1
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Vlasveld M, Callegaro G, Fisher C, Eakins J, Walker P, Lok S, van Oost S, de Jong B, Pellegrino-Coppola D, Burger G, Wink S, van de Water B. The integrated stress response-related expression of CHOP due to mitochondrial toxicity is a warning sign for DILI liability. Liver Int 2024; 44:760-775. [PMID: 38217387 DOI: 10.1111/liv.15822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 11/29/2023] [Accepted: 12/07/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND AND AIMS Drug-induced liver injury (DILI) is one of the most frequent reasons for failure of drugs in clinical trials or market withdrawal. Early assessment of DILI risk remains a major challenge during drug development. Here, we present a mechanism-based weight-of-evidence approach able to identify certain candidate compounds with DILI liabilities due to mitochondrial toxicity. METHODS A total of 1587 FDA-approved drugs and 378 kinase inhibitors were screened for cellular stress response activation associated with DILI using an imaging-based HepG2 BAC-GFP reporter platform including the integrated stress response (CHOP), DNA damage response (P21) and oxidative stress response (SRXN1). RESULTS In total 389, 219 and 104 drugs were able to induce CHOP-GFP, P21-GFP and SRXN1-GFP expression at 50 μM respectively. Concentration response analysis identified 154 FDA-approved drugs as critical CHOP-GFP inducers. Based on predicted and observed (pre-)clinical DILI liabilities of these drugs, nine antimycotic drugs (e.g. butoconazole, miconazole, tioconazole) and 13 central nervous system (CNS) agents (e.g. duloxetine, fluoxetine) were selected for transcriptomic evaluation using whole-genome RNA-sequencing of primary human hepatocytes. Gene network analysis uncovered mitochondrial processes, NRF2 signalling and xenobiotic metabolism as most affected by the antimycotic drugs and CNS agents. Both the selected antimycotics and CNS agents caused impairment of mitochondrial oxygen consumption in both HepG2 and primary human hepatocytes. CONCLUSIONS Together, the results suggest that early pre-clinical screening for CHOP expression could indicate liability of mitochondrial toxicity in the context of DILI, and, therefore, could serve as an important warning signal to consider during decision-making in drug development.
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Affiliation(s)
- Matthijs Vlasveld
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Giulia Callegaro
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | | | | | | | - Samantha Lok
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Siddh van Oost
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Brechtje de Jong
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Damiano Pellegrino-Coppola
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Gerhard Burger
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Steven Wink
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Bob van de Water
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
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2
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Lee S, Yoo S. InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism. J Cheminform 2024; 16:1. [PMID: 38173043 PMCID: PMC10765872 DOI: 10.1186/s13321-023-00796-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024] Open
Abstract
Safety is one of the important factors constraining the distribution of clinical drugs on the market. Drug-induced liver injury (DILI) is the leading cause of safety problems produced by drug side effects. Therefore, the DILI risk of approved drugs and potential drug candidates should be assessed. Currently, in vivo and in vitro methods are used to test DILI risk, but both methods are labor-intensive, time-consuming, and expensive. To overcome these problems, many in silico methods for DILI prediction have been suggested. Previous studies have shown that DILI prediction models can be utilized as prescreening tools, and they achieved a good performance. However, there are still limitations in interpreting the prediction results. Therefore, this study focused on interpreting the model prediction to analyze which features could potentially cause DILI. For this, five publicly available datasets were collected to train and test the model. Then, various machine learning methods were applied using substructure and physicochemical descriptors as inputs and the DILI label as the output. The interpretation of feature importance was analyzed by recognizing the following general-to-specific patterns: (i) identifying general important features of the overall DILI predictions, and (ii) highlighting specific molecular substructures which were highly related to the DILI prediction for each compound. The results indicated that the model not only captured the previously known properties to be related to DILI but also proposed a new DILI potential substructural of physicochemical properties. The models for the DILI prediction achieved an area under the receiver operating characteristic (AUROC) of 0.88-0.97 and an area under the Precision-Recall curve (AUPRC) of 0.81-0.95. From this, we hope the proposed models can help identify the potential DILI risk of drug candidates at an early stage and offer valuable insights for drug development.
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Affiliation(s)
- Soyeon Lee
- Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, 61186, Republic of Korea
- Division of Bioresources Bank, Honam National Institute of Biological Resources, Mokpo, 58762, Republic of Korea
| | - Sunyong Yoo
- Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, 61186, Republic of Korea.
