Pharmacophore Virtual Screening Identifies Riboflavin as an Inhibitor of the Schistosome Cathepsin B1 Protease with Antiparasitic Activity.

Schistosomiasis is a neglected disease of poverty that affects over 200 million people worldwide and relies on a single drug for therapy. The cathepsin B1 cysteine protease (SmCB1) of Schistosoma mansoni has been investigated as a potential target. Here, a structure-based pharmacophore virtual screening (VS) approach was used on a data set of approved drugs to identify potential antischistosomal agents targeting SmCB1. Pharmacophore (PHP) models underwent validation through receiver operating characteristics curves achieving values >0.8. The data highlighted riboflavin (RBF) as a compound of particular interest. A 1 µs molecular dynamics simulation demonstrated that RBF altered the conformation of SmCB1, causing the protease's binding site to close around RBF while maintaining the protease's overall integrity. RBF inhibited the activity of SmCB1 at low micromolar values and killed the parasite in vitro. Finally, in a murine model of S. mansoni infection, oral administration of 100 mg/kg RBF for 7 days significantly decreased worm burdens by ∼20% and had a major impact on intestinal and fecal egg burdens, which were decreased by ∼80%.

Synthesis and evaluation of hybrid sulfonamide-chalcones with potential antileishmanial activity.

Leishmaniasis is an emerging tropical infectious disease caused by a protozoan parasite of the genus Leishmania. In this work, the molecular hybridization between a trimethoxy chalcone and a sulfonamide group was used to generate a series of sulfonamide-chalcones. A series of eight sulfonamide-chalcone hybrids were made with good yields (up to 95%). These sulfonamide-chalcones were tested against promastigotes of Leishmania amazonensis and cytotoxicity against mouse macrophages, which showed good antileishmanial activity with IC50 = 1.72-3.19 µM. Three of them (10c, 10g, and 10h) were also highly active against intracellular amastigotes and had a good selectivity index (SI > 9). Thus, those three compounds were docked in the cytosolic tryparedoxin peroxidase (cTXNPx) enzyme of the parasite, and molecular dynamics simulations were carried out. This enzyme was selected as a target protein for the sulfonamide-chalcones due to the fact of the anterior report, which identified a strong and stable interaction between the chalcone NAT22 (6) and the cTXNPx. In addition, a prediction of the drug-likeness, and the pharmacokinetic profile of all compounds were made, demonstrating a good profile of those chalcones.

Aurones: A Promising Scaffold to Inhibit SARS-CoV-2 Replication.

Aurones are a small subgroup of flavonoids in which the basic C6-C3-C6 skeleton is arranged as (Z)-2-benzylidenebenzofuran-3(2H)-one. These compounds are structural isomers of flavones and flavonols, natural products reported as potent inhibitors of SARS-CoV-2 replication. Herein, we report the design, synthesis, and anti-SARS-CoV-2 activity of a series of 25 aurones bearing different oxygenated groups (OH, OCH3, OCH2OCH3, OCH2O, OCF2H, and OCH2C6H4R) at the A- and/or B-rings using cell-based screening assays. We observed that 12 of the 25 compounds exhibit EC50 < 3 µM (8e, 8h, 8j, 8k, 8l, 8m, 8p, 8q, 8r, 8w, 8x, and 8y), of which five presented EC50 < 1 µM (8h, 8m, 8p, 8q, and 8w) without evident cytotoxic effect in Calu-3 cells. The substitution of the A- and/or B-ring with OCH3, OCH2OCH3, and OCF2H groups seems beneficial for the antiviral activity, while the corresponding phenolic derivatives showed a significant decrease in the anti-SARS-CoV-2 activity. The most potent compound of the series, aurone 8q (EC50 = 0.4 µM, SI = 2441.3), is 2 to 3 times more effective than the polyphenolic flavonoids myricetin (2) and baicalein (1), respectively. Investigation of the five more active compounds as inhibitors of SARS-CoV-2 3CLpro based on molecular dynamic calculations suggested that these aurones should detach from the active site of 3CLpro, and, probably, they could bind to another SARS-CoV-2 protein target (either receptor or enzyme).

