1
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Lucena MI, Villanueva-Paz M, Alvarez-Alvarez I, Aithal GP, Björnsson ES, Cakan-Akdogan G, Cubero FJ, Esteves F, Falcon-Perez JM, Fromenty B, Garcia-Ruiz C, Grove JI, Konu O, Kranendonk M, Kullak-Ublick GA, Miranda JP, Remesal-Doblado A, Sancho-Bru P, Nelson L, Andrade RJ, Daly AK, Fernandez-Checa JC. Roadmap to DILI research in Europe. A proposal from COST action ProEuroDILINet. Pharmacol Res 2024; 200:107046. [PMID: 38159783 DOI: 10.1016/j.phrs.2023.107046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
In the current article the aims for a constructive way forward in Drug-Induced Liver Injury (DILI) are to highlight the most important priorities in research and clinical science, therefore supporting a more informed, focused, and better funded future for European DILI research. This Roadmap aims to identify key challenges, define a shared vision across all stakeholders for the opportunities to overcome these challenges and propose a high-quality research program to achieve progress on the prediction, prevention, diagnosis and management of this condition and impact on healthcare practice in the field of DILI. This will involve 1. Creation of a database encompassing optimised case report form for prospectively identified DILI cases with well-characterised controls with competing diagnoses, biological samples, and imaging data; 2. Establishing of preclinical models to improve the assessment and prediction of hepatotoxicity in humans to guide future drug safety testing; 3. Emphasis on implementation science and 4. Enhanced collaboration between drug-developers, clinicians and regulatory scientists. This proposed operational framework will advance DILI research and may bring together basic, applied, translational and clinical research in DILI.
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Affiliation(s)
- M I Lucena
- Servicios de Aparato Digestivo y Farmacología Clínica, Hospital Universitario Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Universidad de Málaga, Málaga, Spain; Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain; Plataforma de Investigación Clínica y Ensayos Clínicos UICEC-IBIMA, Plataforma ISCIII de Investigación Clínica, Madrid, Spain.
| | - M Villanueva-Paz
- Servicios de Aparato Digestivo y Farmacología Clínica, Hospital Universitario Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Universidad de Málaga, Málaga, Spain; Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
| | - I Alvarez-Alvarez
- Servicios de Aparato Digestivo y Farmacología Clínica, Hospital Universitario Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Universidad de Málaga, Málaga, Spain; Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
| | - G P Aithal
- Nottingham Digestive Diseases Centre, Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, United Kingdom
| | - E S Björnsson
- Faculty of Medicine, University of Iceland, Department of Gastroenterology and Hepatology, Landspitali University Hospital, Reykjavik, Iceland
| | - G Cakan-Akdogan
- Izmir Biomedicine and Genome Center, Izmir, Turkey. Department of Medical Biology, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey
| | - F J Cubero
- Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain; Department of Immunology, Ophthalmology and ORL, Complutense University School of Medicine, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - F Esteves
- Center for Toxicogenomics and Human Health (ToxOmics), NMS | FCM, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - J M Falcon-Perez
- Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain; Exosomes Laboratory, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Derio, Bizkaia, 48160, Spain. IKERBASQUE, Basque Foundation for Science, Bilbao, Bizkaia 48009, Spain
| | - B Fromenty
- INSERM, Univ Rennes, INRAE, Institut NUMECAN (Nutrition Metabolisms and Cancer) UMR_A 1341, UMR_S 1317, F-35000 Rennes, France
| | - C Garcia-Ruiz
- Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. University of Barcelona, Barcelona, Spain; Department of Cell Death and Proliferation, Institute of Biomedical Research of Barcelona (IIBB), CSIC, Barcelona, Spain
| | - J I Grove
- Nottingham Digestive Diseases Centre, Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, United Kingdom
| | - O Konu
- Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University, Ankara, Turkey; Interdisciplinary Neuroscience Program, Bilkent University, Ankara, Turkey; UNAM-Institute of Materials Science and Nanotechnology, Bilkent University, Ankara, Turkey
| | - M Kranendonk
- Center for Toxicogenomics and Human Health (ToxOmics), NMS | FCM, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - G A Kullak-Ublick
- Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; CMO & Patient Safety, Global Drug Development, Novartis Pharma, Basel, Switzerland
| | - J P Miranda
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal
| | - A Remesal-Doblado
- Servicios de Aparato Digestivo y Farmacología Clínica, Hospital Universitario Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Universidad de Málaga, Málaga, Spain
| | - P Sancho-Bru
- Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. University of Barcelona, Barcelona, Spain
| | - L Nelson
- Institute for Bioengineering, School of Engineering, Faraday Building, The University of Edinburgh, Scotland, UK
| | - R J Andrade
- Servicios de Aparato Digestivo y Farmacología Clínica, Hospital Universitario Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Universidad de Málaga, Málaga, Spain; Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
| | - A K Daly
- Translational & Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - J C Fernandez-Checa
- Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. University of Barcelona, Barcelona, Spain; Department of Cell Death and Proliferation, Institute of Biomedical Research of Barcelona (IIBB), CSIC, Barcelona, Spain; Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
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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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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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4
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Cavasotto CN, Scardino V. Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point. ACS OMEGA 2022; 7:47536-47546. [PMID: 36591139 PMCID: PMC9798519 DOI: 10.1021/acsomega.2c05693] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/28/2022] [Indexed: 05/29/2023]
Abstract
Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico. Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted.