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3
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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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4
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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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5
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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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6
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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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7
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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: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [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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8
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Devi V, Awasthi P. Juvenile hormone mimics with phenyl ether and amide functionality to be insect growth regulators (IGRs): synthesis, characterization, computational and biological study. J Biomol Struct Dyn 2022; 40:13246-13264. [PMID: 34622740 DOI: 10.1080/07391102.2021.1985614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
A series of substituted phenyl ethers derivatives as juvenile hormone (JH) mimics (V1-V8) have been synthesized. Substituted phenoxyacetic acid and amino acid ethyl ester hydrochloride were prepared using NaOH, SOCl2. DCC method has been used for amide linkage. The structure of prepared compounds has been confirmed by Fourier Transform Infra-Red (FT-IR), Electrospray ionization-Mass spectrometry (ESI-MS), Proton and Carbon-13 nuclear magnetic resonance (1H-NMR, 13C-NMR) spectroscopic techniques. Biological efficacy of synthesized analogs has been carried out under laboratory conditions. Galleria mellonella (honey bee pest) has been chosen as testing insect. Juvenile hormone (JH) activity of synthesized compounds has been tested at different concentrations and compared with the standard juvenile hormone analogs (JHAs) pyriproxyfen (M1) and fenoxycarb (M2) against the fifth larval instar of G. mellonella. Compound ethyl 2-[2-(4-methylphenoxy)aminoacetyl]-3-phenyl-propanoate (V6) exhibited better activity among all the synthesized compounds (V1-V8) with LC50 and LC90 values of 0.11 mg/mL and 0.56 mg/mL respectively. Compounds showed insect growth regulating (IGR) activity at lower concentrations. In silico screening of all synthesized compounds with the W-cavity of juvenile hormone-binding protein (JHBP) of insect G. mellonella has been carried out. Chemical reactivity of synthesized series has been studied using DFT/B3LYP/6-311 + G(d,2p) method. Non-toxic behavior of molecules has also been observed from ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) study using discovery studio client 3.0.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Vandna Devi
- Department of Chemistry, National Institute of Technology, Hamirpur, Himachal Pradesh, India
| | - Pamita Awasthi
- Department of Chemistry, National Institute of Technology, Hamirpur, Himachal Pradesh, India
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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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Wu L, Yan B, Han J, Li R, Xiao J, He S, Bo X. TOXRIC: a comprehensive database of toxicological data and benchmarks. Nucleic Acids Res 2022; 51:D1432-D1445. [PMID: 36400569 PMCID: PMC9825425 DOI: 10.1093/nar/gkac1074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/10/2022] [Accepted: 10/26/2022] [Indexed: 11/20/2022] Open
Abstract
The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.
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Affiliation(s)
| | | | - Junshan Han
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Ruijiang Li
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jian Xiao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China,Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Song He
- Correspondence may also be addressed to Song He. Tel: +86 01066931450;
| | - Xiaochen Bo
- To whom correspondence should be addressed. Tel: +86 01066931207; ;
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11
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Di Zeo-Sánchez DE, Segovia-Zafra A, Matilla-Cabello G, Pinazo-Bandera JM, Andrade RJ, Lucena MI, Villanueva-Paz M. Modeling drug-induced liver injury: current status and future prospects. Expert Opin Drug Metab Toxicol 2022; 18:555-573. [DOI: 10.1080/17425255.2022.2122810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Daniel E. Di Zeo-Sánchez
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, 29071 Málaga, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029, Madrid, Spain
| | - Antonio Segovia-Zafra
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, 29071 Málaga, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029, Madrid, Spain
| | - Gonzalo Matilla-Cabello
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, 29071 Málaga, Spain
| | - José M. Pinazo-Bandera
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, 29071 Málaga, Spain
| | - Raúl J. Andrade
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, 29071 Málaga, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029, Madrid, Spain
| | - M. Isabel Lucena
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, 29071 Málaga, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029, Madrid, Spain
- Plataforma ISCIII de Ensayos Clínicos. UICEC-IBIMA, 29071, Malaga, Spain
| | - Marina Villanueva-Paz
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, 29071 Málaga, Spain
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12
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Jia X, Wen X, Russo DP, Aleksunes LM, Zhu H. Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay. JOURNAL OF HAZARDOUS MATERIALS 2022; 436:129193. [PMID: 35739723 PMCID: PMC9262097 DOI: 10.1016/j.jhazmat.2022.129193] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 05/20/2023]
Abstract
Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.
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Affiliation(s)
- Xuelian Jia
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Xia Wen
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA.
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13
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Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach. Int J Mol Sci 2022; 23:ijms23158218. [PMID: 35897792 PMCID: PMC9332482 DOI: 10.3390/ijms23158218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 02/04/2023] Open
Abstract
Aedes aegypti is the main vector that transmits viral diseases such as dengue, hemorrhagic dengue, urban yellow fever, zika, and chikungunya. Worldwide, many cases of dengue have been reported in recent years, showing significant growth. The best way to manage diseases transmitted by Aedes aegypti is to control the vector with insecticides, which have already been shown to be toxic to humans; moreover, insects have developed resistance. Thus, the development of new insecticides is considered an emergency. One way to achieve this goal is to apply computational methods based on ligands and target information. In this study, sixteen compounds with acceptable insecticidal activities, with 100% larvicidal activity at low concentrations (2.0 to 0.001 mg·L−1), were selected from the literature. These compounds were used to build up and validate pharmacophore models. Pharmacophore model 6 (AUC = 0.78; BEDROC = 0.6) was used to filter 4793 compounds from the subset of lead-like compounds from the ZINC database; 4142 compounds (dG < 0 kcal/mol) were then aligned to the active site of the juvenile hormone receptor Aedes aegypti (PDB: 5V13), 2240 compounds (LE < −0.40 kcal/mol) were prioritized for molecular docking from the construction of a chitin deacetylase model of Aedes aegypti by the homology modeling of the Bombyx mori species (PDB: 5ZNT), which aligned 1959 compounds (dG < 0 kcal/mol), and 20 compounds (LE < −0.4 kcal/mol) were predicted for pharmacokinetic and toxicological prediction in silico (Preadmet, SwissADMET, and eMolTox programs). Finally, the theoretical routes of compounds M01, M02, M03, M04, and M05 were proposed. Compounds M01−M05 were selected, showing significant differences in pharmacokinetic and toxicological parameters in relation to positive controls and interaction with catalytic residues among key protein sites reported in the literature. For this reason, the molecules investigated here are dual inhibitors of the enzymes chitin synthase and juvenile hormonal protein from insects and humans, characterizing them as potential insecticides against the Aedes aegypti mosquito.
