Promising Effects of Casearins in Tumor-Bearing Mice and Antinociceptive Action against Oncologic Pain: Molecular Docking and In Vivo Findings

Safer analgesic drugs remain a hard challenge because of cardiovascular and/or gastrointestinal toxicity, mainly. So, this study evaluated in vivo the antiproliferative actions of a fraction with casearins (FC) from Casearia sylvestris leaves against human colorectal carcinomas and antihyperalgesic effects on inflammatory- or opiate-based pain relief and oncologic pain in Sarcoma 180 (S180)-bearing mice. Moreover, docking investigations evaluated the binding among Casearin X and NMDA(N-methyl-D-aspartate)-type glutamate receptors. HCT-116 colorectal carcinoma-xenografted mice were treated with FC for 15 days. Antinociceptive assays included chemically induced algesia and investigated mechanisms by pharmacological blockade. Intraplantar region S180-bearing animals received a single dose of FC and were examined for mechanical allodynia and behavior alterations. AutoDock Vina determined molecular interactions among Cas X and NMDA receptor subunits. FC reduced tumor growth at i.p. (5 and 10 mg/kg) and oral (25 mg/kg/day) doses (31.12-39.27%). FC reduced abdominal pain, as confirmed by formalin and glutamate protocols, whose antinociception activity was blocked by naloxone and L-NAME (neurogenic phase) and naloxone, atropine, and flumazenil (inflammatory phase). Meanwhile, glibenclamide potentiated the FC analgesic effects. FC increased the paw withdrawal threshold without producing changes in exploratory parameters or motor coordination. Cas X generated a more stable complex with active sites of the NMDA receptor GluN2B subunits. FC is a promising antitumor agent against colorectal carcinomas, has peripheral analgesic effects by desensitizing secondary afferent neurons, and inhibits glutamate release from presynaptic neurons and/or their action on cognate receptors. These findings emphasize the use of clerodane diterpenes against cancer-related pain conditions.

Design and Identification of Inhibitors for the Spike-ACE2 Target of SARS-CoV-2

When an epidemic started in the Chinese city of Wuhan in December 2019, coronavirus was identified as the cause. Infection by the virus occurs through the interaction of viral S protein with the hosts' angiotensin-converting enzyme 2. By leveraging resources such as the DrugBank database and bioinformatics techniques, ligands with potential activity against the SARS-CoV-2 spike protein were designed and identified in this investigation. The FTMap server and the Molegro software were used to determine the active site of the Spike-ACE2 protein's crystal structure. Virtual screening was performed using a pharmacophore model obtained from antiparasitic drugs, obtaining 2000 molecules from molport^®. The ADME/Tox profiles were used to identify the most promising compounds with desirable drug characteristics. The binding affinity investigation was then conducted with selected candidates. A molecular docking study showed five structures with better binding affinity than hydroxychloroquine. Ligand_003 showed a binding affinity of -8.645 kcal·mol^-1, which was considered an optimal value for the study. The values presented by ligand_033, ligand_013, ligand_044, and ligand_080 meet the profile of novel drugs. To choose compounds with favorable potential for synthesis, synthetic accessibility studies and similarity analyses were carried out. Molecular dynamics and theoretical IC₅₀ values (ranging from 0.459 to 2.371 µM) demonstrate that these candidates are promising for further tests. Chemical descriptors showed that the candidates had strong molecule stability. Theoretical analyses here show that these molecules have potential as SARS-CoV-2 antivirals and therefore warrant further investigation.

Discovery of Novel Epidermal Growth Factor Receptor (EGFR) Inhibitors Using Computational Approaches

The epidermal growth factor receptor (EGFR) signaling pathway plays an important role in cell growth, proliferation, differentiation, and other physiological processes, which makes the EGFR a promising target for anticancer therapies. The discovery of novel EGFR inhibitors may provide a solution to the problem of drug resistance. In this work, we performed a ligand-based virtual screening (LBVS) protocol for finding novel EGFR inhibitors from a 5.3 million compound library. First, the 3D shape-based similarity was used to obtain structurally novel EGFR inhibitors. In this study, we tried three queries; two were crystal structures and one was generated from deep generative models of graphs (DGMG). Next, we have built four structure-activity relationship (SAR) models and three quantitative structure-activity relationship (QSAR) models based on an SVM method for further screening of highly active EGFR inhibitors. Experimental validations led to the identification of nine hits out of 18 tested compounds. Among them, hit 1, hit 5, and hit 6 had IC₅₀ values around 80 nM against EGFR whose interactions with EGFR were further investigated by molecular dynamics simulations.

