An application of two MIFs-based tools (Volsurf+ and Pentacle) to binary QSAR: the case of a palinurin-related data set of non-ATP competitive glycogen synthase kinase 3β (GSK-3β) inhibitors

GSK-3β Allosteric Inhibition: A Dead End or a New Pharmacological Frontier?

Most kinase inhibitors are designed to bind to highly homologous ATP-binding sites, which leads to promiscuity and possible off-target effects. Allostery is an alternative approach to pursuing selectivity. However, allostery is difficult to exploit due to the wide variety of underlying mechanisms and the potential involvement of long-range conformational effects that are difficult to pinpoint. GSK-3β is involved in several pathologies. This critical target has an ATP-binding site that is highly homologous with the orthosteric sites of other kinases. Unsurprisingly, there is also great similarity between the ATP-binding sites of GSK-3β and its isomer, which is not redundant and thus would benefit from selective inhibition. Allostery would also allow for a moderate and tunable inhibition, which is ideal for GSK-3β, because this target is involved in multiple pathways, some of which must be preserved. However, despite considerable research efforts, only one allosteric GSK-3β inhibitor has reached the clinic. Moreover, unlike other kinases, there are no X-ray structures of GSK-3β in complex with allosteric inhibitors in the PDB data bank. This review aims to summarize the state of the art in allosteric GSK-3β inhibitor investigations, highlighting the aspects that make this target challenging for an allosteric approach.

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the k_off values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

Novel coumarins active against Trypanosoma cruzi and toxicity assessment using the animal model Caenorhabditis elegans

BACKGROUND

Chagas disease (CD) is a tropical parasitic disease. Although the number of people infected is very high, the only drugs available to treat CD, nifurtimox (Nfx) and benznidazole, are highly toxic, particularly in the chronic stage of the disease. Coumarins are a large class of compounds that display a wide range of interesting biological properties, such as antiparasitic. Hence, the aim of this work is to find a good antitrypanosomal drug with less toxicity. The use of simple organism models has become increasingly attractive for planning and simplifying efficient drug discovery. Within these models, Caenorhabditis elegans has emerged as a convenient and versatile tool with significant advantages for the toxicological potential identification for new compounds.

METHODS

Trypanocidal activity: Forty-two 4-methylamino-coumarins were assayed against the epimastigote form of Trypanosoma cruzi (Tulahuen 2 strain) by inhibitory concentration 50% (IC50). Toxicity assays: Lethal dose 50% (LD50) and Body Area were determined by Caenorhabditis elegans N2 strain (wild type) after acute exposure. Structure-activity relationship: A classificatory model was built using 3D descriptors.

RESULTS

Two of these coumarins demonstrated near equipotency to Nifurtimox (IC50 = 5.0 ± 1 μM), with values of: 11 h (LaSOM 266), (IC50 = 6.4 ± 1 μM) and 11 g (LaSOM 231), (IC50 = 8.2 ± 2.3 μM). In C. elegans it was possible to observe that Nfx showed greater toxicity in both the LD50 assay and the evaluation of the development of worms. It is possible to observe that the efficacy between Nfx and the synthesized compounds (11 h and 11 g) are similar. On the other hand, the toxicity of Nfx is approximately three times higher than that of the compounds. Results from the QSAR-3D study indicate that the volume and hydrophobicity of the substituents have a significant impact on the trypanocidal activities for derivatives that cause more than 50% of inhibition. These results show that the C. elegans model is efficient for screening potentially toxic compounds.

CONCLUSION

Two coumarins (11 h and 11 g) showed activity against T. cruzi epimastigote similar to Nifurtimox, however with lower toxicity in both LD50 and development of C. elegans assays. These two compounds may be a feasible starting point for the development of new trypanocidal drugs.

Alignment independent 3D-QSAR studies and molecular dynamics simulations for the identification of potent and selective S1P1 receptor agonists

