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Meng L, Zhou B, Liu H, Chen Y, Yuan R, Chen Z, Luo S, Chen H. Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174201. [PMID: 38936709 DOI: 10.1016/j.scitotenv.2024.174201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 06/29/2024]
Abstract
Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.
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Affiliation(s)
- Lingxuan Meng
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Beihai Zhou
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haijun Liu
- School of Resources and Environment, Anqing Normal University, Anqing, China.
| | - Yuefang Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Rongfang Yuan
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhongbing Chen
- Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic.
| | - Shuai Luo
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Huilun Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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2
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Liu J, Khan MKH, Guo W, Dong F, Ge W, Zhang C, Gong P, Patterson TA, Hong H. Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study. Expert Opin Drug Metab Toxicol 2024; 20:665-684. [PMID: 38968091 DOI: 10.1080/17425255.2024.2377593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 06/26/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identifying cardiac safety issues. Various QSAR models exist, but their performance varies. Ongoing improvements show promise, necessitating continued efforts to enhance accuracy using emerging deep learning algorithms in predicting potential hERG blockade. STUDY DESIGN AND METHOD Using a large training dataset, six individual QSAR models were developed. Additionally, three ensemble models were constructed. All models were evaluated using 10-fold cross-validations and two external datasets. RESULTS The 10-fold cross-validations resulted in Mathews correlation coefficient (MCC) values from 0.682 to 0.730, surpassing the best-reported model on the same dataset (0.689). External validations yielded MCC values from 0.520 to 0.715 for the first dataset, exceeding those of previously reported models (0-0.599). For the second dataset, MCC values fell between 0.025 and 0.215, aligning with those of reported models (0.112-0.220). CONCLUSIONS The developed models can assist the pharmaceutical industry and regulatory agencies in predicting hERG blockage activity, thereby enhancing safety assessments and reducing the risk of adverse cardiac events associated with new drug candidates.
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Affiliation(s)
- Jie Liu
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Md Kamrul Hasan Khan
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Wenjing Guo
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Fan Dong
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Weigong Ge
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Ping Gong
- Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
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3
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Zhu Z, Wu R, Luo M, Zeng L, Zhang D, Hu N, Hu Y, Li Y. Two-Dimensional Deep Learning Frameworks for Drug-Induced Cardiotoxicity Detection. ACS Sens 2024; 9:3316-3326. [PMID: 38842187 DOI: 10.1021/acssensors.4c00654] [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: 06/07/2024]
Abstract
The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in vitro models, have severe limitations in accurate identification of cardiotoxic substances. Pioneering a paradigm shift from these conventional techniques, our study presents two deep learning-based frameworks, STFT-CNN and SST-CNN, to assess cardiotoxicity with markedly improved accuracy and reliability. Leveraging the power of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a more human-relevant cell model, we record mechanical beating signals through impedance measurements. These temporal signals were converted into enriched two-dimensional representations through advanced transformation techniques, specifically short-time Fourier transform (STFT) and synchro-squeezing transform (SST). These transformed data are fed into the proposed frameworks for comprehensive analysis, including drug type classification, concentration classification, cardiotoxicity classification, and new drug identification. Compared to traditional models like recurrent neural network (RNN) and 1-dimensional convolutional neural network (1D-CNN), SST-CNN delivered an impressive test accuracy of 98.55% in drug type classification and 99% in distinguishing cardiotoxic and noncardiotoxic drugs. Its feasibility is further highlighted with a stellar 98.5% average accuracy for classification of various concentrations, and the superiority of our proposed frameworks underscores their promise in revolutionizing drug safety assessments. With a potential for scalability, they represent a major leap in drug safety assessments, offering a pathway to more robust, efficient, and human-relevant cardiotoxicity evaluations.