Geissoschizoline, a promising alkaloid for Alzheimer's disease: Inhibition of human cholinesterases, anti-inflammatory effects and molecular docking.

Due to the lack of effective pharmacotherapy options to treats Alzheimer's disease, new strategies have been approached in the search for multi-target molecules as therapeutic options. In this work, four indole alkaloids, geissoschizoline, geissoschizone, geissospermine, and 3',4',5',6'-tetradehydrogeissospermine were isolated from Geissospermum vellosii (Pao pereira) and evaluated for their anticholinesterase activities. While geissospermine inhibited only butyrylcholinesterase (BChE), the other alkaloids behaved as non-selective inhibitors of acetylcholinesterase (AChE) and BChE. In cell viability tests, only geissoschizoline was not cytotoxic. Therefore, geissoschizoline actions were also evaluated in human cholinesterases, where it was twice as potent inhibitor of hBChE (IC50 = 10.21 ± 0.01 µM) than hAChE (IC50 = 20.40 ± 0.93 µM). On enzyme kinetic studies, geissoschizoline presented a mixed-type inhibition mechanism for both enzymes. Molecular docking studies pointed interactions of geissoschizoline with active site and peripheral anionic site of hAChE and hBChE, indicating a dual site inhibitor profile. Moreover, geissoschizoline also played a promising anti-inflammatory role, reducing microglial release of NO and TNF-α at a concentration (1 µM) ten and twenty times lower than the IC50 values of hBChE and hAChE inhibition, respectively. These actions give geissoschizoline a strong neuroprotective character. In addition, the ability to inhibit hAChE and hBChE, with approximate inhibitory potencies, accredits this alkaloid for therapeutic use in the moderate to severe phase of AD. Thus, geissoschizoline emerges as a possible multi-target prototype that can be very useful in preventing neurodegeneration and restore neurotransmission.

New trifluoromethyl triazolopyrimidines as anti-Plasmodium falciparum agents.

According to the World Health Organization, half of the World's population, approximately 3.3 billion people, is at risk for developing malaria. Nearly 700,000 deaths each year are associated with the disease. Control of the disease in humans still relies on chemotherapy. Drug resistance is a limiting factor, and the search for new drugs is important. We have designed and synthesized new 2-(trifluoromethyl)[1,2,4]triazolo[1,5-a]pyrimidine derivatives based on bioisosteric replacement of functional groups on the anti-malarial compounds mefloquine and amodiaquine. This approach enabled us to investigate the impact of: (i) ring bioisosteric replacement; (ii) a CF3 group substituted at the 2-position of the [1,2,4]triazolo[1,5-a]pyrimidine scaffold and (iii) a range of amines as substituents at the 7-position of the of heterocyclic ring; on in vitro activity against Plasmodium falciparum. P. falciparum dihydroorotate dehydrogenase (PfDHODH) through strong hydrogen bonds. The presence of a trifluoromethyl group at the 2-position of the [1,2,4]triazolo[1,5-a]pyrimidine ring led to increased drug activity. Thirteen compounds were found to be active, with IC50 values ranging from 0.023 to 20 µM in the anti-HRP2 and hypoxanthine assays. The selectivity index (SI) of the most active derivatives 5, 8, 11 and 16 was found to vary from 1,003 to 18,478.

CoMFA/CoMSIA 3D-QSAR of pyrimidine inhibitors of Pneumocystis carinii dihydrofolate reductase.

