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.

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.

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.

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.

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.

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.

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.

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).

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.

Molecular Topology QSAR Strategy for Crop Protection: New Natural Fungicides with Chitin Inhibitory Activity.

Nowadays, crop protection is a major concern and how to proceed is a delicate point of contention. New products must be safe and ecofriendly in accordance with the actual legislation. In this context, we present a quantitative structure-activity relationship strategy based on molecular topology as a tool for generating natural products as potential fungicides following a mechanism of action based on the synthesis of chitin inhibition (chitinase inhibition). Two discriminant equations using statistical linear discriminant analysis were used to identify three potential candidates (1-methylxanthine, hematommic acid, and antheraxanthin). The equations showed accuracy and specificity levels above 80%, minimizing the risk of selecting false active compounds.

What place does molecular topology have in today's drug discovery?

INTRODUCTION: Most methods in molecular and drug design are currently based on physicochemical descriptors. However, molecular topology, which relies on topological descriptors, has also shown value for molecular design even if it does not take into account the physical or chemical properties of ligands and receptors, including the ligand-receptor interaction itself. AREAS COVERED: Herein, the authors provide new insights into the importance of molecular topology according to some of the latest discoveries in physics and chemistry. Furthermore, the authors report on the most significant achievements in drug design using molecular topology over the last 5 years and give their expert perspectives on the subject as a whole. EXPERT OPINION: Molecular topology is a new paradigm which is independent of physicochemical molecular descriptors. This fact explains the viability of both the discovery of new lead compounds with a minimum of information derived from mathematical-topological patterns and the interpretation results in structural and physicochemical terms.

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.

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 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.

Alzheimer: A Decade of Drug Design. Why Molecular Topology can be an Extra Edge?

BACKGROUND: The last decade was characterized by a growing awareness about the severity of dementia in the field of age-related and no age-related diseases and about the importance to invest resources in the research of new, effective treatments. Among the dementias, Alzheimer's plays a substantial role because of its extremely high incidence and fatality. Several pharmacological strategies have been tried but still now, Alzheimer keeps being an untreatable disease. In literature, the number of QSAR related drug design attempts about new treatments for Alzheimer is huge, but only few results can be considered noteworthy. Providing a detailed analysis of the actual situation and reporting the most notable results in the field of drug design and discovery, the current review focuses on the potential of molecular topology as a reliable tool in finding new anti-Alzheimer lead compounds. METHODS: Published works on QSAR applied to the search of anti-Alzheimer's drugs during the last 10 years has been tracked. 2D and 3D-QSAR, HQSAR, topological indexes, etc. have been analyzed, as well as different mechanisms of action, such as MAO, AchE, etc. An example of topological indexes' application to the search of potential anti-Alzheimer drugs is reported. RESULTS: Results show that QSAR methods during the last decade represented an excellent approach to the search of new effective drugs against Alzheimer's. In particular, QSAR based on molecular topology allows the establishment of a direct structure-property link that results in the identification of new hits and leads. CONCLUSION: Molecular topology is a powerful tool for the discovery of new anti-Alzheimer drugs covering simultaneously different mechanisms of action, what may help to find a definitive cure for the disease.

Molecular topology: A new strategy for antimicrobial resistance control.

The control of antimicrobial resistance (AMR) seems to have come to an impasse. The use and abuse of antibacterial drugs has had major consequences on the genetic mutability of both pathogenic and nonpathogenic microorganisms, leading to the development of new highly resistant strains. Because of the complexity of this situation, an in silico strategy based on QSAR molecular topology was devised to identify synthetic molecules as antimicrobial agents not susceptible to one or several mechanisms of resistance such as: biofilms formation (BF), ionophore (IA) activity, epimerase (EI) activity or SOS system (RecA inhibition). After selecting a group of 19 compounds, five of them showed significant antimicrobial activity against several strains of Staphylococcus (2 S. aureus, including 1 methicillin resistant, and 1 S. epidermidis), with MIC values between 16 and 32 mg/L. Among the compounds active on RecA, one showed a marked activity in decreasing RecA gene expression in Escherichia coli.

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.

Latest advances in molecular topology applications for drug discovery.

INTRODUCTION: Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. In the last decade, its application has become more and more popular among the leading research groups in the field of quantitative structure-activity relationships (QSAR) and drug design. This has, in turn, contributed to the rapid development of new techniques and applications of MT in QSAR studies, as well as the introduction of new topological indices. AREAS COVERED: This review collates the main innovative techniques in the field of MT and provides a description of the novel topological indices recently introduced, through an exhaustive recompilation of the most significant works carried out by the leading research groups in the field of drug design and discovery. The objective is to show the importance of MT methods combined with the effectiveness of the descriptors. EXPERT OPINION: Recent years have witnessed a remarkable rise in QSAR methods based on MT and its application to drug design. New methodologies have been introduced in the area such as QSAR multi-target, Markov networks or perturbation methods. Moreover, novel topological indices, such as Bourgas' descriptors and other new concepts as the derivative of a graph or cliques capable to distinguish between conformers, have also been introduced. New drugs have also been discovered, including anticonvulsants, anineoplastics, antimalarials or antiallergics, just to name a few. In the authors' opinion, MT and QSAR have moved from an attractive possibility to representing a foundation stone in the process of drug discovery.

Novel cancer chemotherapy hits by molecular topology: dual Akt and Beta-catenin inhibitors.

BACKGROUND AND PURPOSE: Colorectal and prostate cancers are two of the most common types and cause of a high rate of deaths worldwide. Therefore, any strategy to stop or at least slacken the development and progression of malignant cells is an important therapeutic choice. The aim of the present work is the identification of novel cancer chemotherapy agents. Nowadays, many different drug discovery approaches are available, but this paper focuses on Molecular Topology, which has already demonstrated its extraordinary efficacy in this field, particularly in the identification of new hit and lead compounds against cancer. This methodology uses the graph theoretical formalism to numerically characterize molecular structures through the so called topological indices. Once obtained a specific framework, it allows the construction of complex mathematical models that can be used to predict physical, chemical or biological properties of compounds. In addition, Molecular Topology is highly efficient in selecting and designing new hit and lead drugs. According to the aforementioned, Molecular Topology has been applied here for the construction of specific Akt/mTOR and ß-catenin inhibition mathematical models in order to identify and select novel antitumor agents. EXPERIMENTAL APPROACH: Based on the results obtained by the selected mathematical models, six novel potential inhibitors of the Akt/mTOR and ß-catenin pathways were identified. These compounds were then tested in vitro to confirm their biological activity. CONCLUSION AND IMPLICATIONS: Five of the selected compounds, CAS n° 256378-54-8 (Inhibitor n°1), 663203-38-1 (Inhibitor n°2), 247079-73-8 (Inhibitor n°3), 689769-86-6 (Inhibitor n°4) and 431925-096 (Inhibitor n°6) gave positive responses and resulted to be active for Akt/mTOR and/or ß-catenin inhibition. This study confirms once again the Molecular Topology's reliability and efficacy to find out novel drugs in the field of cancer.

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.