关键词: Autonomic nervous system Convulsion biomarker Heart rate variability Machine learning Non-human primates Telemetry

Mesh : Animals Male Machine Learning Seizures / chemically induced Heart Rate / drug effects 4-Aminopyridine / adverse effects Kainic Acid / toxicity Convulsants / toxicity Ranolazine Bupropion / toxicity adverse effects Electrocardiography / drug effects Dose-Response Relationship, Drug Autonomic Nervous System / drug effects physiopathology Telemetry Biomarkers

来  源:   DOI:10.2131/jts.49.231

Abstract:
Drug-induced convulsions are a major challenge to drug development because of the lack of reliable biomarkers. Using machine learning, our previous research indicated the potential use of an index derived from heart rate variability (HRV) analysis in non-human primates as a biomarker for convulsions induced by GABAA receptor antagonists. The present study aimed to explore the application of this methodology to other convulsants and evaluate its specificity by testing non-convulsants that affect the autonomic nervous system. Telemetry-implanted males were administered various convulsants (4-aminopyridine, bupropion, kainic acid, and ranolazine) at different doses. Electrocardiogram data gathered during the pre-dose period were employed as training data, and the convulsive potential was evaluated using HRV and multivariate statistical process control. Our findings show that the Q-statistic-derived convulsive index for 4-aminopyridine increased at doses lower than that of the convulsive dose. Increases were also observed for kainic acid and ranolazine at convulsive doses, whereas bupropion did not change the index up to the highest dose (1/3 of the convulsive dose). When the same analysis was applied to non-convulsants (atropine, atenolol, and clonidine), an increase in the index was noted. Thus, the index elevation appeared to correlate with or even predict alterations in autonomic nerve activity indices, implying that this method might be regarded as a sensitive index to fluctuations within the autonomic nervous system. Despite potential false positives, this methodology offers valuable insights into predicting drug-induced convulsions when the pharmacological profile is used to carefully choose a compound.
摘要:
由于缺乏可靠的生物标志物,药物引起的惊厥是药物开发的主要挑战。使用机器学习,我们之前的研究表明,在非人灵长类动物中,心率变异性(HRV)分析得出的指标可能用作GABAA受体拮抗剂诱发惊厥的生物标志物.本研究旨在探索该方法在其他惊厥中的应用,并通过测试影响自主神经系统的非惊厥来评估其特异性。遥测植入的男性服用了各种惊厥药(4-氨基吡啶,安非他酮,海藻酸,和雷诺嗪)在不同剂量。在给药前期间收集的心电图数据被用作训练数据,使用HRV和多变量统计过程控制评估惊厥电位。我们的发现表明,4-氨基吡啶的Q统计量得出的惊厥指数在低于惊厥剂量的剂量下增加。在惊厥剂量下,也观察到海藻酸和雷诺嗪的增加,而安非他酮在最高剂量(惊厥剂量的1/3)时没有改变指数。当相同的分析应用于非惊厥药(阿托品,阿替洛尔,和可乐定),指数有所上升。因此,指数升高似乎与自主神经活动指数的变化相关,甚至可以预测自主神经活动指数的变化,暗示这种方法可能被视为对自主神经系统内波动的敏感指标。尽管有潜在的误报,当使用药理学谱仔细选择化合物时,该方法为预测药物引起的惊厥提供了有价值的见解.
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