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14
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Identification of New Toxicity Mechanisms in Drug-Induced Liver Injury through Systems Pharmacology. Genes (Basel) 2022; 13:genes13071292. [PMID: 35886075 PMCID: PMC9315637 DOI: 10.3390/genes13071292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 02/05/2023] Open
Abstract
Among adverse drug reactions, drug-induced liver injury presents particular challenges because of its complexity, and the underlying mechanisms are still not completely characterized. Our knowledge of the topic is limited and based on the assumption that a drug acts on one molecular target. We have leveraged drug polypharmacology, i.e., the ability of a drug to bind multiple targets and thus perturb several biological processes, to develop a systems pharmacology platform that integrates all drug–target interactions. Our analysis sheds light on the molecular mechanisms of drugs involved in drug-induced liver injury and provides new hypotheses to study this phenomenon.
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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: 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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16
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Zhang H, Zhang HR, Hu ML, Qi HZ. Development of binary classification models for assessment of drug-induced liver injury in humans using a large set of FDA-approved drugs. J Pharmacol Toxicol Methods 2022; 116:107185. [PMID: 35623583 DOI: 10.1016/j.vascn.2022.107185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 04/13/2022] [Accepted: 05/18/2022] [Indexed: 02/05/2023]
Abstract
Drug-induced liver injury (DILI) has been identified as one of the major causes for drugs withdrawn from the market, and even termination during the late stages of development. Therefore, it is imperative to evaluate the DILI potential of lead compounds during the research and development process. Although various computational models have been developed to predict DILI, most of which applied the DILI data were extracted from preclinical sources. In this investigation, the in silico prediction models for DILI were constructed based on 1140 FDA-approved drugs by using naïve Bayes classifier approach. The genetic algorithm method was applied for the molecular descriptors selection. Among these established prediction models, the NB-11 model based on eight molecular descriptors combined with ECFP_18 showed the best prediction performance for DILI, which gave 91.7% overall prediction accuracy for the training set, and 68.9% concordance for the external test set. Therefore, the established NB-11 prediction model can be used as a reliable virtual screening tool to predict DILI adverse effect in the early stages of drug design. In addition, some new structural alters for DILI were identified, which could be used for structural optimization in the future drug design by medicinal chemists.
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Affiliation(s)
- Hui Zhang
- College of Life Science, Northwest Normal University, Lanzhou, Gansu 730070, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, PR China.
| | - Hong-Rui Zhang
- College of Life Science, Northwest Normal University, Lanzhou, Gansu 730070, PR China
| | - Mei-Ling Hu
- College of Life Science, Northwest Normal University, Lanzhou, Gansu 730070, PR China
| | - Hua-Zhao Qi
- College of Life Science, Northwest Normal University, Lanzhou, Gansu 730070, PR China
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17
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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: 0] [Impact Index Per Article: 0] [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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18
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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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19
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Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci 2022; 23:4645. [PMID: 35563034 PMCID: PMC9104788 DOI: 10.3390/ijms23094645] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
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Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - Faryal Mehwish Awan
- Department of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan;
| | - Anam Naz
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Oscar Alvarez
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | - Ana Cernea
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | | | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43205, USA
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20
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Devi V, Kumari N, Awasthi P. Synthesis, characterization and insect growth regulating study of beta-alanine substituted sulfonamide derivatives as juvenile hormone mimics. PHOSPHORUS SULFUR 2022. [DOI: 10.1080/10426507.2022.2061970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Vandna Devi
- Department of Chemistry, National Institute of Technology, Hamirpur, India
| | - Neetika Kumari
- Department of Chemistry, National Institute of Technology, Hamirpur, India
| | - Pamita Awasthi
- Department of Chemistry, National Institute of Technology, Hamirpur, India
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21
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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: 1] [Impact Index Per Article: 0.5] [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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22
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Liu J, Guo W, Sakkiah S, Ji Z, Yavas G, Zou W, Chen M, Tong W, Patterson TA, Hong H. Machine Learning Models for Predicting Liver Toxicity. Methods Mol Biol 2022; 2425:393-415. [PMID: 35188640 DOI: 10.1007/978-1-0716-1960-5_15] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.
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Affiliation(s)
- Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Zuowei Ji
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Gokhan Yavas
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Wen Zou
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Minjun Chen
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.
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23
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Ellison C, Hewitt M, Przybylak K. In Silico Models for Hepatotoxicity. Methods Mol Biol 2022; 2425:355-392. [PMID: 35188639 DOI: 10.1007/978-1-0716-1960-5_14] [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] [Indexed: 06/14/2023]
Abstract
In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.
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Affiliation(s)
- Claire Ellison
- Human and Natural Sciences Directorate, School of Science, Engineering and Environment, University of Salford, Manchester, UK
| | - Mark Hewitt
- School of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK.
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24
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Kumar P, Kumar A. Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling. Nanotoxicology 2021; 15:1199-1214. [PMID: 34961428 DOI: 10.1080/17435390.2021.2008039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.
