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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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2
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Steger-Hartmann T, Kreuchwig A, Wang K, Birzele F, Draganov D, Gaudio S, Rothfuss A. Perspectives of data science in preclinical safety assessment. Drug Discov Today 2023:103642. [PMID: 37244565 DOI: 10.1016/j.drudis.2023.103642] [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: 03/15/2023] [Revised: 05/12/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
The data landscape in preclinical safety assessment is fundamentally changing because of not only emerging new data types, such as human systems biology, or real-world data (RWD) from clinical trials, but also technological advancements in data-processing software and analytical tools based on deep learning approaches. The recent developments of data science are illustrated with use cases for the three factors: predictive safety (new in silico tools), insight generation (new data for outstanding questions); and reverse translation (extrapolating from clinical experience to resolve preclinical questions). Further advances in this field can be expected if companies focus on overcoming identified challenges related to a lack of platforms and data silos and assuring appropriate training of data scientists within the preclinical safety teams.
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Affiliation(s)
| | - Annika Kreuchwig
- Investigational Toxicology, Bayer AG, Pharmaceuticals, 13353 Berlin, Germany
| | - Ken Wang
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Fabian Birzele
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Dragomir Draganov
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Stefano Gaudio
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Andreas Rothfuss
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
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3
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QSAR, molecular docking, and molecular dynamics simulation–based design of novel anti-cancer drugs targeting thioredoxin reductase enzyme. Struct Chem 2023. [DOI: 10.1007/s11224-022-02111-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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4
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Meng J, Zhang L, Wang L, Li S, Xie D, Zhang Y, Liu H. TSSF-hERG: A machine-learning-based hERG potassium channel-specific scoring function for chemical cardiotoxicity prediction. Toxicology 2021; 464:153018. [PMID: 34757159 DOI: 10.1016/j.tox.2021.153018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 10/15/2021] [Accepted: 10/26/2021] [Indexed: 11/27/2022]
Abstract
The human ether-à-go-go-related gene (hERG) encodes the Kv11.1 voltage-gated potassium ion (K+) channel that conducts the rapidly activating delayed rectifier current (IKr) in cardiomyocytes to regulate the repolarization process. Some drugs, as blockers of hERG potassium channels, cannot be marketed due to prolonged QT intervals, as well known as cardiotoxicity. Predetermining the binding affinity values between drugs and hERG through in silico methods can greatly reduce the time and cost required for experimental verification. In this study, we collected 9,215 compounds with AutoDock Vina's docking structures as training set, and collected compounds from four references as test sets. A series of models for predicting the binding affinities of hERG blockers were built based on five machine learning algorithms and combinations of interaction features and ligand features. The model built by support vector regression (SVR) using the combination of all features achieved the best performance on both tenfold cross-validation and external verification, which was selected and named as TSSF-hERG (target-specific scoring function for hERG). TSSF-hERG is more accurate than the classic scoring function of AutoDock Vina and the machine-learning-based generic scoring function RF-Score, with a Pearson's correlation coefficient (Rp) of 0.765, a Spearman's rank correlation coefficient (Rs) of 0.757, a root-mean-square error (RMSE) of 0.585 in a tenfold cross-validation study. All results demonstrated that TSSF-hERG would be useful for improving the power of binding affinity prediction between hERG and compounds, which can be further used for prediction or virtual screening of the hERG-related cardiotoxicity of drug candidates.
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Affiliation(s)
- Jinhui Meng
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China; Technology Innovation Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China
| | - Lianxin Wang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Shimeng Li
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Di Xie
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Yuxi Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Hongsheng Liu
- Technology Innovation Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China; School of Pharmacy, Liaoning University, Shenyang, 110036, China.
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5
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Begum S, Shareef MZ, Bharathi K. Part-II- in silico drug design: application and success. PHYSICAL SCIENCES REVIEWS 2021. [DOI: 10.1515/psr-2018-0160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.
