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1
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Cui H, He Y, Wang Z, Liu K, Li W, Han W. Unveiling drug-induced osteotoxicity: A machine learning approach and webserver. JOURNAL OF HAZARDOUS MATERIALS 2025; 492:138044. [PMID: 40158503 DOI: 10.1016/j.jhazmat.2025.138044] [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: 10/24/2024] [Revised: 03/04/2025] [Accepted: 03/20/2025] [Indexed: 04/02/2025]
Abstract
Drug-induced osteotoxicity refers to the harmful effects certain pharmaceuticals have on the skeletal system, posing significant safety risks. These toxic effects are critical concerns in clinical practice, drug development, and environmental management. However, current toxicity assessment models lack specialized datasets and algorithms specifically designed to predict osteotoxicity In this study, we compiled a dataset of osteotoxic molecules and used clustering analysis to classify them into four distinct groups Furthermore, target prediction identified key genes (IL6, TNF, ESR1, and MAPK3), while GO and KEGG analyses were employed to explore the complex underlying mechanisms Additionally, we developed prediction models based on molecular fingerprints and descriptors. We further advanced our approach by incorporating models such as Transformer, SVM, XGBoost, and molecular graphs integrated with Weave GNN, ViT, and a pre-trained KPGT model. Specifically, the descriptor-based model achieved an accuracy of 0.82 and an AUC of 0.89; the molecular graph model reached an accuracy of 0.84 and an AUC of 0.86; and the KPGT model attained both an accuracy and an AUC of 0.86. These findings led to the creation of Bonetox, the first online platform specifically designed for predicting osteotoxicity. This tool aids in assessing the impact of hazardous substances on bone health during drug development, thereby improving safety protocols, mitigating skeletal side effects, and ultimately enhancing therapeutic outcomes and public safety.
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Affiliation(s)
- Huizi Cui
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China
| | - Yi He
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China
| | - Zhibang Wang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China
| | - Kaifeng Liu
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China
| | - Wannan Li
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China.
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China.
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2
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Nie X, Hu F, Cheng X, Ma J, Peng X, Sun F, Ni X, Zhan S. Drug-Induced Thrombocytopenia Severity and Toxicity (DITPst): binary classification of drugs by human thrombocytopenia toxicity. Expert Opin Drug Saf 2025; 24:731-743. [PMID: 39875141 DOI: 10.1080/14740338.2025.2460439] [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: 09/23/2024] [Revised: 01/05/2025] [Accepted: 01/08/2025] [Indexed: 01/30/2025]
Abstract
BACKGROUND Drug-induced thrombocytopenia (DITP) often occurs in patients during clinical treatment. However, clinicians usually fail to distinguish which drugs can be plausible culprits accurately. We aimed to develop a large comprehensive drug benchmark database with DITP toxicity using the recommended method by FDA. RESEARCH DESIGN AND METHODS We collected information from six databases that involved drug labeling information, literature, safety signal mining and laboratory testing to generate the annotated drug list with DITP toxicity. Then, we descripted the DITP positive-negative distribution based on the Anatomical Therapeutic Chemical (ATC) coding system; hotspot analysis was conducted to identify therapeutic categories of drugs within each organ system that warrant attention regarding DITP. RESULTS The DITPst database comprised 1,765 drugs, of which 858 were DITP-positives, whereas 907 were negatives. The investigation of distribution across various therapeutic categories revealed the most frequent DITP-positive categories were immunostimulants (10/11), anti-inflammatory, and antirheumatic products (28/32), and antibacterials for systemic use (102/121). On the contrary, the least frequent DITP-positive therapeutic categories were diagnostic radiopharmaceuticals (12/12), pituitary and hypothalamic hormones and analogues (17/18), and drugs for constipation (16/17). CONCLUSIONS We consider the DITPst benchmark database to be an invaluable resource for the community to improve DITP safety research and drug development.
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Affiliation(s)
- Xiaolu Nie
- Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Hainan Institute of Real World Data, Qionghai, Hainan, China
- Ministry of Education, Key Laboratory of Epidemiology of Major Diseases (Peking University), Beijing, China
| | - Fang Hu
- Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, China
| | - Xiaoling Cheng
- Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Jingyao Ma
- Department II of Hematology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xiaoxia Peng
- Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, China
- Hainan Institute of Real World Data, Qionghai, Hainan, China
| | - Feng Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Hainan Institute of Real World Data, Qionghai, Hainan, China
- Ministry of Education, Key Laboratory of Epidemiology of Major Diseases (Peking University), Beijing, China
| | - Xin Ni
- Dean's Office, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Siyan Zhan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Ministry of Education, Key Laboratory of Epidemiology of Major Diseases (Peking University), Beijing, China
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3
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Mou Z, Volarath P, Racz R, Cross KP, Girireddy M, Chakravarti S, Stavitskaya L. Quantitative Structure-Activity Relationship Models to Predict Cardiac Adverse Effects. Chem Res Toxicol 2024; 37:1924-1933. [PMID: 39535830 DOI: 10.1021/acs.chemrestox.4c00186] [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: 11/16/2024]
Abstract
Drug-induced cardiotoxicity represents one of the most common causes of attrition of drug candidates in preclinical and clinical development. For this reason, the evaluation of cardiac toxicity is essential during drug development and regulatory review. In the present study, drug-induced postmarket adverse event combinations from the FDA Adverse Event Reporting System were extracted for 2002 drugs using 243 cardiac toxicity-related preferred terms (PTs). These PTs were combined into 12 groups based on their clinical relevance to serve as training sets. The optimal classification scheme was determined using a combination of data sources that included drug labeling information, published literature, clinical study data, and postmarket surveillance data. Two commercial QSAR platforms were used to construct 12 models, including general cardiac toxicity, cardiac ischemia, heart failure, cardiac valve disease, myocardial disease, pericardial disease, structural heart disease, cardiac arrhythmia, Torsades de Pointes, long QT syndrome, atrial fibrillation and ventricular arrhythmia, and cardiac arrest. The cross-validated performance for the new models reached a sensitivity of up to 80% and negative predictivity of up to 80%. These new models covering a wide range of cardiac endpoints will provide fast, reliable, and comprehensive predictions of potential cardiotoxic compounds in drug discovery and regulatory safety assessment.
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Affiliation(s)
- Zhongyu Mou
- FDA Center for Drug Evaluation and Research (CDER), Silver Spring, Maryland 20903, United States
| | - Patra Volarath
- FDA Center for Drug Evaluation and Research (CDER), Silver Spring, Maryland 20903, United States
| | - Rebecca Racz
- FDA Center for Drug Evaluation and Research (CDER), Silver Spring, Maryland 20903, United States
| | | | | | | | - Lidiya Stavitskaya
- FDA Center for Drug Evaluation and Research (CDER), Silver Spring, Maryland 20903, United States
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4
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Yang Y, Yang Z, Pang X, Cao H, Sun Y, Wang L, Zhou Z, Wang P, Liang Y, Wang Y. Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 953:176095. [PMID: 39245376 DOI: 10.1016/j.scitotenv.2024.176095] [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: 07/04/2024] [Revised: 09/03/2024] [Accepted: 09/04/2024] [Indexed: 09/10/2024]
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health threats. Consequently, striking a balance between maximizing product efficiency and minimizing environmental and health risks by tailoring the molecular structure of PFAS has become a pivotal challenge in the fields of environmental chemistry and sustainable development. To address this issue, a computational workflow was proposed for designing an environmentally friendly PFAS by incorporating deep learning (DL) and molecular generative models. The hybrid DL architecture MolHGT+ based on heterogeneous graph neural network with transformer-like attention was applied to predict the surface tension, bioaccumulation, and hepatotoxicity of the molecules. Through virtual screening of the PFAS master database using MolHGT+, the findings indicate that incorporating the siloxane group and betaine fragment can effectively decrease both the bioaccumulation and hepatotoxicity of PFAS while preserving low surface tension. In addition, molecular generative models were employed to create a structurally diverse pool of novel PFASs with the aforementioned hit molecules serving as the initial template structures. Overall, our study presents a promising AI-driven method for advancing the development of environmentally friendly PFAS.
