] [Accepted: 08/23/2023] [Indexed: 08/28/2023]
Abstract
The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the International Council for Harmonization (ICH) guidelines. Building on this in silico approach, here we describe DeepAmes, a high performance and robust model developed with a novel deep learning (DL) approach for potential utility in regulatory science. DeepAmes was developed with a large and consistent Ames dataset (>10,000 compounds) and was compared with other five standard Machine Learning (ML) methods. Using a test set of 1,543 compounds, DeepAmes was the best performer in predicting the outcome of Ames assay. In addition, DeepAmes yielded the best and most stable performance up to when compounds were >30% outside of the applicability domain (AD). Regarding the potential for regulatory application, a revised version of DeepAmes with a much-improved sensitivity of 0.87 from 0.47. In conclusion, DeepAmes provides a DL-powered Ames test predictive model for predicting the results of Ames tests; with its defined AD and clear context of use, DeepAmes has potential for utility in regulatory application.
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Affiliation(s)
- Ting Li
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA
| | - Zhichao Liu
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA
| | - Shraddha Thakkar
- Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | - Ruth Roberts
- ApconiX Ltd, Alderley Park, Alderley Edge, SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Weida Tong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.
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7
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Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel) 2023; 16:1259. [PMID: 37765069 PMCID: PMC10537003 DOI: 10.3390/ph16091259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
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Affiliation(s)
| | | | | | | | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
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8
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Sinha K, Ghosh N, Sil PC. A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies. Chem Res Toxicol 2023; 36:1174-1205. [PMID: 37561655 DOI: 10.1021/acs.chemrestox.2c00375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
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Affiliation(s)
- Krishnendu Sinha
- Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India
| | - Nabanita Ghosh
- Department of Zoology, Maulana Azad College, Kolkata 700013, West Bengal, India
| | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata 700054, West Bengal, India
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9
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Mittal A, Ahuja G. Advancing chemical carcinogenicity prediction modeling: opportunities and challenges. Trends Pharmacol Sci 2023; 44:400-410. [PMID: 37183054 DOI: 10.1016/j.tips.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 05/16/2023]
Abstract
Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity. This highlights the urgency to devise alternative AI strategies. We propose multiple strategies, including implementing data-driven (integrated databases) and known carcinogen-characteristic-derived features to overcome these apparent shortcomings. In summary, these next-generation approaches will continue facilitating robust chemical carcinogenicity prediction, concomitant with deeper mechanistic insights.
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Affiliation(s)
- Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, 110020, India.
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, 110020, India.
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10
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Li T, Roberts R, Liu Z, Tong W. TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes. Chem Res Toxicol 2023; 36:916-925. [PMID: 37200521 PMCID: PMC10433534 DOI: 10.1021/acs.chemrestox.3c00037] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Indexed: 05/20/2023]
Abstract
Animal studies are required for the evaluation of candidate drugs to ensure patient and volunteer safety. Toxicogenomics is often applied in these studies to gain understanding of the underlying mechanisms of toxicity, which is usually focused on critical organs such as the liver or kidney in young male rats. There is a strong ethical reason to reduce, refine and replace animal use (the 3Rs), where the mapping of data between organs, sexes and ages could reduce the cost and time of drug development. Herein, we proposed a generative adversarial network (GAN)-based framework entitled TransOrGAN that allowed the molecular mapping of gene expression profiles in different rodent organ systems and across sex and age groups. We carried out a proof-of-concept study based on rat RNA-seq data from 288 samples in 9 different organs of both sexes and 4 developmental stages. First, we demonstrated that TransOrGAN could infer transcriptomic profiles between any 2 of the 9 organs studied, yielding an average cosine similarity of 0.984 between synthetic transcriptomic profiles and their corresponding real profiles. Second, we found that TransOrGAN could infer transcriptomic profiles observed in females from males, with an average cosine similarity of 0.984. Third, we found that TransOrGAN could infer transcriptomic profiles in juvenile, adult, and aged animals from adolescent animals with an average cosine similarity of 0.981, 0.983, and 0.989, respectively. Altogether, TransOrGAN is an innovative approach to infer transcriptomic profiles between ages, sexes, and organ systems, offering the opportunity to reduce animal usage and to provide an integrated assessment of toxicity in the whole organism irrespective of sex or age.