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Affiliation(s)
- Shaheen Begum
- Institute of Pharmaceutical Technology , Sri Padmavati Mahila Visvavidyalayam , 517501 Tirupati , Andhra Pradesh , India
| | - Mohammad Zubair Shareef
- Institute of Pharmaceutical Technology , Sri Padmavati Mahila Visvavidyalayam , 517501 Tirupati , Andhra Pradesh , India
| | - Koganti Bharathi
- Institute of Pharmaceutical Technology , Sri Padmavati Mahila Visvavidyalayam , 517501 Tirupati , Andhra Pradesh , India
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Creanza TM, Delre P, Ancona N, Lentini G, Saviano M, Mangiatordi GF. Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study. J Chem Inf Model 2021; 61:4758-4770. [PMID: 34506150 PMCID: PMC9282647 DOI: 10.1021/acs.jcim.1c00744] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
![]()
Drug-induced blockade of the human
ether-à-go-go-related
gene (hERG) channel is today considered the main
cause of cardiotoxicity in postmarketing surveillance. Hence, several
ligand-based approaches were developed in the last years and are currently
employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates.
Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support
vector machines were applied to integrate docking scores and protein–ligand
interaction fingerprints. A total of 396 models were trained and validated
based on: (i) high-quality experimental bioactivity information returned
by 8337 curated compounds extracted from ChEMBL (version 25) and (ii)
structural predictor data. Molecular docking simulations were performed
using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures
obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and
two published homology models selected for comparison. Interestingly,
some classifiers return performances comparable to ligand-based models
in terms of area under the ROC curve (AUCMAX = 0.86 ±
0.01) and negative predictive values (NPVMAX = 0.81 ±
0.01), thus putting forward the herein proposed computational workflow
as a valuable tool for predicting hERG-related cardiotoxicity
without the limitations of ligand-based models, typically affected
by low interpretability and a limited applicability domain. From a
methodological point of view, our study represents the first example
of a successful integration of docking scores and protein–ligand
interaction fingerprints (IFs) through a support vector machine (SVM)
LASSO regularized strategy. Finally, the study highlights the importance
of using hERG structural models accounting for ligand-induced
fit effects and allowed us to select the best-performing protein conformation
(made available in the Supporting Information, SI) to be employed
for a reliable structure-based prediction of hERG-related cardiotoxicity.
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Affiliation(s)
- Teresa Maria Creanza
- CNR-Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - Pietro Delre
- Chemistry Department, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy.,CNR-Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Nicola Ancona
- CNR-Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - Giovanni Lentini
- Department of Pharmacy-Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy
| | - Michele Saviano
- CNR-Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
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Wang Y, Huang L, Jiang S, Wang Y, Zou J, Fu H, Yang S. Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers. Front Pharmacol 2020; 10:1631. [PMID: 32063849 PMCID: PMC6997788 DOI: 10.3389/fphar.2019.01631] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/13/2019] [Indexed: 02/05/2023] Open
Abstract
Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.
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Affiliation(s)
- Yiwei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- College of Preclinical Medicine, Southwest Medical University, Luzhou, China
| | - Lei Huang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Basic Teaching Department, Sichuan College of Architectural Technology, Deyang, China
| | - Siwen Jiang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jun Zou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hongguang Fu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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8
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Abstract
Background Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates. Result In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models. Conclusion The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.
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Munawar S, Windley MJ, Tse EG, Todd MH, Hill AP, Vandenberg JI, Jabeen I. Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities. Front Pharmacol 2018; 9:1035. [PMID: 30333745 PMCID: PMC6176658 DOI: 10.3389/fphar.2018.01035] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 08/27/2018] [Indexed: 12/17/2022] Open
Abstract
The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC50) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC50) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC50) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC50) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.
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Affiliation(s)
- Saba Munawar
- Research Center for Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan.,Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | | | - Edwin G Tse
- School of Chemistry, The University of Sydney, Sydney, NSW, Australia
| | - Matthew H Todd
- School of Chemistry, The University of Sydney, Sydney, NSW, Australia
| | - Adam P Hill
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | | | - Ishrat Jabeen
- Research Center for Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan
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10
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Saeui CT, Liu L, Urias E, Morrissette-McAlmon J, Bhattacharya R, Yarema KJ. Pharmacological, Physiochemical, and Drug-Relevant Biological Properties of Short Chain Fatty Acid Hexosamine Analogues Used in Metabolic Glycoengineering. Mol Pharm 2018; 15:705-720. [PMID: 28853901 PMCID: PMC6292510 DOI: 10.1021/acs.molpharmaceut.7b00525] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In this study, we catalog structure activity relationships (SAR) of several short chain fatty acid (SCFA)-modified hexosamine analogues used in metabolic glycoengineering (MGE) by comparing in silico and experimental measurements of physiochemical properties important in drug design. We then describe the impact of these compounds on selected biological parameters that influence the pharmacological properties and safety of drug candidates by monitoring P-glycoprotein (Pgp) efflux, inhibition of cytochrome P450 3A4 (CYP3A4), hERG channel inhibition, and cardiomyocyte cytotoxicity. These parameters are influenced by length of the SCFAs (e.g., acetate vs n-butyrate), which are added to MGE analogues to increase the efficiency of cellular uptake, the regioisomeric arrangement of the SCFAs on the core sugar, the structure of the core sugar itself, and by the type of N-acyl modification (e.g., N-acetyl vs N-azido). By cataloging the influence of these SAR on pharmacological properties of MGE analogues, this study outlines design considerations for tuning the pharmacological, physiochemical, and the toxicological parameters of this emerging class of small molecule drug candidates.