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Affiliation(s)
- Ying Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Zeguo Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Xudi Pang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Huiming Cao
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China.
| | - Yuzhen Sun
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Ling Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Zhen Zhou
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Pu Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yong Liang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China.
| | - Yawei Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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5
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Banerjee A, Roy K. The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset. Sci Rep 2024; 14:20812. [PMID: 39242880 PMCID: PMC11379871 DOI: 10.1038/s41598-024-71892-4] [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: 07/19/2024] [Accepted: 09/02/2024] [Indexed: 09/09/2024] Open
Abstract
With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descriptors, which represent the structural, physicochemical, and electronic properties of a particular compound. Deviating from the conventional approach, in this investigation, we have employed the classification Read-Across Structure-Activity Relationship (c-RASAR), which involves the amalgamation of the concepts of classification-based quantitative structure-activity relationship (QSAR) and Read-Across to incorporate Read-Across-derived similarity and error-based descriptors into a statistical and machine learning modeling framework. ML models developed from these RASAR descriptors use similarity-based information from the close source neighbors of a particular query compound. We have employed different classification modeling algorithms on the selected QSAR and RASAR descriptors to develop predictive models for efficient prediction of query compounds' hepatotoxicity. The predictivity of each of these models was evaluated on a large number of test set compounds. The best-performing model was also used to screen a true external data set. The concepts of explainable AI (XAI) coupled with Read-Across were used to interpret the contributions of the RASAR descriptors in the best c-RASAR model and to explain the chemical diversity in the dataset. The application of various unsupervised dimensionality reduction techniques like t-SNE and UMAP and the supervised ARKA framework showed the usefulness of the RASAR descriptors over the selected QSAR descriptors in their ability to group similar compounds, enhancing the modelability of the dataset and efficiently identifying activity cliffs. Furthermore, the activity cliffs were also identified from Read-Across by observing the nature of compounds constituting the nearest neighbors for a particular query compound. On comparing our simple linear c-RASAR model with the previously reported models developed using the same dataset derived from the US FDA Orange Book ( https://www.accessdata.fda.gov/scripts/cder/ob/index.cfm ), it was observed that our model is simple, reproducible, transferable, and highly predictive. The performance of the LDA c-RASAR model on the true external set supersedes that of the previously reported work. Therefore, the present simple LDA c-RASAR model can efficiently be used to predict the hepatotoxicity of query chemicals.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
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6
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Zhao Y, Zhang Z, Kong X, Wang K, Wang Y, Jia J, Li H, Tian S. Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches. Chem Biol Drug Des 2024; 104:e14607. [PMID: 39179521 DOI: 10.1111/cbdd.14607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/24/2024] [Accepted: 08/01/2024] [Indexed: 08/26/2024]
Abstract
The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional in vivo and in vitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.
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Affiliation(s)
- Yulong Zhao
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Zhoudong Zhang
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Xiaotian Kong
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, China
| | - Kai Wang
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Yaxuan Wang
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Jie Jia
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Huanqiu Li
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Sheng Tian
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
- College of Chemistry and Life Science, Beijing University of Technology, Beijing, China
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7
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Wu W, Qian J, Liang C, Yang J, Ge G, Zhou Q, Guan X. GeoDILI: A Robust and Interpretable Model for Drug-Induced Liver Injury Prediction Using Graph Neural Network-Based Molecular Geometric Representation. Chem Res Toxicol 2023; 36:1717-1730. [PMID: 37839069 DOI: 10.1021/acs.chemrestox.3c00199] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Drug-induced liver injury (DILI) is a significant cause of drug failure and withdrawal due to liver damage. Accurate prediction of hepatotoxic compounds is crucial for safe drug development. Several DILI prediction models have been published, but they are built on different data sets, making it difficult to compare model performance. Moreover, most existing models are based on molecular fingerprints or descriptors, neglecting molecular geometric properties and lacking interpretability. To address these limitations, we developed GeoDILI, an interpretable graph neural network that uses a molecular geometric representation. First, we utilized a geometry-based pretrained molecular representation and optimized it on the DILI data set to improve predictive performance. Second, we leveraged gradient information to obtain high-precision atomic-level weights and deduce the dominant substructure. We benchmarked GeoDILI against recently published DILI prediction models, as well as popular GNN models and fingerprint-based machine learning models using the same data set, showing superior predictive performance of our proposed model. We applied the interpretable method in the DILI data set and derived seven precise and mechanistically elucidated structural alerts. Overall, GeoDILI provides a promising approach for accurate and interpretable DILI prediction with potential applications in drug discovery and safety assessment. The data and source code are available at GitHub repository (https://github.com/CSU-QJY/GeoDILI).
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Affiliation(s)
- Wenxuan Wu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jiayu Qian
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Changjie Liang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jingya Yang
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Guangbo Ge
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Qingping Zhou
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Xiaoqing Guan
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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8
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Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, Patterson TA, Hong H. Review of machine learning and deep learning models for toxicity prediction. Exp Biol Med (Maywood) 2023; 248:1952-1973. [PMID: 38057999 PMCID: PMC10798180 DOI: 10.1177/15353702231209421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023] Open
Abstract
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.
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Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Meng Song
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Zoe Li
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Md Kamrul Hasan Khan
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
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9
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Shin HK, Huang R, Chen M. In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review. Food Chem Toxicol 2023; 179:113948. [PMID: 37460037 PMCID: PMC10640386 DOI: 10.1016/j.fct.2023.113948] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 07/25/2023]
Abstract
New approach methods (NAMs) have been developed to predict a wide range of toxicities through innovative technologies. Liver injury is one of the most extensively studied endpoints due to its severity and frequency, occurring among populations that consume drugs or dietary supplements. In this review, we focus on recent developments of in silico modeling for liver injury prediction using deep learning and in vitro data based on adverse outcome pathways (AOPs). Despite these models being mainly developed using datasets generated from drug-like molecules, they were also applied to the prediction of hepatotoxicity caused by herbal products. As deep learning has achieved great success in many different fields, advanced machine learning algorithms have been actively applied to improve the accuracy of in silico models. Additionally, the development of liver AOPs, combined with big data in toxicology, has been valuable in developing in silico models with enhanced predictive performance and interpretability. Specifically, one approach involves developing structure-based models for predicting molecular initiating events of liver AOPs, while others use in vitro data with structure information as model inputs for making predictions. Even though liver injury remains a difficult endpoint to predict, advancements in machine learning algorithms and the expansion of in vitro databases with relevant biological knowledge have made a huge impact on improving in silico modeling for drug-induced liver injury prediction.
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Affiliation(s)
- Hyun Kil Shin
- Department of Predictive Toxicology, Korea Institute of Toxicology (KIT), 34114, Daejeon, Republic of Korea
| | - Ruili Huang
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA.
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR, 72079, USA.
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10
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Moein M, Heinonen M, Mesens N, Chamanza R, Amuzie C, Will Y, Ceulemans H, Kaski S, Herman D. Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data. Chem Res Toxicol 2023; 36:1238-1247. [PMID: 37556769 PMCID: PMC10445287 DOI: 10.1021/acs.chemrestox.2c00378] [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: 11/30/2022] [Indexed: 08/11/2023]
Abstract
Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound's fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance.
