Multivariate QSAR, similarity search and ADMET studies based in a set of methylamine derivatives described as dopamine transporter inhibitors.

The dopamine transporter (DAT), responsible for the regulation of dopaminergic neurotransmission, is implicated in the etiology of several neuropsychiatric disorders which, in turn, have contributed to high rates of disability and numerous deaths in recent years, significantly impacting the global health system. Although the research for new drugs for the treatment of neuropsychiatric disorders has evolved in recent years, the availability of DAT-selective drugs that do not generate the same psychostimulant effects observed in drugs of abuse remains scarce. Therefore, we performed a QSAR study based on a dataset of 36 methylamine derivatives described as DAT inhibitors. The model was obtained based only in descriptors derived from 2D structures, and it was validated and generated satisfactory results considering the metrics used for internal and external validation. Subsequently, a virtual screening step also based on 2D similarity was performed, where it was possible to identify a total of 1157 compounds. After a series of reductions of the set using toxicity filters, applicability domain evaluation, and pharmacokinetic properties in silico assessment, seven hit compounds were selected as the most promising to be used, in future studies, as new scaffolds for the development of new DAT inhibitors.

Pharmacophore modeling, molecular docking, and molecular dynamics studies to identify new 5-HT2AR antagonists with the potential for design of new atypical antipsychotics.

Some important atypical antipsychotic drugs target the serotonergic receptor 2A (5-HT2AR). Currently, new therapeutic strategies are needed to offer faster onset of action with fewer side effects and, therefore, greater efficacy in a substantial proportion of patients with neuropsychological disorders such as Autism and Parkinson. The main objective of this work was to use SBDD methods to identify new hit compounds potentially useful as precursors of novel and selective 5-HT2AR antagonists. A structure-based pharmacophore screening study based on a selective antagonist was carried out in ten databases. The set obtained was refined using molecular docking, and the five most promising compounds were subjected to molecular dynamics simulations. The most stable and promising hit occupied a side pocket present in the 5-HT2AR, a site that can be explored to obtain selective ligands. Simulations against 5-HT2CR and D2R showed that the best hit could not form stable complexes with these targets, strengthening the hypothesis that the hit presents selective binding by the receptor of interest. The selected hits showed some predicted toxicity risk or violated some drug-likeness property. However, it can be concluded that the identified hits are the most promising for performing in vitro assays. Once the presence of activity is confirmed, they could become precursors of optimized and selective antagonists of 5-HT2AR. An SBDD study was carried out to identify new selective 5-HT2AR ligands potentially useful for designing selective atypical antipsychotics.

Bioactive compounds as potential angiotensin-converting enzyme II inhibitors against COVID-19: a scoping review.

OBJECTIVE AND DESIGN: The current study aimed to summarize the evidence of compounds contained in plant species with the ability to block the angiotensin-converting enzyme 2 (ACE-II), through a scoping review. METHODS: PubMed and Scopus electronic databases were used for the systematic search and a manual search was performed RESULTS: Studies included were characterized as in silico. Among the 200 studies retrieved, 139 studies listed after the exclusion of duplicates and 74 were included for the full read. Among them, 32 studies were considered eligible for the qualitative synthesis. The most evaluated class of secondary metabolites was flavonoids with quercetin and curcumin as most actives substances and terpenes (isothymol, limonin, curcumenol, anabsinthin, and artemisinin). Other classes that were also evaluated were alkaloid, saponin, quinone, substances found in essential oils, and primary metabolites as the aminoacid L-tyrosine and the lipidic compound 2-monolinolenin. CONCLUSION: This review suggests the most active substance from each class of metabolites, which presented the strongest affinity to the ACE-II receptor, what contributes as a basis for choosing compounds and directing the further experimental and clinical investigation on the applications these compounds in biotechnological and health processes as in COVID-19 pandemic.

Correction: A structure-activity relationship study of the toxicity of ionic liquids using an adapted Ferreira-Kiralj hydrophobicity parameter.

Correction for 'A structure-activity relationship study of the toxicity of ionic liquids using an adapted Ferreira-Kiralj hydrophobicity parameter' by Eduardo Borges de Melo et al., Phys. Chem. Chem. Phys., 2015, 17, 4516-4523.

1,2,3-Triazole-based analogue of benznidazole displays remarkable activity against Trypanosoma cruzi.

