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Khan MZI, Ren JN, Cao C, Ye HYX, Wang H, Guo YM, Yang JR, Chen JZ. Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning. Front Pharmacol 2024; 15:1441587. [PMID: 39234116 PMCID: PMC11373136 DOI: 10.3389/fphar.2024.1441587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Accepted: 07/24/2024] [Indexed: 09/06/2024] Open
Abstract
Background Chemicals may lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost. Methods In current studies, QSAR models were developed for hepatotoxicity predictions using the ensemble strategy to integrate machine learning (ML) and deep learning (DL) algorithms using various molecular features. A large dataset of 2588 chemicals and drugs was randomly divided into training (80%) and test (20%) sets, followed by the training of individual base models using diverse machine learning or deep learning based on three different kinds of descriptors and fingerprints. Feature selection approaches were employed to proceed with model optimizations based on the model performance. Hybrid ensemble approaches were further utilized to determine the method with the best performance. Results The voting ensemble classifier emerged as the optimal model, achieving an excellent prediction accuracy of 80.26%, AUC of 82.84%, and recall of over 93% followed by bagging and stacking ensemble classifiers method. The model was further verified by an external test set, internal 10-fold cross-validation, and rigorous benchmark training, exhibiting much better reliability than the published models. Conclusion The proposed ensemble model offers a dependable assessment with a good performance for the prediction regarding the risk of chemicals and drugs to induce liver damage.
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Affiliation(s)
| | - Jia-Nan Ren
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Cheng Cao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Polytechnic Institute, Zhejiang University, Hangzhou, China
| | - Hong-Yu-Xiang Ye
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Hao Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Ya-Min Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jin-Rong Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Polytechnic Institute, Zhejiang University, Hangzhou, China
| | - Jian-Zhong Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
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2
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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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3
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Yin Y, Hu H, Yang J, Ye C, Goh WWB, Kong AWK, Wu J. OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs. Bioinformatics 2024; 40:btae365. [PMID: 38889277 PMCID: PMC11208724 DOI: 10.1093/bioinformatics/btae365] [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: 10/19/2023] [Revised: 05/14/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. RESULTS We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. AVAILABILITY AND IMPLEMENTATION The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.
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Affiliation(s)
- Yueming Yin
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
- College of Computing and Data Science, Nanyang Technological University, 639798, Singapore
| | - Haifeng Hu
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Jitao Yang
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Chun Ye
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, 637551, Singapore
- School of Biological Sciences, Nanyang Technological University, 637551, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, 637551, Singapore
- Center for AI in Medicine, Nanyang Technological University, 639798, Singapore
- Division of Neurology, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, U.K
| | - Adams Wai-Kin Kong
- College of Computing and Data Science, Nanyang Technological University, 639798, Singapore
| | - Jiansheng Wu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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4
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Mostafa F, Chen M. Computational models for predicting liver toxicity in the deep learning era. FRONTIERS IN TOXICOLOGY 2024; 5:1340860. [PMID: 38312894 PMCID: PMC10834666 DOI: 10.3389/ftox.2023.1340860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 12/22/2023] [Indexed: 02/06/2024] Open
Abstract
Drug-induced liver injury (DILI) is a severe adverse reaction caused by drugs and may result in acute liver failure and even death. Many efforts have centered on mitigating risks associated with potential DILI in humans. Among these, quantitative structure-activity relationship (QSAR) was proven to be a valuable tool for early-stage hepatotoxicity screening. Its advantages include no requirement for physical substances and rapid delivery of results. Deep learning (DL) made rapid advancements recently and has been used for developing QSAR models. This review discusses the use of DL in predicting DILI, focusing on the development of QSAR models employing extensive chemical structure datasets alongside their corresponding DILI outcomes. We undertake a comprehensive evaluation of various DL methods, comparing with those of traditional machine learning (ML) approaches, and explore the strengths and limitations of DL techniques regarding their interpretability, scalability, and generalization. Overall, our review underscores the potential of DL methodologies to enhance DILI prediction and provides insights into future avenues for developing predictive models to mitigate DILI risk in humans.