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Affiliation(s)
- Ting Li
- National
Center for Toxicological Research, Food
and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Ruth Roberts
- ApconiX Ltd, Alderley Park, Alderley Edge SK10 4TG, United Kingdom
- University
of Birmingham, Edgbaston, Birmingham B15 2TT, United
Kingdom
| | - Zhichao Liu
- Integrative
Toxicology, Nonclinical Drug Safety, Boehringer
Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut 06877, United States
| | - Weida Tong
- National
Center for Toxicological Research, Food
and Drug Administration, Jefferson, Arkansas 72079, United States
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11
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Singh AV, Chandrasekar V, Paudel N, Laux P, Luch A, Gemmati D, Tissato V, Prabhu KS, Uddin S, Dakua SP. Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother 2023; 163:114784. [PMID: 37121152 DOI: 10.1016/j.biopha.2023.114784] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/15/2023] [Accepted: 04/24/2023] [Indexed: 05/02/2023] Open
Abstract
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | | | - Namuna Paudel
- Department of Chemistry, Amrit Campus, Institute of Science and Technology, Tribhuvan University, Lainchaur, Kathmandu 44600 Nepal
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Donato Gemmati
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Veronica Tissato
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Kirti S Prabhu
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
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12
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Tran TTV, Surya Wibowo A, Tayara H, Chong KT. Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. J Chem Inf Model 2023; 63:2628-2643. [PMID: 37125780 DOI: 10.1021/acs.jcim.3c00200] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University - Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Agung Surya Wibowo
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Department of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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13
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Hao N, Sun P, Zhao W, Li X. Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 255:114806. [PMID: 36948010 DOI: 10.1016/j.ecoenv.2023.114806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 03/04/2023] [Accepted: 03/16/2023] [Indexed: 06/18/2023]
Abstract
Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.
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Affiliation(s)
- Ning Hao
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Peixuan Sun
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Wenjin Zhao
- College of New Energy and Environment, Jilin University, Changchun 130012, China.
| | - Xixi Li
- State Environmental Protection Key Laboratory of Ecological Effect and Risk Assessment of Chemicals, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, A1B 3×5, Canada.
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14
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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15
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Luconi M, Sogorb MA, Markert UR, Benfenati E, May T, Wolbank S, Roncaglioni A, Schmidt A, Straccia M, Tait S. Human-Based New Approach Methodologies in Developmental Toxicity Testing: A Step Ahead from the State of the Art with a Feto-Placental Organ-on-Chip Platform. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15828. [PMID: 36497907 PMCID: PMC9737555 DOI: 10.3390/ijerph192315828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Developmental toxicity testing urgently requires the implementation of human-relevant new approach methodologies (NAMs) that better recapitulate the peculiar nature of human physiology during pregnancy, especially the placenta and the maternal/fetal interface, which represent a key stage for human lifelong health. Fit-for-purpose NAMs for the placental-fetal interface are desirable to improve the biological knowledge of environmental exposure at the molecular level and to reduce the high cost, time and ethical impact of animal studies. This article reviews the state of the art on the available in vitro (placental, fetal and amniotic cell-based systems) and in silico NAMs of human relevance for developmental toxicity testing purposes; in addition, we considered available Adverse Outcome Pathways related to developmental toxicity. The OECD TG 414 for the identification and assessment of deleterious effects of prenatal exposure to chemicals on developing organisms will be discussed to delineate the regulatory context and to better debate what is missing and needed in the context of the Developmental Origins of Health and Disease hypothesis to significantly improve this sector. Starting from this analysis, the development of a novel human feto-placental organ-on-chip platform will be introduced as an innovative future alternative tool for developmental toxicity testing, considering possible implementation and validation strategies to overcome the limitation of the current animal studies and NAMs available in regulatory toxicology and in the biomedical field.