The current treatment of Chagas disease is based on the use of two drugs, nifurtimox and benznidazole, which present limited efficacy in the chronic stage of the disease and toxic side effects. Although some progress has been made in the development of new drugs to treat this disease, the discovery of novel compounds is urgently required. In this work we report the synthesis and biological evaluation of 1,2,3-triazole-based analogues of benznidazole. A small series of 27 compounds was successfully synthesized via microwave-assisted copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) and ruthenium-catalyzed azide-alkyne cycloaddition (RuAAC) from N-benzyl-2-azidoacetamide (1) and a set of commercial terminal alkynes. Analogues 24 (IC50 40 µM) and 28 (IC50 50 µM) showed comparable activities to benznidazole (IC50 34 µM) against trypomastigote form and analogue 15 (IC50 7 µM) was found to be the most active. Regarding the cytotoxicity assessment of the series, most compounds were not cytotoxic. This work shows that the designed strategy is efficiently capable of generating novel benzindazole analogues and reveals one analogue is more active than benznidazole.

A structure-activity relationship study of the toxicity of ionic liquids using an adapted Ferreira-Kiralj hydrophobicity parameter.

The Ferreira-Kiralj hydrophobicity parameter Wc is a number fraction of hydrophobic carbon atoms and can be regarded as a constitutional descriptor since its calculation depends only on the number of polar and nonpolar carbons in a compound. Hydrophobicity is important to the toxicity of ionic liquids (ILs), which are salts by nature. Herein, a descriptor for this property was calculated using a simple adaptation of the type of polar carbon atoms included (WcAdap) to explore the possibility of its use in quantitative structure-activity relationship (QSAR) studies of ILs. The resulting model was tested using a database of ILs with toxicity against the Leukemia rat cell line IPC-81. Two other models were constructed using Crippen log P and Mannhold log P descriptors, which are both available in the free program PaDEL. The use of WcAdap led to a better and more indicative model. Thus, WcAdap may be a suitable molecular descriptor for the hydrophobicity of ILs in QSAR studies.

Naringenin-4'-glucuronide as a new drug candidate against the COVID-19 Omicron variant: a study based on molecular docking, molecular dynamics, MM/PBSA and MM/GBSA.

This study aimed to identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) receptor binding domain (RBD) of the COVID-19 Omicron variant using computer simulations (in silico). NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening, molecular docking, molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), and molecular mechanics/generalized Born surface area (MM/GBSA). Remdesivir was used as a reference drug in docking and MD calculations. A total of 170,906 compounds were analyzed. Molecular docking screening revealed the top four NBCs with a high affinity with the spike (affinity energy <-7 kcal/mol) to be ZINC000045789238, ZINC000004098448, ZINC000008662732, and ZINC000003995616. In the MD analysis, the four ligands formed a complex with the highest dynamic equilibrium S1 (mean RMSD <0.3 nm), lowest fluctuation of the complex amino acid residues (RMSF <1.3), and solvent accessibility stability. However, the ZINC000045789238-spike complex (naringenin-4'-O glucuronide) was the only one that simultaneously had minus signal (-) MM/PBSA and MM/GBSA binding free energy values (-3.74 kcal/mol and -15.65 kcal/mol, respectively), indicating favorable binding. This ligand (naringenin-4'-O glucuronide) was also the one that produced the highest number of hydrogen bonds in the entire dynamic period (average = 4601 bonds per nanosecond). Six mutant amino acid residues formed these hydrogen bonds from the RBD region of S1 in the Omicron variant: Asn417, Ser494, Ser496, Arg403, Arg408, and His505. Naringenin-4'-O-glucuronide showed promising results as a potential drug candidate against COVID-19. In vitro and preclinical studies are needed to confirm these findings.Communicated by Ramaswamy H. Sarma.

A new quantitative structure-property relationship model to predict bioconcentration factors of polychlorinated biphenyls (PCBs) in fishes using E-state index and topological descriptors.

A quantitative structure-property relationship (QSPR) study for predicting the logarithm of bioconcentration factors (LogBCF) of polychlorinated biphenyls (PCBs) is presented in this work. For this, the descriptors were obtained using only the Simplified Molecular Input Line Entry System (SMILES) strings in the free web server Parameter Client. The model was built using the Partial Least Squares (PLS) regression method. The best model presented five descriptors (one E-state index and four topological descriptors) and a high quality for fit, internal, and external predictions. The leave-N-out (LNO) cross validation and the y-randomization test showed the model is robust and has no shown chance correlation. With a second test set, the model was compared to other models and presented a root mean square error (RMSE) very close to the best model. The mechanistic interpretation was corroborated by other works in the literature and by the descriptors' theory. Thus, the results meet the five Organization for Economic Co-operation and Development (OECD) principles for validation of QSA(P)R models, and it is expected the model can effectively predict the BCF values in fishes of the PCB congeners without highly reliable experimental BCF.