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Affiliation(s)
- Mohammad Moein
- Department
of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
| | - Markus Heinonen
- Department
of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
| | - Natalie Mesens
- Predictive,
Investigative and Translational Toxicology, PSTS, Janssen Research
& Development, Pharmaceutical Companies
of Johnson & Johnson, 2340 Beerse, Belgium
| | - Ronnie Chamanza
- Pathology,
PSTS, Janssen Research & Development, Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
| | - Chidozie Amuzie
- Johnson
& Johnson Innovation-JLABS, 661 University Avenue, CA014 ON Toronto, Canada
| | - Yvonne Will
- Predictive,
Investigative and Translational Toxicology, PSTS, Janssen Research
& Development, Pharmaceutical Companies
of Johnson & Johnson, 3210 Merryfield Row, San Diego, California 92121, United States
| | - Hugo Ceulemans
- In-Silico
Discovery, Janssen Pharmaceutica, Janssen Research & Development, Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
| | - Samuel Kaski
- Department
of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
| | - Dorota Herman
- In-Silico
Discovery, Janssen Pharmaceutica, Janssen Research & Development, Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
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11
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Toropova AP, Toropov AA, Roncaglioni A, Benfenati E. The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity. TOXICS 2023; 11:toxics11050419. [PMID: 37235234 DOI: 10.3390/toxics11050419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/11/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023]
Abstract
Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the active (1)-inactive (0) format. The so-called system of self-consistent models has been suggested as an approach for two tasks: (i) building up a model and (ii) estimating its predictive potential. However, this approach has been tested so far for regression models. Here, the approach is applied to building up and estimating a categorical hepatotoxicity model using the CORAL software. This new process yields good results: sensitivity = 0.77, specificity = 0.75, accuracy = 0.76, and Matthew correlation coefficient = 0.51 (all compounds) and sensitivity = 0.83, specificity = 0.81, accuracy = 0.83 and Matthew correlation coefficient = 0.63 (validation set).
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Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
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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: 59] [Impact Index Per Article: 29.5] [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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Hosack T, Damry D, Biswas S. Drug-induced liver injury: a comprehensive review. Therap Adv Gastroenterol 2023; 16:17562848231163410. [PMID: 36968618 PMCID: PMC10031606 DOI: 10.1177/17562848231163410] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/24/2023] [Indexed: 03/24/2023] Open
Abstract
Drug-induced liver injury (DILI) remains a challenge in clinical practice and is
still a diagnosis of exclusion. Although it has a low incidence amongst the
general population, DILI accounts for most cases of acute liver failure with a
fatality rate of up to 50%. While multiple mechanisms of DILI have been
postulated, there is no clear causal relationship between drugs, risk factors
and mechanisms of DILI. Current best practice relies on a combination of high
clinical suspicion, thorough clinical history of risk factors and timeline, and
extensive hepatological investigations as supported by the international Roussel
Uclaf Causality Assessment Method criteria, the latter considered a key
diagnostic algorithm for DILI. This review focuses on DILI classification, risk
factors, clinical evaluation, future biomarkers and management, with the aim of
facilitating physicians to correctly identify DILI early in presentation.
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Affiliation(s)
| | - Djamil Damry
- Department of Gastroenterology &
Hepatology, Stoke Mandeville Hospital, Buckinghamshire Health NHS Trust,
Aylesbury, Buckinghamshire, UK
| | - Sujata Biswas
- Department of Gastroenterology &
Hepatology, Stoke Mandeville Hospital, Buckinghamshire Health NHS Trust,
Aylesbury, Buckinghamshire, UK
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14
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Lin J, Li M, Mak W, Shi Y, Zhu X, Tang Z, He Q, Xiang X. Applications of In Silico Models to Predict Drug-Induced Liver Injury. TOXICS 2022; 10:788. [PMID: 36548621 PMCID: PMC9785299 DOI: 10.3390/toxics10120788] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.
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Affiliation(s)
| | | | | | | | | | | | - Qingfeng He
- Correspondence: (Q.H.); (X.X.); Tel.: +86-21-51980024 (X.X.)
| | - Xiaoqiang Xiang
- Correspondence: (Q.H.); (X.X.); Tel.: +86-21-51980024 (X.X.)
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15
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Chen Z, Jiang Y, Zhang X, Zheng R, Qiu R, Sun Y, Zhao C, Shang H. The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications. Brief Bioinform 2022; 23:6598880. [PMID: 35656709 DOI: 10.1093/bib/bbac196] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/12/2022] [Accepted: 04/27/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
In the previous study, we developed the generalized drug-induced liver injury (DILI) prediction model—ResNet18DNN to predict DILI based on multi-source combined DILI dataset and achieved better performance than that of previously published described DILI prediction models. Recently, we were honored to receive the invitation from the editor to response the Letter to Editor by Liu Zhichao, et al. We were glad that our research has attracted the attention of Liu’s team and they has put forward their opinions on our research. In this response to Letter to the Editor, we will respond to these comments.
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Affiliation(s)
- Zhao Chen
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yin Jiang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xiaoyu Zhang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Ruijin Qiu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Chen Zhao
- Institute of Basic Research in Clinical Medicine , China Academy of Chinese Medical Sciences, Beijing, China
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- College of Integrated Traditional Chinese and Western Medicine , Hunan University of Chinese Medicine, Changsha, Hunan 410208 , China
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16
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Chen Z, Zhao M, You L, Zheng R, Jiang Y, Zhang X, Qiu R, Sun Y, Pan H, He T, Wei X, Chen Z, Zhao C, Shang H. Developing an artificial intelligence method for screening hepatotoxic compounds in traditional Chinese medicine and Western medicine combination. Chin Med 2022; 17:58. [PMID: 35581608 PMCID: PMC9112584 DOI: 10.1186/s13020-022-00617-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 04/29/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUNDS Traditional Chinese medicine and Western medicine combination (TCM-WMC) increased the complexity of compounds ingested. OBJECTIVE To develop a method for screening hepatotoxic compounds in TCM-WMC based on chemical structures using artificial intelligence (AI) methods. METHODS Drug-induced liver injury (DILI) data was collected from the public databases and published literatures. The total dataset formed by DILI data was randomly divided into training set and test set at a ratio of 3:1 approximately. Machine learning models of SGD (Stochastic Gradient Descent), kNN (k-Nearest Neighbor), SVM (Support Vector Machine), NB (Naive Bayes), DT (Decision Tree), RF (Random Forest), ANN (Artificial Neural Network), AdaBoost, LR (Logistic Regression) and one deep learning model (deep belief network, DBN) were adopted to construct models for screening hepatotoxic compounds. RESULT Dataset of 2035 hepatotoxic compounds was collected in this research, in which 1505 compounds were as training set and 530 compounds were as test set. Results showed that RF obtained 0.838 of classification accuracy (CA), 0.827 of F1-score, 0.832 of Precision, 0.838 of Recall, 0.814 of area under the curve (AUC) on the training set and 0.767 of CA, 0.731 of F1, 0.739 of Precision, 0.767 of Recall, 0.739 of AUC on the test set, which was better than other eight machine learning methods. The DBN obtained 82.2% accuracy on the test set, which was higher than any other machine learning models on the test set. CONCLUSION The DILI AI models were expected to effectively screen hepatotoxic compounds in TCM-WMC.
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Affiliation(s)
- Zhao Chen
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Mengzhu Zhao
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Liangzhen You
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yin Jiang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xiaoyu Zhang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Ruijin Qiu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Haie Pan
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Tianmai He
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xuxu Wei
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Zhineng Chen
- School of Computer Science, Fudan University, Shanghai, China
| | - Chen Zhao
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
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17
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Mirahmad M, Sabourian R, Mahdavi M, Larijani B, Safavi M. In vitro cell-based models of drug-induced hepatotoxicity screening: progress and limitation. Drug Metab Rev 2022; 54:161-193. [PMID: 35403528 DOI: 10.1080/03602532.2022.2064487] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Drug-induced liver injury (DILI) is one of the major causes of post-approval withdrawal of therapeutics. As a result, there is an increasing need for accurate predictive in vitro assays that reliably detect hepatotoxic drug candidates while reducing drug discovery time, costs, and the number of animal experiments. In vitro hepatocyte-based research has led to an improved comprehension of the underlying mechanisms of chemical toxicity and can assist the prioritization of therapeutic choices with low hepatotoxicity risk. Therefore, several in vitro systems have been generated over the last few decades. This review aims to comprehensively present the development and validation of 2D (two-dimensional) and 3D (three-dimensional) culture approaches on hepatotoxicity screening of compounds and highlight the main factors affecting predictive power of experiments. To this end, we first summarize some of the recognized hepatotoxicity mechanisms and related assays used to appraise DILI mechanisms and then discuss the challenges and limitations of in vitro models.
