The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity

Abstract

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 (NPSfinder®) 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 NPSfinder®. 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.

Molecular mechanisms of action of stimulant novel psychoactive substances that target the high-affinity transporter for dopamine

“… Computational docking : We have also employed molecular docking to place the substrate DA and screened NPS compounds ( Figure 2 ) into the rDAT model and compared it with similar docking studies in hDAT [ 11–13 , 31 , 76 ]. Docking as a molecular modelling technique to predict the position and orientation of ligands in the binding site of their target protein has also been utilised in other studies of NPS [ 14 , 32 , 75 , 77 , 78 , 33 , 37 , 39 , 40 , 44 , 46 , 48 , 52 ]. Computational simulation: We and others [ 11–13 , 37 , 41 , 45 , 46 , 48 , 79 ] have observed that conformational changes emerging over long-scale simulations, and free-energy calculations, can indicate the structural and dynamic elements of the mechanisms governing the ligand interactions and pharmacological effects of NPS on DAT.…”

“…Computational docking : We have also employed molecular docking to place the substrate DA and screened NPS compounds ( Figure 2 ) into the rDAT model and compared it with similar docking studies in hDAT [ 11–13 , 31 , 76 ]. Docking as a molecular modelling technique to predict the position and orientation of ligands in the binding site of their target protein has also been utilised in other studies of NPS [ 14 , 32 , 75 , 77 , 78 , 33 , 37 , 39 , 40 , 44 , 46 , 48 , 52 ].…”

“…Nowadays, with the abundance of 3D structural information, ligand-based QSAR has become a much faster way to predict the correlation between modifications in stimulant chemical structures and binding affinities. We have already seen how small modifications can change the binding mechanism for amphetamine-like stimulants [ 12 , 48 ], and likewise, QSAR has been instrumental in differentiating stimulants as releasing agents or cocaine-like reuptake inhibitors by identifying key structural features, such as the nature and size of the substituent modification [ 88 , 89 ] and stereochemistry [ 54 , 77 ], all-important in predicting the biological activity for NPS like benzodiazepines and transporter selectivity [ 88 , 90 ].…”

In silico studies on recreational drugs: 3D quantitative structure activity relationship prediction of classified and de novo designer benzodiazepines

“…In fact, these models are well established in the pharmaceutical field (Bajorath, 2015; Leelananda & Lindert, 2016; Tian et al, 2015) as very successful tools for drug discovery and development (Valerio & Choudhuri, 2012). In addition, they have been extensively applied to NPS studies as important resources for the preliminary (risk) assessment of unknown molecules (Alam & Khan, 2017; Artemenko et al, 2009; Catalani et al, 2021; Durdagi et al, 2007; Ellis et al, 2018; Floresta & Abbate, 2021; Floresta et al, 2019; Waters et al, 2018; Zhang et al, 2007).…”

“…Here, we report three QSAR models (3D field QSAR and two machine‐learning) developed in Forge™ (Cresset, 2021) for the prediction of previously identified classified, unclassified DBZDs (Catalani et al, 2021) and of a new set of potential ligands resulting from scaffold hopping studies conducted with MOE ® (Chemical Computing Group ULC, 2021).…”