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Affiliation(s)
- Zhijing Zhu
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou 310015, China
| | - Ruochen Wu
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Ma Luo
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou 311121, China
| | - Linghui Zeng
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou 310015, China
| | - Diming Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou 311121, China
| | - Ning Hu
- Department of Chemistry, Zhejiang-Israel Joint Laboratory of Self-Assembling Functional Materials, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310058, China
- General Surgery Department, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Ye Hu
- Nanjing Institute for Food and Drug Control, Nanjing, Jiangsu 211198, China
| | - Ying Li
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China
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4
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Mori M, Cocorullo M, Tresoldi A, Cazzaniga G, Gelain A, Stelitano G, Chiarelli LR, Tomaiuolo M, Delre P, Mangiatordi GF, Garofalo M, Cassetta A, Covaceuszach S, Villa S, Meneghetti F. Structural basis for specific inhibition of salicylate synthase from Mycobacterium abscessus. Eur J Med Chem 2024; 265:116073. [PMID: 38169270 DOI: 10.1016/j.ejmech.2023.116073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/15/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024]
Abstract
Blocking iron uptake and metabolism has been emerging as a promising therapeutic strategy for the development of novel antimicrobial compounds. Like all mycobacteria, M. abscessus (Mab) has evolved several countermeasures to scavenge iron from host carrier proteins, including the production of siderophores, which play a crucial role in these processes. In this study, we solved, for the first time, the crystal structure of Mab-SaS, the first enzyme involved in the biosynthesis of siderophores. Moreover, we screened a small, focused library and identified a compound exhibiting a potent inhibitory effect against Mab-SaS (IC50 ≈ 2 μM). Its binding mode was investigated by means of Induced Fit Docking simulations, performed on the crystal structure presented herein. Furthermore, cytotoxicity data and pharmacokinetic predictions revealed the safety and drug-likeness of this class of compounds. Finally, the crystallographic data were used to optimize the model for future virtual screening campaigns. Taken together, the findings of our study pave the way for the identification of potent Mab-SaS inhibitors, based on both established and unexplored chemotypes.
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Affiliation(s)
- Matteo Mori
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
| | - Mario Cocorullo
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via A. Ferrata 9, 27100, Pavia, Italy
| | - Andrea Tresoldi
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
| | - Giulia Cazzaniga
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
| | - Arianna Gelain
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
| | - Giovanni Stelitano
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via A. Ferrata 9, 27100, Pavia, Italy
| | - Laurent R Chiarelli
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via A. Ferrata 9, 27100, Pavia, Italy
| | - Martina Tomaiuolo
- Institute of Crystallography, National Research Council, Trieste Outstation, Area Science Park - Basovizza, S.S.14 - Km. 163.5, 34149, Trieste, Italy
| | - Pietro Delre
- Institute of Crystallography, National Research Council, Via G. Amendola 122/o, 70126, Bari, Italy
| | - Giuseppe F Mangiatordi
- Institute of Crystallography, National Research Council, Via G. Amendola 122/o, 70126, Bari, Italy
| | - Mariangela Garofalo
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via F. Marzolo 5, 35131, Padova, Italy
| | - Alberto Cassetta
- Institute of Crystallography, National Research Council, Trieste Outstation, Area Science Park - Basovizza, S.S.14 - Km. 163.5, 34149, Trieste, Italy
| | - Sonia Covaceuszach
- Institute of Crystallography, National Research Council, Trieste Outstation, Area Science Park - Basovizza, S.S.14 - Km. 163.5, 34149, Trieste, Italy.
| | - Stefania Villa
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy.
| | - Fiorella Meneghetti
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
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Lomuscio M, Abate C, Alberga D, Laghezza A, Corriero N, Colabufo NA, Saviano M, Delre P, Mangiatordi GF. AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis. Mol Pharm 2024; 21:864-872. [PMID: 38134445 PMCID: PMC10853961 DOI: 10.1021/acs.molpharmaceut.3c00964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/24/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023]
Abstract
Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.
