New role of the antidepressant imipramine as a Fascin1 inhibitor in colorectal cancer cells

Colorectal cancer: Antitumor antidepressant The antidepressant drug imipramine can block the activity of a protein that contributes to the progression of certain aggressive tumors. Serrated adenocarcinoma (SAC) is a form of colorectal cancer with a poor prognosis. A key factor in SAC development is the overexpression of the protein fascin1, which promotes the formation of structures that help cancer cells move around, thereby leading to metastasis. Pablo Conesa-Zamora at Santa Lucia University Hospital in Cartagena, Horacio Perez-Sanchez at the Universidad Catolica de Murcia in Guadalupe, Spain, and coworkers demonstrated that imipramine shows promise in binding to fascin1 and blocking its activity. The team analyzed over 9500 compounds as potential fascin1 blockers, identifying imipramine as a possible option. In tests on human tissues and in vivo studies using zebrafish, the drug reduced cancer invasion and metastasis. Serrated adenocarcinoma (SAC) is more invasive, has worse outcomes than conventional colorectal carcinoma (CRC), and is characterized by frequent resistance to anti-epidermal growth factor receptor (EGFR) and overexpression of fascin1, a key protein in actin bundling that plays a causative role in tumor invasion and is overexpressed in different cancer types with poor prognosis. In silico screening of 9591 compounds, including 2037 approved by the Food and Drug Administration (FDA), was performed, and selected compounds were analyzed for their fascin1 binding affinity by differential scanning fluorescence. The results were compared with migrastatin as a typical fascin1 inhibitor. In silico screening and differential scanning fluorescence yielded the FDA-approved antidepressant imipramine as the most evident potential fascin1 blocker. Biophysical and different in vitro actin-bundling assays confirm this activity. Subsequent assays investigating lamellipodia formation and migration and invasion of colorectal cancer cells in vitro using 3D human tissue demonstrated anti-fascin1 and anti-invasive activities of imipramine. Furthermore, expression profiling suggests the activity of imipramine on the actin cytoskeleton. Moreover, in vivo studies using a zebrafish invasion model showed that imipramine is tolerated, its anti-invasive and antimetastatic activities are dose-dependent, and it is associated with both constitutive and induced fascin1 expression. This is the first study that demonstrates an antitumoral role of imipramine as a fascin1 inhibitor and constitutes a foundation for a molecular targeted therapy for SAC and other fascin1-overexpressing tumors.Peer reviewe

SENSAAS (SENsitive Surface As A Shape): utilizing open-source algorithms for 3D point cloud alignment of molecules

Open-source 3D data processing libraries originally developed for computer vision and pattern recognition are used to align and compare molecular shapes and sub-shapes. Here, a shape is represented by a set of points distributed on the van der Waals surface of molecules. Each point is colored by its closest atom, which itself belongs to a user defined class. The strength of this representation is that it allows for comparisons of point clouds of different kind of chemical entities: small molecules, peptides, proteins or cavities (the negative image of th

In vitro antibacterial efficacy of plants used by an Indian aboriginal tribe against pathogenic bacteria isolated from clinical samples

AbstractObjectivesTo evaluate antibacterial efficacies of 21 medicinal plants used by an Indian aboriginal tribe against infectious diseases caused by bacteria isolated from clinical samples.MethodsStandard biochemical procedures were followed for identifying bacteria that were isolated from several clinical samples. All of the bacterial strains were subjected to antibiotic sensitivity tests by Kirby–Bauer's disc diffusion method. From antibiograms of isolated Gram-positive and Gram-negative bacteria, it was discernible that samples were multidrug resistant (MDR). The methanol leaf-extract of Solanum xanthocarpum was subjected to thin layer chromatography (TLC) for phytochemical analysis. Molecular docking of β-lactamase enzyme of Escherichia coli with phytochemicals of S. xanthocarpum was performed to locate effective compounds.ResultsThe most effective 5 plants, which caused the size of the zone of inhibition to range from 21 to 27 mm, were Buchanania latifolia, Careya arborea, Ocimum tenuiflorum, Senna alata and S. xanthocarpum, for MDR bacteria. S. xanthocarpum had the lowest MIC value of 0.67 mg/ml and the lowest MBC value of 1.51 mg/ml against E. coli. In the TLC study, 9 spots of methanol leaf-extract of S. xanthocarpum were recorded with two solvent systems. The phytochemicals of S. xanthocarpum, solasodine and stigmasterol glucoside had the highest docking score values, −10.868 kcal/mol and −10.439 kcal/mol, respectively, against β-lactamase.ConclusionThis study could prove in vitro antimicrobial efficacy of 5 uncommon plants against MDR pathogenic bacteria. Solasodine and stigmasterol glucoside were computationally recorded as the best controlling chemicals from the plant S. xanthocarpum

