Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China.
Department of Mathematics, Michigan State University, East Lansing, MI, United States.
Pain. 2024 Apr 1;165(4):908-921. doi: 10.1097/j.pain.0000000000003089. Epub 2023 Oct 18.
Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein-protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug-target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)-based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.
疼痛是一个全球性的重大健康问题，目前疼痛管理的治疗选择在疗效、副作用和成瘾性方面存在局限性。因此，迫切需要改进疼痛治疗方法并开发新的药物。电压门控钠离子通道，特别是 Nav1.3、Nav1.7、Nav1.8 和 Nav1.9，在神经元兴奋性中发挥着关键作用，主要在周围神经系统中表达。靶向这些通道可能是一种治疗疼痛的方法，同时最大限度地减少中枢和心脏的不良反应。在这项研究中，我们根据与疼痛相关的钠离子通道构建了蛋白质-蛋白质相互作用（PPI）网络，并开发了相应的药物-靶标相互作用网络，以确定潜在的用于疼痛管理的先导化合物。为了确保可靠的机器学习预测，我们从 PPI 网络中 1000 多个靶点的池中仔细选择了 111 个抑制剂数据集。我们使用 3 种不同的机器学习算法，并结合先进的自然语言处理（NLP）-基于嵌入的技术，特别是预训练的转换器和自动编码器表示。通过系统的筛选过程，我们评估了超过 150000 种针对 Nav1.7 和 Nav1.8 钠离子通道的候选药物的副作用和重新定位潜力。此外，我们评估了这些候选药物的 ADMET（吸收、分布、代谢、排泄和毒性）特性，以确定具有近乎最佳特性的先导化合物。我们的策略为疼痛治疗的药理学开发提供了一个创新的平台，有可能提高疗效和减少副作用。