SMILES-based 2D-QSAR and similarity search for identification of potential new scaffolds for development of SARS-CoV-2 MPRO inhibitors.

COVID-19, whose etiological agent is the SARS-CoV-2 virus, has caused over 537.5 million cases and killed over 6.3 million people since its discovery in 2019. Despite the recent development of the first drugs indicated for treating people already infected, the great need to develop new anti-SARS-CoV-2 drugs still exists, mainly due to the possible emergence of new variants of this virus and resistant strains of the current variants. Thus, this work presents the results of QSAR and similarity search studies based only on 2D structures from a set of 32 bicycloproline derivatives, aiming to quickly, reproducibly, and reliably identify potentially useful compounds as scaffolds of new major protease inhibitors (Mpro) of the virus. The obtained QSAR model is based only on topological molecular descriptors. The model has good internal and external statistics, is robust, and does not present a chance correlation. This model was used as one of the tools to support the virtual screening stage carried out in the SwissADME web tool. Five molecules, from an initial set of 2695 molecules, proved to be the most promising, as they were within the model's applicability domain and linearity range, with low potential to cause carcinogenic, teratogenic, and reproductive toxicity effects and promising pharmacokinetic properties. These five compounds were then selected as the most competent to generate, in future studies, new anti-SARS-CoV-2 agents with drug-likeness properties suitable for use in therapy.

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.

Synthesis and 2D-QSAR studies of neolignan-based diaryl-tetrahydrofuran and -furan analogues with remarkable activity against Trypanosoma cruzi and assessment of the trypanothione reductase activity.

Two series of diaryl-tetrahydrofuran and -furan were synthesised and screened for anti-trypanosomal activity against trypomastigote and amastigote forms of Trypanosoma cruzi, the causative agent of Chagas disease. Based on evidence that modification of a natural product may result in a more effective drug than the natural product itself, and using known neolignan inhibitors veraguensin 1 and grandisin 2 as templates to synthesise simpler analogues, remarkable anti-trypanosomal activity and selectivity were found for 3,5-dimethoxylated diaryl-furan 5c and 2,4-dimethoxylated diaryl-tetrahydrofuran 4e analogues with EC50 0.01 µM and EC50 0.75 µM, respectively, the former being 260-fold more potent than veraguensin 1 and 150-fold better than benznidazole, the current available drugs for Chagas disease treatment. The ability of the most potent anti-trypanosomal compounds to penetrate LLC-MK2 cells infected with T. cruzi amastigotes parasite was tested, which revealed 4e and 5e analogues as the most effective, causing no damage to mammalian cells. In particular, the majority of the derivatives were non-toxic against mice spleen cells. 2D-QSAR studies show the rigid central core and the position of dimethoxy-aryl substituents dramatically affect the anti-trypanosomal activity. The mode of action of the most active anti-trypanosomal derivatives was investigated by exploring the anti-oxidant functions of Trypanothione reductase (TR). As a result, diarylfuran series displayed the strongest inhibition, highlighting compounds 5d-e (IC50 19.2 and 17.7 µM) and 5f-g (IC50 8.9 and 7.4 µM), respectively, with similar or 2-fold higher than the reference inhibitor clomipramine (IC50 15.2 µM).

A best​ comprehension about the toxicity of phenylsulfonyl carboxylates in Vibrio fischeri using quantitative structure activity/property relationship methods.

Aromatic sulfones comprise a class of chemicals used in agrochemical and pharmaceutical industries and as floatation and extractant agents in petrochemical and metallurgy industries. In this study, new QSA(P)R studies were carried out to predict the toxicity against Vibrio fischeri of a set of 52 aromatic sulfones. The same approach was used to evaluate the relationship between these endpoint and the water solubility, another important environmental endpoint. The study resulted in models of good statistical quality and mechanistic interpretation with a possible correlation between the two endpoints, but the toxic effect is also likely to depend on other physicochemical properties. The use of the PLS2, a method not commonly used in QSA(P)R studies, also produced models of greater reliability, and the relationship between the two endpoints was reinforced to some degree. These results are useful for better understanding the process by which these compounds exert their environmental toxicity, thus aiding in the development of industrially useful compounds with less potential environmental damage.

