Catalani Valeria, Botha Michelle, Corkery John Martin, Guirguis Amira, Vento Alessandro, Scherbaum Norbert, Schifano Fabrizio
Psychopharmacology, Drug Misuse & Novel Psychoactive Substances Research Unit, School of Life & Medical Sciences, University of Hertfordshire, College Lane Campus, Hatfield AL10 9AB, UK.
Department of Pharmacy, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Singleton Campus, Wales SA2 8PP, UK.
Pharmaceuticals (Basel). 2021 Jul 26;14(8):720. doi: 10.3390/ph14080720.
Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPS) and to predict their possible activity/affinity on the gamma-aminobutyric acid A receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPS. The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the importance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable information on biological activity for index novel DBZDs, as preliminary data for further investigations.
设计苯二氮䓬类药物(DBZD)引发了严重的健康问题,且在多药滥用相关死亡案例中的报道日益增多。当新的DBZD被发现时,关于其药效学的信息非常有限。在此,利用计算模型(即定量构效关系/QSAR和分子对接)来分析由自动网络爬虫(NPS)在网上识别出的DBZD,并预测它们对γ-氨基丁酸A受体(GABA-AR)的可能活性/亲和力。使用计算软件MOE来计算二维QSAR模型,对结晶的GABA-A受体(6HUO、6HUP)进行对接研究,并根据对接构象结果生成药效团查询。NPS在网上识别出了101种DBZD。经过验证的QSAR模型预测,其中41%的DBZD具有较高的生物活性值。这些预测得到了对接研究(良好的结合亲和力)的支持,药效团建模证实了QSAR所识别的疏水和极性官能团的存在及位置的重要性。本研究再次证实了基于网络的分析在评估药物情况(DBZD)中的重要性,以及计算模型如何能够用于获取关于新型DBZD生物活性的快速可靠信息,作为进一步研究的初步数据。