Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR.

Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.

Drug screening of α-amylase inhibitors as candidates for treating diabetes.

In the present study, the identification of potential α-amylase inhibitors is explored as a potential strategy for treating type-2 diabetes mellitus. A computationally driven approach using molecular docking was employed to search for new α-amylase inhibitors. The interactions of potential drugs with the enzyme's active site were investigated and compared with the contacts established by acarbose (a reference drug for α-amylase inhibition) in the crystallographic structure 1B2Y. For this active site characterization, both molecular docking and molecular dynamics simulations were performed, and the residues involved in the α-amylase-acarbose complex were considered to analyse the potential drug's interaction with the enzyme. Two potential α-amylase inhibitors (AN-153I105594 and AN-153I104845) have been selected following this computational strategy. Both compounds established a large number of interactions with key binding site α-amylase amino acids and obtained a comparable docking score concerning the reference drug (acarbose). Aiming to further analyse candidates' properties, their ADME (absorption, distribution, metabolism, excretion) parameters, druglikeness, organ toxicity, toxicological endpoints and median lethal dose (LD50 ) were estimated. Overall estimations are promising for both candidates, and in silico toxicity predictions suggest that a low toxicity should be expected.

Macrolides May Prevent Severe Acute Respiratory Syndrome Coronavirus 2 Entry into Cells: A Quantitative Structure Activity Relationship Study and Experimental Validation.

The global pandemic caused by the emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening the health and economic systems worldwide. Despite the enormous efforts of scientists and clinicians around the world, there is still no drug or vaccine available worldwide for the treatment and prevention of the infection. A rapid strategy for the identification of new treatments is based on repurposing existing clinically approved drugs that show antiviral activity against SARS-CoV-2 infection. In this study, after developing a quantitative structure activity relationship analysis based on molecular topology, several macrolide antibiotics are identified as promising SARS-CoV-2 spike protein inhibitors. To confirm the in silico results, the best candidates were tested against two human coronaviruses (i.e., 229E-GFP and SARS-CoV-2) in cell culture. Time-of-addition experiments and a surrogate model of viral cell entry were used to identify the steps in the virus life cycle inhibited by the compounds. Infection experiments demonstrated that azithromycin, clarithromycin, and lexithromycin reduce the intracellular accumulation of viral RNA and virus spread as well as prevent virus-induced cell death, by inhibiting the SARS-CoV-2 entry into cells. Even though the three macrolide antibiotics display a narrow antiviral activity window against SARS-CoV-2, it may be of interest to further investigate their effect on the viral spike protein and their potential in combination therapies for the coronavirus disease 19 early stage of infection.

Targeting the JAK/STAT Pathway: A Combined Ligand- and Target-Based Approach.

Janus kinases (JAKs) are a family of proinflammatory enzymes able to mediate the immune responses and the inflammatory cascade by modulating multiple cytokine expressions as well as various growth factors. In the present study, the inhibition of the JAK-signal transducer and activator of transcription (STAT) signaling pathway is explored as a potential strategy for treating autoimmune and inflammatory disorders. A computationally driven approach aimed at identifying novel JAK inhibitors based on molecular topology, docking, and molecular dynamics simulations was carried out. For the best candidates selected, the inhibitory activity against JAK2 was evaluated in vitro. Two hit compounds with a novel chemical scaffold, 4 (IC50 = 0.81 µM) and 7 (IC50 = 0.64 µM), showed promising results when compared with the reference drug Tofacitinib (IC50 = 0.031 µM).

Identification of New Templates for the Synthesis of BEA, BEC, and ISV Zeolites Using Molecular Topology and Monte Carlo Techniques.