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Affiliation(s)
| | - Carmen Abate
- CNR—Institute
of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
- Department
of Pharmacy-Pharmaceutical Sciences, University
of the Studies of Bari “Aldo Moro”, Via E.Orabona 4, 70125 Bari, Italy
| | - Domenico Alberga
- CNR—Institute
of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Antonio Laghezza
- Department
of Pharmacy-Pharmaceutical Sciences, University
of the Studies of Bari “Aldo Moro”, Via E.Orabona 4, 70125 Bari, Italy
| | - Nicola Corriero
- CNR—Institute
of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Nicola Antonio Colabufo
- Department
of Pharmacy-Pharmaceutical Sciences, University
of the Studies of Bari “Aldo Moro”, Via E.Orabona 4, 70125 Bari, Italy
| | - Michele Saviano
- CNR—Institute
of Crystallography, Via
Vivaldi 43, 81100 Caserta, Italy
| | - Pietro Delre
- CNR—Institute
of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
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6
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Chen S, Yang Y, Yuan Y, Bo Liu. Targeting PIM kinases in cancer therapy: An update on pharmacological small-molecule inhibitors. Eur J Med Chem 2024; 264:116016. [PMID: 38071792 DOI: 10.1016/j.ejmech.2023.116016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/15/2023] [Accepted: 11/28/2023] [Indexed: 12/30/2023]
Abstract
PIM kinases, a serine/threonine kinase family with three isoforms, has been well-known to participate in multiple physiological processes by phosphorylating various downstream targets. Accumulating evidence has recently unveiled that aberrant upregulation of PIM kinases (PIM1, PIM2, and PIM3) are closely associated with tumor cell proliferation, migration, survival, and even resistance. Inhibiting or silencing of PIM kinases has been reported have remarkable antitumor effects, such as anti-proliferation, pro-apoptosis and resensitivity, indicating the therapeutic potential of PIM kinases as potential druggable targets in many types of human cancers. More recently, several pharmacological small-molecule inhibitors have been preclinically and clinically evaluated and showed their therapeutic potential; however, none of them has been approved for clinical application so far. Thus, in this perspective, we focus on summarizing the oncogenic roles of PIM kinases, key signaling network, and pharmacological small-molecule inhibitors, which will provide a new clue on discovering more candidate antitumor drugs targeting PIM kinases in the future.
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Affiliation(s)
- Siwei Chen
- Department of Thoracic Surgery, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yushang Yang
- Department of Thoracic Surgery, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yong Yuan
- Department of Thoracic Surgery, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Bo Liu
- Department of Thoracic Surgery, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
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7
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Kim T, Chung KC, Park H. Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm. Pharmaceuticals (Basel) 2023; 16:1509. [PMID: 38004375 PMCID: PMC10675541 DOI: 10.3390/ph16111509] [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: 09/25/2023] [Revised: 10/14/2023] [Accepted: 10/20/2023] [Indexed: 11/26/2023] Open
Abstract
The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.
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Affiliation(s)
| | - Kee-Choo Chung
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Republic of Korea;
| | - Hwangseo Park
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Republic of Korea;
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8
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Wang H, Zhu G, Izu LT, Chen-Izu Y, Ono N, Altaf-Ul-Amin MD, Kanaya S, Huang M. On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme. Front Physiol 2023; 14:1156286. [PMID: 37228825 PMCID: PMC10203956 DOI: 10.3389/fphys.2023.1156286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/05/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction: Given the direct association with malignant ventricular arrhythmias, cardiotoxicity is a major concern in drug design. In the past decades, computational models based on the quantitative structure-activity relationship have been proposed to screen out cardiotoxic compounds and have shown promising results. The combination of molecular fingerprint and the machine learning model shows stable performance for a wide spectrum of problems; however, not long after the advent of the graph neural network (GNN) deep learning model and its variant (e.g., graph transformer), it has become the principal way of quantitative structure-activity relationship-based modeling for its high flexibility in feature extraction and decision rule generation. Despite all these progresses, the expressiveness (the ability of a program to identify non-isomorphic graph structures) of the GNN model is bounded by the WL isomorphism test, and a suitable thresholding scheme that relates directly to the sensitivity and credibility of a model is still an open question. Methods: In this research, we further improved the expressiveness of the GNN model by introducing the substructure-aware bias by the graph subgraph transformer network model. Moreover, to propose the most appropriate thresholding scheme, a comprehensive comparison of the thresholding schemes was conducted. Results: Based on these improvements, the best model attains performance with 90.4% precision, 90.4% recall, and 90.5% F1-score with a dual-threshold scheme (active: < 1 μ M ; non-active: > 30 μ M ). The improved pipeline (graph subgraph transformer network model and thresholding scheme) also shows its advantages in terms of the activity cliff problem and model interpretability.