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Affiliation(s)
- Claudio N. Cavasotto
- Computational
Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones
en Medicina Traslacional (IIMT), CONICET-Universidad
Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Austral
Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Facultad
de Ciencias Biomédicas, Facultad de Ingenierá, Universidad Austral, Pilar, B1630FHB Buenos
Aires, Argentina
| | - Valeria Scardino
- Austral
Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Meton
AI, Inc., Wilmington, Delaware 19801, United
States
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5
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Kong W, Huang W, Peng C, Zhang B, Duan G, Ma W, Huang Z. Multiple machine learning methods aided virtual screening of Na V 1.5 inhibitors. J Cell Mol Med 2022; 27:266-276. [PMID: 36573431 PMCID: PMC9843531 DOI: 10.1111/jcmm.17652] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/30/2022] [Accepted: 12/06/2022] [Indexed: 12/28/2022] Open
Abstract
Nav 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Nav 1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Nav 1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Nav 1.5 ion channel and key privileged substructures with high affinity were also extracted.
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Affiliation(s)
- Weikaixin Kong
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina,Institute for Molecular Medicine Finland (FIMM)HiLIFE, University of HelsinkiHelsinkiFinland,Institute Sanqu Technology (Hangzhou) Co., Ltd.HangzhouChina
| | - Weiran Huang
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina
| | - Chao Peng
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina
| | - Bowen Zhang
- ComMedX (Computational Medicine Beijing Co., Ltd.)BeijingChina
| | - Guifang Duan
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina
| | - Weining Ma
- Department of NeurologyShengjing Hospital affiliated to China Medical UniversityShenyangChina
| | - Zhuo Huang
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina,State Key Laboratory of Natural and Biomimetic Drugs, Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina
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6
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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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7
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Zhong C, Ai J, Yang Y, Ma F, Sun W. Small Molecular Drug Screening Based on Clinical Therapeutic Effect. Molecules 2022; 27:molecules27154807. [PMID: 35956770 PMCID: PMC9369618 DOI: 10.3390/molecules27154807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.
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Affiliation(s)
| | | | | | | | - Wei Sun
- Correspondence: ; Tel.: +86-10-64445826
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8
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Hua Y, Cui X, Liu B, Shi Y, Guo H, Zhang R, Li X. SApredictor: An Expert System for Screening Chemicals Against Structural Alerts. Front Chem 2022; 10:916614. [PMID: 35910729 PMCID: PMC9326022 DOI: 10.3389/fchem.2022.916614] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be “black box”, which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.
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Affiliation(s)
- Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Bo Liu
- Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Yinping Shi
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
- Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
- *Correspondence: Xiao Li, , , orcid.org/0000-0002-1148-9898
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9
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Yu Z, Wu Z, Li W, Liu G, Tang Y. ADENet: a novel network-based inference method for prediction of drug adverse events. Brief Bioinform 2022; 23:6510157. [PMID: 35039845 DOI: 10.1093/bib/bbab580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/02/2021] [Accepted: 12/19/2021] [Indexed: 11/13/2022] Open
Abstract
Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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10
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Deng X, Wu Y, Xu H, Yan J, Liu H, Zhang B. Recent research progress in galactose-based fluorescent probes for detection of biomarkers of liver diseases. Chem Commun (Camb) 2022; 58:12518-12527. [DOI: 10.1039/d2cc04180d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This highlight illustrates the challenges and latest progress in galactose-based fluorescent probes for early diagnosis of liver diseases.
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Affiliation(s)
- Xiaojing Deng
- College of Medical Laboratory, Dalian Medical University, Dalian 116044, China
| | - Yingxu Wu
- College of Medical Laboratory, Dalian Medical University, Dalian 116044, China
| | - Hu Xu
- Advanced Institute for Medical Sciences, Dalian Medical University, Dalian 16044, China
| | - Jiawei Yan
- College of Medical Laboratory, Dalian Medical University, Dalian 116044, China
| | - Huanying Liu
- School of Mechanical and Power Engineering, Dalian Ocean University, Dalian 116023, China
| | - Boyu Zhang
- College of Medical Laboratory, Dalian Medical University, Dalian 116044, China
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11
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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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12
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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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13
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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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14
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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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15
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Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
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16
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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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17
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Huang X, Tang F, Hua Y, Li X. In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods. Chem Biol Drug Des 2021; 98:248-257. [PMID: 34013639 DOI: 10.1111/cbdd.13894] [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] [Received: 02/20/2021] [Revised: 04/27/2021] [Accepted: 05/15/2021] [Indexed: 12/27/2022]
Abstract
Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment.
