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.

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.

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.

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.

Predicción de la actividad anti-Trypanosoma brucei rhodesiense de un grupo de 3, 5-Difenilisoxazoles dicationicos por medio de la Topología Molecular / Predicting anti-Trypanosoma brucei rhodesiense activity of a group of Dicationic 3, 5-diphenylisoxazols by Molecular Topology

Actualmente más de 65 millones de personas se encuentran en riesgo de contraer la enfermedad del sueño producida por Trypanosoma brucei. Los tratamientos actuales para esta parasitosis son limitados y poseen efectos colaterales, por lo que es necesario buscar nuevos compuestos activos que sean potentes y de baja toxicidad. En este estudio se utilizaron los métodos de relación cuantitativa estructura-actividad (QSAR) para predecir potencia y actividad anti-Trypanosoma brucei rhodesiense de un grupo de 3,5-Difenilisoxazoles Dicationicos. Mediante el Análisis Lineal Discriminante (ALD) se clasificó correctamente la actividad anti-Trypanosoma brucei en el 100% de los casos (sensibilidad) y la inactividad en el 86,7% (especificidad). Adicionalmente, a través del análisis de regresión multilineal (MLRA) se predijo la bioactividad teórica (pIC50) de los compuestos estudiados, mostrando una elevada correlación de los resultados obtenidos in vitro e in silico (r2=0,82). Finalmente, los modelos topológicos obtenidos en el ALD y MLRA fueron aplicados a un grupo de análogos dicatiónicos para cribado molecular que presentaron una teórica elevada actividad y podrían ser seleccionados para su futuro análisis in vitro ahorrando tiempo y costes en la búsqueda de futuros tratamientos (AU)

Currently, there are more than 65 million people at risk of contracting sleeping sickness caused by Trypanosoma brucei. Available treatments for this parasitic disease are limited and have side effects. There is a necessity to investigate novel, effective compounds that also yield low in toxicity. Quantitative Structure-Activity Relationship models (QSAR) have been used in this study for predicting anti-Trypanosoma brucei activity and potency in a group of dicationic 3,5-Diphenylisoxazoles. Linear Discriminant Analysis (LDA) correctly classified anti-Trypanosoma brucei activity in 100 % of cases (sensitivity) and inactivity in 86.7 % (specificity). Theoretical bioactivity (pIC50) of the studied compounds was predicted using Multilinear Regression Analysis (MLRA), demonstrating a high correlation between in vitro and in silico results (r2=0.82). Topologic models obtained with LDA and MLRA were applied to the screening of a group of dicationic analogs which presented a high in silico activity. These compounds when selected for in vitro analysis could reduce both time and costs in future drug research (AU)

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.

Aplicación de la Topología Molecular para la predicción de la actividad frente a leishmania de un grupo de compuestos derivados del pirrol [1, 2-alfa] quinoxalina / Application of molecular topology for predicting the leishmanicidal activity of a group of compounds derived from pyrrolo [1, 2-alfa] quinoxaline

La leishmaniasis se encuentra entre las seis enfermedades más importantes en el Programa Especial de Investigación y Adiestramiento en Enfermedades Tropicales (TDR) de la OMS. Los fármacos disponibles actualmente presentan altas toxicidades o se han desarrollado resistencias para los mismos, de modo que buscar nuevos compuestos que posean actividad anti-leishmánica es una opción quimioterapéutica atractiva. En el presente estudio hemos utilizado la topología molecular y el análisis de regresión multilineal para el desarrollo de un modelo QSAR capaz de predecir la actividad frente a L. major y L. donovani de un grupo de compuestos derivados del pirrol [1,2-alfa] quinoxalina. Validados los modelos topológicos seleccionados, se ha realizado un cribado molecular y se han seleccionado nuevos derivados de la quinoxalina con potencial actividad anti-leishmánica

Leishmaniasis is present among the six greatest diseases reported in the WHO's Special Programme for Research and Training in Tropical Diseases (TDR). Nowadays, the available drugs show high toxicities or resistance has been developed. Therefore the search of new compounds that possess anti-leishmania activity is an attractive chemotherapeutic option. In the present study we have applied molecular topology and multilineal regression analysis to develop a QSAR model able to predict the activity of a series of pyrrolo [1,2-alfa] quinoxaline derived compounds against L. major and L. donovani. Once validated the selected topological models, a molecular screening has been performed and new quinoxaline derivatives with potential anti-leishmania activity have been selected

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.

