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Tomarchio R, Patamia V, Zagni C, Crocetti L, Cilibrizzi A, Floresta G, Rescifina A. Steered Molecular Dynamics Simulations Study on FABP4 Inhibitors. Molecules 2023; 28:molecules28062731. [PMID: 36985701 PMCID: PMC10058326 DOI: 10.3390/molecules28062731] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/15/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Ordinary small molecule de novo drug design is time-consuming and expensive. Recently, computational tools were employed and proved their efficacy in accelerating the overall drug design process. Molecular dynamics (MD) simulations and a derivative of MD, steered molecular dynamics (SMD), turned out to be promising rational drug design tools. In this paper, we report the first application of SMD to evaluate the binding properties of small molecules toward FABP4, considering our recent interest in inhibiting fatty acid binding protein 4 (FABP4). FABP4 inhibitors (FABP4is) are small molecules of therapeutic interest, and ongoing clinical studies indicate that they are promising for treating cancer and other diseases such as metabolic syndrome and diabetes.
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Affiliation(s)
- Rosario Tomarchio
- Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Vincenzo Patamia
- Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Chiara Zagni
- Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Letizia Crocetti
- Department Neurofarba, Pharmaceutical and Nutraceutical Section, via Ugo Schiff 6, 50019 Sesto Fiorentino, Italy
| | - Agostino Cilibrizzi
- Institute of Pharmaceutical Science, King's College London, Stamford Street, London SE1 9NH, UK
- Centre for Therapeutic Innovation, University of Bath, Bath BA2 7AY, UK
| | - Giuseppe Floresta
- Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Antonio Rescifina
- Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
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Heparan Sulfate and Enoxaparin Interact at the Interface of the Spike Protein of HCoV-229E but Not with HCoV-OC43. Viruses 2023; 15:v15030663. [PMID: 36992372 PMCID: PMC10056857 DOI: 10.3390/v15030663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/20/2022] [Accepted: 01/05/2023] [Indexed: 03/05/2023] Open
Abstract
It is known that the spike protein of human coronaviruses can bind to a secondary receptor, or coreceptor, to facilitate the virus entry. While HCoV-229E uses human aminopeptidase N (hAPN) as a receptor, HCoV-OC43 binds to 9-O-acetyl-sialic acid (9-O-Ac-Sia), which is linked in a terminal way to the oligosaccharides that decorate glycoproteins and gangliosides on the surface of the host cell. Thus, evaluating the possible inhibitory activity of heparan sulfate, a linear polysaccharide found in animal tissues, and enoxaparin sodium on these viral strains can be considered attractive. Therefore, our study also aims to evaluate these molecules’ antiviral activity as possible adsorption inhibitors against non-SARS-CoV. Once the molecules’ activity was verified in in vitro experiments, the binding was studied by molecular docking and molecular dynamic simulations confirming the interactions at the interface of the spike proteins.
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Catalani V, Floresta G, Botha M, Corkery JM, Guirguis A, Vento A, Abbate V, Schifano F. In silico studies on recreational drugs: 3D quantitative structure activity relationship prediction of classified and de novo designer benzodiazepines. Chem Biol Drug Des 2023; 101:40-51. [PMID: 35838189 DOI: 10.1111/cbdd.14119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/17/2022] [Accepted: 07/08/2022] [Indexed: 12/15/2022]
Abstract
Currently, increasing availability and popularity of designer benzodiazepines (DBZDs) constitutes a primary threat to public health. To assess this threat, the biological activity/potency of DBZDs was investigated using in silico studies. Specific Quantitative Structure Activity Relationship (QSAR) models were developed in Forge™ for the prediction of biological activity (IC50 ) on the γ-aminobutyric acid A receptor (GABA-AR) of previously identified classified and unclassified DBDZs. A set of new potential ligands resulting from scaffold hopping studies conducted with MOE® was also evaluated. Two generated QSAR models (i.e. 3D-field QSAR and RVM) returned very good performance statistics (r2 = 0.98 [both] and q2 = 0.75 and 0.72, respectively). The DBZDs predicted to be the most active were flubrotizolam, clonazolam, pynazolam and flucotizolam, consistently with what reported in literature and/or drug discussion fora. The scaffold hopping studies strongly suggest that replacement of the pendant phenyl moiety with a five-membered ring could increase biological activity and highlight the existence of a still unexplored chemical space for DBZDs. QSAR could be of use as a preliminary risk assessment model for (newly) identified DBZDs, as well as scaffold hopping for the creation of computational libraries that could be used by regulatory bodies as support tools for scheduling procedures.
