Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100, China.
First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510006, China.
Biosens Bioelectron. 2023 Feb 15;222:114923. doi: 10.1016/j.bios.2022.114923. Epub 2022 Nov 17.
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.
药物诱导的心脏毒性的临床前研究对于药物开发很重要。为了评估这种心脏毒性，广泛使用基于相互交织的电极的体外高通量记录心肌细胞机械跳动来进行评估。为了自动分析跳动信号以进行药物诱导的心脏毒性评估，通常采用人工神经网络分析，并将信号分段为周期，在周期中定位特征点。然而，不同信号形状的信号分段和特征点定位需要设计特定的算法。因此，这可能会降低研究效率，并且这些算法在不同形态的信号中的应用受到限制。在这里，我们提出了一种生物传感系统，该系统采用非线性动态分析辅助神经网络 (NDANN) 来避免信号分段过程，并直接从跳动信号时间序列中提取特征。通过处理具有固定持续时间的跳动时间序列来避免信号分段过程，这个基于 NDANN 的生物传感系统可以识别药物诱导的心脏毒性，准确率超过 0.99。个体药物的分类准确率超过 0.94，并且可以准确预测药物诱导的心脏毒性水平。我们还通过独立数据集评估了基于 NDANN 的生物传感系统在评估药物诱导的心脏毒性方面的泛化性能。该系统在识别药物诱导的心脏毒性方面，对于不同的药物浓度，准确率为 0.85-0.95。这一结果表明，我们的基于 NDANN 的生物传感系统具有筛选新开发药物的能力，这在实际应用中至关重要。这个基于 NDANN 的生物传感系统可以作为药物诱导的心脏毒性的新筛选平台，提高生物信号处理的效率。