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Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
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25
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Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury. J CHEM-NY 2021. [DOI: 10.1155/2021/2293871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.
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Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space. Molecules 2021; 26:molecules26247548. [PMID: 34946636 PMCID: PMC8707960 DOI: 10.3390/molecules26247548] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/09/2021] [Accepted: 12/09/2021] [Indexed: 01/22/2023] Open
Abstract
Drug-induced liver injury (DILI) is a major concern for drug developers, regulators, and clinicians. However, there is no adequate model system to assess drug-associated DILI risk in humans. In the big data era, computational models are expected to play a revolutionary role in this field. This study aimed to develop a deep neural network (DNN)-based model using extended connectivity fingerprints of diameter 4 (ECFP4) to predict DILI risk. Each data set for the predictive model was retrieved and curated from DILIrank, LiverTox, and other literature. The best model was constructed through ten iterations of stratified 10-fold cross-validation, and the applicability domain was defined based on integer ECFP4 bits of the training set which represented substructures. For the robustness test, we employed the concept of the endurance level. The best model showed an accuracy of 0.731, a sensitivity of 0.714, and a specificity of 0.750 on the validation data set in the complete applicability domain. The model was further evaluated with four external data sets and attained an accuracy of 0.867 on 15 drugs with DILI cases reported since 2019. Overall, the results suggested that the ECFP4-based DNN model represents a new tool to identify DILI risk for the evaluation of drug safety.
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27
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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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Segovia-Zafra A, Di Zeo-Sánchez DE, López-Gómez C, Pérez-Valdés Z, García-Fuentes E, Andrade RJ, Lucena MI, Villanueva-Paz M. Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction. Acta Pharm Sin B 2021; 11:3685-3726. [PMID: 35024301 PMCID: PMC8727925 DOI: 10.1016/j.apsb.2021.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/07/2021] [Accepted: 11/10/2021] [Indexed: 02/08/2023] Open
Abstract
Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical human in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.
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Affiliation(s)
- Antonio Segovia-Zafra
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid 28029, Spain
| | - Daniel E. Di Zeo-Sánchez
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
| | - Carlos López-Gómez
- Unidad de Gestión Clínica de Aparato Digestivo, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga 29010, Spain
| | - Zeus Pérez-Valdés
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
| | - Eduardo García-Fuentes
- Unidad de Gestión Clínica de Aparato Digestivo, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga 29010, Spain
| | - Raúl J. Andrade
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid 28029, Spain
| | - M. Isabel Lucena
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid 28029, Spain
- Platform ISCIII de Ensayos Clínicos, UICEC-IBIMA, Málaga 29071, Spain
| | - Marina Villanueva-Paz
- Unidad de Gestión Clínica de Gastroenterología, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga 29071, Spain
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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
| | - Glenn J. Myatt
- Instem, 1393 Dublin Road, Columbus, OH 43215. USA
- Corresponding author. (G.J. Myatt)
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30
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Jaganathan K, Tayara H, Chong KT. Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets. Int J Mol Sci 2021; 22:8073. [PMID: 34360838 PMCID: PMC8348336 DOI: 10.3390/ijms22158073] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/18/2021] [Accepted: 07/23/2021] [Indexed: 02/05/2023] Open
Abstract
Drug-induced liver toxicity is one of the significant safety challenges for the patient's health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. Thus, it is essential to identify hepatotoxic compounds in the initial stages of drug development process. The purpose of this study is to construct quantitative structure activity relationship models using machine learning algorithms and systematical feature selection methods for molecular descriptor sets. The models were built from a large and diverse set of 1253 drug compounds and were validated internally with 10-fold cross-validation. In this study, we applied a variety of feature selection techniques to extract the optimal subset of descriptors as modeling features to improve the prediction performance. Experimental results suggested that the support vector machine-based classifier had achieved a better classification accuracy with reduced molecular descriptors. The final optimal model provides an accuracy of 0.811, a sensitivity of 0.840, a specificity of 0.783 and Mathew's correlation coefficient of 0.623 with an internal validation set. Furthermore, this model outperformed the prior studies while evaluated in both the internal and external test sets. The utilization of distinct optimal molecular descriptors as modeling features produce an in silico model with a superior performance.
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Affiliation(s)
- Keerthana Jaganathan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
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31
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Zhao L, Russo DP, Wang W, Aleksunes LM, Zhu H. Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data. Toxicol Sci 2021; 174:178-188. [PMID: 32073637 DOI: 10.1093/toxsci/kfaa005] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [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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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: 26] [Impact Index Per Article: 8.7] [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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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: 4.3] [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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Liu A, Walter M, Wright P, Bartosik A, Dolciami D, Elbasir A, Yang H, Bender A. Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure. Biol Direct 2021; 16:6. [PMID: 33461600 PMCID: PMC7814730 DOI: 10.1186/s13062-020-00285-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 12/01/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models. RESULTS As part of the Critical Assessment of Massive Data Analysis (CAMDA) "CMap Drug Safety Challenge" 2019 ( http://camda2019.bioinf.jku.at ), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose. CONCLUSION Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.