Pneumocystis carinii is typically a non-pathogenic fungus found in the respiratory tract of healthy humans. However, it may cause P. carinii pneumonia (PCP) in people with immune deficiency, affecting mainly premature babies, cancer patients and transplant recipients, and people with acquired immunodeficiency syndrome (AIDS). In the latter group, PCP occurs in approximately 80% of patients, a major cause of death. Currently, there are many available therapies to treat PCP patients, including P. carinii dihydrofolate reductase (PcDHFR) inhibitors, such as trimetrexate (TMX), piritrexim (PTX), trimethoprim (TMP), and pyrimethamine (PMT). Nevertheless, the high percentage of adverse side effects and the limited therapeutic success of the current drug therapy justify the search for new drugs rationally planned against PCP. This work focuses on the study of pyrimidine inhibitors of PcDHFR, using both CoMFA and CoMSIA 3D-QSAR methods.

Design and synthesis of new N-(5-trifluoromethyl)-1H-1,2,4-triazol-3-yl benzenesulfonamides as possible antimalarial prototypes.

A rational approach was used to synthesize a new set of 15 1H-1,2,4-triazol-3-yl benzenesulfonamide derivatives with the aim of developing new antimalarial lead compounds. These derivatives were prepared in yields between 50% and 62%, and their structures were elucidated using IR, ¹H-, ¹³C-, ¹9F-NMR, MS and elemental analysis. A docking study based on sulfonamides previously used against malaria identified trifluoromethyl-substituted derivatives to be the best lead compounds for new antimalarial drug development.

The receptor-dependent QSAR paradigm: an overview of the current state of the art.

The original quantitative structure-activity relationship (QSAR) formulation was proposed by Hansch and Fujita in the 1960's and, since then QSAR analysis has evolved as a mature science, due mainly to the advances that occurred in the past two decades in the fields of molecular modeling, data analysis algorithms, chemoinformatics, and the application of graph theory in chemistry. Moreover, it is also worthy of note the exponential progress that have occurred in software and hardware development. In this context, a myriad of QSAR methods exist; from the considered "classical" approaches (known as two-dimensional (2D) QSAR), to three-dimensional (3D) and multidimensional (nD) QSAR ones. A distinct QSAR approach has been recently proposed, the receptor-dependent-QSAR, where explicit information regarding the receptor structure (usually a protein) is extensively used during modeling process. Indeed, a limited, but growing number of receptor-dependent QSAR methods are reported in the literature. With no intention to be comprehensive, an overview of receptor-dependent QSAR methods will be discussed along with an in-depth examination of their applications in drug design, virtual screen, and ADMET modeling in silico.

Using molecular docking, 3D-QSAR, and cluster analysis for screening structurally diverse data sets of pharmacological interest.

In this study, we propose a drug design approach which includes docking, molecular fingerprints based cluster analysis, and 'induced' descriptors based receptor-dependent 3D-QSAR. The method was shown to be very useful for screening and modeling structurally diverse data sets of pharmacological interest. Different from other receptor-dependent 3D-QSAR, no ambiguous alignments are required for the construction of the models, and the computational cost is relatively lower. Moreover, 'induced' descriptors were shown to be very powerful in "capturing" ligand-receptor intermolecular interactions. The methodology was validated for eight data sets sampled from the literature and from public databases: human sex hormone-binding globulin, human corticosteroid-binding globulin, anthrax lethal factor, HIV-1 reverse transcriptase, neuraminidase A, thrombin, trypsin, and Pneumocystis carinii dihydrofolate reductase data sets. The resulting models were interpretable; the constructed QSAR equations have high statistical significance and predictive strength; and the drug design solutions were shown to be useful for guiding ligand modification for the development of new inhibitors for a broad range of molecular targets.

Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors.

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

Combined 4D-fingerprint and clustering based membrane-interaction QSAR analyses for constructing consensus Caco-2 cell permeation virtual screens.