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Affiliation(s)
- Valeria Catalani
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, UK
| | - Giuseppe Floresta
- Department of Analytical, Environmental and Forensic Sciences, King's College London, London, UK
| | - Michelle Botha
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, UK
| | - John Martin Corkery
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, UK
| | - Amira Guirguis
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, UK
- Swansea University Medical School, The Grove, Swansea University, Swansea, UK
| | - Alessandro Vento
- Department of Psychology, Guglielmo Marconi University, Rome, Italy
| | - Vincenzo Abbate
- Department of Analytical, Environmental and Forensic Sciences, King's College London, London, UK
| | - Fabrizio Schifano
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, UK
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Callis TB, Garrett TR, Montgomery AP, Danon JJ, Kassiou M. Recent Scaffold Hopping Applications in Central Nervous System Drug Discovery. J Med Chem 2022; 65:13483-13504. [PMID: 36206553 DOI: 10.1021/acs.jmedchem.2c00969] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The concept of bioisosterism and the implementation of bioisosteric replacement is fundamental to medicinal chemistry. The exploration of bioisosteres is often used to probe key structural features of candidate pharmacophores and enhance pharmacokinetic properties. As the understanding of bioisosterism has evolved, capabilities to undertake more ambitious bioisosteric replacements have emerged. Scaffold hopping is a broadly used term in the literature referring to a variety of different bioisosteric replacement strategies, ranging from simple heterocyclic replacements to topological structural overhauls. In this work, we have highlighted recent applications of scaffold hopping in the central nervous system drug discovery space. While we have highlighted the benefits of using scaffold hopping approaches in central nervous system drug discovery, these are also widely applicable to other medicinal chemistry fields. We also recommend a shift toward the use of more refined and meaningful terminology within the realm of scaffold hopping.
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Affiliation(s)
- Timothy B Callis
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - Taylor R Garrett
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | | | - Jonathan J Danon
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - Michael Kassiou
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
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Gentile D, Coco A, Patamia V, Zagni C, Floresta G, Rescifina A. Targeting the SARS-CoV-2 HR1 with Small Molecules as Inhibitors of the Fusion Process. Int J Mol Sci 2022; 23:10067. [PMID: 36077465 PMCID: PMC9456533 DOI: 10.3390/ijms231710067] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 11/16/2022] Open
Abstract
The rapid and global propagation of the novel human coronavirus that causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced an immediate urgency to discover promising targets for the treatment of this virus. In this paper, we studied the spike protein S2 domain of SARS-CoV-2 as it is the most conserved component and controls the crucial fusion process of SARS-CoV-2 as a target for different databases of small organic compounds. Our in silico methodology, based on pharmacophore modeling, docking simulation and molecular dynamics simulations, was first validated with ADS-J1, a potent small-molecule HIV fusion inhibitor that has already proved effective in binding the HR1 domain and inhibiting the fusion core of SARS-CoV-1. It then focused on finding novel small molecules and new peptides as fusion inhibitors. Our methodology identified several small molecules and peptides as potential inhibitors of the fusion process. Among these, NF 023 hydrate (MolPort-006-822-583) is one of the best-scored compounds. Other compounds of interest are ZINC00097961973, Salvianolic acid, Thalassiolin A and marine_160925_88_2. Two interesting active peptides were also identified: AP00094 (Temporin A) and AVP1227 (GBVA5). The inhibition of the spike protein of SARS-CoV-2 is a valid target to inhibit the virus entry in human cells. The discussed compounds reported in this paper led to encouraging results for future in vitro tests against SARS-CoV-2.
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Affiliation(s)
| | | | | | | | | | - Antonio Rescifina
- Dipartimento di Scienze del Farmaco e Della Salute, Università di Catania, Viale A. Doria 6, 95125 Catania, Italy
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The Psychonauts' Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity. Pharmaceuticals (Basel) 2021; 14:ph14080720. [PMID: 34451817 PMCID: PMC8398354 DOI: 10.3390/ph14080720] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 12/28/2022] Open
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.
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Abstract
This paper is the forty-second consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2019 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).
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Affiliation(s)
- Richard J Bodnar
- Department of Psychology and Neuropsychology Doctoral Sub-Program, Queens College, City University of New York, 65-30 Kissena Blvd., Flushing, NY, 11367, United States.
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Sakamuru S, Zhao J, Xia M, Hong H, Simeonov A, Vaisman I, Huang R. Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors. J Chem Inf Model 2021; 61:2675-2685. [PMID: 34047186 DOI: 10.1021/acs.jcim.1c00439] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.