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Affiliation(s)
- Anika Liu
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
| | - Moritz Walter
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Peter Wright
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Aleksandra Bartosik
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Daniela Dolciami
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- Department of Pharmaceutical Sciences, University of Perugia, Via del Liceo 1, 06123, Perugia, Italy
| | - Abdurrahman Elbasir
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- ICT Department, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Hongbin Yang
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
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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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36
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Đukić-Ćosić D, Baralić K, Jorgovanović D, Živančević K, Javorac D, Stojilković N, Radović B, Marić Đ, Ćurčić M, Buha-Đorđević A, Bulat Z, Antonijević-Miljaković E, Antonijević B. 'In silico' toxicology methods in drug safety assessment. ARHIV ZA FARMACIJU 2021. [DOI: 10.5937/arhfarm71-32966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
While experimental animal investigation has historically been the most conventional approach conducted to assess drug safety and is currently considered the main method for determining drug toxicity, these studies are constricted by cost, time, and ethical approvals. Over the last 20 years, there have been significant advances in computational sciences and computer data processing, while knowledge of alternative techniques and their application has developed into a valuable skill in toxicology. Thus, the application of in silico methods in drug safety assessment is constantly increasing. They are very complex and are grounded on accumulated knowledge from toxicology, bioinformatics, biochemistry, statistics, mathematics, as well as molecular biology. This review will summarize current state-of-the-art scientific data on the use of in silico methods in toxicity testing, taking into account their shortcomings, and highlighting the strategies that should deliver consistent results, while covering the applications of in silico methods in preclinical trials and drug impurities toxicity testing.
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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: 1.3] [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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Li T, Tong W, Roberts R, Liu Z, Thakkar S. DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation. Chem Res Toxicol 2020; 34:550-565. [PMID: 33356151 DOI: 10.1021/acs.chemrestox.0c00374] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Drug-induced liver injury (DILI) is the most frequently reported single cause of safety-related withdrawal of marketed drugs. It is essential to identify drugs with DILI potential at the early stages of drug development. In this study, we describe a deep learning-powered DILI (DeepDILI) prediction model created by combining model-level representation generated by conventional machine learning (ML) algorithms with a deep learning framework based on Mold2 descriptors. We conducted a comprehensive evaluation of the proposed DeepDILI model performance by posing several critical questions: (1) Could the DILI potential of newly approved drugs be predicted by accumulated knowledge of early approved ones? (2) is model-level representation more informative than molecule-based representation for DILI prediction? and (3) could improved model explainability be established? For question 1, we developed the DeepDILI model using drugs approved before 1997 to predict the DILI potential of those approved thereafter. As a result, the DeepDILI model outperformed the five conventional ML algorithms and two state-of-the-art ensemble methods with a Matthews correlation coefficient (MCC) value of 0.331. For question 2, we demonstrated that the DeepDILI model's performance was significantly improved (i.e., a MCC improvement of 25.86% in test set) compared with deep neural networks based on molecule-based representation. For question 3, we found 21 chemical descriptors that were enriched, suggesting a strong association with DILI outcome. Furthermore, we found that the DeepDILI model has more discrimination power to identify the DILI potential of drugs belonging to the World Health Organization therapeutic category of 'alimentary tract and metabolism'. Moreover, the DeepDILI model based on Mold2 descriptors outperformed the ones with Mol2vec and MACCS descriptors. Finally, the DeepDILI model was applied to the recent real-world problem of predicting any DILI concern for potential COVID-19 treatments from repositioning drug candidates. Altogether, this developed DeepDILI model could serve as a promising tool for screening for DILI risk of compounds in the preclinical setting, and the DeepDILI model is publicly available through https://github.com/TingLi2016/DeepDILI.
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Affiliation(s)
- Ting Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas 72079, United States.,University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Joint Bioinformatics Program, Little Rock, Arkansas 72204, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Ruth Roberts
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas 72079, United States.,ApconiX Ltd., Alderley Park, Alderley Edge SK10 4TG, United Kingdom.,University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Shraddha Thakkar
- Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
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Artificial intelligence in the early stages of drug discovery. Arch Biochem Biophys 2020; 698:108730. [PMID: 33347838 DOI: 10.1016/j.abb.2020.108730] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/11/2020] [Accepted: 12/14/2020] [Indexed: 02/07/2023]
Abstract
Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.
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Dai W, Tang T, Dai Z, Shi D, Mo L, Zhang Y. Probing the Mechanism of Hepatotoxicity of Hexabromocyclododecanes through Toxicological Network Analysis. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:15235-15245. [PMID: 33190479 DOI: 10.1021/acs.est.0c03998] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The prediction and mechanism analysis of hepatotoxicity of contaminants, because of their various phenotypes and complex mechanisms, is still a key problem in environmental research. We applied a toxicological network analysis method to predict the hepatotoxicity of three hexabromocyclododecane (HBCD) diastereoisomers (α-HBCD, β-HBCD, and γ-HBCD) and explore their potential mechanisms. First, we collected the hepatotoxicity related genes and found that those genes were significantly localized in the human interactome. Therefore, these genes form a disease module of hepatotoxicity. We also collected targets of α-, β-, and γ-HBCD and found that their targets overlap with the hepatotoxicity disease module. Then, we trained a model to predict hepatotoxicity of three HBCD diastereoisomers based on the relationship between the hepatotoxicity disease module and targets of compounds. We found that 593 genes were significantly located in the hepatotoxicity disease module (Z = 11.9, p < 0.001) involved in oxidative stress, cellular immunity, and proliferation, and the accuracy of hepatotoxicity prediction of HBCD was 0.7095 ± 0.0193 and the recall score was 0.8355 ± 0.0352. HBCD mainly affects the core disease module genes to mediate the adenosine monophosphate-activated kinase, p38MAPK, PI3K/Akt, and TNFα pathways to regulate the immune reaction and inflammation. HBCD also induces the secretion of IL6 and STAT3 to lead hepatotoxicity by regulating NR3C1. This approach is transferable to other toxicity research studies of environmental pollutants.