Impact of different protonation states on virtual screening performance against cruzain

The cysteine protease cruzain is a Chagas disease target, exploited in computational studies. However, there is no consensus on the protonation states of the active site residues Cys25, His162, and Glu208 at the enzyme's active pH range. We evaluated the impact of different protonation states of these residues on docking calculations. Through a retrospective study with cruzain inhibitors and decoys, we compared the performance of virtual screening using four grids, varying protonation states of Cys25, His162, and Glu208. Based on enrichment factors and ROC plots, docking with the four grids affected compound ranking and the overall charge of top-ranking compounds. Different grids can be complementary and synergistic, increasing the odds of finding different ligands with diverse chemical properties.

Design of Inhibitors for Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH) Enzyme of <i>Leishmania mexicana</i>

BACKGROUND

Leishmaniosis is a neglected tropical disease and glyceraldehyde 3- phosphate dehydrogenase (GAPDH) is a key enzyme in the design of new drugs to fight this disease.

OBJECTIVE

The present study aimed to evaluate potential inhibitors of GAPDH enzyme found in Leishmania mexicana (L. mexicana).

METHODS

A search for novel antileishmanial molecules was carried out based on similarities from the pharmacophoric point of view related to the binding site of the crystallographic enzyme using the ZINCPharmer server. The molecules selected in this screening were subjected to molecular docking and molecular dynamics simulations.

RESULTS

Consensual analysis of the docking energy values was performed, resulting in the selection of ten compounds. These ligand-receptor complexes were visually inspected in order to analyze the main interactions and subjected to toxicophoric evaluation, culminating in the selection of three compounds, which were subsequently submitted to molecular dynamics simulations. The docking results showed that the selected compounds interacted with GAPDH from L. mexicana, especially by hydrogen bonds with Cys166, Arg249, His194, Thr167, and Thr226. From the results obtained from molecular dynamics, it was observed that one of the loop regions, corresponding to the residues 195-222, can be related to the fitting of the substrate at the binding site, assisting in the positioning and the molecular recognition via residues responsible for the catalytic activity.

CONCLUSION

The use of molecular modeling techniques enabled the identification of promising compounds as inhibitors of the GAPDH enzyme from L. mexicana, and the results obtained here can serve as a starting point to design new and more effective compounds than those currently available.

Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage

Chagas disease affects 8-11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomena have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival and immune evasion. In this work, we compiled and curated a database of diverse cruzain inhibitors previously reported in the literature. From this data set, quantitative structure-activity relationship (QSAR) models for the prediction of their pIC50 values were generated using k-nearest neighbors and random forest algorithms. Local and global models were calculated and compared. The statistical parameters for internal and external validation indicate a significant predictability, with q loo 2 values around 0.66 and 0.61 and external R 2 coefficients of 0.725 and 0.766. The applicability domain is quantitatively defined, according to QSAR good practices, using the leverage and similarity methods. The models described in this work are readily available in a Python script for the discovery of novel cruzain inhibitors.

Molecular design aided by random forests and synthesis of potent trypanocidal agents as cruzain inhibitors for Chagas disease treatment

Cruzain is an established target for the identification of novel trypanocidal agents, but how good are in vitro/in vivo correlations? This work describes the development of a random forests model for the prediction of the bioavailability of cruzain inhibitors that are Trypanosoma cruzi killers. Some common properties that characterize drug-likeness are poorly represented in many established cruzain inhibitors. This correlates with the evidence that many high-affinity cruzain inhibitors are not trypanocidal agents against T. cruzi. On the other hand, T. cruzi killers that present typical drug-like characteristics are likely to show better trypanocidal action than those without such features. The random forests model was not outperformed by other machine learning methods (such as artificial neural networks and support vector machines), and it was validated with the synthesis of two new trypanocidal agents. Specifically, we report a new lead compound, Neq0565, which was tested on T. cruzi Tulahuen (β-galactosidase) with a pEC₅₀ of 4.9. It is inactive in the host cell line showing a selectivity index (SI = EC₅₀ ^cyto /EC₅₀ ^T. cruzi ) higher than 50.

