Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data

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

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly re-ported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (e.g., quantita-tive 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 activi-ty/affinity on the gamma-aminobutyric acid 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 im-portance 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 in-formation on biological activity for index novel DBZDs, as preliminary data for further investiga-tions.Peer reviewe

5-HT2B Receptor-mediated Cardiac Valvulopathy

5-HT2B receptor agonism causes cardiac valvulopathy, a condition characterized by thickening of the heart valves and as a result, regurgitation of blood within the heart. The anti-obesity drug fenfluramine, which was originally prescribed as an anorectic, was withdrawn from the market due to causing cardiac valvulopathy. Fenfluramine, after metabolism by N-dealkylation, produces the metabolite norfenfluramine, which acts as a more potent valvulopathogen. The same was seen with MDMA (ecstasy), a popular drug of abuse, which is metabolized by N-dealkylation to produce MDA, a more potent valvulopathogen. Glennon and co-workers. studied a series of 2,5-dimethoxy-4- substituted phenylisopropylamines (DOX type) hallucinogens and determined their affinities at the three types of 5-HT2 receptors. A high correlation was found between the affinities of these molecules at 5-HT2A and 5-HT2B receptors. Therefore, these hallucinogens have a high possibility of causing valvulopathy, which gives rise to a new class of valvulopathogens. Since certain hallucinogens have the common phenylisopropylamine structural scaffold as that of MDA and norfenfluramine, we conducted 3D-QSAR studies to identify the common structural features of these molecules that are responsible for their high affinities. We were unable to obtain a suitable CoMFA and CoMSIA model for 5-HT2B receptors, but we were able to obtain an internally and externally validated model for 5-HT2A receptor affinities which indicated the hydrophobicity of the substituent at the 4- position was essential for high affinity. Following up with this evidence, we conducted a correlation analysis for the hydrophobicity (π-value) of the 4-position substituent and found a positive correlation between the π-value and the affinity of the molecules. The same results were not observed for the volume of the substituents. We docked the molecules into the 5-HT2B receptor and successfully generated models of the putative interactions made by the DOX molecules and the receptor. In order to compare their binding modes with respect to known valvulopathogens, we also generated models for norfenfluramine and MDA. Our docking results revealed that DOX molecules bind in a more or less similar manner to valvulopathogens MDA and norfenfluramine. Ours is the first in silico model developed for the potent valvulopathogen MDA and the hallucinogenic DOX series of molecules

Assessing the Pharmacological Properties of Novel Psychoactive Substances (NPS) Identified Online: In Silico Studies on Designer Benzodiazepines and Novel Synthetic Opioids