Next generation 3D pharmacophore modeling

3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond

Rational drug design of antineoplastic agents using 3D-QSAR, cheminformatic, and virtual screening approaches

Support was kindly provided by the EU COST Action CM1406 and CA15135. KN and JV kindly acknowledge national project number 172033 supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.PostprintPeer reviewe

Computational Ligand-Based CNS Therapeutic Design: The Search for Novel-Scaffold Norepinephrine Transporter Inhibitors

Monoamine transporter (MAT) proteins are responsible for regulating cellular signal transduction through control of neurotransmitter reuptake in the synapse, and are therefore relevant to diseases including addiction, psychosis, anxiety and depression. MATs, specifically the serotonin transporter (SERT or 5-HTT), norepinephrine transporter (NET), and dopamine transporter (DAT), serve as the principal targets for antidepressant drugs, such as SSRIs (selective serotonin reuptake inhibitors), NRIs (norepinephrine reuptake inhibitors) and TCAs (tricyclic antidepressants), as well as psychostimulant drugs of abuse such as cocaine and the amphetamines. Due to a lack of crystallographic MAT data, it is unclear as to which of two MAT protein ligand binding sites these drugs bind, hindering knowledge of the specific binding modes of MAT ligands. In this study an in silico pharmacophore model was created using a ligand-based method aimed at drug screening for the ability to specifically inhibit NET, using Molecular Operating Environment software. A group of four structurally-diverse compounds with high NET binding affinities comprised the training set used to generate the model. A test set, which included ten compounds with a range of known NET affinities, served in the validation of the model. The constructed pharmacophore model selected all high affinity NET inhibitors and one relatively inactive compound from the test set. Following model validation, the ZINC small molecule structural database was virtually screened to identify novel MAT inhibitor candidates. Hit compounds were ranked by an overlay score, which calculated how well novel compounds aligned to the original training set alignment. Six top-ranking compounds were purchased and evaluated via in vitro pharmacology to determine the binding affinity at the MATs. Although no significant inhibition was observed at the MATs, compound AC-1 showed a 15% inhibition at the DAT in radioligand binding assays. This result suggests that with further refinement of key pharmacophore features or alteration of the AC-1 structure, more potent MAT inhibitors could be discovered. Pharmacophore-based drug design has become one of the most important tools in drug discovery. Using the molecular modeling approaches described in this study, it is possible to rationally design novel and more selective central nervous system drugs

SARs for the Antiparasitic Plant metabolite Pulchrol

Pulchrol, a natural compound isolated from the roots of the vegetal specie Bourreria pulchra has been shown to possess potential antiparasitic activity toward Trypanosomatids, particularly against Trypanosoma cruzi, which causes the Chagas disease; and moderately against Leishmania species, responsible for Leishmaniasis. In this investigation, several pulchrol analogues were prepared and assayed toward T. cruzi epimastigotes, and L. braziliensis and L. amazonensis promastigotes, to develop structure activity relationship studies (SARs). Analogues with transformations in the three rings of the pulchrol’s scaffold were prepared. Initially, compounds with transformations at the benzylic position in the A-ring were assayed to evaluate the role of the benzylic alcohol in pulchrol. The results showed that an hydrogen bond acceptor group is important for the antitrypanosomatid activity and that ester groups with bulky alkyl substituents increase the potency toward all parasites. Analogues with transformations in the B- and C-rings, were focused on the variation of lipophilicity. In the B-ring, the methyl substituents placed at position 6 in pulchrol were replaced for two hydrogen atoms, just one methyl substituent, or two longer alkyl substituents. The biological activity results showed that longer chains with less than four carbon atoms are benefitial for the activity. A methoxy subtituent is placed at position 2 in pulchrol’s C-ring, in this study, analogues with the methoxy subtituent placed in different positions or replaced with alkyl subtituents were prepared, the results showed that compounds with hydrophobic groups in the C-ring incresed the potency.Several analogues with more than one modification in different rings were also prepared. The combination of carbonyl groups in the A-ring with bulky alkyl groups in the C-ring was the most benefitial for the activity. In contrast, esters subtituted with a hydrophobic group in the A-ring and bulky alkyl groups in the C-ring hampered the activity. A hydrogen bond acceptor at the benzylic position in the A-ring, as well as an additional hydroxyl group at position 1 in the C-ring (as in cannabinol) appeared to be important for the activity. The combination of different functionalities also seemed to have and effect in the orientation of the molecule inside the target protein. Our results showed that differences between the active sites for the different parasites may exist, however, preliminary pharmacophore hypotheses based on our biological results showed that the main pharmacophoric features are two hydrogen bond acceptor groups (one at the benzylic position and one on the B-ring’s oxigen) and three hydrophobic features (two in the B-ring at position 6, and one in the C-ring at position 2 or 3).A qualitative evaluation of ADMET-descriptors calculated in silico, showed that most of the molecules have potential as orally administered substances, however, further studies focused on the development of compounds with more potency and focused on the optimization of the ADME characteristics are recommended