Identification of New Rofecoxib-Based Cyclooxygenase-2 Inhibitors: A Bioinformatics Approach

The cyclooxygenase-2 receptor is a therapeutic target for planning potential drugs with anti-inflammatory activity. The selective cyclooxygenase-2 (COX-2) inhibitor rofecoxib was selected as a pivot molecule to perform virtual ligand-based screening from six commercial databases. We performed the search for similarly shaped Rapid Overlay of Chemical Structures (ROCS) and electrostatic (EON) compounds. After, we used pharmacokinetic and toxicological parameters to determine the best potential compounds, obtained through the softwares QikProp and Derek, respectively. Then, the compounds proceeded to the molecular anchorage study, which showed promising results of binding affinity with the hCOX-2 receptor: LMQC72 (∆G = -11.0 kcal/mol), LMQC36 (∆G = -10.6 kcal/mol), and LMQC50 (∆G = -10.2 kcal/mol). LMQC72 and LMQC36 showed higher binding affinity compared to rofecoxib (∆G = -10.4 kcal/mol). Finally, molecular dynamics (MD) simulations were used to evaluate the interaction of the compounds with the target hCOX-2 during 150 ns. In all MD simulation trajectories, the ligands remained interacting with the protein until the end of the simulation. The compounds were also complexing with hCOX-2 favorably. The compounds obtained the following affinity energy values: rofecoxib: ΔGbind = -45.31 kcal/mol; LMQC72: ΔGbind = -38.58 kcal/mol; LMQC36: ΔGbind = -36.10 kcal/mol; and LMQC50: ΔGbind = -39.40 kcal/mol. The selected LMQC72, LMQC50, and LMQC36 structures showed satisfactory pharmacokinetic results related to absorption and distribution. The toxicological predictions of these compounds did not display alerts for possible toxic groups and lower risk of cardiotoxicity compared to rofecoxib. Therefore, future in vitro and in vivo studies are needed to confirm the anti-inflammatory potential of the compounds selected here with bioinformatics approaches based on rofecoxib ligand.

Exploring different approaches to improve the success of drug discovery and development projects: a review

Background

There has been a significant increase in the cost and timeline of delivering new drugs for clinical use over the last three decades. Despite the increased investments in research infrastructure by pharmaceutical companies and technological advances in the scientific tools available, efforts to increase the number of molecules coming through the drug development pipeline have largely been unfruitful.

Main body

A non-systematic review of the current literature was undertaken to enumerate the various strategies employed to improve the success rates in the pharmaceutical research and development. The review covers the exploitation of genomics and proteomics, complementarity of target-based and phenotypic efficacy screening platforms, drug repurposing and repositioning, collaborative research, focusing on underserved therapeutic fields, outsourcing strategy, and pharmaceutical modeling and artificial intelligence. Examples of successful drug discoveries achieved through application of these strategies are highlighted and discussed herein.

Conclusions

Genomics and proteomics have uncovered a wide array of potential drug targets and are facilitative of enhanced scrupulous target identification and validation thus reducing efficacy-related drug attrition. When used complementarily, phenotypic and target-based screening platforms would likely allow serendipitous drug discovery while increasing rationality in drug design. Drug repurposing and repositioning reduces financial risks in drug development accompanied by cost and time savings, while prolonging patent exclusivity hence increased returns on investment to the innovator company. Equally important, collaborative research is facilitative of cross-fertilization and refinement of ideas, while sharing resources and expertise, hence reducing overhead costs in the early stages of drug discovery. Underserved therapeutic fields are niche drug discovery areas that may be used to experiment and launch novel drug targets, while exploiting incentivized benefits afforded by drug regulatory authorities. Outsourcing allows the pharma industries to focus on their core competencies while deriving greater efficiency of specialist contract research organizations. The existing and emerging pharmaceutical modeling and artificial intelligence softwares and tools allow for in silico computation enabling more efficient computer-aided drug design. Careful selection and application of these strategies, singly or in combination, may potentially harness pharmaceutical research and innovation.