The presence of organic structure directing agents (templates) in the synthesis of zeolites allows the synthesis to be directed, in many cases, toward structures in which there is a large stabilization between the template and the zeolite micropore due to dispersion interactions. Although other factors are also important (temperature, pH, Si/Al ratio, etc.), systems with strong zeolite-template interactions are good candidates for an application of new computational algorithms, for instance those based in molecular topology (MT), that can be used in combination with large databases of organic molecules. Computational design of new templates allows the synthesis of existing and new zeolites to be expanded and refined. Three zeolites with similar 3-D large pore systems, BEA, BEC, and ISV, were selected with the aim of finding new templates for their selective syntheses. Using a training set of active and inactive templates (obtained from the literature) for the synthesis of target zeolites, it was possible to select chemical descriptors related to activity, meaning a good candidate template. With a discriminant function defined upon MT, the screening through a database of organic molecules led to a small subset (preselection) of candidate templates for the synthesis of BEA, BEC, and ISV. As far as we know, this is the first time that topological/topochemical descriptors, which do not consider 3-D information on the molecules, have been used to predict the activity of zeolite structure directing agents (SDAs). Following the prediction of SDAs using MT, an automated approach of sequential template filling of micropores based on a combination of Monte Carlo and lattice energy minimization was applied for all the candidate templates in the three zeolites. Two results can be obtained from this: an evaluation of the quality of the molecular topology QSAR models leading to the preselection of templates, and a final selection of candidate templates for the selective synthesis of BEA, BEC, and ISV. Regarding the latter, a good template will be that which maximizes the zeolite-template dispersion interactions with one, and only one, of the three zeolites. The presented methodology can be used to find alternative (maybe cheaper or perhaps more selective) templates than those already known.

Molecular topology and QSAR multi-target analysis to boost the in silico research for fungicides in agricultural chemistry.

The aim of the present study is to show how molecular topology can be a powerful in silico tool for the prediction of the fungicidal activity of several diphenylamine derivatives against three fungal species (cucumber downy mildew, rice blast and cucumber gray mold). A multi-target QSAR model was developed, and two strategies were followed. First is the construction of a virtual library of molecules using DesMol2 program and a subsequent selection of potential active ones. Second is the selection of molecules from the literature on the basis of molecular scaffolds. More than 700 diphenylamine derivatives designed and other 60 fluazinam's derivatives with structural similarity higher than 80% were studied. Almost twenty percent of the molecules analyzed show potential activity against the three fungal species.

DesMol2, an Effective Tool for the Construction of Molecular Libraries and Its Application to QSAR Using Molecular Topology.

A web application, DesMol2, which offers two main functionalities, is presented: the construction of molecular libraries and the calculation of topological indices. These functionalities are explained through a practical example of research of active molecules to the formylpeptide receptor (FPR), a receptor associated with chronic inflammation in systemic amyloidosis and Alzheimer's disease. Starting from a data(base) of 106 dioxopiperazine pyrrolidin piperazine derivatives and their respective constant values of binding affinity to FPR, multilinear regression and discriminant analyses are performed to calculate several predictive topological-mathematical models. Next, using the DesMol2 application, a molecular library consisting of 6,120 molecules is built and performed for each predictive model. The best potential active candidates are selected and compared with results from other previous works.

Molecular topology: a strategy to identify novel compounds against ulcerative colitis.

In the present paper, a strategy to identify novel compounds against ulcerative colitis (UC) by molecular topology (MT) is presented. Several quantitative structure-activity relationship (QSAR) models based on molecular topology have been developed to predict inducible nitric oxide synthase (iNOS) and tumor necrosis factor alpha ([Formula: see text]) mediated anti-ulcerative colitis (UC) activity and protective activity against a dextran sulfate sodium (DSS)-induced UC model. Each one has been used for the screening of four previously selected compounds as potential therapeutic agents for UC: alizarin-3-methyliminodiacetic acid (AMA), Calcein, (+)-dibenzyl-L-tartrate, and Ro 41-0960. These four compounds were then tested in vitro and in vivo and confirmed AMA and Ro 41-0960 as the best lead candidates for further development against UC.

Predicting antiprotozoal activity of benzyl phenyl ether diamine derivatives through QSAR multi-target and molecular topology.