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Affiliation(s)
- Huijia Wang
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Guangxian Zhu
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Leighton T. Izu
- Department of Pharmacology, University of California, Davis, CA, United States
| | - Ye Chen-Izu
- Department of Biomedical Engineering, University of California, Davis, CA, United States
| | - Naoaki Ono
- Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan
| | - MD Altaf-Ul-Amin
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Shigehiko Kanaya
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Ming Huang
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
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9
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AlRawashdeh S, Chandrasekaran S, Barakat KH. Structural analysis of hERG channel blockers and the implications for drug design. J Mol Graph Model 2023; 120:108405. [PMID: 36680816 DOI: 10.1016/j.jmgm.2023.108405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/26/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023]
Abstract
The repolarizing current (Ikr) produced by the hERG potassium channel forms a major component of the cardiac action potential and blocking this current by small molecule drugs can lead to life-threatening cardiotoxicity. Understanding the mechanisms of drug-mediated hERG inhibition is essential to develop a second generation of safe drugs, with minimal cardiotoxic effects. Although various computational tools and drug design guidelines have been developed to avoid binding of drugs to the hERG pore domain, there are many other aspects that are still open for investigation. This includes the use computational modelling to study the implications of hERG mutations on hERG structure and trafficking, the interactions of hERG with hERG chaperone proteins and with membrane-soluble molecules, the mechanisms of drugs that inhibit hERG trafficking and drugs that rescue hERG mutations. The plethora of available experimental data regarding all these aspects can guide the construction of much needed robust computational structural models to study these mechanisms for the rational design of safe drugs.
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Affiliation(s)
- Sara AlRawashdeh
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada
| | | | - Khaled H Barakat
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
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10
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Mangiatordi GF, Cavalluzzi MM, Delre P, Lamanna G, Lumuscio MC, Saviano M, Majoral JP, Mignani S, Duranti A, Lentini G. Endocannabinoid Degradation Enzyme Inhibitors as Potential Antipsychotics: A Medicinal Chemistry Perspective. Biomedicines 2023; 11:biomedicines11020469. [PMID: 36831006 PMCID: PMC9953700 DOI: 10.3390/biomedicines11020469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 02/08/2023] Open
Abstract
The endocannabinoid system (ECS) plays a very important role in numerous physiological and pharmacological processes, such as those related to the central nervous system (CNS), including learning, memory, emotional processing, as well pain control, inflammatory and immune response, and as a biomarker in certain psychiatric disorders. Unfortunately, the half-life of the natural ligands responsible for these effects is very short. This perspective describes the potential role of the inhibitors of the enzymes fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MGL), which are mainly responsible for the degradation of endogenous ligands in psychic disorders and related pathologies. The examination was carried out considering both the impact that the classical exogenous ligands such as Δ9-tetrahydrocannabinol (THC) and (-)-trans-cannabidiol (CBD) have on the ECS and through an analysis focused on the possibility of predicting the potential toxicity of the inhibitors before they are subjected to clinical studies. In particular, cardiotoxicity (hERG liability), probably the worst early adverse reaction studied during clinical studies focused on acute toxicity, was predicted, and some of the most used and robust metrics available were considered to select which of the analyzed compounds could be repositioned as possible oral antipsychotics.
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Affiliation(s)
| | - Maria Maddalena Cavalluzzi
- Department of Pharmacy—Pharmaceutical Sciences, University of Bari Aldo Moro, Via E. Orabona 4, 70125 Bari, Italy
| | - Pietro Delre
- Institute of Crystallography, National Research Council of Italy, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Giuseppe Lamanna
- Institute of Crystallography, National Research Council of Italy, Via G. Amendola 122/O, 70126 Bari, Italy
- Department of Chemistry, University of Bari Aldo Moro, Via E. Orabona 4, 70125 Bari, Italy
| | - Maria Cristina Lumuscio
- Institute of Crystallography, National Research Council of Italy, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Michele Saviano
- Institute of Crystallography, National Research Council of Italy, Via Vivaldi 43, 81100 Caserta, Italy
| | - Jean-Pierre Majoral
- Laboratoire de Chimie de Coordination du CNRS, 205 Route de Narbonne, CEDEX 4, 31077 Toulouse, France
- Université Toulouse, 118 Route de Narbonne, CEDEX 4, 31077 Toulouse, France
| | - Serge Mignani
- CERMN (Centre d’Etudes et de Recherche sur le Médicament de Normandie), Université de Caen, 14032 Caen, France
- CQM—Centro de Química da Madeira, MMRG (Molecular Materials Research Group), Campus da Penteada, Universidade da Madeira, 9020-105 Funchal, Portugal
| | - Andrea Duranti
- Department of Biomolecular Sciences, University of Urbino Carlo Bo, Piazza del Rinascimento 6, 61029 Urbino, Italy
- Correspondence: ; Tel.: +39-0722-303501
| | - Giovanni Lentini
- Department of Pharmacy—Pharmaceutical Sciences, University of Bari Aldo Moro, Via E. Orabona 4, 70125 Bari, Italy
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