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Affiliation(s)
- Maryam Mirahmad
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Reyhaneh Sabourian
- Department of Drug and Food Control, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Mahdavi
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Maliheh Safavi
- Department of Biotechnology, Iranian Research Organization for Science and Technology, Tehran, Iran
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18
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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. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 20:100187. [PMID: 35340402 PMCID: PMC8955833 DOI: 10.1016/j.comtox.2021.100187] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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
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19
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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: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [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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20
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Vall A, Sabnis Y, Shi J, Class R, Hochreiter S, Klambauer G. The Promise of AI for DILI Prediction. Front Artif Intell 2021; 4:638410. [PMID: 33937745 PMCID: PMC8080874 DOI: 10.3389/frai.2021.638410] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 02/02/2021] [Indexed: 12/11/2022] Open
Abstract
Drug-induced liver injury (DILI) is a common reason for the withdrawal of a drug from the market. Early assessment of DILI risk is an essential part of drug development, but it is rendered challenging prior to clinical trials by the complex factors that give rise to liver damage. Artificial intelligence (AI) approaches, particularly those building on machine learning, range from random forests to more recent techniques such as deep learning, and provide tools that can analyze chemical compounds and accurately predict some of their properties based purely on their structure. This article reviews existing AI approaches to predicting DILI and elaborates on the challenges that arise from the as yet limited availability of data. Future directions are discussed focusing on rich data modalities, such as 3D spheroids, and the slow but steady increase in drugs annotated with DILI risk labels.
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Affiliation(s)
- Andreu Vall
- LIT AI Lab, Johannes Kepler University Linz, Linz, Austria.,Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | | | - Jiye Shi
- UCB Biopharma SRL, Braine-l'Alleud, Belgium
| | | | - Sepp Hochreiter
- LIT AI Lab, Johannes Kepler University Linz, Linz, Austria.,Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.,Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
| | - Günter Klambauer
- LIT AI Lab, Johannes Kepler University Linz, Linz, Austria.,Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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21
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Ma H, An W, Wang Y, Sun H, Huang R, Huang J. Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury. Chem Res Toxicol 2021; 34:495-506. [PMID: 33347312 PMCID: PMC9887540 DOI: 10.1021/acs.chemrestox.0c00322] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an in silico screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI. To serve the screening goal, we apply several computational techniques to predict the DILI property, including traditional machine learning methods and graph-based deep learning techniques. While deep learning models require large training data to tune huge model parameters, the DILI data set only contains a few hundred annotated molecules. To alleviate the data scarcity problem, we propose a property augmentation strategy to include massive training data with other property information. Extensive experiments demonstrate that our proposed method significantly outperforms all existing baselines on the DILI data set by obtaining a 81.4% accuracy using cross-validation with random splitting, 78.7% using leave-one-out cross-validation, and 76.5% using cross-validation with scaffold splitting.
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Affiliation(s)
- Hehuan Ma
- Department of Computer Science, University of Texas at Arlington, Arlington, Texas, USA
| | - Weizhi An
- Department of Computer Science, University of Texas at Arlington, Arlington, Texas, USA
| | - Yuhong Wang
- National Center for Advancing Translating Sciences, NIH Rockville, Maryland, USA
| | - Hongmao Sun
- National Center for Advancing Translating Sciences, NIH Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translating Sciences, NIH Rockville, Maryland, USA
| | - Junzhou Huang
- Department of Computer Science, University of Texas at Arlington, Arlington, Texas, USA
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22
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Béquignon OJ, Pawar G, van de Water B, Cronin MT, van Westen GJ. Computational Approaches for Drug-Induced Liver Injury (DILI) Prediction: State of the Art and Challenges. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11535-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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23
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Rathman J, Yang C, Ribeiro JV, Mostrag A, Thakkar S, Tong W, Hobocienski B, Sacher O, Magdziarz T, Bienfait B. Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment. Chem Res Toxicol 2020; 34:601-615. [PMID: 33356149 DOI: 10.1021/acs.chemrestox.0c00423] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.
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Affiliation(s)
- James Rathman
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany.,Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | - Chihae Yang
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - J Vinicius Ribeiro
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Aleksandra Mostrag
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Shraddha Thakkar
- National Center for Toxicology Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Weida Tong
- National Center for Toxicology Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Bryan Hobocienski
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Oliver Sacher
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Tomasz Magdziarz
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Bruno Bienfait
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
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24
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Wu Q, Taboureau O, Audouze K. Development of an adverse drug event network to predict drug toxicity. Curr Res Toxicol 2020; 1:48-55. [PMID: 34345836 PMCID: PMC8320634 DOI: 10.1016/j.crtox.2020.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/31/2020] [Accepted: 06/04/2020] [Indexed: 11/28/2022] Open
Abstract
Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.
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Key Words
- ADE, adverse drug event
- ADR, adverse drug reaction
- AOP, adverse outcome pathway
- Adverse event network
- Computational toxicology
- FAERS, FDA Adverse Event Reporting System
- FDA, Food and Drug Administration
- HMS-PCI, high-throughput mass spectrometric protein complex identification
- LRT, Likelihood Ratio Test
- MedDRA, Medical Dictionary for Regulatory Activities
- Network science
- PPAN, protein-protein association network
- PT, Preferred Term
- Predictive toxicity
- QSAR, Quantitative structure-activity relationships
- SE, side effect
- SOC, System Organ Class
- System toxicology
- TAP–MS, tandem-affinity-purification method coupled to mass spectrometry
- pullS, pull-down score
- wS, weighted score
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Affiliation(s)
- Qier Wu
- Université de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France
| | - Olivier Taboureau
- Université de Paris, BFA, CNRS UMR 8251, ERL Inserm U1133, CNRS UMR 8251, F-75013 Paris, France
| | - Karine Audouze
- Université de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France
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25
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Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem. Molecules 2020; 25:molecules25030481. [PMID: 31979300 PMCID: PMC7037161 DOI: 10.3390/molecules25030481] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 12/13/2022] Open
Abstract
Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming in vitro and in vivo studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are in silico approaches, which present a cost-efficient method for toxicity prediction. The aim of our study was to explore the capabilities of counter-propagation artificial neural networks (CPANNs) for the classification of an imbalanced dataset related to idiosyncratic drug-induced liver injury and to develop a model for prediction of the hepatotoxic potential of drugs. Genetic algorithm optimization of CPANN models was used to build models for the classification of drugs into hepatotoxic and non-hepatotoxic class using molecular descriptors. For the classification of an imbalanced dataset, we modified the classical CPANN training algorithm by integrating random subsampling into the training procedure of CPANN to improve the classification ability of CPANN. According to the number of models accepted by internal validation and according to the prediction statistics on the external set, we concluded that using an imbalanced set with balanced subsampling in each learning epoch is a better approach compared to using a fixed balanced set in the case of the counter-propagation artificial neural network learning methodology.