Structural Pattern Recognition for Chemical-Compound Virtual Screening

Les molècules es configuren de manera natural com a xarxes, de manera que són ideals per estudiar utilitzant les seves representacions gràfiques, on els nodes representen àtoms i les vores representen els enllaços químics. Una alternativa per a aquesta representació directa és el gràfic reduït ampliat, que resumeix les estructures químiques mitjançant descripcions de nodes de tipus farmacòfor per codificar les propietats moleculars rellevants. Un cop tenim una manera adequada de representar les molècules com a gràfics, hem de triar l’eina adequada per comparar-les i analitzar-les. La distància d'edició de gràfics s'utilitza per resoldre la concordança de gràfics tolerant als errors; aquesta metodologia calcula la distància entre dos gràfics determinant el nombre mínim de modificacions necessàries per transformar un gràfic en l’altre. Aquestes modificacions (conegudes com a operacions d’edició) tenen associat un cost d’edició (també conegut com a cost de transformació), que s’ha de determinar en funció del problema. Aquest estudi investiga l’eficàcia d’una comparació molecular basada només en gràfics que utilitza gràfics reduïts ampliats i distància d’edició de gràfics com a eina per a aplicacions de cribratge virtual basades en lligands. Aquestes aplicacions estimen la bioactivitat d'una substància química que utilitza la bioactivitat de compostos similars. Una part essencial d’aquest estudi es centra en l’ús d’aprenentatge automàtic i tècniques de processament del llenguatge natural per optimitzar els costos de transformació utilitzats en les comparacions moleculars amb la distància d’edició de gràfics.Las moléculas tienen la forma natural de redes, lo que las hace ideales para estudiar mediante el empleo de sus representaciones gráficas, donde los nodos representan los átomos y los bordes representan los enlaces químicos. Una alternativa para esta representación sencilla es el gráfico reducido extendido, que resume las estructuras químicas utilizando descripciones de nodos de tipo farmacóforo para codificar las propiedades moleculares relevantes. Una vez que tenemos una forma adecuada de representar moléculas como gráficos, debemos elegir la herramienta adecuada para compararlas y analizarlas. La distancia de edición de gráficos se utiliza para resolver la coincidencia de gráficos tolerante a errores; esta metodología estima una distancia entre dos gráficos determinando el número mínimo de modificaciones necesarias para transformar un gráfico en el otro. Estas modificaciones (conocidas como operaciones de edición) tienen un costo de edición (también conocido como costo de transformación) asociado, que debe determinarse en función del problema. Este estudio investiga la efectividad de una comparación molecular basada solo en gráficos que emplea gráficos reducidos extendidos y distancia de edición de gráficos como una herramienta para aplicaciones de detección virtual basadas en ligandos. Estas aplicaciones estiman la bioactividad de una sustancia química empleando la bioactividad de compuestos similares. Una parte esencial de este estudio se centra en el uso de técnicas de procesamiento de lenguaje natural y aprendizaje automático para optimizar los costos de transformación utilizados en las comparaciones moleculares con la distancia de edición de gráficos.Molecules are naturally shaped as networks, making them ideal for studying by employing their graph representations, where nodes represent atoms and edges represent the chemical bonds. An alternative for this straightforward representation is the extended reduced graph, which summarizes the chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Once we have a suitable way to represent molecules as graphs, we need to choose the right tool to compare and analyze them. Graph edit distance is used to solve the error-tolerant graph matching; this methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications (known as edit operations) have an edit cost (also known as transformation cost) associated, which must be determined depending on the problem. This study investigates the effectiveness of a graph-only driven molecular comparison employing extended reduced graphs and graph edit distance as a tool for ligand-based virtual screening applications. Those applications estimate the bioactivity of a chemical employing the bioactivity of similar compounds. An essential part of this study focuses on using machine learning and natural language processing techniques to optimize the transformation costs used in the molecular comparisons with the graph edit distance. Overall, this work shows a framework that combines graph reduction and comparison with optimization tools and natural language processing to identify bioactivity similarities in a structurally diverse group of molecules. We confirm the efficiency of this framework with several chemoinformatic tests applied to regression and classification problems over different publicly available datasets