María Gálvez-Llompart

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.

Application of molecular topology for the prediction of the reaction times and yields under solvent-free conditions

Ball milling and conventional magnetic stirring can be used to support different laboratory techniques with a highly efficient mixing of reagents under solvent-free conditions. By using multilinear regression and linear discriminant analysis, topological-mathematical models have been built to predict the yield and the reaction time for organocatalytic reactions, Suzuki reactions and reactions of synthesis of heterocyclic compounds. The results from the in silico predictions confirm the usefulness of the approach followed.

Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: The nitroindazolinone chemotype

In this report, we propose the combination of computational methods and in vivo primary screening in zebrafish larvae and confirmatory in mice models as a novel strategy to accelerate anti-inflammatory drug discovery. Initially, a database of 1213 organic chemicals with great structural variability - 587 of them anti-inflammatory agents plus 626 compounds with other clinical uses - was divided into training and test groups. Atom-based quadratic indices - a TOMOCOMD-CARDD molecular descriptors family - and linear discriminant analysis (LDA) were used to develop a total of 13 models to describe the anti-inflammatory activity. The best model (Eq. (13)) shows an accuracy of 87.70% in the training set, and values of Matthews correlation coefficient (C) of 0.75. The robustness of the models was demonstrated using an external test set as validation method, i.e., Eq. (13) revealing classification of 88.44% (C = 0.77) in this series. All models were employed to develop ensemble a QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. The fusion model was used for the identification of novel anti-inflammatory compounds using virtual screening of 145 molecules available in our in-house library of indazole, indole, cinnoline and quinoxaline derivatives. Out of these, 34 chemicals were selected, synthesized and tested in a lipopolysaccharide (LPS)-induced leukocyte migration assay in zebrafish larvae. This activity was evaluated based on leukocyte migration to the injury zone of tail-transected larvae. Compounds 18 (3 μM), 24 (10 μM), 25 (10 μM), 6 (10 μM), 15 (30 μM), 11 (30 μM) and 12 (30 μM) gave the best results displaying relative leukocyte migration (RLM) values of 0.24, 0.27, 0.35, 0.41, 0.17, 0. 26 and 0.27 respectively, date that suggest an anti-inflammatory activity of 76, 73, 65, 59, 83, 84 and 73%, respectively. Compound 18 was the most potent but showed high toxicity together with compound 6. Next, we used the tetradecanoylphorbol acetate (TPA)-induced mouse ear oedema model to evaluate the most potent compounds in the zebrafish larvae tail transection assay. All assayed compounds, with the exception of chemical 15, showed anti-inflammatory activity in mice. Compound 12 (VA5-13l, 2-benzyl-1-methyl-5-nitro-1,2-dihydro-3H-indazol-3-one) was the most active and completely abolished the oedema. Compounds 6, 11 and 24 showed inhibition percentages in the range of the reference drug (indomethacin), whereas compounds 18 and 25 reduced the oedema in a lesser extent (inhibition of 73 and 80%, respectively). In addition, all compounds except chemical 15, significantly reduced neutrophil infiltration, measured as myeloperoxidase activity on TPA application test. Compounds 6, 11, 12 and 18 showed values comparable to indomethacin (inhibition percentage of 61), but compounds 6 and 18 were toxic in zebrafish and showed unspecific cytotoxicity in murine macrophages at 100 μg/mL, while the remaining compounds 11, 12 and 25 were inactive at most levels. Evidently, this study suggests a new support structure (12, 11 and 24; a nitroindazolinone chemotype) that constitutes a novel promising lead and may represent an important therapeutic alternative for the treatment of inflammatory conditions.

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.

Molecular topology as a novel approach for drug discovery

Introduction: Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. One key part of MT is that, in the process of drug design/discovery, there is no need for an explicit knowledge of a drugs mechanism of action unlike other drug discovery methods. Areas covered: In this review, the authors introduce the topic by explaining briefly the most common methodology used today in drug design/discovery and address the most important concepts of MT and the methodology followed (QSAR equations, LDA, etc.). Furthermore, the significant results achieved, from this approach, are outlined and discussed. Expert opinion: The results outlined herein can be explained by considering that MT represents a new paradigm in the field of drug design. This means that it is not only an alternative method to the conventional methods, but it is also independent, that is, it represents a pathway to connect directly molecular structure with the experimental properties of the compounds (particularly drugs). Moreover, the process can be realized also in the reverse pathway, that is, designing new molecules from their topological pattern, what opens almost limitless expectations in new drugs development, given that the virtual universe of molecules is much greater than that of the existing ones.

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.Graphical Abstract

Application of Molecular Topology for the Prediction of Reaction Yields and Anti-Inflammatory Activity of Heterocyclic Amidine Derivatives

Topological-mathematical models based on multiple linear regression analyses have been built to predict the reaction yields and the anti-inflammatory activity of a set of heterocylic amidine derivatives, synthesized under environmental friendly conditions, using microwave irradiation. Two models with three variables each were selected. The models were validated by cross-validation and randomization tests. The final outcome demonstrates a good agreement between the predicted and experimental results, confirming the robustness of the method. These models also enabled the screening of virtual libraries for new amidine derivatives predicted to show higher values of reaction yields and anti-inflammatory activity.

Introduction to molecular topology: basic concepts and application to drug design.

In this review it is dealt the use of molecular topology (MT) in the selection and design of new drugs. After an introduction of the actual methods used for drug design, the basic concepts of MT are defined, including examples of calculation of topological indices, which are numerical descriptors of molecular structures. The goal is making this calculation familiar to the potential students and allowing a straightforward comprehension of the topic. Finally, the achievements obtained in this field are detailed, so that the reader can figure out the great interest of this approach.

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.