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Affiliation(s)
- Christopher T. Saeui
- Department of Biomedical Engineering and the Translational Tissue Engineering Center, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Lingshu Liu
- Department of Biomedical Engineering and the Translational Tissue Engineering Center, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Esteban Urias
- Department of Biomedical Engineering and the Translational Tissue Engineering Center, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Justin Morrissette-McAlmon
- Department of Biomedical Engineering and the Translational Tissue Engineering Center, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Rahul Bhattacharya
- Department of Biomedical Engineering and the Translational Tissue Engineering Center, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Kevin J. Yarema
- Department of Biomedical Engineering and the Translational Tissue Engineering Center, The Johns Hopkins University, Baltimore, Maryland, USA
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11
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Kratz JM, Grienke U, Scheel O, Mann SA, Rollinger JM. Natural products modulating the hERG channel: heartaches and hope. Nat Prod Rep 2017; 34:957-980. [PMID: 28497823 PMCID: PMC5708533 DOI: 10.1039/c7np00014f] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This review covers natural products modulating the hERG potassium channel. Risk assessment strategies, structural features of blockers, and the duality target/antitarget are discussed.
Covering: 1996–December 2016 The human Ether-à-go-go Related Gene (hERG) channel is a voltage-gated potassium channel playing an essential role in the normal electrical activity in the heart. It is involved in the repolarization and termination of action potentials in excitable cardiac cells. Mutations in the hERG gene and hERG channel blockage by small molecules are associated with increased risk of fatal arrhythmias. Several drugs have been withdrawn from the market due to hERG channel-related cardiotoxicity. Moreover, as a result of its notorious ligand promiscuity, this ion channel has emerged as an important antitarget in early drug discovery and development. Surprisingly, the hERG channel blocking profile of natural compounds present in frequently consumed botanicals (i.e. dietary supplements, spices, and herbal medicinal products) is not routinely assessed. This comprehensive review will address these issues and provide a critical compilation of hERG channel data for isolated natural products and extracts over the past two decades (1996–2016). In addition, the review will provide (i) a solid basis for the molecular understanding of the physiological functions of the hERG channel, (ii) the translational potential of in vitro/in vivo results to cardiotoxicity in humans, (iii) approaches for the identification of hERG channel blockers from natural sources, (iv) future perspectives for cardiac safety guidelines and their applications within phytopharmaceuticals and dietary supplements, and (v) novel applications of hERG channel modulation (e.g. as a drug target).
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Affiliation(s)
- Jadel M Kratz
- Department of Pharmacognosy, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090 Vienna, Austria.
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12
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Ford KA. Refinement, Reduction, and Replacement of Animal Toxicity Tests by Computational Methods. ILAR J 2017; 57:226-233. [DOI: 10.1093/ilar/ilw031] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 10/12/2016] [Indexed: 12/16/2022] Open
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Wang S, Sun H, Liu H, Li D, Li Y, Hou T. ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches. Mol Pharm 2016; 13:2855-66. [PMID: 27379394 DOI: 10.1021/acs.molpharmaceut.6b00471] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.
