In Silico Prediction of Torsadogenic Drug-Induced Proarrhythmias from Action Potential Waveforms in O'Hara-Rudy Human Cardiac Ventricular Model

AbstractCardiac arrhythmias are the most common cause of sudden cardiac death worldwide. Lengthening the ventricular action potential duration (APD) either congenitally or via pathologic or pharmacologic means, predisposes to a life-threatening ventricular arrhythmia, Torsade de Pointes. IKs, a slowly activating K+ current plays a role in action potential repolarization. In this study, we screened a chemical library in silico by docking compounds to the voltage sensing domain (VSD) of the IKs channel. Here we show that C28 specifically shifted IKs VSD activation in ventricle to more negative voltages and reversed drug-induced lengthening of APD. At the same dosage, C28 did not cause significant changes of the normal APD in either ventricle or atrium. This study provides evidence in support of a computational prediction of IKs VSD activation as a potential therapeutic approach for all forms of APD prolongation. This outcome could expand the therapeutic efficacy of a myriad of currently approved drugs that may trigger arrhythmias.Significance statementC28, identified by in silico screening, specifically facilitated voltage dependent activation of a cardiac potassium ion channel, IKs. C28 reversed drug-induced prolongation of action potentials, but minimally affected the normal action potential at the same dosage. This outcome supports a computational prediction of modulating IKs activation as a potential therapy for all forms of action potential prolongation, and could expand therapeutic efficacy of many currently approved drugs that may trigger arrhythmias.

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Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using naïve Bayes classifier method

Food and Chemical Toxicology ◽

10.1016/j.fct.2018.09.051 ◽

2018 ◽

Vol 121 ◽

pp. 593-603 ◽

Cited By ~ 9

Author(s):

Hui Zhang ◽

Jin-Xiang Ma ◽

Chun-Tao Liu ◽

Ji-Xia Ren ◽

Lan Ding

Keyword(s):

Prediction Model ◽

In Silico ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

In Silico Prediction ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Drug Induced ◽

Respiratory Toxicity

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In silico prediction of drug-induced liver injury: Quo vadis?

Advances in Molecular Toxicology ◽

10.1016/b978-0-444-64293-6.00002-6 ◽

2020 ◽

pp. 31-68

Author(s):

Ching-Feng Weng ◽

Max K. Leong

Keyword(s):

Liver Injury ◽

In Silico ◽

In Silico Prediction ◽

Drug Induced ◽

Drug Induced Liver Injury ◽

Quo Vadis

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In Silico Prediction of Drug-Induced Liver Injury Based on Adverse Drug Reaction Reports

Toxicological Sciences ◽

10.1093/toxsci/kfx099 ◽

2017 ◽

Vol 158 (2) ◽

pp. 391-400 ◽

Cited By ~ 11

Author(s):

Xiang-Wei Zhu ◽

Shao-Jing Li

Keyword(s):

Adverse Drug Reaction ◽

Drug Reaction ◽

Liver Injury ◽

In Silico ◽

In Silico Prediction ◽

Drug Induced ◽

Drug Induced Liver Injury

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In silico prediction of drug-induced developmental toxicity by using machine learning approaches

Molecular Diversity ◽

10.1007/s11030-019-09991-y ◽

2019 ◽

Vol 24 (4) ◽

pp. 1281-1290

Author(s):

Hui Zhang ◽

Jun Mao ◽

Hua-Zhao Qi ◽

Lan Ding

Keyword(s):

Machine Learning ◽

In Silico ◽

Developmental Toxicity ◽

Learning Approaches ◽

In Silico Prediction ◽

Drug Induced

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In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

Frontiers in Pharmacology ◽

10.3389/fphar.2021.793332 ◽

2022 ◽

Vol 12 ◽

Author(s):

Yinping Shi ◽

Yuqing Hua ◽

Baobao Wang ◽

Ruiqiu Zhang ◽

Xiao Li

Keyword(s):

Drug Development ◽

In Silico ◽

Molecular Properties ◽

Structural Basis ◽

Consensus Model ◽

In Silico Prediction ◽

Drug Induced ◽

Data Set ◽

Chemical Structures ◽

Structural Alerts

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.

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