Background By 2022, a total of 1,127 of Novel Psychoactive Substances (NPS) have been identified worldwide and officially reported by the United Nations Office on Drugs and Crime (UNODC) and the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA). An analysis of the surface web via the use of a web crawler, NPSfinder®, indicated that the number of NPS could be almost four times higher than that known to both the UNODC and EMCDDA. This is of particular concern, especially if one considers the public health risks and harms associated with NPS use/abuse and the paucity of data related to their pharmacological/toxicity profiles. In particular, in the last few years two NPS classes, i.e. novel synthetic opioids (NSOs) and designer benzodiazepines (DBZDs) were associated with serious side-effects and life-threatening scenarios (i.e., fatalities and overdoses). Gaps in knowledge Hence, with online NPS numbers exceeding those reported by official sources, there is a strong need to address the gap in knowledge concerning the discrepancies between the online and the evidence based NPS market(s); as well as the gap in knowledge concerning lack of pharmacological profiles for most of the newly-identified NPS. Objectives This programme of research aimed to: use data available from NPSfinder®, the UNODC and EMCDDA to assess the current general NPS scenarios, and in particular for DBZDs and NSOs; use in silico computational techniques to predict the biological activity of the emerging NPS; use the predicted values to infer possible health threats associated with the consumption of these substances, underscoring which of the NPS identified online could indeed represent a serious threat to public health; assess the potential of in silico methodologies as preliminary risk assessment tools; and subsequently inform relevant stakeholders about the risks associated with these new NPS. Methods The NPSfinder® web crawler was used to identify NPS which are available/discussed online. A comparison with UNODC and EMCDDA databases was then carried out to assess the extent of the total NPS scenario, and the numbers of the NSOs and DBZDs classes. To appreciate and predict the biological activities of NSOs and DBZDs, in silico models (e.g., quantitative structure-activity relationship (QSAR), Molecular Docking (MD) and pharmacophore mapping) were used as reliable, time- and cost- effective alternatives to the classical approaches such as in vivo, in vitro or preclinical studies. Results and Discussion A total of 4,231 NPS were identified on the surface web, almost four times the numbers reported by both UNDOC and EMCDDA databases (circa 1,127). These results suggest how the online content analysis should be considered as an important source for the assessment of the NPS scenario. The same discrepancy in the total NPS numbers was observed for each NPS class and a total of 115 DBZDs and 371 NSOs were identified compared to 33 and 123 reported by the UNODC respectively. To assess pharmacological profiles of these NSOs and DBZDs identified online, specific QSAR models were developed in MOE® and Forge™. For the prediction of biological activities of DBZDs, the γ-aminobutyric acid A receptor (GABA-AR) was used; the mu opioid receptor (MOR) was used for the NSOs. In addition, for the DBZDs, a set of new potential ligands resulting from “scaffold hopping” exercises conducted with MOE® was also evaluated. The generated QSAR models returned good performance statistics confirming their strong reliability in predicting the biological activity of an unknown or a newly-identified molecule. The DBZDs predicted to be the most active were flubrotizolam, clonazolam, pynazolam and, fluclotizolam, consistently with reported literature and/or drug discussion forums. In particular with flubrotizolam and fluclotizolam, it was found they were discussed on drug fora but not previously identified either by the UNODC or EMCDDA (flubrotizolam only). This suggests the possible presence on the market of very potent NPS which are still unknown to international agencies, potentially representing a serious threat to public health. Worrisome results were also obtained for the class of NSOs, with the identification of new and potent analogues of carfentanyl (10,000 more potent than morphine), i.e., 2-methyl carfentanyl, n-methyl-carfentanyl and butyryl-carfentanyl. Moreover, the scaffold hopping exercise conducted for the DBZDs class, strongly suggested that structural replacement of the pendant phenyl moiety could increase biological activity and highlighted the existence of a still unexplored chemical space for this NPS class. The results obtained with QSAR analysis were supported by molecular docking exercises, which gave an indication of the binding affinity of these NPS towards their respective receptors. Moreover, the binding affinity of a set of DBZDs was assessed for the MOR, in an attempt to assess a possible multi-receptor activity of these molecules. Conclusions The online identification of a great number of NPS, including very potent central nervous system depressants, represents a serious challenge, in particular if one considers that DBZDs and NSOs are usually consumed either together or in combination with stimulants for recreational purposes and self-medication. The high numbers of available molecules, their patterns of use and the paucity of pharmacological data could lead to worrisome outcomes, including the synergy of each NPS class side-effects, which could (and are) increasing the likelihood of respiratory depression, coma, and deaths. To retrieve an extensive picture of the current NPS drug scenario, the online analysis has proven very useful, if not fundamental. Its ability to identify novel mentioned NPS, in a timely manner, makes it a very important tool for a range of activities, including informing law-enforcement and public health stakeholders, supporting the European and United Nations Early Warning Systems impacting and influencing law-making and guiding monitoring/surveillance. Moreover, in silico methodologies, proven as reliable tools for a fast prediction of biological activity, could be used in describing the activity/toxicity profile of novel NPS, aiming at supporting both law enforcement in scheduling process and public health stakeholders in drafting treatment/management educational packages. Finally, the combination of online and in silico analysis could support and improve the risk assessment procedures currently in place for NPS