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Yang W, Ouyang Q, Zhu Z, Wu Y, Fan M, Liao Y, Guo X, Xu Z, Zhang X, Zhang Y, Hu N, Zhang D. A biosensing system employing nonlinear dynamic analysis-assisted neural network for drug-induced cardiotoxicity assessment. Biosens Bioelectron 2023; 222:114923. [PMID: 36455375 DOI: 10.1016/j.bios.2022.114923] [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: 07/07/2022] [Accepted: 11/16/2022] [Indexed: 11/18/2022]
Abstract
Preclinical investigation of drug-induced cardiotoxicity is of importance for drug development. To evaluate such cardiotoxicity, in vitro high-throughput interdigitated electrode-based recording of cardiomyocytes mechanical beating is widely used. To automatically analyze the features from the beating signals for drug-induced cardiotoxicity assessment, artificial neural network analysis is conventionally employed and signals are segmented into cycles and feature points are located in the cycles. However, signal segmentation and location of feature points for different signal shapes require design of specific algorithms. Consequently, this may lower the efficiency of research and the applications of such algorithms in signals with different morphologies are limited. Here, we present a biosensing system that employs nonlinear dynamic analysis-assisted neural network (NDANN) to avoid the signal segmentation process and directly extract features from beating signal time series. By processing beating time series with fixed time duration to avoid the signal segmentation process, this NDANN-based biosensing system can identify drug-induced cardiotoxicity with accuracy over 0.99. The individual drugs were classified with high accuracies over 0.94 and drug-induced cardiotoxicity levels were accurately predicted. We also evaluated the generalization performance of the NDANN-based biosensing system in assessing drug-induced cardiotoxicity through an independent dataset. This system achieved accuracy of 0.85-0.95 for different drug concentrations in identification of drug-induced cardiotoxicity. This result demonstrates that our NDANN-based biosensing system has the capacity of screening newly developed drugs, which is crucial in practical applications. This NDANN-based biosensing system can work as a new screening platform for drug-induced cardiotoxicity and improve the efficiency of bio-signal processing.
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Affiliation(s)
- Wenjian Yang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China
| | - Qiangqiang Ouyang
- First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Zhijing Zhu
- Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, School of Computer & Computing Science, Zhejiang University City College, Hangzhou, 310015, China; School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Yue Wu
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China.
| | - Minzhi Fan
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China
| | - Yuheng Liao
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China
| | - Xinyu Guo
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China
| | - Zhongyuan Xu
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China
| | - Xiaoyu Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China
| | - Yunshan Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China
| | - Ning Hu
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 311200, China; Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou, 310058, China
| | - Diming Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China.
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