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26
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Tian QJ, Zhao XY, Wang Y, Wee A, Soon GST, Gouw ASH, Li M, Yang RY, Wang L, Wang QY, Duan WJ, Wang Y, Wang XM, Kong YY, Ou XJ, You H, Jia JD. Histologic pattern is better correlated with clinical outcomes than biochemical classification in patients with drug-induced liver injury. Mod Pathol 2019; 32:1795-1805. [PMID: 31300804 DOI: 10.1038/s41379-019-0314-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 05/30/2019] [Accepted: 05/30/2019] [Indexed: 12/19/2022]
Abstract
Histologically, drug-induced liver injury could be classified into acute hepatitis, chronic hepatitis, acute cholestasis, chronic cholestasis, and cholestatic hepatitis. The correlation between these histologic patterns and long-term clinical outcomes has not been well established. Therefore, we conducted a retrospective cohort study to investigate the association of histologic patterns and long-term clinical outcomes defined as biochemical normalization, persistent abnormal liver biochemistry or death at designated time points. In this study, biochemical classification was determined by R-values; histologic injury pattern was determined by morphological features. Predictive ability of clinical outcomes by these two classifications was assessed using Receiver Operating Characteristic Curves. Logistic regression was performed to identify histologic factors associated with outcomes. Totally, 88 patients with drug-induced liver injury were included for final analysis. Biochemical and histologic classification were consistent in 50 (57%) cases. 53 (60%) cases showed biochemical normalization within 6 months, and a further 11 (13%), 16 (18%), and 6 (7%) cases within 1, 2, and 3 years, respectively. Compared with biochemical classification, histologic injury pattern had better predictive ability for abnormal biochemistry at 6 months (Areas under Receiver Operating Characteristic Curves 0.92 versus 0.60, P < 0.001) and 1 year (Areas under Receiver Operating Characteristic Curves 0.94 versus 0.69, P < 0.001). Interlobular bile duct loss in >25% portal areas was independently associated with abnormal biochemistry at 6 months, 1 year, and 2 years. In conclusion, histologic injury pattern is better correlated with clinical outcome at 6 months and 1 year than biochemical classification. Moderate bile duct loss is an important histologic feature associated with persistent biochemical abnormality at 6 months, 1 year, and 2 years.
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Affiliation(s)
- Qiu-Ju Tian
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Xin-Yan Zhao
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Yan Wang
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Aileen Wee
- Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, National University Hospital, Singapore, Singapore
| | | | - Annette S H Gouw
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Min Li
- Clinical Epidemiology and Evidence-Based Medicine Unit, National Clinical Research Center for Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Rui-Yuan Yang
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Lan Wang
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Qian-Yi Wang
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Wei-Jia Duan
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Yu Wang
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Xiao-Ming Wang
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Yuan-Yuan Kong
- Clinical Epidemiology and Evidence-Based Medicine Unit, National Clinical Research Center for Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xiao-Juan Ou
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Hong You
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Ji-Dong Jia
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China.
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27
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Thakkar S, Li T, Liu Z, Wu L, Roberts R, Tong W. Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity. Drug Discov Today 2019; 25:201-208. [PMID: 31669330 DOI: 10.1016/j.drudis.2019.09.022] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 09/22/2019] [Accepted: 09/26/2019] [Indexed: 12/12/2022]
Abstract
Drug-induced liver injury (DILI) is of significant concern to drug development and regulatory review because of the limited success with existing preclinical models. For developing alternative methods, a large drug list is needed with known DILI severity and toxicity. We augmented the DILIrank data set [annotated using US Food and Drug Administration (FDA) drug labeling)] with four literature datasets (N >350 drugs) to generate the largest drug list with DILI classification, called DILIst (DILI severity and toxicity). DILIst comprises 1279 drugs, of which 768 were DILI positives (increase of 65% from DILIrank), whereas 511 were DILI negatives (increase of 65%). The investigation of DILI positive-negative distribution across various therapeutic categories revealed the most and least frequent DILI categories. Thus, we consider DILIst to be an invaluable resource for the community to improve DILI research.
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Affiliation(s)
- Shraddha Thakkar
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Ting Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Leihong Wu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Ruth Roberts
- ApconiX Ltd, Alderley Park, Alderley Edge, SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
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28
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Ai H, Chen W, Zhang L, Huang L, Yin Z, Hu H, Zhao Q, Zhao J, Liu H. Predicting Drug-Induced Liver Injury Using Ensemble Learning Methods and Molecular Fingerprints. Toxicol Sci 2019; 165:100-107. [PMID: 29788510 DOI: 10.1093/toxsci/kfy121] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Drug-induced liver injury (DILI) is a major safety concern in the drug-development process, and various methods have been proposed to predict the hepatotoxicity of compounds during the early stages of drug trials. In this study, we developed an ensemble model using 3 machine learning algorithms and 12 molecular fingerprints from a dataset containing 1241 diverse compounds. The ensemble model achieved an average accuracy of 71.1 ± 2.6%, sensitivity (SE) of 79.9 ± 3.6%, specificity (SP) of 60.3 ± 4.8%, and area under the receiver-operating characteristic curve (AUC) of 0.764 ± 0.026 in 5-fold cross-validation and an accuracy of 84.3%, SE of 86.9%, SP of 75.4%, and AUC of 0.904 in an external validation dataset of 286 compounds collected from the Liver Toxicity Knowledge Base. Compared with previous methods, the ensemble model achieved relatively high accuracy and SE. We also identified several substructures related to DILI. In addition, we provide a web server offering access to our models (http://ccsipb.lnu.edu.cn/toxicity/HepatoPred-EL/).
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Affiliation(s)
- Haixin Ai
- School of Life Science, Liaoning University, Shenyang 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang 110036, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang 110036, China
| | | | - Li Zhang
- School of Life Science, Liaoning University, Shenyang 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang 110036, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang 110036, China
| | | | | | - Huan Hu
- School of Life Science, Liaoning University, Shenyang 110036, China
| | - Qi Zhao
- School of Mathematics, Liaoning University, Shenyang 110036, China
| | - Jian Zhao
- School of Life Science, Liaoning University, Shenyang 110036, China
| | - Hongsheng Liu
- School of Life Science, Liaoning University, Shenyang 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang 110036, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang 110036, China
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29
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Peng Y, Wu Z, Yang H, Cai Y, Liu G, Li W, Tang Y. Insights into mechanisms and severity of drug-induced liver injury via computational systems toxicology approach. Toxicol Lett 2019; 312:22-33. [DOI: 10.1016/j.toxlet.2019.05.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 04/10/2019] [Accepted: 05/03/2019] [Indexed: 12/14/2022]
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30
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Liu Z, He X, Wang L, Zhang Y, Hai Y, Gao R. Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database. Curr Drug Metab 2019; 20:138-146. [PMID: 30101702 PMCID: PMC6635764 DOI: 10.2174/1389200219666180813144114] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 05/28/2018] [Accepted: 06/27/2018] [Indexed: 12/17/2022]
Abstract
Background: Due to the special nature of Chinese Herbal medicine and the complexity of its clinical use, it is difficult to identify and evaluate its toxicity and resulting herb induced liver injury (HILI). Methods: First, the database would provide full profile of HILI from the basic ingredients to clinical out-comes by the most advanced algorithms of artificial intelligence, and it is also possible that we can predict possibilities of HILI after patients taking Chinese herbs by individual patient evaluation and prediction. Second, the database would solve the chaos and lack of the relevant data faced by the current basic re-search and clinical practice of Chinese Herbal Medicine. Third, we can also screen the susceptible patients from the database and thus prevent the accidents of HILI from the very beginning. Results: The Roussel Uclaf Causality Assessment Method (RUCAM) is the most accepted method to evalu-ate DILI, but at present before using the RUCAM evaluation method, data resource collection and analysis are yet to be perfected. Based on existing research on drug-metabolizing enzymes mediating reactive me-tabolites (RMs), the aim of this study is to explore the possibilities and methods of building multidimen-sional hierarchical database composing of RMs evidence library, Chinese herbal evidence library, and indi-vidualized reports evidence library of herb induced liver injury HILI. Conclusion: The potential benefits lie in its ability to organize, use vast amounts of evidence and use big data mining techniques at the center for Chinese herbal medicine liver toxicity research, which is the most difficult key point of scientific research to be investigated in the next few years.
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Affiliation(s)
- Zhi Liu
- Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.,Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin 300193, China
| | - Xin He
- Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.,Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin 300193, China
| | - Lili Wang
- Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.,Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin 300193, China
| | - Yunhua Zhang
- Tianjin Clinda Medical Technology Co., Ltd., Tianjin, China
| | - Yue Hai
- Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Rui Gao
- Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
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31
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He S, Ye T, Wang R, Zhang C, Zhang X, Sun G, Sun X. An In Silico Model for Predicting Drug-Induced Hepatotoxicity. Int J Mol Sci 2019; 20:E1897. [PMID: 30999595 PMCID: PMC6515336 DOI: 10.3390/ijms20081897] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 04/09/2019] [Accepted: 04/15/2019] [Indexed: 01/10/2023] Open
Abstract
As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure-activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.