A set of 30 structurally diverse molecules, for which Caco-2 cell permeation coefficients were determined, formed the training set for construction of Caco-2 cell permeation models based upon membrane-interaction (MI) QSAR analysis and a new QSAR method called 4D-fingerprint QSAR analysis. The descriptor terms of the 4D-fingerprints equation are molecular similarity eigenvalues, and this set of descriptors is being evaluated as a potential "universal" QSAR descriptor set. The 4D-fingerprint model suggests that Caco-2 cell permeation is governed by the spatial distribution of hydrogen bonding and nonpolar groups over the molecular shape of a molecule. Moreover, a complementary resampling of the original Caco-2 cell permeation training set, followed by the construction of several "clustered" MI-QSAR models, led to a consensus model consistent in interpretation with the 4D-fingerprint model.

Structure-based QSAR analysis of a set of 4-hydroxy-5,6-dihydropyrones as inhibitors of HIV-1 protease: an application of the receptor-dependent (RD) 4D-QSAR formalism.

Receptor-dependent (RD) 4D-QSAR models were constructed for a set of 39 4-hydroxy-5,6-dihydropyrone analogue HIV-1 protease inhibitors. The receptor model used in this QSAR analysis was derived from the HIV-1 protease (PDB ID ) crystal structure. The bound ligand in the active site of the enzyme, also a 4-hydroxy-5,6-dihydropyrone analogue, was used as the reference ligand for docking the data set compounds. The optimized RD 4D-QSAR models are not only statistically significant (r(2) = 0.86, q(2) = 0.80 for four- and greater-term models) but also possess reasonable predictivity based on test set predictions. The proposed "active" conformations of the docked analogues in the active site of the enzyme are consistent in overall molecular shape with those suggested from crystallographic studies. Moreover, the RD 4D-QSAR models also "capture" the existence of specific induced-fit interactions between the enzyme active site and each specific inhibitor. Hydrophobic interactions, steric shape requirements, and hydrogen bonding of the 4-hydroxy-5,6-dihydropyrone analogues with the HIV-1 protease binding site model dominate the RD 4D-QSAR models in a manner again consistent with experimental conclusions. Some possible hypotheses for the development of new lead HIV-1 protease inhibitors can be inferred from the RD 4D-QSAR models.

Rational design of new antituberculosis agents: receptor-independent four-dimensional quantitative structure-activity relationship analysis of a set of isoniazid derivatives.

A 4D-QSAR analysis was carried out for a set of 37 hydrazides whose minimum inhibitory concentrations against M. tuberculosis var. bovis were evaluated. These ligands are thought to act like isoniazid in mycolic acid biosynthesis. Results indicate that nonpolar groups in the acyl moiety of ligands markedly decrease biological activity. Molecular modifications of the ligand NAD moiety, including nonpolar groups and hydrogen bond donor and acceptor groups, seemingly improve ligand interactions with amino acid residues of the InhA active site.

Characterization of skin penetration processes of organic molecules using molecular similarity and QSAR analysis.

Molecular similarity and QSAR analyses have been used to develop compact, robust, and definitive models for skin penetration by organic compounds. The QSAR models have been sought to provide an interpretation and characterization of plausible molecular mechanisms of skin penetration. A training set of 40 structurally diverse compounds were selected to be representative of a parent set of 152 compounds in terms of both structural diversity and range in measured skin penetration. The subset of 40 compounds was used in a series of QSAR analyses in the search for the most significant, compact, and straightforward skin penetration QSAR models. Molecular dynamics simulations were employed to determine a set of MI (membrane-interaction) descriptors for each test compound (solute) interacting with a model DMPC monolayer membrane model. The MI-QSAR models may capture features of cellular membrane lateral transverse transport involved in the overall skin penetration process by organic compounds. An additional set of intramolecular solute descriptors, the non-MI-QSAR descriptors, were computed and added to the trial pool of descriptors for building QSAR models. All QSAR models were constructed using multidimensional linear regression fitting and a genetic algorithm optimization function. QSAR models were constructed using only non-MI-QSAR descriptors and using a combination of both these descriptor sets. It was found that a combination of non-MI-QSAR and MI-QSAR descriptors yielded the optimum models, not only with respect to the statistical measures of fit but also regarding model predictivity.