Multi-target QSAR is a novel approach that can predict simultaneously the activity of a given chemical compound on different pharmacological targets. In this work, we have used molecular topology and statistical tools such as multilinear regression analysis and artificial neural networks, to achieve a multi-target QSAR model capable to predict the antiprotozoal activity of a group of benzyl phenyl ether diamine derivatives. The activity was related to three parasites with a high prevalence rate in humans: Trypanosoma brucei rhodesiense, Plasmodium falciparum, and Leishmania donovani. The multi-target model showed a high regression coefficient (R(2) = 0.9644 and R(2) = 0.9235 for training and test sets, respectively) and a low standard error of estimate (SEE = 0.279). Model validation was performed with an external test (R(2) = 0.9001) and a randomization analysis. Finally, the model was applied to the search of potential new active compounds.

Molecular topology applied to the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a non-ligand-binding-pocket antiandrogen.

We report the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a novel non-ligand binding pocket (non-LBP) antagonist of the androgen receptor (AR) through the application of molecular topology techniques. This compound, validated through time-resolved fluorescence resonance energy transfer and fluorescence polarization biological assays, provides the basis for lead optimization and structure-activity relationship analysis of a new series of non-LBP AR antagonists. Induced-fit docking and molecular dynamics studies have been performed to establish a consistent hypothesis for the interaction of the new active molecule on the AR surface.

Rational Design of a Potential New Nematicide Targeting Chitin Deacetylase.

In a previously published study, the authors devised a molecular topology QSAR (quantitative structure-activity relationship) approach to detect novel fungicides acting as inhibitors of chitin deacetylase (CDA). Several of the chosen compounds exhibited noteworthy activity. Due to the close relationship between chitin-related proteins present in fungi and other chitin-containing plant-parasitic species, the authors decided to test these molecules against nematodes, based on their negative impact on agriculture. From an overall of 20 fungal CDA inhibitors, six showed to be active against Caenorhabditis elegans. These experimental results made it possible to develop two new molecular topology-based QSAR algorithms for the rational design of potential nematicides with CDA inhibitor activity for crop protection. Linear discriminant analysis was employed to create the two algorithms, one for identifying the chemo-mathematical pattern of commercial nematicides and the other for identifying nematicides with activity on CDA. After creating and validating the QSAR models, the authors screened several natural and synthetic compound databases, searching for alternatives to current nematicides. Finally one compound, the N2-(dimethylsulfamoyl)-N-{2-[(2-methyl-2-propanyl)sulfanyl]ethyl}-N2-phenylglycinamide or nematode chitin deacetylase inhibitor, was selected as the best candidate and was further investigated both in silico, through molecular docking and molecular dynamic simulations, and in vitro, through specific experimental assays. The molecule shows favorable binding behavior on the catalytic pocket of C. elegans CDA and the experimental assays confirm potential nematicide activity.

Molecular topology - dissimilar similarities.

The present paper depicts the role of molecular topology in the study of similarity-dissimilarity between molecular structures. It proves that molecular topology represents a very useful tool for getting common patterns of pharmacological activity and hence an efficient approach for the search of novel lead drugs.

Novel potential agents for ulcerative colitis by molecular topology: suppression of IL-6 production in Caco-2 and RAW 264.7 cell lines.

Ulcerative colitis (UC) is an immune-mediated chronic and relapsing intestinal inflammatory disease. Interleukin (IL)-6, a pro-inflammatory cytokine, plays a key role in the uncontrolled intestinal inflammatory process, which is a main characteristic of UC. In this work, a quantitative structure-activity relationship model based on molecular topology (MT) has been built up to predict the IL-6 mediated anti-UC activity. After an external validation of the model, a virtual screening of the MicroSource Pure Natural Products Collection and Sigma-Aldrich databases was carried out looking for potential new active compounds. From the entire set of compounds labeled as active by the model, four of them, namely alizarin-3-methylimino-N,N-diacetic acid (AMA), Calcein, (+)-dibenzyl-L-tartrate (DLT), and Ro 41-0960, were tested in vitro by determination of IL-6 production in two cell lines (RAW 264.7 and Caco-2). The results demonstrate that three of them were able to significantly reduce IL-6 levels in both cell lines and particularly one, namely Ro 41-0960. These results confirm MT's efficacy as a tool for the selection of compounds potentially active in UC.