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Affiliation(s)
- Xin Huang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Fang Tang
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,School of Pharmacy, Shandong First Medical University (Shandong Academy of Medical Science), Taian, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
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18
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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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19
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Wang MWH, Goodman JM, Allen TEH. Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. Chem Res Toxicol 2020; 34:217-239. [PMID: 33356168 DOI: 10.1021/acs.chemrestox.0c00316] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.
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Affiliation(s)
- Marcus W H Wang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.,MRC Toxicology Unit, University of Cambridge, Hodgkin Building, Lancaster Road, Leicester LE1 7HB, United Kingdom
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20
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Allen TEH, Wedlake AJ, Gelžinytė E, Gong C, Goodman JM, Gutsell S, Russell PJ. Neural network activation similarity: a new measure to assist decision making in chemical toxicology. Chem Sci 2020; 11:7335-7348. [PMID: 34123016 PMCID: PMC8159362 DOI: 10.1039/d0sc01637c] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 06/23/2020] [Indexed: 12/03/2022] Open
Abstract
Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093 and ROC-AUC 0.96 ± 0.04). A new molecular similarity measure, Neural Network Activation Similarity, has been developed, based on signal propagation through the network. This is complementary to standard Tanimoto similarity, and the combined use increases confidence in the computer's prediction of activity for new chemicals by providing a greater understanding of the underlying justification. The in silico prediction of these human molecular initiating events is central to the future of chemical safety risk assessment and improves the efficiency of safety decision making.
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Affiliation(s)
- Timothy E H Allen
- MRC Toxicology Unit, University of Cambridge Hodgkin Building, Lancaster Road Leicester LE1 7HB UK
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
| | - Andrew J Wedlake
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
| | - Elena Gelžinytė
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
| | - Charles Gong
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
| | - Steve Gutsell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park Sharnbrook Bedfordshire MK44 1LQ UK
| | - Paul J Russell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park Sharnbrook Bedfordshire MK44 1LQ UK
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21
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Kong W, Tu X, Huang W, Yang Y, Xie Z, Huang Z. Prediction and Optimization of NaV1.7 Sodium Channel Inhibitors Based on Machine Learning and Simulated Annealing. J Chem Inf Model 2020; 60:2739-2753. [DOI: 10.1021/acs.jcim.9b01180] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Weikaixin Kong
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University Health Science Center, 38 Xueyuan Lu,
Haidian district, Beijing 100191, China
| | - Xinyu Tu
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University Health Science Center, 38 Xueyuan Lu,
Haidian district, Beijing 100191, China
| | - Weiran Huang
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University Health Science Center, 38 Xueyuan Lu,
Haidian district, Beijing 100191, China
| | - Yang Yang
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, West Lafayette, Indiana 47907, United States
| | - Zhengwei Xie
- Peking University International Cancer Institute and Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, 38 Xueyuan Lu, Haidian district, Beijing 100191, China
| | - Zhuo Huang
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University Health Science Center, 38 Xueyuan Lu,
Haidian district, Beijing 100191, China
- State Key Laboratory of Natural and Biomimetic Drugs, Department of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University Health Science Center, 38 Xueyuan Lu,
Haidian district, Beijing 100191, China
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22
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Mora JR, Marrero-Ponce Y, García-Jacas CR, Suarez Causado A. Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches. Chem Res Toxicol 2020; 33:1855-1873. [PMID: 32406679 DOI: 10.1021/acs.chemrestox.0c00030] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Drug-induced liver injury (DILI) is a key safety issue in the drug discovery pipeline and a regulatory concern. Thus, many in silico tools have been proposed to improve the hepatotoxicity prediction of organic-type chemicals. Here, classifiers for the prediction of DILI were developed by using QuBiLS-MAS 0-2.5D molecular descriptors and shallow machine learning techniques, on a training set composed of 1075 molecules. The best ensemble model build, E13, was obtained with good statistical parameters for the learning series, namely, the following: accuracy = 0.840, sensibility = 0.890, specificity = 0.761, Matthew's correlation coefficient = 0.660, and area under the ROC curve = 0.904. The model was also satisfactorily evaluated with Y-scrambling test, and repeated k-fold cross-validation and repeated k-holdout validation. In addition, an exhaustive external validation was also carried out by using two test sets and five external test sets, with an average accuracy value equal to 0.854 (±0.062) and a coverage equal to 98.4% according to its applicability domain. A statistical comparison of the performance of the E13 model, with regard to results and tools (e.g., Padel DDPredictor Software, Deep Learning DILIserver, and Vslead) reported in the literature, was also performed. In general, E13 presented the best global performance in all experiments. The sum of the ranking differences procedure provided a very similar grouping pattern to that of the M-ANOVA statistical analysis, where E13 was identified as the best model for DILI predictions. A noncommercial and fully cross-platform software for the DILI prediction was also developed, which is freely available at http://tomocomd.com/apps/ptoxra. This software was used for the screening of seven data sets, containing natural products, leads, toxic materials, and FDA approved drugs, to assess the usefulness of the QSAR models in the DILI labeling of organic substances; it was found that 50-92% of the evaluated molecules are positive-DILI compounds. All in all, it can be stated that the E13 model is a relevant method for the prediction of DILI risk in humans, as it shows the best results among all of the methods analyzed.
