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

Modellierung von Metalloenzymen: 3D-QSAR-Untersuchungen an Carboanhydrase-Isoenzymen und virtuelles Screening nach Peptiddeformylase-Inhibitoren

In der modernen Arzneistoffentwicklung unterscheidet man die Phasen der Leitstrukturfindung und der Leitstrukturoptimierung. Die vorliegende Dissertationsschrift beinhaltet Beiträge zu beiden Bereichen. Der erste Teil der Arbeit befasst sich mit der Entwicklung und Evaluierung von Computermodellen zur Vorhersage von Affinität und Selektivität und entstammt daher dem Bereich der Leitstrukturoptimierung. Selektivitätsaspekte spielen eine wichtige Rolle, da sie das Risiko von Nebenwirkungen maßgeblich beeinflussen. Zur Modellierung und Vorhersage von Affinitäts- und Selektivitätsparametern wurden QSAR-Methoden angewendet. Das Modellsystem stellten Carboanhydrasen (CAs) dar; diese zinkhaltigen Hydrolasen katalysieren die reversible Hydratisierung von Kohlendioxid zu Bicarbonat und einem Proton. Sie sind daher in eine Vielzahl (patho)physiologischer Prozesse involviert und stellen interessante therapeutische Targets dar. Die zahlreichen Isoenzyme der CAs besitzen im Bereich der Bindetasche hohe Ähnlichkeiten in Bezug auf physikochemische Eigenschaften, so dass die Entwicklung selektiver Inhibitoren kein triviales Problem darstellt. Im Mittelpunkt der Untersuchungen standen insbesondere 3D-QSAR-Verfahren. Es wurden statistisch hochsignifikante und robuste Modelle abgeleitet, um Affinität und Selektivität von Sulfonamidinhibitoren bezüglich der Isoenzyme CA I, II und IV vorherzusagen. Es zeigte sich, dass die geringen Unterschiede im strukturbasierten Alignment unter Verwendung der drei Bindetaschen nur geringen Einfluss auf die statistischen Parameter besitzen und dass bessere Ergebnisse erzielt werden, wenn für alle Isoenzyme das auf CA II basierende Alignment benutzt wird anstelle des Alignments in der jeweiligen Bindetasche. Ursache hierfür ist wahrscheinlich die Vielzahl an Kristallstrukturen, die für CA II existieren und damit das Alignment verlässlicher machen. Die erhaltenen Isokonturkarten erlaubten eine Interpretation der Modelle im Hinblick auf die Bedeutung physikochemischer Eigenschaften für die Affinität/Selektivität. Der Vergleich zu qualitativen proteinbasierten Isokonturkarten unterstreicht den komplementären Charakter beider Methoden: Während die ligandbasierten QSAR-Verfahren implizit teilweise die Struktur der Bindetasche widerspiegeln, aber auch von den Eigenheiten des Trainingsdatensatzes abhängen, vermögen die proteinbasierten Analysen auch Informationen über Bereiche der Bindetasche zu geben, die keine Interaktionen mit Liganden des Trainingsdatensatzes ausbilden. Ein weiteres Ziel bestand darin, QSAR-Methoden für das Screening größerer Datenbanken zu verwenden. Dies erlaubt die Identifizierung besonders interessanter (d.h. affiner/selektiver) Kandidaten zur Synthese im Sinne einer Leitstrukturoptimierung. Für 3D-QSAR-Methoden musste zunächst ein Protokoll zur Automatisierung des Alignments entwickelt und validiert werden. Es zeigte sich hierbei, dass ein ligandbasiertes Alignment vergleichbare Ergebnisse zu manuellen stukturbasierten Alignmentmethoden erzielt. Die 3D-Modelle erwiesen sich als überlegen im Vergleich zu fragmentbasierten 2D-Methoden oder insbesondere zu den eigenschaftsbasierten 1D-Methoden. Als praktisches Anwendungsbeispiel der entwickelten Modelle wurde eine mehrere tausend Einträge umfassende virtuelle Ligandbibliothek aufgebaut und mit den leistungsfähigsten Modellen bewertet. Der zweite Teil der Arbeit beinhaltet ein virtuelles Screening nach neuartigen Inhibitoren der Peptiddeformylasen (PDFs) und gehört somit in den Bereich der Leitstrukturfindung. PDFs sind (meist) eisenhaltige Enzyme, die die Deformylierung von in Mitochondrien, Plastiden oder Bakterien synthetisierten Proteinen katalysieren. Ausgehend von Kristallstrukturen potenter PDF-Inhibitoren wurden 3D-Pharmakophormodelle entwickelt und validiert. Diese waren in der Lage, strukturell diverse, aus der Literatur bekannte Inhibitoren zu identifizieren (hinreichende Sensitivität) und gleichzeitig die zu durchsuchenden Datenbanken stark zu reduzieren (hinreichende Spezifität). Die Pharmakophormodelle wurden zum Screening von Datenbanken kommerziell erhältlicher Moleküle mit wirkstoffartigen Eigenschaften benutzt. Durch Docking und Scoring wurden schließlich aus etwa zwei Millionen Verbindungen elf Substanzen identifiziert und erworben, die einer biologischen Testung unterzogen werden sollen. Erste vorliegende Messergebnisse zeigen, dass mindestens zwei der Substanzen mit einem IC50-Wert von 60 nM bzw. 190 nM potente Inhibitoren der PDF1B aus E. coli sind. Dies belegt die Güte der Modelle und des angewendeten Screening-Protokolls. Inhibitoren der PDF könnten Anwendung als Herbizide, Antibiotika und Antimalaria-Therapeutika finden