Identification of potential cruzain inhibitors using de novo design, molecular docking and dynamics simulations studies

Communicated by Ramaswamy H. Sarma.

Cink4T, a quinazolinone-based dual inhibitor of Cdk4 and tubulin polymerization, identified via ligand-based virtual screening, for efficient anticancer therapy

Inhibition of cyclin dependent kinase 4 (Cdk4) prevents cancer cells from entering the early G₀/G₁ phase of the cell division cycle whereas inhibiting tubulin polymerization blocks cancer cells' ability to undergo mitosis (M) late in the cell cycle. We had reported earlier that two non-planar and relatively non-toxic fascaplysin derivatives, an indole and a tryptoline, inhibit Cdk4 with IC₅₀ values of 6.2 and 10 μM, respectively. Serendipitously, we had also found that they inhibited tubulin polymerization. The molecules were efficacious in mouse tumor models. We have now identified Cink4T in a 59-compound quinazolinone library, designed on the basis of ligand-based virtual screening, as a compound that inhibits Cdk4 and tubulin. Its IC₅₀ value for Cdk4 inhibition is 0.47 μM and >50 μM for inhibition of Cdk1, Cdk2, Cdk6, Cdk9. Cink4T inhibits tubulin polymerization with an IC₅₀ of 0.6 μM. Molecular modelling studies on Cink4T with Cdk4 and tubulin crystal structures lend support to these observations. Cancer cell cycle analyses confirm that Cink4T blocks cells at both G₀/G₁ and M phases as it should if it were to inhibit both Cdk4 and tubulin polymerization. Our results show, for the very first time, that virtual screening can be used to design novel inhibitors that can potently block two crucial phases of the cell division cycle.

Behavioral effects induced by antitumor cleronade diterpenes from Casearia sylvestris and in silico interactions with neuron receptors

ETHNOPHARMACOLOGICAL RELEVANCE

Casearia sylvestris is a medicinal plant traditionally used to treat snakebites, wounds, inflammation and gastric ulcers and scientific supports for have demonstrated its antitumor, antihyperlipidemic and antiparasitic properties.

AIM OF THE STUDY

To assess the effects of a fraction with casearins (FC) on adult mice using classical experimental models of animal behavior and theoretical calculations to verify the interaction of Casearin X (Cas X) with neuron receptors.

MATERIALS AND METHODS

Animals divided in 6 groups (n=9/group) were intraperitoneally treated with vehicle (DMSO 4%), FC (2.5, 5, 10 and 25mg/kg/day) and diazepam (2mg/kg) for 7 days. Thirty minutes after the last dose of treatment, acute toxicity and behavioral experiments were performed.

RESULTS

The highest dose of FC (25mg/kg/day) caused diarrhea, weight loss and death of one animal. Elevated plus maze test showed that lower doses [2.5mg/kg/day (36.4±5.1s) and 5mg/kg/day (43.9±6.2s)] increased the time spent in open arms (TSOA). Open field test revealed reduction in the number of crossings (54.9%, 51.1%, 48% and 67.7% for 2.5, 5, 10 and 25mg/kg/day, respectively) in all doses of FC studied and decrease of rearings at 25mg/kg/day (p<0.05). Computational calculations showed that the inhibition constant (Ki) for the Cas X-D₁ complex is up to 1000-fold more favourable than the Cas X-GABA_A complex. All ∆G° values obtained for Cas X-D₁ complexes were more negative than those seen with Cas X-GABA_A complexes.