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Affiliation(s)
- Weina Dai
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China
| | - Tiantian Tang
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China
| | - Zhenghua Dai
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China
- Chongqing Academy of Metrology and Quality Inspection, Chongqing 401123, China
| | - Da Shi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Lingyun Mo
- The Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control, College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China
- Technical Innovation Center for Mine Geological Environment Restoration Engineering in Shishan Area of South China, Ministry of Natural Resources, Nanning 530028, China
| | - Yonghong Zhang
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China
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41
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Tugcu G, Kırmızıbekmez H, Aydın A. The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum. Food Chem Toxicol 2020; 145:111663. [PMID: 32827561 DOI: 10.1016/j.fct.2020.111663] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 06/27/2020] [Accepted: 07/30/2020] [Indexed: 02/07/2023]
Abstract
Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.
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Affiliation(s)
- Gulcin Tugcu
- Yeditepe University, Faculty of Pharmacy, Department of Toxicology, 34755, Atasehir, Istanbul, Turkey
| | - Hasan Kırmızıbekmez
- Yeditepe University, Faculty of Pharmacy, Department of Pharmacognosy, 34755, Atasehir, Istanbul, Turkey
| | - Ahmet Aydın
- Yeditepe University, Faculty of Pharmacy, Department of Toxicology, 34755, Atasehir, Istanbul, Turkey.
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Zhou S, Li W, Tian M, Zhang N, Yang X, Li W, Peng Y, Zheng J. Metabolic Activation of Pirfenidone Mediated by Cytochrome P450s and Sulfotransferases. J Med Chem 2020; 63:8059-8068. [DOI: 10.1021/acs.jmedchem.9b02073] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shenzhi Zhou
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P. R. China
| | - Wei Li
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P. R. China
| | - Min Tian
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P. R. China
| | - Na Zhang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P. R. China
| | - Xiaojing Yang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P. R. China
| | - Weiwei Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang, Guizhou 550025, P. R. China
| | - Ying Peng
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P. R. China
| | - Jiang Zheng
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P. R. China
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang, Guizhou 550025, P. R. China
- Key Laboratory of Environmental Pollution, Monitoring and Disease Control (J.Z.), Ministry of Education, Guizhou Medical University, Guiyang 550025, P. R. China
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43
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Mechanism-based integrated assay systems for the prediction of drug-induced liver injury. Toxicol Appl Pharmacol 2020; 394:114958. [PMID: 32198022 DOI: 10.1016/j.taap.2020.114958] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 02/29/2020] [Accepted: 03/13/2020] [Indexed: 12/18/2022]
Abstract
Drug-induced liver injury (DILI) can cause hepatic failure and result in drug withdrawal from the market. It has host-related and compound-dependent mechanisms. Preclinical prediction of DILI risk is very challenging and safety assessments based on animals inadequately forecast human DILI risk. In contrast, human-derived in vitro cell culture-based models could improve DILI risk prediction accuracy. Here, we developed and validated an innovative method to assess DILI risk associated with various compounds. Fifty-four marketed and withdrawn drugs classified as DILI risks of "most concern", "less concern", and "no concern" were tested using a combination of four assays addressing mitochondrial injury, intrahepatic lipid accumulation, inhibition of bile canalicular network formation, and bile acid accumulation. Using the inhibitory potencies of the drugs evaluated in these in vitro tests, an algorithm with the highest available DILI risk prediction power was built by artificial neural network (ANN) analysis. It had an overall forecasting accuracy of 73%. We excluded the intrahepatic lipid accumulation assay to avoid overfitting. The accuracy of the algorithm in terms of predicting DILI risks was 62% when it was constructed by ANN but only 49% when it was built by the point-added scoring method. The final algorithm based on three assays made no DILI risk prediction errors such as "most concern " instead of "no concern" and vice-versa. Our mechanistic approach may accurately predict DILI risks associated with numerous candidate drugs.