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Affiliation(s)
- Fahad Mostafa
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, United States
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
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5
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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: 1] [Impact Index Per Article: 1.0] [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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6
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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: 3] [Impact Index Per Article: 3.0] [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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7
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Sinha K, Ghosh N, Sil PC. A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies. Chem Res Toxicol 2023; 36:1174-1205. [PMID: 37561655 DOI: 10.1021/acs.chemrestox.2c00375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
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Affiliation(s)
- Krishnendu Sinha
- Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India
| | - Nabanita Ghosh
- Department of Zoology, Maulana Azad College, Kolkata 700013, West Bengal, India
| | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata 700054, West Bengal, India
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8
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Tran TTV, Surya Wibowo A, Tayara H, Chong KT. Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. J Chem Inf Model 2023; 63:2628-2643. [PMID: 37125780 DOI: 10.1021/acs.jcim.3c00200] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University - Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Agung Surya Wibowo
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Department of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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9
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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: 3] [Impact Index Per Article: 1.5] [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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10
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Ye L, Ngan DK, Xu T, Liu Z, Zhao J, Sakamuru S, Zhang L, Zhao T, Xia M, Simeonov A, Huang R. Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data. Toxicol Appl Pharmacol 2022; 454:116250. [PMID: 36150479 PMCID: PMC9561045 DOI: 10.1016/j.taap.2022.116250] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/24/2022] [Accepted: 09/14/2022] [Indexed: 11/18/2022]
Abstract
Drug-induced liver injury (DILI) and cardiotoxicity (DICT) are major adverse effects triggered by many clinically important drugs. To provide an alternative to in vivo toxicity testing, the U.S. Tox21 consortium has screened a collection of ∼10K compounds, including drugs in clinical use, against >70 cell-based assays in a quantitative high-throughput screening (qHTS) format. In this study, we compiled reference compound lists for DILI and DICT and compared the potential of Tox21 assay data with chemical structure information in building prediction models for human in vivo hepatotoxicity and cardiotoxicity. Models were built with four different machine learning algorithms (e.g., Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine) and model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). Chemical structure-based models showed reasonable predictive power for DILI (best AUC-ROC = 0.75 ± 0.03) and DICT (best AUC-ROC = 0.83 ± 0.03), while Tox21 assay data alone only showed better than random performance. DILI and DICT prediction models built using a combination of assay data and chemical structure information did not have a positive impact on model performance. The suboptimal predictive performance of the assay data is likely due to insufficient coverage of an adequately predictive number of toxicity mechanisms. The Tox21 consortium is currently expanding coverage of biological response space with additional assays that probe toxicologically important targets and under-represented pathways that may improve the prediction of in vivo toxicity such as DILI and DICT.
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Affiliation(s)
- Lin Ye
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Deborah K Ngan
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Tuan Xu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Zhichao Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration (FDA), Jefferson, AR 72079, USA
| | - Jinghua Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Srilatha Sakamuru
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Li Zhang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Tongan Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Menghang Xia
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Anton Simeonov
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA.
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11
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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: 1.0] [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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12
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An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning. Molecules 2022; 27:molecules27103112. [PMID: 35630587 PMCID: PMC9147181 DOI: 10.3390/molecules27103112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/10/2022] [Indexed: 11/19/2022] Open
Abstract
In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.
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13
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Varela-Rial A, Maryanow I, Majewski M, Doerr S, Schapin N, Jiménez-Luna J, De Fabritiis G. PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks. J Chem Inf Model 2022; 62:225-231. [PMID: 34978201 PMCID: PMC8790755 DOI: 10.1021/acs.jcim.1c00691] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
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Deep learning has
been successfully applied to structure-based
protein–ligand affinity prediction, yet the black box nature
of these models raises some questions. In a previous study, we presented
KDEEP, a convolutional neural network that predicted the
binding affinity of a given protein–ligand complex while reaching
state-of-the-art performance. However, it was unclear what this model
was learning. In this work, we present a new application to visualize
the contribution of each input atom to the prediction made by the
convolutional neural network, aiding in the interpretability of such
predictions. The results suggest that KDEEP is able to
learn meaningful chemistry signals from the data, but it has also
exposed the inaccuracies of the current model, serving as a guideline
for further optimization of our prediction tools.
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Affiliation(s)
- Alejandro Varela-Rial
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.,Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain
| | - Iain Maryanow
- Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain
| | - Maciej Majewski
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Stefan Doerr
- Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain
| | - Nikolai Schapin
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.,Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain
| | - José Jiménez-Luna
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.,Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
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14
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Chen Z, Jiang Y, Zhang X, Zheng R, Qiu R, Sun Y, Zhao C, Shang H. ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18. Brief Bioinform 2021; 23:6457162. [PMID: 34882224 DOI: 10.1093/bib/bbab503] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/27/2021] [Accepted: 11/02/2021] [Indexed: 01/22/2023] Open
Abstract
Drug-induced liver injury (DILI) has always been the focus of clinicians and drug researchers. How to improve the performance of the DILI prediction model to accurately predict liver injury was an urgent problem for researchers in the field of medical research. In order to solve this scientific problem, this research collected a comprehensive and accurate dataset of DILI with high recognition and high quality based on clinically confirmed DILI compound datasets, including 1446 chemical compounds. Then, the residual neural network with 18-layer by using more 5-layer blocks (ResNet18) with deep neural network (ResNet18DNN) model was proposed to predict DILI, which was an improved model for DILI prediction through vectorization of compound structure image. In predicting DILI, the ResNet18DNN learned greatly and outperformed the existing state-of-the-art DILI predictors. The results of DILI prediction model based on ResNet18DNN showed that the AUC (area under the curve), accuracy, recall, precision, F1-score and specificity of the training set were 0.973, 0.992, 0.995, 0.994, 0.995 and 0.975; those of test set were, respectively, 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913, which were better than the performance of previously published described DILI prediction models. This method adopted ResNet18 embedding method to vectorize molecular structure images and the evaluation indicators of Resnet18DNN were obtained after 10 000 iterations. This prediction approach will greatly improve the performance of the predictive model of DILI and provide an accurate and precise early warning method for DILI in drug development and clinical medication.
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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 100700, China
| | - Yin Jiang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Xiaoyu Zhang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Ruijin Qiu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Chen Zhao
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.,College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
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15
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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: 3] [Impact Index Per Article: 1.0] [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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Kleinstreuer NC, Tetko IV, Tong W. Introduction to Special Issue: Computational Toxicology. Chem Res Toxicol 2021; 34:171-175. [PMID: 33583184 DOI: 10.1021/acs.chemrestox.1c00032] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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