The prediction of human intestinal absorption based on the molecular structure.

Human Intestinal Absorption (HIA) has been modeled many times by using classification models. However, regression models are scarce. Here, Artificial Neural Networks (ANNs) are implemented for this purpose. A dataset of structurally diverse chemicals with their respective experimental HIA were used to design robust, true predictive and widespread applicable ANN models. An input variables pool was made up of structural invariants calculated by using either Dragon or our software Desmol 1. The selection of best variables was performed following three steps using the entire dataset of molecules. Firstly, variables poorly correlated with the experimental data were eliminated. Secondly, input variable selection was performed by stepwise multilinear regression. Thirdly, correlation matrix in the set of selected variables was then obtained to eliminate those variables strongly intercorrelated. Backpropagation ANNs were trained for these variables finally selected as inputs, and HIA as output. The training and selection procedure to find robust models consisted of randomly partitioning the dataset into three sets: training set, with 50% of the population, test set with 25%, and validation set with the other 25%. With each partitioning, diverse numbers of hidden nodes were assayed to optimize the performance in the prediction for the three sets. Models with r(2) greater than 0.6 for the three sets were considered as robust. A randomization test following all these steps was performed, and the poor results obtained confirm the validity of the method presented in this paper to predict HIA for datasets of structurally diverse organic compounds.

QSAR multi-target in drug discovery: a review.

The main purpose of the present review is to summarize the most significant works up to date in the field of multi-target QSAR (mt-QSAR), in order to emphasize the importance that this technique has acquired over the last decade. Unlike traditional QSAR techniques, mt-QSAR permits to calculate the probability of activity of a given compound against different biological or pharmacological targets. In simple terms, a single equation for multiple outputs. To emphasize more the importance of the mt-QSAR in the field of drug discovery, we also present a novel mt-QSAR model, made on purpose by our research group, for the prediction of the susceptibility of Gram + and Gram - anaerobic bacteria.

Antiprotozoan lead discovery by aligning dry and wet screening: prediction, synthesis, and biological assay of novel quinoxalinones.

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients (C) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development of novel classes of broad-antiprotozoan-spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses.

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.

QSAR methods for the discovery of new inflammatory bowel disease drugs.

INTRODUCTION: Inflammatory bowel disease (IBD) represents an important class of chronic gastrointestinal tract disease. And although there are already several useful treatments to reduce and control the symptoms, there is still no cure. One drug discovery technique used is the computer-aided (in silico) discovery approach which has largely demonstrated efficacy. Computational techniques, when used in combination with traditional drug discovery methodology, greatly increase the chance of drug discovery in a sustainable and economical fashion. AREAS COVERED: This review aims to provide the most recent and important advances of in silico IBD drug discovery. While this review is mainly focused on QSAR methods, especially those based on molecular topology (MT), additional topics, such as docking or comparative field analysis are also addressed. EXPERT OPINION: IBD is a worldwide growing health concern that can only be currently treated in symptomatic and palliative way; thus, the search for new drugs is imperative. Computer-aided methods, which focus on the drug-receptor interaction, are essential tool in this regard. It is noted, however that a major problem is that although there are many known receptors associated with IBD, none of these have yet been found essential. The use of other approaches, including QSAR methodology, is certainly a complementary and attractive alternative; especially QSAR methods based on MT, which has proven successful in other drug discovery.

Modeling anti-allergic natural compounds by molecular topology.

Molecular topology has been applied to the search of QSAR models able to identify the anti-allergic activity of a wide group of heterogeneous compounds. Through the linear discriminant analysis and artificial neural networks, correct classification percentages above 85% for both the training set and the test set have been obtained. After carrying out a virtual screening with a natural product library, about thirty compounds with theoretical anti-allergic activity have been selected. Among them, hesperidin, naringin, salinomycin, sorbitol, curcumol, myricitrin, diosmin and kinetin stand out. Some of these compounds have already been referenced as having anti-allergic activity.