Mesh : Helicobacter pylori / drug effects Berberine / pharmacology chemistry pharmacokinetics Anti-Bacterial Agents / pharmacology chemistry Humans Deep Learning Helicobacter Infections / drug therapy microbiology Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial / drug effects genetics Animals Omeprazole / pharmacology Clarithromycin / pharmacology Amoxicillin / pharmacology

来  源:   DOI:10.1038/s41392-024-01895-0   PDF(Pubmed)

Abstract:
Helicobacter pylori (H. pylori) is currently recognized as the primary carcinogenic pathogen associated with gastric tumorigenesis, and its high prevalence and resistance make it difficult to tackle. A graph neural network-based deep learning model, employing different training sets of 13,638 molecules for pre-training and fine-tuning, was aided in predicting and exploring novel molecules against H. pylori. A positively predicted novel berberine derivative 8 with 3,13-disubstituted alkene exhibited a potency against all tested drug-susceptible and resistant H. pylori strains with minimum inhibitory concentrations (MICs) of 0.25-0.5 μg/mL. Pharmacokinetic studies demonstrated an ideal gastric retention of 8, with the stomach concentration significantly higher than its MIC at 24 h post dose. Oral administration of 8 and omeprazole (OPZ) showed a comparable gastric bacterial reduction (2.2-log reduction) to the triple-therapy, namely OPZ + amoxicillin (AMX) + clarithromycin (CLA) without obvious disturbance on the intestinal flora. A combination of OPZ, AMX, CLA, and 8 could further decrease the bacteria load (2.8-log reduction). More importantly, the mono-therapy of 8 exhibited comparable eradication to both triple-therapy (OPZ + AMX + CLA) and quadruple-therapy (OPZ + AMX + CLA + bismuth citrate) groups. SecA and BamD, playing a major role in outer membrane protein (OMP) transport and assembling, were identified and verified as the direct targets of 8 by employing the chemoproteomics technique. In summary, by targeting the relatively conserved OMPs transport and assembling system, 8 has the potential to be developed as a novel anti-H. pylori candidate, especially for the eradication of drug-resistant strains.
摘要:
幽门螺杆菌(H.pylori)是目前公认的与胃肿瘤发生相关的主要致癌病原体,它的高流行率和抵抗力使其难以解决。基于图神经网络的深度学习模型,采用13638个分子的不同训练集进行预训练和微调,有助于预测和探索抗幽门螺杆菌的新分子。具有3,13-二取代的烯烃的阳性预测的新型小檗碱衍生物8表现出对所有测试的药物敏感和抗性幽门螺杆菌菌株的效力,其最小抑制浓度(MIC)为0.25-0.5μg/mL。药代动力学研究表明,理想的胃潴留为8,在给药后24小时,胃浓度明显高于其MIC。口服8和奥美拉唑(OPZ)显示与三联疗法相当的胃细菌减少(2.2-log减少),即OPZ+阿莫西林(AMX)+克拉霉素(CLA)对肠道菌群无明显干扰。OPZ的组合,AMX,CLA,8可以进一步降低细菌负荷(减少2.8-log)。更重要的是,单药治疗8例的根除效果与三联疗法(OPZ+AMX+CLA)和四联疗法(OPZ+AMX+CLA+柠檬酸铋)组相当.SecA和BamD,在外膜蛋白(OMP)的运输和组装中起主要作用,通过使用化学蛋白质组学技术鉴定并验证为8的直接靶标。总之,通过瞄准相对保守的OMP运输和组装系统,8有可能被开发为一种新型的抗H。幽门螺杆菌候选,尤其是根除耐药菌株。
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