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Affiliation(s)
- Shuangquan Wang
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China
| | - Huiyong Sun
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China
| | - Hui Liu
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University , Suzhou, Jiangsu 215123, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.,State Key Lab of CAD&CG, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
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Gobbi M, Beeg M, Toropova MA, Toropov AA, Salmona M. Monte Carlo method for predicting of cardiac toxicity: hERG blocker compounds. Toxicol Lett 2016; 250-251:42-6. [DOI: 10.1016/j.toxlet.2016.04.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 04/05/2016] [Accepted: 04/07/2016] [Indexed: 11/27/2022]
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Patlewicz G, Fitzpatrick JM. Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity. Chem Res Toxicol 2016; 29:438-51. [PMID: 26686752 DOI: 10.1021/acs.chemrestox.5b00388] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Exploiting non-testing approaches to predict toxicity early in the drug discovery development cycle is a helpful component in minimizing expensive drug failures due to toxicity being identified in late development or even during clinical trials. Changes in regulations in the industrial chemicals and cosmetics sectors in recent years have prompted a significant number of advances in the development, application, and assessment of non-testing approaches, such as (Q)SARs. Many efforts have also been undertaken to establish guiding principles for performing read-across within category and analogue approaches. This review offers a perspective, as taken from these sectors, of the current status of non-testing approaches, their evolution in light of the advances in high-throughput approaches and constructs such as adverse outcome pathways, and their potential relevance for drug discovery. It also proposes a workflow for how non-testing approaches could be practically integrated within testing and assessment strategies.
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Affiliation(s)
- Grace Patlewicz
- National Center for Computational Toxicology (NCCT), U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Jeremy M Fitzpatrick
- National Center for Computational Toxicology (NCCT), U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
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Computational investigations of hERG channel blockers: New insights and current predictive models. Adv Drug Deliv Rev 2015; 86:72-82. [PMID: 25770776 DOI: 10.1016/j.addr.2015.03.003] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 01/13/2015] [Accepted: 03/04/2015] [Indexed: 01/08/2023]
Abstract
Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel blockers is an essential part of the drug development and drug safety process in pharmaceutical industries or academic drug discovery centers, as they may lead to drug-induced QT prolongation, arrhythmia and Torsade de Pointes. Recent reports also suggest starting to address such issues at the hit selection stage. In order to prioritize molecules during the early drug discovery phase and to reduce the risk of drug attrition due to cardiotoxicity during pre-clinical and clinical stages, computational approaches have been developed to predict the potential hERG blockage of new drug candidates. In this review, we will describe the current in silico methods developed and applied to predict and to understand the mechanism of actions of hERG blockers, including ligand-based and structure-based approaches. We then discuss ongoing research on other ion channels and hERG polymorphism susceptible to be involved in LQTS and how systemic approaches can help in the drug safety decision.
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Ai N, Fan X, Ekins S. In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond. Adv Drug Deliv Rev 2015; 86:46-60. [PMID: 25796619 DOI: 10.1016/j.addr.2015.03.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 01/05/2015] [Accepted: 03/11/2015] [Indexed: 12/13/2022]
Abstract
Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.
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Affiliation(s)
- Ni Ai
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, PR China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, PR China.
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
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Abstract
The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure–activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).
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Ma S, Tan S, Fang D, Zhang R, Zhou S, Wu W, Zheng K. Probing the binding mechanism of novel dual NF-κB/AP-1 inhibitors by 3D-QSAR, docking and molecular dynamics simulations. RSC Adv 2015. [DOI: 10.1039/c5ra10831d] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Potent dual NF-κB/AP-1 inhibitors could effectively treat immunoinflammatory diseases. An integrated computational study was carried out to identify the most favourable binding sites, the structural features and the interaction mechanisms.