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Shin HK, Kang MG, Park D, Park T, Yoon S. Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures. Front Pharmacol 2020; 11:67. [PMID: 32116729 PMCID: PMC7034408 DOI: 10.3389/fphar.2020.00067] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 01/23/2020] [Indexed: 12/18/2022] Open
Abstract
Drug-induced liver injury (DILI) is one of the major reasons for termination of drug development. Due to the importance of predicting DILI in early phases of drug development, diverse in silico models have been developed to filter out DILI-causing candidates before clinical study. However, no computational models have achieved sufficient prediction power for screening DILI in early phases because 1) drugs often cause liver injury through reactive metabolites, 2) different clinical outcomes of DILI have different mechanisms, and 3) the DILI label on drugs is not clearly defined. In this study, we developed binary classification models to predict drug-induced cholestasis, cirrhosis, hepatitis, and steatosis based on the structure of drugs and their metabolites. DILI-positive data was obtained from post-market reports of drugs and DILI-negative data from DILIrank, a database curated by the Food and Drug Administration (FDA). Support vector machine (SVM) and random forest (RF) were used in developing models with nine fingerprints and one 2D molecular descriptor calculated from drug (152 DILI-positives and 102 DILI-negatives) and drug metabolite (192 DILI-positives and 126 DILI-negatives) structures. Models were developed according to Organisation for Economic Co-operation and Development (OECD) guidelines for quantitative structure-activity relationship (QSAR) validation. Internal and external validation was performed with a randomization test in order to thoroughly examine model predictability and avoid random correlation between structural features and adverse outcomes. The applicability domain was defined with a leverage method for reliable prediction of new chemicals. The best models for each liver disease were selected based on external validation results from drugs (cholestasis: 70%, cirrhosis: 90%, hepatitis: 83%, and steatosis: 85%) and drug metabolites (cholestasis: 86%, cirrhosis: 88%, hepatitis: 86%, and steatosis: 83%) with applicability domain analysis. Compiled data sets were further exploited to derive privileged substructures that were more frequent in DILI-positive sets compared to DILI-negative sets and in drug metabolite structures compared to drug structures with a Morgan fingerprint level 2.
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Affiliation(s)
- Hyun Kil Shin
- Toxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South Korea
| | - Myung-Gyun Kang
- Toxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South Korea
| | - Daeui Park
- Toxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South Korea.,Department of Human and Environmental Toxicology, University of Science and Technology, Daejeon, South Korea
| | - Tamina Park
- Toxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South Korea.,Department of Human and Environmental Toxicology, University of Science and Technology, Daejeon, South Korea
| | - Seokjoo Yoon
- Toxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, South Korea.,Department of Human and Environmental Toxicology, University of Science and Technology, Daejeon, South Korea
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Xue R, Liao J, Shao X, Han K, Long J, Shao L, Ai N, Fan X. Prediction of Adverse Drug Reactions by Combining Biomedical Tripartite Network and Graph Representation Model. Chem Res Toxicol 2019; 33:202-210. [PMID: 31777246 DOI: 10.1021/acs.chemrestox.9b00238] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
As one of the primary contributors to high clinical attrition rates of drugs, toxicity evaluation is of critical significance to new drug discovery. Unsurprisingly, a vast number of computational methods have been developed at various stages of development pipeline to evaluate potential adverse drug reactions (ADRs). Despite previous success of these methods on individual ADR or certain drug family, there are great challenges to toxicity evaluation. In this study, a novel strategy was developed to predict the drug-ADR associations by combining deep learning and the biomedical tripartite network. This heterogeneous network contains biomedical linked data of three entities, for example, drugs, targets, and ADRs. For the first time, GraRep, a deep learning method for distributed representations, is introduced to learn graph representations and identify hidden features from the tripartite network which are further used for ADR prediction. Through this approach, drug-ADR associations could possibly be discovered from a systemic perspective. The accuracy of our method is 0.95 based on internal resource validation and 0.88 based on external resource validation. Moreover, our results show the prediction accuracy using the tripartite network is better than the one with bipartite network, suggesting the model performance can be improved with further enrichment on information. According to the result of 10-fold cross validation, the deep learning model outperforms two traditional methods (topology-based measures and chemical structure-based measures). Additionally, predictive models are also constructed using other deep learning methods, and comparable results are achieved. In summary, the biomedical tripartite network-based deep learning model proposed here proves to offer a promising solution for prediction of ADRs.
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Affiliation(s)
- Rui Xue
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Ke Han
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Jingbo Long
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Li Shao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine , Zhejiang University , 79 Qingchun Road , Hangzhou , 310003 , China
| | - Ni Ai
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
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Tachibana K, Kass GE, Ono A, Yamada T, Tong W, Doerge DR, Yamazoe Y. A Summary Report of FSCJ Workshop "Future Challenges and Opportunities in Developing Methodologies for Improved Human Risk Assessments". Food Saf (Tokyo) 2019; 7:83-89. [PMID: 31998592 PMCID: PMC6957455 DOI: 10.14252/foodsafetyfscj.2018017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 10/08/2019] [Indexed: 11/21/2022] Open
Abstract
This is a summary report of FSCJ (Food Safety Commission of Japan) workshop entitled "Future Challenges and Opportunities in Developing Methodologies for Improved Human Risk Assessments, which held in November 2018. Scientific advancements have facilitated the development of new methods for chemical risk assessments with the expansion of toxicological databases. They are promising tools to overcome challenges, such as situations of data insufficiency, estimation of internal exposure and prediction of hazard, and enable us to improve our human health risk assessment in food safety. In this review, current understandings on developments in chemical risk assessments, especially focusing on Threshold of Toxicological Concern (TTC) approach, non-testing and in-silico approaches (e.g. read-across), and physiologically based pharmacokinetics (PBPK) modeling are discussed as possible promising tools. It also discusses future challenges and opportunities regarding social environment buildings in which all stakeholders including scientific experts, risk managers and consumers are able to accept these new risk assessment technologies. International collaboration would increase and enhance the efficiency in forming innovative ideas and in translating them into regulatory practices. It would strengthen technical capacity of experts who contribute to regulatory decisions and also promote acceptance of new methodologies among stakeholders. Cross-sectional collaboration such as making good use of human data of pharmaceutical drugs will facilitate a development of fresh tools for food safety domains. Once a new methodology is recognized in risk assessment agencies as implementable, it needs to be acknowledged and accepted by wider range of different stakeholders. Such stakeholders include scientific experts who conduct risk assessment for the risk assessment agencies, food industries and consumers. Transparency in the risk assessment work performed by regulatory agencies should strengthen their credibility and promote the acceptance of risk assessment including the new methodologies used in it. At the same time, efforts should be continued by regulatory agencies to further communicate with consumers about the concept of risk-based assessment as well as the concept of uncertainty.