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Affiliation(s)
- Jose R Mora
- Grupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Universidad San Francisco de Quito (USFQ), Quito 17-1200-841, Ecuador.,Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y Vía Interoceánica, Quito 17-1200-841, Ecuador
| | - Yovani Marrero-Ponce
- Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y Vía Interoceánica, Quito 17-1200-841, Ecuador.,Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, and Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y vía Interoceánica, Quito, Pichincha 170157, Ecuador
| | - César R García-Jacas
- Cátedras Conacyt-Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California 22860, México
| | - Amileth Suarez Causado
- Grupo de Investigación Prometeus & Biomedicina Aplicada a las Ciencias Clínicas, Área de Bioquímica, Campus de Zaragocilla, Facultad de Medicina, Universidad de Cartagena, Cartagena de Indias 130001, Colombia
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23
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Cui X, Yang R, Li S, Liu J, Wu Q, Li X. Modeling and insights into molecular basis of low molecular weight respiratory sensitizers. Mol Divers 2020; 25:847-859. [PMID: 32166484 DOI: 10.1007/s11030-020-10069-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 03/03/2020] [Indexed: 01/10/2023]
Abstract
Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.
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Affiliation(s)
- Xueyan Cui
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Rui Yang
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Siwen Li
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Juan Liu
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Qiuyun Wu
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Xiao Li
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China. .,Department of Clinical pharmacy, The First Affiliated Hospital of Shandong First Medical University, Shandong First Medical University, Jinan, 250014, China.
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24
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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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25
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Cui X, Liu J, Zhang J, Wu Q, Li X. In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts. J Appl Toxicol 2019; 39:1224-1232. [DOI: 10.1002/jat.3808] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 03/13/2019] [Accepted: 03/17/2019] [Indexed: 12/21/2022]
Affiliation(s)
- Xueyan Cui
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
| | - Juan Liu
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
| | - Jinfeng Zhang
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
| | - Qiuyun Wu
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
| | - Xiao Li
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
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26
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Saini N, Bakshi S, Sharma S. In-silico approach for drug induced liver injury prediction: Recent advances. Toxicol Lett 2018; 295:288-295. [PMID: 29981923 DOI: 10.1016/j.toxlet.2018.06.1216] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 06/06/2018] [Accepted: 06/25/2018] [Indexed: 02/07/2023]
Abstract
Drug induced liver injury (DILI) is the prime cause of liver disfunction which may lead to mild non-specific symptoms to more severe signs like hepatitis, cholestasis, cirrhosis and jaundice. Not only the prescription medications, but the consumption of herbs and health supplements have also been reported to cause these adverse reactions resulting into high mortality rates and post marketing withdrawal of drugs. Due to the continuously increasing DILI incidences in recent years, robust prediction methods with high accuracy, specificity and sensitivity are of priority. Bioinformatics is the emerging field of science that has been used in the past few years to explore the mechanisms of DILI. The major emphasis of this review is the recent advances of in silico tools for the diagnostic and therapeutic interventions of DILI. These tools have been developed and widely used in the past few years for the prediction of pathways induced from both hepatotoxic as well as hepatoprotective Chinese drugs and for the identification of DILI specific biomarkers for prognostic purpose. In addition to this, advanced machine learning models have been developed for the classification of drugs into DILI causing and non-DILI causing. Moreover, development of 3 class models over 2 class offers better understanding of multi-class DILI risks and at the same time providing authentic prediction of toxicity during drug designing before clinical trials.
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Affiliation(s)
- Neha Saini
- Department of Biochemistry, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
| | - Shikha Bakshi
- Department of Biochemistry, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
| | - Sadhna Sharma
- Department of Biochemistry, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
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