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

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Using fuzzy methods for rule extraction in the discrimination of class C GPCR subtypes from their subsequences

G-Protein-Coupled receptors (GPCR) are cell membrane proteins that regulate many of the cell functions and transduce signals between the intracellular and extracellular domains. This makes them relevant in pharmacology as therapeutic targets. As members of this superfamily, class C GPCRs in particular regulate a number of important physiological functions. Proteins of the class must be studied from their primary sequences, as only one of their 3-D structures has been fully determined, earlier this year. Protein function investigation requires the identification of motifs, or functional subsequences. In this thesis, we will describe the discrimination of class C GPCR subtypes through interpretable rules from a specific alignment free transformation of the sequences, namely amino acid composition. The Fuzzy Inductive Reasoning methodology was used as the basis to extract these linguistic rules

Antimicrobial Drug Repurposing Through Molecular Modelling: Acquisition, Analyis and Prediction

Antimicrobial resistance has sparked unprecedented medical crises around the world, not only increasing the mortality rate but also impacting nosocomial resources. Methicillin-resistant Staphylococcus aureus (MRSA) has consistently evaded the available range of antibiotics and is a typical case study for new generation drugs. Drug development has been conventionally suffering from exceedingly high costs and overdrawn timelines. Drug Repurposing can be a solution to alleviate those burdens. Put simply, DR is a mechanism to identify new usages of existing drugs, typically targeted to treat diseases different to the ones that these were initially intended for. This inherently interdisciplinary research targets to identify the best MRSA drug candidates analysing protein (BIG) data, in the process developing a combination of techniques from stochastic mathematics, statistics and data analytics that can generically identify drug targets from the databank. Structure-based virtual screening was used to repurpose an extensive range of marketed drugs and Phase I/II/III trials. Molecular docking methods were used for virtual screening against MRSA targets based on sequence alignment to match gene sequences against proteins in the Protein Data Bank (PDB). Ligands from the Database of Useful Decoys - Enhanced were docked against MRSA-oriented target proteins using 10 open-source docking programmes for benchmark. The novel consensus scoring methods prove superior to other reported consensus scores in terms of discrimination between decoys and active ligands concerning MRSA drug target identification. The consensus scoring predictions are then applied to docking data between MRSA targets and compounds from the Repurposing Hub to identify a list of potential drug candidates for anti-MRSA treatment. MRSA is currently an apocalypse across the world with limited prevention and medications. This study provided more potential candidates to help fight against MRSA. The consensus scoring developed in this study can be generically implemented to unlock other antimicrobial drug candidates