CONCLUSIONS

Findings indicate a probable anxiolytic action of the FC since it reduces the number of crossings and rearings and prolonged the time spent in open arms, without sedative and myorelaxant effects, probably due to the interaction of Cas X with dopaminergic system.

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

Integration of methods in cheminformatics and biocalorimetry for the design of trypanosomatid enzyme inhibitors

Background: The enzyme GAPDH, which acts in the glycolytic pathway, is seen as a potential target for pharmaceutical intervention of Chagas disease. Results: Herein, we report the discovery of new Trypanosoma cruzi GAPDH (TcGAPDH) inhibitors from target- and ligand-based virtual screening protocols using isothermal titration calorimetry (ITC) and molecular dynamics. Molecular dynamics simulations were used to gain insight on the binding poses of newly identified inhibitors acting at the TcGAPDH substrate (G3P) site. Conclusion: Nequimed125, the most potent inhibitor to act upon TcGAPDH so far, which sits on the G3P site without any contact with the co-factor (NAD+) site, underpins the result obtained by ITC that it is a G3P-competitive inhibitor. Molecular dynamics simulation provides biding poses of TcGAPDH inhibitors that correlate with mechanisms of inhibition observed by ITC. Overall, a new class of dihydroindole compounds that act upon TcGAPDH through a competitive mechanism of inhibition as proven by ITC measurements also kills T. cruzi.

Non-peptidic cruzain inhibitors with trypanocidal activity discovered by virtual screening and in vitro assay

A multi-step cascade strategy using integrated ligand- and target-based virtual screening methods was developed to select a small number of compounds from the ZINC database to be evaluated for trypanocidal activity. Winnowing the database to 23 selected compounds, 12 non-covalent binding cruzain inhibitors with affinity values (K_i) in the low micromolar range (3–60 µM) acting through a competitive inhibition mechanism were identified. This mechanism has been confirmed by determining the binding mode of the cruzain inhibitor Nequimed176 through X-ray crystallographic studies. Cruzain, a validated therapeutic target for new chemotherapy for Chagas disease, also shares high similarity with the mammalian homolog cathepsin L. Because increased activity of cathepsin L is related to invasive properties and has been linked to metastatic cancer cells, cruzain inhibitors from the same library were assayed against it. Affinity values were in a similar range (4–80 µM), yielding poor selectivity towards cruzain but raising the possibility of investigating such inhibitors for their effect on cell proliferation. In order to select the most promising enzyme inhibitors retaining trypanocidal activity for structure-activity relationship (SAR) studies, the most potent cruzain inhibitors were assayed against T. cruzi-infected cells. Two compounds were found to have trypanocidal activity. Using compound Nequimed42 as precursor, an SAR was established in which the 2-acetamidothiophene-3-carboxamide group was identified as essential for enzyme and parasite inhibition activities. The IC₅₀ value for compound Nequimed42 acting against the trypomastigote form of the Tulahuen lacZ strain was found to be 10.6±0.1 µM, tenfold lower than that obtained for benznidazole, which was taken as positive control. In addition, by employing the strategy of molecular simplification, a smaller compound derived from Nequimed42 with a ligand efficiency (LE) of 0.33 kcal mol⁻¹ atom⁻¹ (compound Nequimed176) is highlighted as a novel non-peptidic, non-covalent cruzain inhibitor as a trypanocidal agent candidate for optimization.

Chagas disease (American trypanosomiasis) is a parasitic infection that kills millions of mostly poverty-stricken people in Latin America. In recent years it has also spread to nonendemic countries – the United States, Canada, Europe, Australia and Japan – as a result of immigration. The only available drugs for its treatment were introduced more than forty years ago, have low efficacy, and cause various severe side effects. This dire public health situation has prompted us to search for new small molecules to act as drug candidates to treat Chagas disease. The T. cruzi enzyme cruzain, a key biological catalyst used by the protozoan to digest host proteins, is a validated drug target for Chagas disease. By combining in silico molecular design, X-ray crystallography and biological screening, we found a new class of non-covalent small molecules that inhibit cruzain in low micromolar concentrations.

Established and emerging trends in computational drug discovery in the structural genomics era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.