Elucidating the mechanism of action of mycotoxins through machine learning-driven QSAR models: Focus on lipid peroxidation.

Understanding the mechanisms of mycotoxin toxicity is crucial for establishing effective guidelines and preventive strategies. In this study, machine learning models based on quantitative structure-activity relationship (QSAR) were employed to predict the lipid peroxidation activity of mycotoxins. Two different algorithms using Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs) have been trained using a dataset of 70 mycotoxins. The LDA model had an average correct classification rate of 91%, while the ANN model achieved a perfect 100% classification rate. Following an internal validation process, the models were utilized to predict mycotoxins with known lipid peroxidation activity. The machine learning models achieved an 88% correct classification rate for these mycotoxins. Finally, by utilizing classified algorithms, the study aimed to infer the mechanism of action related to lipid peroxidation for 91 unstudied mycotoxins. These models provide a fast, accurate, and cost-effective means to assess the potential toxicity and mechanism of action of mycotoxins. The findings of this study contribute to a comprehensive understanding of mycotoxin toxicology and assist researchers and toxicologists in evaluating health risks associated with mycotoxin exposure and developing appropriate preventive strategies and potential therapeutic interventions to mitigate the effects of mycotoxins.

Modeling drug-induced anorexia by molecular topology.

Molecular topology (MT) has demonstrated to be a very good technique for describing molecular structures and to predict physical, chemical, and biological properties of compounds. In this paper, a topological-mathematical model based on MT has been developed for identifying drug compounds showing anorexia as a side effect. An external validation (test set) has been carried out, yielding over an 80% correct classification in the active and inactive compounds. These results reinforce the role of MT as a potential useful tool for predicting drug side effects.

How Molecular Topology Can Help in Amyotrophic Lateral Sclerosis (ALS) Drug Development: A Revolutionary Paradigm for a Merciless Disease.

Even if amyotrophic lateral sclerosis is still considered an orphan disease to date, its prevalence among the population is growing fast. Despite the efforts made by researchers and pharmaceutical companies, the cryptic information related to the biological and physiological onset mechanisms, as well as the complexity in identifying specific pharmacological targets, make it almost impossible to find effective treatments. Furthermore, because of complex ethical and economic aspects, it is usually hard to find all the necessary resources when searching for drugs for new orphan diseases. In this context, computational methods, based either on receptors or ligands, share the capability to improve the success rate when searching and selecting potential candidates for further experimentation and, consequently, reduce the number of resources and time taken when delivering a new drug to the market. In the present work, a computational strategy based on Molecular Topology, a mathematical paradigm capable of relating the chemical structure of a molecule to a specific biological or pharmacological property by means of numbers, is presented. The result was the creation of a reliable and accessible tool to help during the early in silico stages in the identification and repositioning of potential hits for ALS treatment, which can also apply to other orphan diseases. Considering that further computational and experimental results will be required for the final identification of viable hits, three linear discriminant equations combined with molecular docking simulations on specific proteins involved in ALS are reported, along with virtual screening of the Drugbank database as a practical example. In this particular case, as reported, a clinical trial has been already started for one of the drugs proposed in the present study.

COVID-19 and the Importance of Being Prepared: A Multidisciplinary Strategy for the Discovery of Antivirals to Combat Pandemics.

During an emergency, such as a pandemic in which time and resources are extremely scarce, it is important to find effective and rapid solutions when searching for possible treatments. One possibility in this regard is the repurposing of available "on the market" drugs. This is a proof of the concept study showing the potential of a collaboration between two research groups, engaged in computer-aided drug design and control of viral infections, for the development of early strategies to combat future pandemics. We describe a QSAR (quantitative structure activity relationship) based repurposing study on molecular topology and molecular docking for identifying inhibitors of the main protease (Mpro) of SARS-CoV-2, the causative agent of COVID-19. The aim of this computational strategy was to create an agile, rapid, and efficient way to enable the selection of molecules capable of inhibiting SARS-CoV-2 protease. Molecules selected through in silico method were tested in vitro using human coronavirus 229E as a surrogate for SARS-CoV-2. Three strategies were used to screen the antiviral activity of these molecules against human coronavirus 229E in cell cultures, e.g., pre-treatment, co-treatment, and post-treatment. We found >99% of virus inhibition during pre-treatment and co-treatment and 90−99% inhibition when the molecules were applied post-treatment (after infection with the virus). From all tested compounds, Molport-046-067-769 and Molport-046-568-802 are here reported for the first time as potential anti-SARS-CoV-2 compounds.

