New ligand-based approach for the discovery of antitrypanosomal compounds

Computational discovery of novel anthelmintic natural compounds from Agave Brittoniana trel. Spp. Brachypus

Helminth infections are a medical problem in the world nowadays. This report used bond-based 2D quadratic indices, a bond-level QuBiLs-MAS molecular descriptor family, and Linear Discriminant Analysis (LDA) to obtain a quantitative linear model that discriminates between anthelmintic and non-anthelmintic drug-like organic-compounds. The model obtained correctly classified 87.46% and 81.82% of the training and external data sets, respectively. The developed model was used in a virtual screening to predict the biological activity of all chemicals (19) previously obtained and chemically characterized by some authors of this report from Agave brittoniana Trel. spp. Brachypus. The model identified several metabolites (12) as possible anthelmintics, and a group of 5 novel natural products was tested in an in vitro assay against Fasciola hepatica (100% effectivity at 500 µg/mL). Finally, the two best hits were evaluated in vivo in bald/c mice and the same helminth parasite using a 25 mg/kg dose. Compound 8 (Karatavinoside A) showed an efficacy of 92.2% in vivo. It is important to remark that this natural compound exhibits similar-to-superior activity as triclabendazole, the best human fasciolicide available in the market against Fasciola hepatica, resulting in a novel lead scaffold with anti-helminthic activity. Keywords: TOMOCOMD-CARDD Software; QuBiLs-MAS, nonstochastic and stochastic bond-based quadratic indices; LDA-based QSAR model; Computational Screening, Anthelmintic Agent; Agave brittoniana Trel. spp. Brachypus, Fasciola hepatica.

Quantitative structure-activity relationship models for compounds with anticonvulsant activity

Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

Experimental models in Chagas disease: a review of the methodologies applied for screening compounds against Trypanosoma cruzi

One of the main problems of Chagas disease (CD), the parasitic infection caused by Trypanosoma cruzi, is the lack of a completely satisfactory treatment, which is currently based on two old nitroheterocyclic drugs (i.e., nifurtimox and benznidazole) that show important limitations for treating patients. In this context, many laboratories look for alternative therapies potentially applicable to the treatment, and therefore, research in CD chemotherapy works in the design of experimental protocols for detecting molecules with activity against T. cruzi. Phenotypic assays are considered the most valuable strategy for screening these antiparasitic compounds. Among them, in vitro experiments are the first step to test potential anti-T. cruzi drugs directly on the different parasite forms (i.e., epimastigotes, trypomastigotes, and amastigotes) and to detect cytotoxicity. Once the putative trypanocidal drug has been identified in vitro, it must be moved to in vivo models of T. cruzi infection, to explore (i) acute toxicity, (ii) efficacy during the acute infection, and (iii) efficacy in the chronic disease. Moreover, in silico approaches for predicting activity have emerged as a supporting tool for drug screening procedures. Accordingly, this work reviews those in vitro, in vivo, and in silico methods that have been routinely applied during the last decades, aiming to discover trypanocidal compounds that contribute to developing more effective CD treatments.

Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling

A quantitative structure-activity (QSAR) model has been developed for enriched tubulin inhibitors, which were retrieved from sequence similarity searches and applicability domain analysis. Using partial least square (PLS) method and leave-one-out (LOO) validation approach, the model was generated with the correlation statistics of [Formula: see text] and [Formula: see text] of 0.68 and 0.69, respectively. The present study indicates that topological descriptors, viz. BIC, CH_3_C, IC, JX and Kappa_2 correlate well with biological activity. ADME and toxicity (or ADME/T) assessment showed that out of 260 molecules, 255 molecules successfully passed the ADME/T assessment test, wherein the drug-likeness attributes were exhibited. These results showed that topological indices and the colchicine binding domain directly influence the aetiology of helminthic infections. Further, we anticipate that our model can be applied for guiding and designing potential anthelmintic inhibitors.

Drug repositioning for novel antitrichomonas from known antiprotozoan drugs using hierarchical screening

Aim: Metronidazole is the most widely used drug in trichomoniasis therapy. However, the emergence of metronidazole-resistant Trichomonas vaginalis isolates calls for the search for new drugs to counter the pathogenicity of these parasites. Results: Classification models for predicting the antitrichomonas activity of molecules were built. These models were employed to screen antiprotozoal drugs, from which 20 were classified as active. The in vitro experiments showed moderate to high activity for 19 of the molecules at 10 μg/ml, while 3 compounds yielded higher activity than the reference at 1 μg/ml. The 11 most active chemicals were evaluated in vivo using Naval Medical Research Institute (NMRI) mice. Conclusion: Benznidazole showed similar results as metronidazole, and can thus be considered as a potential candidate in antitrichomonas therapy.

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

Background

Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity.

Methodology/Principal Findings

In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC₅₀ < 10μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi.

Conclusions/ Significance

We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.

Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The disease is endemic to Latin America but is increasingly found in North America and Europe, primarily through immigration, and the spread of this disease is bringing new attention to the need for novel, safe, and effective therapeutics to treat T. cruzi infection. We have used data from a phenotypic screen to build Bayesian models to predict anti-parasitic activity against T. cruzi in vitro. These models were used to score various small libraries of molecules. We selected less than 100 compounds for testing and found in vitro actives, some of which were tested in an in vivo efficacy model. We identified the antimalarial pyronaridine as having in vivo efficacy and provides us with a new starting point for further investigation and optimization.

Synthesis and in vitro and in vivo biological evaluation of substituted nitroquinoxalin-2-ones and 2,3-diones as novel trichomonacidal agents

Two series of ten novel 7-nitroquinoxalin-2-ones and ten 6-nitroquinoxaline-2,3-diones with diverse substituents at positions 1 and 4 were synthesized and evaluated against the sexually transmitted parasite Trichomonas vaginalis. Furthermore, diverse molecular and drug-likeness properties were analyzed to predict the oral bioavailability following the Lipinski's "rule of five". 7-Nitroquinoxalin-2-one derivatives displayed moderate to high in vitro activity while the efficiency of most nitroquinoxaline-2,3-diones was rather low; both kinds of compounds did not show cytotoxic effects in mammalian cells. 7-Nitro-4-(3-piperidinopropyl)quinoxalin-2-one 9 achieved the highest trichomonacidal activity (IC50 = 18.26 μM) and was subsequently assayed in vivo in a murine model of trichomonosis. A 46.13% and a 50.70% reduction of pathogenic injuries were observed in the experimental groups treated orally during 7 days with 50 mg/kg and 100 mg/kg doses. The results obtained in the biological assays against T. vaginalis indicate that compounds with ω-(dialkylamino)alkyl substituents and a keto group at positions 4 and 2 of quinoxaline ring, respectively, provide interesting structural cores to develop novel prototypes to enhance the nitroquinoxalinones activity as trichomonacidal agents with interesting ADME properties according to virtual screening analysis.

QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents

The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correctly classify 92.16% and 87.56% of 706 compounds in an external test set. A comparison of the statistical parameters of the QuBiLs-MAS LDA-based models with those for models reported in the literature reveals comparable to superior performance, although the latter were built over much smaller and less diverse datasets, representing fewer mechanisms of action. It may therefore be inferred that the QuBiLs-MAS method constitutes a valuable tool useful in the design and/or selection of new and broad spectrum agents against life-threatening fungal infections.

Drug discovery and human African trypanosomiasis: a disease less neglected?

Human African trypanosomiasis (HAT) has been neglected for a long time. The most recent drug to treat this disease, eflornithine, was approved by the US FDA in 2000. Current treatments exhibit numerous problematic side effects and are often ineffective against the debilitating CNS resident stage of the disease. Fortunately, several partnerships and initiatives have been formed over the last 20 years in an effort to eradicate HAT, along with a number of other neglected diseases. This has led to an increasing number of foundations and research institutions that are currently working on the development of new drugs for HAT and tools with which to diagnose and treat patients. New biochemical pathways as therapeutic targets are emerging, accompanied by increasing numbers of new antitrypanosomal compound classes. The future looks promising that this collaborative approach will facilitate eagerly awaited breakthroughs in the treatment of HAT.

In vitro assessment of the acaricidal activity of computer-selected analogues of carvacrol and salicylic acid on Rhipicephalus (Boophilus) microplus

Rhipicephalus (Boophilus) microplus is a tick that causes huge economic losses in cattle. The indiscriminate use of acaricides has generated resistance to most compounds present on the market. This makes further investigation on other potential acaricides necessary, the in silico assay being an alternative to the design of new compounds. In the present study a biosilico assay was performed using TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer-Aided Rational Drug Design) and WEKA (Waikato Environment for Knowledge Analysis) software. Two carvacrol and four salicylic acid derivatives, synthesized by conventional methods and evaluated with the larval packet test on larvae of R. (B.) microplus were selected. All evaluated compounds presented acaricidal activity; however, ethyl 2-methoxybenzoate (91.8 ± 1.7 % mortality) and ethyl 2,5-dihydroxybenzoate (89.1 ± 1.6 % mortality) showed greater activity than salicylic acid. With regard to the carvacrol analogues, carvacrol acetate (67.8 ± 2.1 % mortality) and carvacrol methyl ether (71.7 ± 1.6 % mortality) also showed greater activity than carvacrol (35.9 ± 3.2 % mortality). TOMOCOMD-CARDD and WEKA software were helpful tools in the search for alternative structures with potential acaricidal activity on R. (B.) microplus.