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Affiliation(s)
- Shaojie Ma
- Department of Physical Chemistry
- College of Pharmacy
- Guangdong Pharmaceutical University
- Guangzhou 510006
- PR China
| | - Shepei Tan
- Department of Physical Chemistry
- College of Pharmacy
- Guangdong Pharmaceutical University
- Guangzhou 510006
- PR China
| | - Danqing Fang
- Department of Cardiothoracic Surgery
- Affiliated Second Hospital of Guangzhou Medical University
- Guangzhou 510260
- PR China
| | - Rong Zhang
- Department of Physical Chemistry
- College of Pharmacy
- Guangdong Pharmaceutical University
- Guangzhou 510006
- PR China
| | - Shengfu Zhou
- Department of Physical Chemistry
- College of Pharmacy
- Guangdong Pharmaceutical University
- Guangzhou 510006
- PR China
| | - Wenjuan Wu
- Department of Physical Chemistry
- College of Pharmacy
- Guangdong Pharmaceutical University
- Guangzhou 510006
- PR China
| | - Kangcheng Zheng
- School of Chemistry and Chemical Engineering
- SunYat-Sen University
- Guangzhou 510275
- PR China
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Kratz JM, Schuster D, Edtbauer M, Saxena P, Mair CE, Kirchebner J, Matuszczak B, Baburin I, Hering S, Rollinger JM. Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools. J Chem Inf Model 2014; 54:2887-901. [PMID: 25148533 DOI: 10.1021/ci5001955] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The goal of this study was to design, experimentally validate, and apply a virtual screening workflow to identify novel hERG channel blockers. The hERG channel is an important antitarget in drug development since cardiotoxic risks remain as a major cause of attrition. A ligand-based pharmacophore model collection was developed and theoretically validated. The seven most complementary and suitable models were used for virtual screening of in-house and commercially available compound libraries. From the hit lists, 50 compounds were selected for experimental validation through bioactivity assessment using patch clamp techniques. Twenty compounds inhibited hERG channels expressed in HEK 293 cells with IC50 values ranging from 0.13 to 2.77 μM, attesting to the suitability of the models as cardiotoxicity prediction tools in a preclinical stage.
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Affiliation(s)
- Jadel M Kratz
- Departamento de Ciências Farmacêuticas, Universidade Federal de Santa Catarina , 88.040-900 Florianópolis, Santa Catarina, Brazil
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Novel Bayesian classification models for predicting compounds blocking hERG potassium channels. Acta Pharmacol Sin 2014; 35:1093-102. [PMID: 24976154 DOI: 10.1038/aps.2014.35] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 04/10/2014] [Indexed: 02/03/2023] Open
Abstract
AIM A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels. METHODS Doddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation. RESULTS A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation. CONCLUSION The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.
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Kramer C, Fuchs JE, Whitebread S, Gedeck P, Liedl KR. Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty. J Med Chem 2014; 57:3786-802. [DOI: 10.1021/jm500317a] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Christian Kramer
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Julian E. Fuchs
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Steven Whitebread
- Preclinical
Safety Profiling, Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Peter Gedeck
- Novartis Institute for Tropical Diseases, 10 Biopolis Road, No. 05-01 Chromos, Singapore 138670, Singapore
| | - Klaus R. Liedl
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
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Ekins S. Progress in computational toxicology. J Pharmacol Toxicol Methods 2013; 69:115-40. [PMID: 24361690 DOI: 10.1016/j.vascn.2013.12.003] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2013] [Accepted: 12/08/2013] [Indexed: 01/02/2023]
Abstract
INTRODUCTION Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed. METHODS A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods. RESULTS The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data. DISCUSSION Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, USA; Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA; Department of Pharmacology, Rutgers University-Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ 08854, USA; Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599-7355, USA.
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Gordon D, Chen R, Chung SH. Computational methods of studying the binding of toxins from venomous animals to biological ion channels: theory and applications. Physiol Rev 2013; 93:767-802. [PMID: 23589832 PMCID: PMC3768100 DOI: 10.1152/physrev.00035.2012] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The discovery of new drugs that selectively block or modulate ion channels has great potential to provide new treatments for a host of conditions. One promising avenue revolves around modifying or mimicking certain naturally occurring ion channel modulator toxins. This strategy appears to offer the prospect of designing drugs that are both potent and specific. The use of computational modeling is crucial to this endeavor, as it has the potential to provide lower cost alternatives for exploring the effects of new compounds on ion channels. In addition, computational modeling can provide structural information and theoretical understanding that is not easily derivable from experimental results. In this review, we look at the theory and computational methods that are applicable to the study of ion channel modulators. The first section provides an introduction to various theoretical concepts, including force-fields and the statistical mechanics of binding. We then look at various computational techniques available to the researcher, including molecular dynamics, brownian dynamics, and molecular docking systems. The latter section of the review explores applications of these techniques, concentrating on pore blocker and gating modifier toxins of potassium and sodium channels. After first discussing the structural features of these channels, and their modes of block, we provide an in-depth review of past computational work that has been carried out. Finally, we discuss prospects for future developments in the field.
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Affiliation(s)
- Dan Gordon
- Research School of Biology, The Australian National University, Acton, ACT 0200, Australia.
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