Rational Design of Chitin Deacetylase Inhibitors for Sustainable Agricultural Use Based on Molecular Topology.

Fungicide resistance is a major concern in modern agriculture; therefore, there is a pressing demand to develop new, greener chemicals. Chitin is a major component of the fungal cell wall and a well-known elicitor of plant immunity. To overcome chitin recognition, fungal pathogens developed different strategies, with chitin deacetylase (CDA) activity being the most conserved. This enzyme is responsible for hydrolyzing the N-acetamido group in N-acetylglucosamine units of chitin to convert it to chitosan, a compound that can no longer be recognized by the plant. In previous works, we observed that treatments with CDA inhibitors, such as carboxylic acids, reduced the symptoms of cucurbit powdery mildew and induced rapid activation of chitin-triggered immunity, indicating that CDA could be an interesting target for fungicide development. In this work, we developed an in silico strategy based on QSAR (quantitative structure-activity relationship) and molecular topology (MT) to discover new, specific, and potent CAD inhibitors. Starting with the chemical structures of few carboxylic acids, with and without disease control activity, three predictive equations based on the MT paradigm were developed to identify a group of potential molecules. Their fungicidal activity was experimentally tested, and their specificity as CDA inhibitors was studied for the three best candidates by molecular docking simulations. To our knowledge, this is the first time that MT has been used for the identification of potential CDA inhibitors to be used against resistant powdery mildew strains. In this sense, we consider of special interest the discovery of molecules capable of stimulating the immune system of plants by triggering a defensive response against fungal species that are highly resistant to fungicides such as powdery mildew.

Topological virtual screening: a way to find new compounds active in ulcerative colitis by inhibiting NF-κB.

Ulcerative colitis and Crohn's disease are chronic, immune-mediated inflammatory diseases of the gastrointestinal tract. Nuclear Factor Kappa B (NF-κB) is a transcription factor that plays a key role in regulating expression of multiple inflammatory and immune genes. In this study, a Topological Virtual Screening study has been carried out to achieve a model capable of finding new compounds active in ulcerative colitis by inhibiting NF-κB. Different topological indices were used as structural descriptors, and their relation to biological activity was determined using linear discriminant analysis. A topological model consisting of two discriminant functions was built up. The first function focused in the discrimination between NF-κB active and inactive compounds, and the second one in distinguishing between compounds active and inactive on ulcerative colitis. The model was then applied sequentially to a large database of compounds with unknown activity. Twenty-eight of such compounds were predicted to be active and selected for in vitro and in vivo testing.

Modeling natural anti-inflammatory compounds by molecular topology.

One of the main pharmacological problems today in the treatment of chronic inflammation diseases consists of the fact that anti-inflammatory drugs usually exhibit side effects. The natural products offer a great hope in the identification of bioactive lead compounds and their development into drugs for treating inflammatory diseases. Computer-aided drug design has proved to be a very useful tool for discovering new drugs and, specifically, Molecular Topology has become a good technique for such a goal. A topological-mathematical model, obtained by linear discriminant analysis, has been developed for the search of new anti-inflammatory natural compounds. An external validation obtained with the remaining compounds (those not used in building up the model), has been carried out. Finally, a virtual screening on natural products was performed and 74 compounds showed actual anti-inflammatory activity. From them, 54 had been previously described as anti-inflammatory in the literature. This can be seen as a plus in the model validation and as a reinforcement of the role of Molecular Topology as an efficient tool for the discovery of new anti-inflammatory natural compounds.