Biological assay of a novel quinoxalinone with antimalarial efficacy on Plasmodium yoelii yoelii

Compound 1-methyl-7-nitro-4-(5-(piperidin-1-yl)pentyl)-3,4-dihydroquinoxalin-2(1H)-one (VAM2-6) was evaluated against a blood-induced infection with chloroquine-sensitive Plasmodium yoelii yoelii lethal strain in CD1 mice in a 4-day test scheme. LD50 of the compound was 56.51 mg/kg and LD10 was 20.58 mg/kg (taken as the highest dose). Animals were treated by oral gavage of 20, 10, and 5 mg/kg. Mice in the untreated control group showed a progressively increasing parasitemia leading to mouse death on 6 days post-infection; in this group, all mice showed parasites in the blood on the fifth day of sampling; the mean parasitemia on that day was 19.4%. A 4-day dosage of 20 mg/kg of VAM2-6 showed a 97% chemosuppression of total parasitemia on the fifth day, a 28 days survival time, and 20% of cured animals. A 4-day dosage of 10 and 5 mg/kg showed 85 and 37%, respectively, chemosuppression of total parasitemia on the fifth day; but all mice died from days 6 to 9 post-infection with increasing parasitemia. Mice treated with chloroquine at 5 mg/kg survived during the experiment. The results obtained in this study showed that the infection outcome of P. yoelii yoelii-infected mice is affected by VAM2-6 compound by slowing down the parasite replication, retarding the patency time, and increasing their survival time. Although compound VAM2-6 was active at higher doses than chloroquine, these results leaves a door open to the study of its structure in order to improve its antimalarial activity.

Bond-based 2D quadratic fingerprints in QSAR studies: virtual and in vitro tyrosinase inhibitory activity elucidation

In this report, we show the results of quantitative structure-activity relationship (QSAR) studies of tyrosinase inhibitory activity, by using the bond-based quadratic indices as molecular descriptors (MDs) and linear discriminant analysis (LDA), to generate discriminant functions to predict the anti-tyrosinase activity. The best two models [Eqs (6) and (12)] out of the total 12 QSAR models developed here show accuracies of 93.51% and 91.21%, as well as high Matthews correlation coefficients (C) of 0.86 and 0.82, respectively, in the training set. The validation external series depicts values of 90.00% and 89.44% for these best two equations (6) and (12), respectively. Afterwards, a second external prediction data are used to perform a virtual screening of compounds reported in the literature as active (tyrosinase inhibitors). In a final step, a series of lignans is analysed using the in silico-developed models, and in vitro corroboration of the activity is carried out. An issue of great importance to remark here is that all compounds present greater inhibition values than Kojic acid (standard tyrosinase inhibitor: IC₅₀ = 16.67 μm). The current obtained results could be used as a framework to increase the speed, in the biosilico discovery of leads for the treatment of skin disorders.

Bond-based 2D TOMOCOMD-CARDD approach for drug discovery: aiding decision-making in 'in silico' selection of new lead tyrosinase inhibitors

In this paper, we present a new set of bond-level TOMOCOMD-CARDD molecular descriptors (MDs), the bond-based bilinear indices, based on a bilinear map similar to those defined in linear algebra. These novel MDs are used here in Quantitative Structure-Activity Relationship (QSAR) studies of tyrosinase inhibitors, for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. In total 14 models were obtained and the best two discriminant functions (Eqs. 32 and 33) shown globally good classification of 91.00% and 90.17%, respectively, in the training set. The test set had accuracies of 93.33% and 88.89% for the models 32 and 33, correspondingly. A simulated virtual screening was also carried out to prove the quality of the determined models. In a final step, the fitted models were used in the biosilico identification of new synthesized tetraketones, where a good agreement could be observed between the theoretical and experimental results. Four compounds of the novel bioactive chemicals discovered as tyrosinase inhibitors: TK10 (IC(50) = 2.09 microM), TK11 (IC(50) = 2.61 microM), TK21 (IC(50) = 2.06 microM), TK23 (IC(50) = 3.19 microM), showed more potent activity than L-mimose (IC(50) = 3.68 microM). Besides, for this study a heterogeneous database of tyrosinase inhibitors was collected, and could be a useful tool for the scientist in the domain of tyrosinase enzyme researches. The current report could help to shed some clues in the identification of new chemicals that inhibits enzyme tyrosinase, for entering in the pipeline of drug discovery development.

Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

The great cost associated with the development of new anabolic-androgenic steroid (AASs) makes necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, quantum, and physicochemical molecular descriptors, plus linear discriminant analysis (LDA) were used to analyze the anabolic/androgenic activity of structurally diverse steroids and to discover novel AASs, as well as also to give a structural interpretation of their anabolic-androgenic ratio (AAR). The obtained models are able to correctly classify 91.67% (86.27%) of the AASs in the training (test) sets, respectively. The results of predictions on the 10% full-out cross-validation test also evidence the robustness of the obtained model. Moreover, these classification functions are applied to an "in house" library of chemicals, to find novel AASs. Two new AASs are synthesized and tested for in vivo activity. Although both AASs are less active than some commercially AASs, this result leaves a door open to a virtual variational study of the structure of the two compounds, to improve their biological activity. The LDA-assisted QSAR models presented here, could significantly reduce the number of synthesized and tested AASs, as well as could increase the chance of finding new chemical entities with higher AAR.