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Affiliation(s)
- Shuaibing He
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Tianyuan Ye
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Ruiying Wang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Chenyang Zhang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Xuelian Zhang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Guibo Sun
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Xiaobo Sun
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
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32
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Jain S, Ecker GF. In Silico Approaches to Predict Drug-Transporter Interaction Profiles: Data Mining, Model Generation, and Link to Cholestasis. Methods Mol Biol 2019; 1981:383-396. [PMID: 31016669 DOI: 10.1007/978-1-4939-9420-5_26] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Transport proteins play a crucial role in drug distribution, disposition, and clearance by mediating cellular drug influx and efflux. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury, such as cholestasis, which comprises a major challenge in drug development process. Thus, computer-based (in silico) models that can predict the pharmacological and toxicological profiles of these small molecules with respect to liver transporters may help in the early prioritization of compounds and hence may lower the high attrition rates. In this chapter, we provide a protocol for in silico prediction of cholestasis by generating validated predictive models. In addition to the two-dimensional molecular descriptors, we include transporter inhibition predictions as descriptors and evaluate the influence of the same on the performance of the cholestasis models.
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Affiliation(s)
- Sankalp Jain
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, Vienna, 1090, Austria
| | - Gerhard F Ecker
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, Vienna, 1090, Austria.
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33
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Chen M, Zhu J, Ashby K, Wu L, Liu Z, Gong P, Zhang C, Borlak J, Hong H, Tong W. Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2019:259-278. [DOI: 10.1007/978-3-030-16443-0_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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34
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Liu L, Fu L, Zhang JW, Wei H, Ye WL, Deng ZK, Zhang L, Cheng Y, Ouyang D, Cao Q, Cao DS. Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects. Mol Pharm 2018; 16:393-408. [PMID: 30475633 DOI: 10.1021/acs.molpharmaceut.8b01048] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Hepatotoxicity is a major cause of drug withdrawal from the market. To reduce the drug attrition induced by hepatotoxicity, an accurate and efficient hepatotoxicity prediction system must be constructed. In the present study, we constructed a three-level hepatotoxicity prediction system based on different levels of adverse hepatic effects (AHEs) combined with machine learning, using (1) an end point, hepatotoxicity; (2) four hepatotoxicity severity degrees; and (3) specific AHEs. After collecting and curing 15 873 compound-AHE pairs associated with 2017 compounds and 403 AHEs, we constructed 27 models with three end point levels with the random forest algorithm, and obtained accuracies ranging from 67.0 to 78.2% and the area under receiver operating characteristic curves (AUCs) of 0.715-0.875. The 27 models were fully integrated into a tiered hepatotoxicity prediction system. The existence of hepatotoxicity existence, severity degree, and potential AHEs for a given compound could be inferred simultaneously and systematically. Thus, the tiered hepatotoxicity prediction system allows researchers to have significant confidence in confirming compound hepatotoxicity, analyzing hepatotoxicity from multiple perspectives, obtaining warnings for the potential hepatotoxicity severity, and even rapidly selecting the proper in vitro experiments for hepatotoxicity verification. We also applied three external sets (11 drugs or candidates that failed in clinical trials or were withdrawn from the market, the PharmGKB (offsides) database, and an herbal hepatotoxicity data set) to test and validate the prediction ability of our system. Furthermore, the hepatotoxicity prediction system was adapted into a flow framework based on the Konstanz Information Miner, which was made available for researchers.
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Affiliation(s)
- Lu Liu
- Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China
| | - Jin-Wei Zhang
- Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China
| | - Hui Wei
- Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China
| | - Wen-Ling Ye
- Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China
| | - Zhen-Ke Deng
- Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China
| | - Lin Zhang
- Hunan Key Laboratory of Processed Food for Special Medical Purpose Central South University of Forestry and Technology , Changsha 410004 , People's Republic of China
| | - Yan Cheng
- Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS) , University of Macau , Macau , China
| | - Qian Cao
- Beijing Rehabilitation Hospital Affiliated to Capital Medical University , Beijing 100001 , People's Republic of China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China
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35
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Substrate selectivity of human aldehyde oxidase 1 in reduction of nitroaromatic drugs. Arch Biochem Biophys 2018; 659:85-92. [DOI: 10.1016/j.abb.2018.10.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 09/27/2018] [Accepted: 10/23/2018] [Indexed: 11/17/2022]
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36
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Yang H, Sun L, Li W, Liu G, Tang Y. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts. Front Chem 2018; 6:30. [PMID: 29515993 PMCID: PMC5826228 DOI: 10.3389/fchem.2018.00030] [Citation(s) in RCA: 121] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 02/05/2018] [Indexed: 12/17/2022] Open
Abstract
During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
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Affiliation(s)
| | | | | | | | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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37
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Identification of enzymes responsible for nitrazepam metabolism and toxicity in human. Biochem Pharmacol 2017; 140:150-160. [DOI: 10.1016/j.bcp.2017.06.114] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Accepted: 06/07/2017] [Indexed: 12/16/2022]
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38
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Cronin MTD, Enoch SJ, Mellor CL, Przybylak KR, Richarz AN, Madden JC. In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects. Toxicol Res 2017; 33:173-182. [PMID: 28744348 PMCID: PMC5523554 DOI: 10.5487/tr.2017.33.3.173] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 04/04/2017] [Accepted: 04/06/2017] [Indexed: 11/20/2022] Open
Abstract
In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.
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Affiliation(s)
- Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England
| | - Claire L Mellor
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England
| | - Katarzyna R Przybylak
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England
| | - Andrea-Nicole Richarz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England
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39
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Kotsampasakou E, Montanari F, Ecker GF. Predicting drug-induced liver injury: The importance of data curation. Toxicology 2017; 389:139-145. [PMID: 28652195 PMCID: PMC6422282 DOI: 10.1016/j.tox.2017.06.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 06/10/2017] [Accepted: 06/15/2017] [Indexed: 12/12/2022]
Abstract
Drug-induced liver injury (DILI) is a major issue for both patients and pharmaceutical industry due to insufficient means of prevention/prediction. In the current work we present a 2-class classification model for DILI, generated with Random Forest and 2D molecular descriptors on a dataset of 966 compounds. In addition, predicted transporter inhibition profiles were also included into the models. The initially compiled dataset of 1773 compounds was reduced via a 2-step approach to 966 compounds, resulting in a significant increase (p-value < 0.05) in model performance. The models have been validated via 10-fold cross-validation and against three external test sets of 921, 341 and 96 compounds, respectively. The final model showed an accuracy of 64% (AUC 68%) for 10-fold cross-validation (average of 50 iterations) and comparable values for two test sets (AUC 59%, 71% and 66%, respectively). In the study we also examined whether the predictions of our in-house transporter inhibition models for BSEP, BCRP, P-glycoprotein, and OATP1B1 and 1B3 contributed in improvement of the DILI mode. Finally, the model was implemented with open-source 2D RDKit descriptors in order to be provided to the community as a Python script.
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Affiliation(s)
- Eleni Kotsampasakou
- University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
| | - Floriane Montanari
- University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
| | - Gerhard F Ecker
- University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria.
