Computational Prediction and In Vitro Validation of Potential Molecular Targets Associated with Adverse Reactions of Antimicrobial Drugs in Neonates.

IF 1.8 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Assay and drug development technologies Pub Date : 2026-04-01 Epub Date: 2026-04-14 DOI:10.1177/1540658X251413074
Xiaojuan He, Rui Jing, Na Wu, Xiaojing Li, Shasha Qiao, Zhaojuan Yang, Ying Zhang, Yuanyuan Han
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引用次数: 0

Abstract

Neonates are particularly susceptible to a range of adverse reactions when administered antimicrobial drugs, including allergic reactions, gastrointestinal discomfort, and hepatic or renal dysfunction. These adverse reactions can have significant long-term impacts on neonatal health. This study aimed to investigate the common molecular targets associated with these adverse reactions using reverse target identification and molecular dynamics (MD) simulations, and to experimentally assess the inhibitory effects of selected drugs on these targets. We initially identified adverse reactions associated with commonly used antimicrobial drugs such as Ceftazidime, Meropenem, Levofloxacin, Cefdinir, and Avibactam through DrugBank. Potential targets for these drugs were predicted using SwissTargetPrediction software, and common targets were identified using a Venn diagram. Molecular docking and MD simulations were conducted to assess the binding affinity and stability of the drug-target interactions. Finally, the inhibitory potential of selected antimicrobial drugs on the target proteins was evaluated through in vitro bioactivity assays. The study identified AKR1B1 and Neprilysin as common potential off-targets. Bioactivity assays confirmed that the tested antimicrobial drugs exhibited significant inhibitory effects on these two proteins, with inhibitory concentration at 50% (IC50) values generally in the micromolar range. MD simulations indicated that the complexes of these drugs with the target proteins maintained relatively stable root mean square deviation (RMSD) values throughout the simulation period. Analysis of intermolecular forces revealed key attractive charge interactions and hydrogen bonds between the drugs and target proteins. This study systematically analyzed the molecular targets associated with antimicrobial drug adverse reactions in neonates by integrating computational and experimental approaches. The findings suggest that these drugs may cause adverse reactions by affecting the function of specific off-target proteins. This discovery provides valuable molecular insights into the mechanisms of adverse reactions and establishes a scientific basis for the rational design of safer drug analogs and for informing future antimicrobial treatment research for neonates.

新生儿抗菌药物不良反应相关潜在分子靶点的计算预测和体外验证。
当使用抗菌药物时,新生儿特别容易发生一系列不良反应,包括过敏反应、胃肠道不适和肝肾功能障碍。这些不良反应可对新生儿健康产生重大的长期影响。本研究旨在通过反向靶标识别和分子动力学(MD)模拟研究与这些不良反应相关的常见分子靶点,并通过实验评估所选药物对这些靶点的抑制作用。我们最初通过DrugBank确定了常用抗菌药物(如头孢他啶、美罗培南、左氧氟沙星、头孢地尼和阿维巴坦)的不良反应。使用SwissTargetPrediction软件预测这些药物的潜在靶点,并使用维恩图确定共同靶点。通过分子对接和MD模拟来评估药物-靶标相互作用的结合亲和力和稳定性。最后,通过体外生物活性测定评价所选抗菌药物对目标蛋白的抑制潜力。该研究确定AKR1B1和Neprilysin是常见的潜在脱靶。生物活性测定证实,所试抗菌药物对这两种蛋白具有显著的抑制作用,抑菌浓度在50% (IC50)值时一般在微摩尔范围内。MD模拟表明,这些药物与靶蛋白的复合物在整个模拟期间保持相对稳定的均方根偏差(RMSD)值。分子间作用力分析揭示了药物与靶蛋白之间的关键吸引电荷相互作用和氢键。本研究采用计算与实验相结合的方法,系统分析了与新生儿抗菌药物不良反应相关的分子靶点。研究结果表明,这些药物可能通过影响特异性脱靶蛋白的功能而引起不良反应。这一发现为不良反应的分子机制提供了有价值的见解,为合理设计更安全的药物类似物和为未来的新生儿抗菌治疗研究提供了科学依据。
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来源期刊
Assay and drug development technologies
Assay and drug development technologies 医学-生化研究方法
CiteScore
3.60
自引率
0.00%
发文量
33
审稿时长
>12 weeks
期刊介绍: ASSAY and Drug Development Technologies provides access to novel techniques and robust tools that enable critical advances in early-stage screening. This research published in the Journal leads to important therapeutics and platforms for drug discovery and development. This reputable peer-reviewed journal features original papers application-oriented technology reviews, topical issues on novel and burgeoning areas of research, and reports in methodology and technology application. ASSAY and Drug Development Technologies coverage includes: -Assay design, target development, and high-throughput technologies- Hit to Lead optimization and medicinal chemistry through preclinical candidate selection- Lab automation, sample management, bioinformatics, data mining, virtual screening, and data analysis- Approaches to assays configured for gene families, inherited, and infectious diseases- Assays and strategies for adapting model organisms to drug discovery- The use of stem cells as models of disease- Translation of phenotypic outputs to target identification- Exploration and mechanistic studies of the technical basis for assay and screening artifacts
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