Sphingosine 1-phosphate type 1 (S1P₁) receptors are expressed on lymphocytes and regulate immune cells trafficking. Sphingosine 1-phosphate and its analogues cause internalization and degradation of S1P₁ receptors, preventing the auto reactivity of immune cells in the target tissues. It has been shown that S1P₁ receptor agonists such as fingolimod can be suitable candidates for treatment of autoimmune diseases. The current study aimed to generate GRIND-based 3D-QSAR predictive models for agonistic activities of 2-imino-thiazolidin-4-one derivatives on S1P₁ to be used in virtual screening of chemical libraries. The developed model for the S1P₁ receptor agonists showed appropriate power of predictivity in internal (r²_acc 0.93 and SDEC 0.18) and external (r² 0.75 and MAE (95% data), 0.28) validations. The generated model revealed the importance of variables DRY-N1 and DRY-O in the potency and selectivity of these compounds towards S1P₁ receptor. To propose potential chemical entities with S1P₁ agonistic activity, PubChem chemicals database was searched and the selected compounds were virtually tested for S1P₁ receptor agonistic activity using the generated models, which resulted in four potential compounds with high potency and selectivity towards S1P₁ receptor. Moreover, the affinities of the identified compounds towards S1P₁ receptor were evaluated using molecular dynamics simulations. The results indicated that the binding energies of the compounds were in the range of −39.31 to −46.18 and −3.20 to −9.75 kcal mol⁻¹, calculated by MM-GBSA and MM-PBSA algorithms, respectively. The findings in the current work may be useful for the identification of potent and selective S1P₁ receptor agonists with potential use in diseases such as multiple sclerosis.

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A set of S1P₁ and S1P₃ receptor agonists was used to develop 3D-QSAR predictive models.

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The predictivity of the generated models were validated using external and internal validation methods.

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PubChem chemicals database was searched for identification of selective S1P₁ receptor agonists.

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Molecular dynamics simulations were used to calculate ligands binding energies to S1P₁ receptor.

The emerging chemical patterns applied in predicting human toll-like receptor 8 agonists

Toll-like receptors (TLRs) are important pattern recognition receptors to human innate immunity, which can recognize pathogen-associated molecular patterns and initiate innate immune responses. As the receptor of single stranded RNA (ssRNA), toll-like receptor 8 (TLR8) has potential in the treatment of tumors, microbial infection, and inflammatory diseases. Herein, an emerging chemical pattern (ECP) method was utilized to predict the key chemical patterns of TLR8 agonists. Based on the ECPs discovered, a robust and predictive ECP model was derived with prediction accuracies of 83.3%, 81.0%, and 80.0% for 132 training samples, 79 validation samples, and 75 test samples, respectively. When the ECP model was applied with a molecular docking method, the hit rate of TLR8 agonists was greatly enhanced. The results of ECP-based hierarchical cluster analysis and Connolly surface analysis of the TLR8 receptor showed that the H-bonding, hydrophilic and hydrophobic potentials as well as the unbalanced degree of property distributions are very important for distinguishing the TLR8 agonists from non-agonists.

A detailed structural study of cytotoxicity effect of ionic liquids on the leukemia rat cell line IPC-81 by three dimensional quantitative structure toxicity relationship

In the present study, a very thorough and in-depth three-dimensional quantitative structure-toxicity relationship (3D-QSTR) analysis has been implemented to make a correlation between the structural information of the ionic liquids (ILs) and their cytotoxicity towards Leukemia rat cell line IPC-81, as one of the ILs' toxicological consequences. To do this, alignment free GRid-INdependent Descriptors (GRINDs), which were derived from molecular interaction fields (MIFs), were correlated to the cytotoxicity values by partial least squares (PLS) and support vector regression (SVR). Genetic algorithm (GA), as a powerful linear tool, was used to select the best and interpretative subset of variables for the predictive model building. The selected variables with the capability to screen the effective structural features, showed direct and inverse contribution to the cytotoxicity. In silico modeling can reduce the amount of cellular testing necessary by predicting the toxicological functions of the chemical structures. Acceptable predictions of both internal and external validation sets made it possible to develop the predictive models for a large set of 269 diverse ILs containing 9 cationic cores and 44 types of anions. The constructed 3D-QSTR models use simple and interpretable descriptors to provide an in-depth and mechanistic interpretation of structural characteristics. This helps provide a clear understanding of the cytotoxicity effects of the understudy ILs. The effects of the nature of the cations, anions, and substituents on the cytotoxicities were evaluated and discussed.

Synthesis and SAR of new isoxazole-triazole bis-heterocyclic compounds as analogues of natural lignans with antiparasitic activity

Despite the impressive scientific and technological advances of recent decades, no effective treatment is currently available for Chagas disease. Our research group has been studying the design and synthesis of analogues of natural lignans aiming to identify compounds with antiparasitic activity. This article reports the synthesis of 42 novel bis-heterocyclic derivatives and the structure-activity relationship study conducted based on results of biological assays against Trypanosoma cruzi amastigotes. Thirty-seven compounds were active, and eight of them had GI₅₀ values lower than 100 μM (GI₅₀ 88.4-12.2 μM). A qualitative structure activity relationship study using three dimensional descriptors was carried out and showed a correlation between growth inhibitory potency and the presence of bulky hydrophobic groups located at rings A and D of the compounds. Compound 3-(3,4-dimethoxyphenyl)-5-((4-(4-pentylphenyl)-1H-1,2,3-triazol-1-yl)methyl)isoxazole (31) was the most active in the series (GI₅₀ 12.2 μM), showing, in vitro, low toxicity and potency similar to benznidazole (GI₅₀ 10.2 μM). These results suggest that this compound can be a promising scaffold for the design of new trypanocidal compounds.

Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches

Persistent organic products are compounds used for various purposes, such as personal care products, surfactants, colorants, industrial additives, food, pesticides and pharmaceuticals. These substances are constantly introduced into the environment and many of these pollutants are difficult to degrade. Toxic compounds classified as MoA 1 (Mode of Action 1) are low toxicity compounds that comprise nonreactive chemicals. In silico methods such as Quantitative Structure-Activity Relationships (QSARs) have been used to develop important models for prediction in several areas of science, as well as aquatic toxicity studies. The aim of the present study was to build a QSAR model-based set of theoretical Volsurf molecular descriptors using the fish acute toxicity values of compounds defined as MoA 1 to identify the molecular properties related to this mechanism. The selected Partial Least Squares (PLS) results based on the values of cross-validation coefficients of determination (Q_cv²) show the following values: Q_cv² = 0.793, coefficient of determination (R²) = 0.823, explained variance in external prediction (Q_ext²) = 0.87. From the selected descriptors, not only the hydrophobicity is related to the toxicity as already mentioned in previously published studies but other physicochemical properties combined contribute to the activity of these compounds. The symmetric distribution of the hydrophobic moieties in the structure of the compounds as well as the shape, as branched chains, are important features that are related to the toxicity. This information from the model can be useful in predicting so as to minimize the toxicity of organic compounds.

Constructing Interconsistent, Reasonable, and Predictive Models for Both the Kinetic and Thermodynamic Properties of HIV-1 Protease Inhibitors

Accumulated evidence suggests that the in vivo biological potency of a ligand is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of the protein-ligand interaction (PLI). However, the existing experimental and computational techniques are largely insufficient and limited in large-scale measurements or accurate predictions of the kinetic properties of PLI. In this work, elaborate efforts have been made to develop interconsistent, reasonable, and predictive models of the association rate constant (k_on), dissociation rate constant (k_off), and equilibrium dissociation constant (K_D) of a series of HIV protease inhibitors with different structural skeletons. The results showed that nine Volsurf descriptors derived from water (OH2) and hydrophobic (DRY) probes are key molecular determinants for the kinetic and thermodynamic properties of HIV-1 protease inhibitors. To the best of our knowledge, this is the first time that interconsistent and reasonable models with strong prediction power have been established for both the kinetic and thermodynamic properties of HIV protease inhibitors.

GSK-3 Inhibitors: Preclinical and Clinical Focus on CNS

Inhibiting glycogen synthase kinase-3 (GSK-3) activity via pharmacological intervention has become an important strategy for treating neurodegenerative and psychiatric disorders. The known GSK-3 inhibitors are of diverse chemotypes and mechanisms of action and include compounds isolated from natural sources, cations, synthetic small-molecule ATP-competitive inhibitors, non-ATP-competitive inhibitors, and substrate-competitive inhibitors. Here we describe the variety of GSK-3 inhibitors with a specific emphasis on their biological activities in neurons and neurological disorders. We further highlight our current progress in the development of non-ATP-competitive inhibitors of GSK-3. The available data raise the hope that one or more of these drug design approaches will prove successful at stabilizing or even reversing the aberrant neuropathology and cognitive deficits of certain central nervous system disorders.

Classification of anti-HIV compounds using counterpropagation artificial neural networks and decision trees

The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure-activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm-pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the molecules according to their inhibition mechanisms and activities. Some general parameters such as molecular weight, average molecular weight, number of hydrogen atoms and number of hydroxyl groups were found to be important for describing the inhibition behaviour of anti-HIV agents. The developed classifier models in this work can be used to screen large libraries of compounds to identify those likely to display activity as anti-HIV agents.

Glycogen synthase kinase 3 inhibitors in the next horizon for Alzheimer's disease treatment

Glycogen synthase kinase 3 (GSK-3), a proline/serine protein kinase ubiquitously expressed and involved in many cellular signaling pathways, plays a key role in the pathogenesis of Alzheimer's disease (AD) being probably the link between β-amyloid and tau pathology. A great effort has recently been done in the discovery and development of different new molecules, of synthetic and natural origin, able to inhibit this enzyme, and several kinetics mechanisms of binding have been described. The small molecule called tideglusib belonging to the thiadiazolidindione family is currently on phase IIb clinical trials for AD. The potential risks and benefits of this new kind of disease modifying drugs for the future therapy of AD are discussed in this paper.