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40
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McEuen K, Borlak J, Tong W, Chen M. Associations of Drug Lipophilicity and Extent of Metabolism with Drug-Induced Liver Injury. Int J Mol Sci 2017; 18:E1335. [PMID: 28640208 PMCID: PMC5535828 DOI: 10.3390/ijms18071335] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 06/13/2017] [Accepted: 06/15/2017] [Indexed: 12/11/2022] Open
Abstract
Drug-induced liver injury (DILI), although rare, is a frequent cause of adverse drug reactions resulting in warnings and withdrawals of numerous medications. Despite the research community's best efforts, current testing strategies aimed at identifying hepatotoxic drugs prior to human trials are not sufficiently powered to predict the complex mechanisms leading to DILI. In our previous studies, we demonstrated lipophilicity and dose to be associated with increased DILI risk and, and in our latest work, we factored reactive metabolites into the algorithm to predict DILI. Given the inconsistency in determining the potential for drugs to cause DILI, the present study comprehensively assesses the relationship between DILI risk and lipophilicity and the extent of metabolism using a large published dataset of 1036 Food and Drug Administration (FDA)-approved drugs by considering five independent DILI annotations. We found that lipophilicity and the extent of metabolism alone were associated with increased risk for DILI. Moreover, when analyzed in combination with high daily dose (≥100 mg), lipophilicity was statistically significantly associated with the risk of DILI across all datasets (p < 0.05). Similarly, the combination of extensive hepatic metabolism (≥50%) and high daily dose (≥100 mg) was also strongly associated with an increased risk of DILI among all datasets analyzed (p < 0.05). Our results suggest that both lipophilicity and the extent of hepatic metabolism can be considered important risk factors for DILI in humans, and that this relationship to DILI risk is much stronger when considered in combination with dose. The proposed paradigm allows the convergence of different published annotations to a more uniform assessment.
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Affiliation(s)
- Kristin McEuen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA.
| | - Jürgen Borlak
- Center of Pharmacology and Toxicology, Hannover Medical School, Hannover 30625, Germany.
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
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41
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Kotsampasakou E, Ecker GF. Predicting Drug-Induced Cholestasis with the Help of Hepatic Transporters-An in Silico Modeling Approach. J Chem Inf Model 2017; 57:608-615. [PMID: 28166633 PMCID: PMC5411109 DOI: 10.1021/acs.jcim.6b00518] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Cholestasis represents one out of three types of drug induced liver injury (DILI), which comprises a major challenge in drug development. In this study we applied a two-class classification scheme based on k-nearest neighbors in order to predict cholestasis, using a set of 93 two-dimensional (2D) physicochemical descriptors and predictions of selected hepatic transporters' inhibition (BSEP, BCRP, P-gp, OATP1B1, and OATP1B3). In order to assess the potential contribution of transporter inhibition, we compared whether the inclusion of the transporters' inhibition predictions contributes to a significant increase in model performance in comparison to the plain use of the 93 2D physicochemical descriptors. Our findings were in agreement with literature findings, indicating a contribution not only from BSEP inhibition but a rather synergistic effect deriving from the whole set of transporters. The final optimal model was validated via both 10-fold cross validation and external validation. It performs quite satisfactorily resulting in 0.686 ± 0.013 for accuracy and 0.722 ± 0.014 for area under the receiver operating characteristic curve (AUC) for 10-fold cross-validation (mean ± standard deviation from 50 iterations).
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Affiliation(s)
- Eleni Kotsampasakou
- University of Vienna , Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
| | - Gerhard F Ecker
- University of Vienna , Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
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42
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Ivanov S, Semin M, Lagunin A, Filimonov D, Poroikov V. In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions. Mol Inform 2017; 36. [PMID: 28145637 DOI: 10.1002/minf.201600142] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 01/16/2017] [Indexed: 12/13/2022]
Abstract
Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research.
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Affiliation(s)
- Sergey Ivanov
- Institute of Biomedical Chemistry 10 building 8, Pogodinskaya str., 119121, Moscow, Russia.,Pirogov Russian National Research Medical University, Medico-Biological Faculty 1, Ostrovitianova str., 117997, Moscow, Russia
| | - Maxim Semin
- Institute of Biomedical Chemistry 10 building 8, Pogodinskaya str., 119121, Moscow, Russia.,Pirogov Russian National Research Medical University, Medico-Biological Faculty 1, Ostrovitianova str., 117997, Moscow, Russia
| | - Alexey Lagunin
- Institute of Biomedical Chemistry 10 building 8, Pogodinskaya str., 119121, Moscow, Russia.,Pirogov Russian National Research Medical University, Medico-Biological Faculty 1, Ostrovitianova str., 117997, Moscow, Russia
| | - Dmitry Filimonov
- Institute of Biomedical Chemistry 10 building 8, Pogodinskaya str., 119121, Moscow, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry 10 building 8, Pogodinskaya str., 119121, Moscow, Russia
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43
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Toropova AP, Toropov AA. CORAL: Binary classifications (active/inactive) for drug-induced liver injury. Toxicol Lett 2017; 268:51-57. [PMID: 28111161 DOI: 10.1016/j.toxlet.2017.01.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 12/16/2016] [Accepted: 01/16/2017] [Indexed: 12/16/2022]
Abstract
INTRODUCTION The data on human hepatotoxcity (drug-induced liver injury) is extremely important information from point of view of drug discovery. Experimental clinical data on this endpoint is scarce. Experimental way to extend databases on this endpoint is extremely difficult. Quantitative structure - activity relationships (QSAR) is attractive alternative of the experimental approach. METHODS Predictive models for human hepatotoxicity (drug-induced liver injury) have been built up by the Monte Carlo method with using of the CORAL software (http://www.insilico.eu/coral). These models are the binary classifications into active class and inactive class. These models are calculated with so-called "semi correlations" described in this work. The Mattews correlation coefficient of these models for external validation sets ranged from 0.52 to 0.62. RESULTS DISCUSSION The approach has been checked up with a group of random splits into the training and validation sets. These stochastic experiments have shown the stability of results: predictability of the models for various splits. Thus, the attempt to build up the classification QSAR model by means of the Monte Carlo technique, based on representation of the molecular structure via simplified molecular input line entry systems (SMILES) and hydrogen suppressed graph (HSG) using the CORAL software (http://www.insilico.eu/coral) has shown ability of this approach to provide quite good prediction of the examined endpoint (drug-induced liver injury).
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Affiliation(s)
- Alla P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milano, Italy.
| | - Andrey A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milano, Italy
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44
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Raschi E, De Ponti F. Drug-induced liver injury: Towards early prediction and risk stratification. World J Hepatol 2017; 9:30-37. [PMID: 28105256 PMCID: PMC5220269 DOI: 10.4254/wjh.v9.i1.30] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 09/29/2016] [Accepted: 11/27/2016] [Indexed: 02/06/2023] Open
Abstract
Drug-induced liver injury (DILI) is a hot topic for clinicians, academia, drug companies and regulators, as shown by the steadily increasing number of publications and agents listed as causing liver damage (http://livertox.nih.gov/). As it was the case in the past decade with drug-induced QT prolongation/arrhythmia, there is an urgent unmet clinical need to develop tools for risk assessment and stratification in clinical practice and, in parallel, to improve prediction of pre-clinical models to support regulatory steps and facilitate early detection of liver-specific adverse drug events. Although drug discontinuation and therapy reconciliation still remain the mainstay in patient management to minimize occurrence of DILI, especially acute liver failure events, different multidisciplinary attempts have been proposed in 2016 to predict and assess drug-related risk in individual patients; these promising, albeit preliminary, results strongly support the need to pursue this innovative pathway.
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Affiliation(s)
- Emanuel Raschi
- Emanuel Raschi, Fabrizio De Ponti, Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
| | - Fabrizio De Ponti
- Emanuel Raschi, Fabrizio De Ponti, Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
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45
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Ford KA. Refinement, Reduction, and Replacement of Animal Toxicity Tests by Computational Methods. ILAR J 2017; 57:226-233. [DOI: 10.1093/ilar/ilw031] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 10/12/2016] [Indexed: 12/16/2022] Open
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46
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Kotsampasakou E, Escher SE, Ecker GF. Linking organic anion transporting polypeptide 1B1 and 1B3 (OATP1B1 and OATP1B3) interaction profiles to hepatotoxicity - The hyperbilirubinemia use case. Eur J Pharm Sci 2017; 100:9-16. [PMID: 28063966 DOI: 10.1016/j.ejps.2017.01.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 11/26/2016] [Accepted: 01/02/2017] [Indexed: 02/07/2023]
Abstract
Hyperbilirubinemia is a pathological condition of excessive accumulation of conjugated or unconjugated bilirubin in blood. It has been associated with neurotoxicity and non-neural organ dysfunctions, while it can also be a warning of liver side effects. Hyperbilirubinemia can either be a result of overproduction of bilirubin due to hemolysis or dyserythropoiesis, or the outcome of impaired bilirubin elimination due to liver transporter malfunction or inhibition. There are several reports in literature that inhibition of organic anion transporting polypeptides 1B1 and 1B3 (OATP1B1 and OATP1B3) might lead to hyperbilirubinemia. In this study we created a set of classification models for hyperbilirubinemia, which, besides physicochemical descriptors, also include the output of classification models of human OATP1B1 and 1B3 inhibition. Models were based on either human data derived from public toxicity reports or animal data extracted from the eTOX database VITIC. The generated models showed satisfactory accuracy (68%) and area under the curve (AUC) for human data and 71% accuracy and 70% AUC for animal data. However, our results did not indicate strong association between OATP inhibition and hyperbilirubinemia, neither for humans nor for animals.