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Affiliation(s)
- Srilatha Sakamuru
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States.,Bioinformatics and Computational Biology, School of Systems Biology, College of Science, George Mason University, Manassas, Virginia 20110, United States
| | - Jinghua Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Menghang Xia
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration (FDA), Jefferson, Arkansas 72079, United States
| | - Anton Simeonov
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Iosif Vaisman
- Bioinformatics and Computational Biology, School of Systems Biology, College of Science, George Mason University, Manassas, Virginia 20110, United States
| | - Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
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Floresta G, Abbate V. Machine learning vs. field 3D-QSAR models for serotonin 2A receptor psychoactive substances identification. RSC Adv 2021; 11:14587-14595. [PMID: 35424006 PMCID: PMC8697832 DOI: 10.1039/d1ra01335a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/13/2021] [Indexed: 02/06/2023] Open
Abstract
Serotonergic psychedelics, substances exerting their effects primarily through the serotonin 2A receptor (5HT2AR), continue to comprise a substantial portion of reported new psychoactive substances (NPS). In this paper five quantitative structure-activity relationship (QSAR) models for predicting the affinity of 5-HT2AR ligands have been developed. The resulting models, exploiting the accessibility of the QSAR equations, generate a useful tool for the investigation and identification of unclassified molecules. The models have been built using a set of 375 molecules using Forge software, and the quality was confirmed by statistical analysis, resulting in effective tools with respect to their predictive and descriptive capabilities. The best performing algorithm among the machine learning approaches and the classical field 3D-QSAR model were then combined to produce a consensus model and were exploited, together with a pharmacophorefilter, to explore the 5-HT2AR activity of 523 105 natural products, to classify a set of recently reported 5-HT2AR NPS and to design new potential active molecules. The findings of this study should facilitate the identification and classification of emerging 5-HT2AR ligands including NPS.
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Affiliation(s)
- Giuseppe Floresta
- Department of Analytical, Environmental and Forensic Sciences, King's College London London UK
| | - Vincenzo Abbate
- Department of Analytical, Environmental and Forensic Sciences, King's College London London UK
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Jia X, Ciallella HL, Russo DP, Zhao L, James MH, Zhu H. Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2021; 9:3909-3919. [PMID: 34239782 PMCID: PMC8259887 DOI: 10.1021/acssuschemeng.0c09139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow proven to be applicable by screening new analgesic opioids. To this end, a large opioid data set was used as the probe to automatically obtain bioassay data from the PubChem portal. There were 114 PubChem bioassays selected to build quantitative structure-activity relationship (QSAR) models based on the testing results across the probe compounds. The compounds tested in each bioassay were used to develop 12 models using the combination of three machine learning approaches and four types of chemical descriptors. The model performance was evaluated by the coefficient of determination (R 2) obtained from 5-fold cross-validation. In total, 49 models developed for 14 bioassays were selected based on the criteria and were identified to be mainly associated with binding affinities to different opioid receptors. The models for these 14 bioassays were further used to fill data gaps in the probe opioids data set and to predict general drug compounds in the DrugBank data set. This study provides a universal modeling strategy that can take advantage of large public data sets for computer-aided drug design (CADD).
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Affiliation(s)
- Xuelian Jia
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States
| | - Heather L Ciallella
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States
| | - Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States
| | - Morgan H James
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University and Rutgers Biomedical Health Sciences, Piscataway, New Jersey 08854, United States; Brain Health Institute, Rutgers University and Rutgers Biomedical and Health Sciences, Piscataway, New Jersey 08854, United States
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States; Department of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
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Overview of the major classes of new psychoactive substances, psychoactive effects, analytical determination and conformational analysis of selected illegal drugs. OPEN CHEM 2021. [DOI: 10.1515/chem-2021-0196] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Abstract
The misuse of psychoactive substances is attracting a great deal of attention from the general public. An increase use of psychoactive substances is observed among young people who do not have enough awareness of the harmful effects of these substances. Easy access to illicit drugs at low cost and lack of effective means of routine screening for new psychoactive substances (NPS) have contributed to the rapid increase in their use. New research and evidence suggest that drug use can cause a variety of adverse psychological and physiological effects on human health (anxiety, panic, paranoia, psychosis, and seizures). We describe different classes of these NPS drugs with emphasis on the methods used to identify them and the identification of their metabolites in biological specimens. This is the first review that thoroughly gives the literature on both natural and synthetic illegal drugs with old known data and very hot new topics and investigations, which enables the researcher to use it as a starting point in the literature exploration and planning of the own research. For the first time, the conformational analysis was done for selected illegal drugs, giving rise to the search of the biologically active conformations both theoretically and using lab experiments.
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Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach. Mar Drugs 2019; 17:md17110624. [PMID: 31683588 PMCID: PMC6891735 DOI: 10.3390/md17110624] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 10/17/2019] [Accepted: 10/29/2019] [Indexed: 12/12/2022] Open
Abstract
Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have received interest following the recent publication of their pharmacologically beneficial effects. Recently, it was revealed that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered, but, unfortunately, none have reached the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FABP4 inhibitors; hence, different structure- and ligand-based computational approaches have been used for their identification. In this paper, we searched for new potentially active FABP4 ligands in the Marine Natural Products (MNP) database. We retrieved 14,492 compounds from this database and filtered through them with a statistical and computational filter. Seven compounds were suggested by our methodology to possess a potential inhibitory activity upon FABP4 in the range of 97–331 nM. ADMET property prediction was performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives. From these analyses, three molecules that are excellent candidates for becoming new drugs were found.
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