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Affiliation(s)
- Kaoruko Tachibana
- Department of Environmental Medicine, Kochi Medical School, Kohasu, Oko-cho, Nankoku-shi, Kochi
783-8505, Japan
- Food Safety Commission of Japan, Cabinet Office, Government of Japan, Akasaka Park Bldg, 22F, 5-2-20 Akasaka, Minatoku, Tokyo 107-6122, Japan
| | - George E.N. Kass
- European Food Safety Authority, Via Carlo Magno 1A, 43126 Parma, Italy
| | - Atsushi Ono
- Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 1-1-1 Tsushimanaka Kita-ku, Okayama-shi, Okayama 700-8530, Japan
| | - Takashi Yamada
- National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-shi, Kanagawa 210-0821, Japan
| | - Weida Tong
- National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, United States of America
| | - Daniel R. Doerge
- National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, United States of America
| | - Yasushi Yamazoe
- Food Safety Commission of Japan, Cabinet Office, Government of Japan, Akasaka Park Bldg, 22F, 5-2-20 Akasaka, Minatoku, Tokyo 107-6122, Japan
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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: 59] [Impact Index Per Article: 11.8] [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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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: 2.4] [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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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: 52] [Impact Index Per Article: 10.4] [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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50
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Aleo MD, Aubrecht J, D Bonin P, Burt DA, Colangelo J, Luo L, Schomaker S, Swiss R, Kirby S, C Rigdon G, Dua P. Phase I study of PF‐04895162, a Kv7 channel opener, reveals unexpected hepatotoxicity in healthy subjects, but not rats or monkeys: clinical evidence of disrupted bile acid homeostasis. Pharmacol Res Perspect 2019; 7:e00467. [PMID: 30784208 PMCID: PMC6370995 DOI: 10.1002/prp2.467] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 01/11/2019] [Accepted: 01/13/2019] [Indexed: 12/28/2022] Open
Abstract
During a randomized Phase 1 clinical trial the drug candidate, PF‐04895162 (ICA‐105665), caused transaminase elevations (≥grade 1) in six of eight healthy subjects treated at 300 mg twice daily for 2‐weeks (NCT01691274). This was unexpected since studies in rats (<6 months) and cynomolgus monkeys (<9 months) treated up to 100 mg/kg/day did not identify the liver as a target organ. Mechanistic studies showed PF‐04895162 had low cytotoxic potential in human hepatocytes, but inhibited liver mitochondrial function and bile salt export protein (BSEP) transport. Clinical relevance of these postulated mechanisms of liver injury was explored in three treated subjects that consented to analysis of residual pharmacokinetic plasma samples. Compared to a nonresponder, two subjects with transaminase elevations displayed higher levels of miRNA122 and total/conjugated bile acid species, whereas one demonstrated impaired postprandial clearance of systemic bile acids. Elevated taurine and glycine conjugated to unconjugated bile acid ratios were observed in two subjects, one before the onset of elevated transaminases. Based on the affinity of conjugated bile acid species for transport by BSEP, the profile of plasma conjugated/unconjugated bile acid species was consistent with inhibition of BSEP. These data collectively suggest that the human liver injury by PF‐04895162 was due to alterations in bile acid handling driven by dual BSEP/mitochondrial inhibition, two important risk factors associated with drug‐induced liver injury in humans. Alterations in systemic bile acid composition were more important than total bile acids in the manifestation of clinical liver injury and may be a very early biomarker of BSEP inhibition.
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Affiliation(s)
- Michael D Aleo
- Investigative Toxicology, Drug Safety Research and Development, Pfizer Inc., Groton, Connecticut
| | - Jiri Aubrecht
- Safety Biomarkers, Drug Safety Research and Development, Pfizer Inc., Groton, Connecticut
| | - Paul D Bonin
- Medicine Design, Primary Pharmacology Group, Pfizer Inc., Groton, Connecticut
| | - Deborah A Burt
- Safety Biomarkers, Drug Safety Research and Development, Pfizer Inc., Groton, Connecticut
| | - Jennifer Colangelo
- Safety Biomarkers, Drug Safety Research and Development, Pfizer Inc., Groton, Connecticut
| | - Lina Luo
- Safety Biomarkers, Drug Safety Research and Development, Pfizer Inc., Groton, Connecticut
| | - Shelli Schomaker
- Safety Biomarkers, Drug Safety Research and Development, Pfizer Inc., Groton, Connecticut
| | - Rachel Swiss
- Compound Safety Prediction, Worldwide Medicinal Chemistry, Pfizer Inc., Groton, Connecticut
| | - Simon Kirby
- Global Biometrics and Data Management, Pfizer Inc., Cambridge, UK
| | - Greg C Rigdon
- Neusentis Research Unit, Pfizer Inc., Durham, North Carolina
| | - Pinky Dua
- Clinical Pharmacology, Early Clinical Development, Pfizer Inc., Cambridge, UK
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