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Affiliation(s)
- Eleni Kotsampasakou
- University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
| | - Sylvia E Escher
- Fraunhofer Institute of Toxicology and Experimental Medicine (ITEM), Nikolai-Fuchs-Strasse 1, 30625 Hannover, Germany
| | - Gerhard F Ecker
- University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria.
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47
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Predicting drug-induced liver injury in human with Naïve Bayes classifier approach. J Comput Aided Mol Des 2016; 30:889-898. [PMID: 27640149 DOI: 10.1007/s10822-016-9972-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Accepted: 09/12/2016] [Indexed: 02/05/2023]
Abstract
Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.
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48
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Key Challenges and Opportunities Associated with the Use of In Vitro Models to Detect Human DILI: Integrated Risk Assessment and Mitigation Plans. BIOMED RESEARCH INTERNATIONAL 2016; 2016:9737920. [PMID: 27689095 PMCID: PMC5027328 DOI: 10.1155/2016/9737920] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 06/22/2016] [Indexed: 01/10/2023]
Abstract
Drug-induced liver injury (DILI) is a major cause of late-stage clinical drug attrition, market withdrawal, black-box warnings, and acute liver failure. Consequently, it has been an area of focus for toxicologists and clinicians for several decades. In spite of considerable efforts, limited improvements in DILI prediction have been made and efforts to improve existing preclinical models or develop new test systems remain a high priority. While prediction of intrinsic DILI has improved, identifying compounds with a risk for idiosyncratic DILI (iDILI) remains extremely challenging because of the lack of a clear mechanistic understanding and the multifactorial pathogenesis of idiosyncratic drug reactions. Well-defined clinical diagnostic criteria and risk factors are also missing. This paper summarizes key data interpretation challenges, practical considerations, model limitations, and the need for an integrated risk assessment. As demonstrated through selected initiatives to address other types of toxicities, opportunities exist however for improvement, especially through better concerted efforts at harmonization of current, emerging and novel in vitro systems or through the establishment of strategies for implementation of preclinical DILI models across the pharmaceutical industry. Perspectives on the incorporation of newer technologies and the value of precompetitive consortia to identify useful practices are also discussed.
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49
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Oh E, Liu R, Nel A, Gemill KB, Bilal M, Cohen Y, Medintz IL. Meta-analysis of cellular toxicity for cadmium-containing quantum dots. NATURE NANOTECHNOLOGY 2016; 11:479-86. [PMID: 26925827 DOI: 10.1038/nnano.2015.338] [Citation(s) in RCA: 292] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 12/16/2015] [Indexed: 04/14/2023]
Abstract
Understanding the relationships between the physicochemical properties of engineered nanomaterials and their toxicity is critical for environmental and health risk analysis. However, this task is confounded by material diversity, heterogeneity of published data and limited sampling within individual studies. Here, we present an approach for analysing and extracting pertinent knowledge from published studies focusing on the cellular toxicity of cadmium-containing semiconductor quantum dots. From 307 publications, we obtain 1,741 cell viability-related data samples, each with 24 qualitative and quantitative attributes describing the material properties and experimental conditions. Using random forest regression models to analyse the data, we show that toxicity is closely correlated with quantum dot surface properties (including shell, ligand and surface modifications), diameter, assay type and exposure time. Our approach of integrating quantitative and categorical data provides a roadmap for interrogating the wide-ranging toxicity data in the literature and suggests that meta-analysis can help develop methods for predicting the toxicity of engineered nanomaterials.
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Affiliation(s)
- Eunkeu Oh
- Optical Sciences Division, Code 5611, US Naval Research Laboratory, Washington, Washington DC 20375, USA
- Sotera Defense Solutions, Columbia, Maryland 21046, USA
| | - Rong Liu
- Institute of the Environment and Sustainability, University of California, Los Angeles, California 90095-1496, USA
- Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, California 90095-7227, USA
| | - Andre Nel
- Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, California 90095-7227, USA
- Department of Medicine, Division of NanoMedicine, University of California, Los Angeles, California 90095, USA
| | - Kelly Boeneman Gemill
- Center for Bio/Molecular Science and Engineering, Code 6900, US Naval Research Laboratory, SW Washington, Washington DC 20375, USA
| | - Muhammad Bilal
- Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, California 90095-7227, USA
| | - Yoram Cohen
- Institute of the Environment and Sustainability, University of California, Los Angeles, California 90095-1496, USA
- Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, California 90095-7227, USA
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095-1592, USA
| | - Igor L Medintz
- Center for Bio/Molecular Science and Engineering, Code 6900, US Naval Research Laboratory, SW Washington, Washington DC 20375, USA
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Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data. ENVIRONMENTAL HEALTH PERSPECTIVES 2016; 124:634-41. [PMID: 26383846 PMCID: PMC4858396 DOI: 10.1289/ehp.1509763] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 09/16/2015] [Indexed: 05/18/2023]
Abstract
BACKGROUND Hepatotoxicity accounts for a substantial number of drugs being withdrawn from the market. Using traditional animal models to detect hepatotoxicity is expensive and time-consuming. Alternative in vitro methods, in particular cell-based high-throughput screening (HTS) studies, have provided the research community with a large amount of data from toxicity assays. Among the various assays used to screen potential toxicants is the antioxidant response element beta lactamase reporter gene assay (ARE-bla), which identifies chemicals that have the potential to induce oxidative stress and was used to test > 10,000 compounds from the Tox21 program. OBJECTIVE The ARE-bla computational model and HTS data from a big data source (PubChem) were used to profile environmental and pharmaceutical compounds with hepatotoxicity data. METHODS Quantitative structure-activity relationship (QSAR) models were developed based on ARE-bla data. The models predicted the potential oxidative stress response for known liver toxicants when no ARE-bla data were available. Liver toxicants were used as probe compounds to search PubChem Bioassay and generate a response profile, which contained thousands of bioassays (> 10 million data points). By ranking the in vitro-in vivo correlations (IVIVCs), the most relevant bioassay(s) related to hepatotoxicity were identified. RESULTS The liver toxicants profile contained the ARE-bla and relevant PubChem assays. Potential toxicophores for well-known toxicants were created by identifying chemical features that existed only in compounds with high IVIVCs. CONCLUSION Profiling chemical IVIVCs created an opportunity to fully explore the source-to-outcome continuum of modern experimental toxicology using cheminformatics approaches and big data sources. CITATION Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. 2016. Mechanism profiling of hepatotoxicity caused by oxidative stress using antioxidant response element reporter gene assay models and big data. Environ Health Perspect 124:634-641; http://dx.doi.org/10.1289/ehp.1509763.
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Affiliation(s)
- Marlene Thai Kim
- Department of Chemistry, Rutgers University, Camden, New Jersey, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Alexander Sedykh
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
- Multicase Inc., Beachwood, Ohio, USA
| | - Wenyi Wang
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Hao Zhu
- Department of Chemistry, Rutgers University, Camden, New Jersey, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
- Address correspondence to H. Zhu, 315 Penn St., Rutgers University, Camden, NJ 08102 USA. Telephone: (856) 225-6781. E-mail:
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