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Affiliation(s)
- Michaela Luconi
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy
- I.N.B.B. (Istituto Nazionale Biostrutture e Biosistemi), Viale Medaglie d’Oro 305, 00136 Rome, Italy
| | - Miguel A. Sogorb
- Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, 03202 Elche, Spain
| | - Udo R. Markert
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Tobias May
- InSCREENeX GmbH, Inhoffenstr. 7, 38124 Braunschweig, Germany
| | - Susanne Wolbank
- Ludwig Boltzmann Institut for Traumatology, The Research Center in Cooperation with AUVA, Austrian Cluster for Tissue Regeneration, Donaueschingenstrasse 13, 1200 Vienna, Austria
| | - Alessandra Roncaglioni
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Astrid Schmidt
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - Marco Straccia
- FRESCI by Science&Strategy SL, C/Roure Monjo 33, Vacarisses, 08233 Barcelona, Spain
| | - Sabrina Tait
- Centre for Gender-Specific Medicine, Istituto Superiore di Sanità, 00161 Rome, Italy
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16
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Hurben AK, Erber L. Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment. Chem Res Toxicol 2022; 35:1925-1928. [PMID: 36179327 PMCID: PMC9886504 DOI: 10.1021/acs.chemrestox.2c00269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research presented at the Fall 2022 American Chemical Society meeting, highlighting how AI is being applied across various facets of drug design, development, and safety assessment.
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Affiliation(s)
- Alexander K Hurben
- Department of Medicinal Chemistry and Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Luke Erber
- Department of Medicinal Chemistry and Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
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17
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Limbu S, Dakshanamurthy S. Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22218185. [PMID: 36365881 PMCID: PMC9653664 DOI: 10.3390/s22218185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/11/2022] [Accepted: 10/23/2022] [Indexed: 05/28/2023]
Abstract
Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.
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18
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Abstract
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared to in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.
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Affiliation(s)
- Zhoumeng Lin
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.,Center for Environmental and Human Toxicology, University of Florida, FL, 32608, USA
| | - Wei-Chun Chou
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.,Center for Environmental and Human Toxicology, University of Florida, FL, 32608, USA
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19
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Toropova AP, Toropov AA, Viganò EL, Colombo E, Roncaglioni A, Benfenati E. Carcinogenicity prediction using the index of ideality of correlation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:419-428. [PMID: 35642587 DOI: 10.1080/1062936x.2022.2076736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Carcinogenicity testing is necessary to protect human health and comply with regulations, but testing it with the traditionally used two-year rodent studies is time-consuming and expensive. In certain cases, such as for impurities, alternative methods may be convenient. Thus there is an urgent need for alternative approaches for reliable and robust assessments of carcinogenicity. The Monte Carlo technique with CORAL software is a tool to tackle this task for unknown compounds using available experimental data for a representative set of compounds. The models can be constructed with the simplified molecular input line entry system without additional physicochemical descriptors. We describe here a model based on a data set of 1167 substances. Matthew's correlation coefficient values for calibration and validation sets are 0.747 and 0.577, respectively. Double bonds between carbon atoms and double bonds of oxygen atoms are the molecular features that indicate the carcinogenic potential of a compound.
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Affiliation(s)
- A P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - E L Viganò
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - E Colombo
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A Roncaglioni
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - E Benfenati
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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20
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Chicco D, Jurman G. An Invitation to Greater Use of Matthews Correlation Coefficient in Robotics and Artificial Intelligence. Front Robot AI 2022; 9:876814. [PMID: 35402520 PMCID: PMC8993212 DOI: 10.3389/frobt.2022.876814] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 03/07/2022] [Indexed: 11/17/2022] Open
Affiliation(s)
- Davide Chicco
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada
| | - Giuseppe Jurman
- Data Science for Health Unit, Fondazione Bruno Kessler, Trento, Italy
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