Modeling structure-activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS.

In the present study, we performed a multivariate quantitative structure-activity relationship (QSAR) analysis of 52 prodiginines with antimalarial activity. Variable selection was based on the genetic algorithm (GA) and ordered predictor selection (OPS) approaches, and the models were built using the multiple linear regression (MLR) and partial least squares (PLS) regression methods. The leave-N-out crossvalidation and y-randomization tests showed that the models were robust and free from chance correlation. The mechanistic interpretation of the results was supported by earlier findings. In addition, the comparison of our models with those previously described indicated that the OPS/PLS-based model had a higher quality of external prediction. Thus, this study provides a comprehensive approach to the evaluation of the antimalarial activity of prodiginines, which may be used as a support tool in designing new therapeutic agents for malaria.

An alternative approach for the use of water solubility of nonionic pesticides in the modeling of the soil sorption coefficients.

The collection of data to study the damage caused by pesticides to the environment and its ecosystems is slowly acquired and costly. Large incentives have been established to encourage research projects aimed at building mathematical models for predicting physical, chemical or biological properties of environmental interest. The organic carbon normalized soil sorption coefficient (K(oc)) is an important physicochemical property used in environmental risk assessments for compounds released into the environment. Many models for predicting logK(oc) that have used the parameters logP or logS as descriptors have been published in recent decades. The strong correlation between these properties (logP and logS) prevents them from being used together in multiple linear regressions. Because the sorption of a chemical compound in soil depends on both its water solubility and its water/organic matter partitioning, we assume that models capable of combining these two properties can generate more realistic results. Therefore, the objective of this study was to propose an alternative approach for modeling logK(oc), using a simple descriptor of solubility, here designated as the logarithm of solubility corrected by octanol/water partitioning (logS(P)). Thus, different models were built with this descriptor and with the conventional descriptors logP and logS, alone or associated with other explanatory variables representing easy-to-interpret physicochemical properties. The obtained models were validated according to current recommendations in the literature, and they were compared with other previously published models. The results showed that the use of logS(p) instead of conventional descriptors led to simple models with greater statistical quality and predictive power than other more complex models found in the literature. Therefore, logS(P) can be a good alternative to consider for the modeling of logK(oc) and other properties that relate to both solubility and water/organic matter partitioning.

The effect of different log P algorithms on the modeling of the soil sorption coefficient of nonionic pesticides.

Collecting data on the effects of pesticides on the environment is a slow and costly process. Therefore, significant efforts have been focused on the development of models that predict physical, chemical or biological properties of environmental interest. The soil sorption coefficient normalized to the organic carbon content (Koc) is a key parameter that is used in environmental risk assessments. Thus, several log Koc prediction models that use the hydrophobic parameter log P as a descriptor have been reported in the literature. Often, algorithms are used to calculate the value of log P due to the lack of experimental values for this property. Despite the availability of various algorithms, previous studies fail to describe the procedure used to select the appropriate algorithm. In this study, models that correlate log Koc with log P were developed for a heterogeneous group of nonionic pesticides using different freeware algorithms. The statistical qualities and predictive power of all of the models were evaluated. Thus, this study was conducted to assess the effect of the log P algorithm choice on log Koc modeling. The results clearly demonstrate that the lack of a selection criterion may result in inappropriate prediction models. Seven algorithms were tested, of which only two (ALOGPS and KOWWIN) produced good results. A sensible choice may result in simple models with statistical qualities and predictive power values that are comparable to those of more complex models. Therefore, the selection of the appropriate log P algorithm for modeling log Koc cannot be arbitrary but must be based on the chemical structure of compounds and the characteristics of the available algorithms.

A QSAR Study of Matrix Metalloproteinases Type 2 (MMP-2) Inhibitors with Cinnamoyl Pyrrolidine Derivatives.

A multivariate PLS-QSAR study with a data set of 31 cinnamoyl pyrrolidine derivatives described as type 2 matrix metalloproteinases (MMP-2) inhibitors is presented in this paper. The variable selection was performed with the Ordered Predictors Selection (OPS) algorithm. The PLS model presented six descriptors and three Latent Variables (LV) that cumulated 71.845% of variance. Leave-N-out (LNO) cross validation and y-randomization tests showed that the model presented robustness and no chance correlation, respectively. The descriptors indicated that MMP-2 inhibition depends mainly on the electronic properties of the compounds. The model obtained can be useful as a support tool in the design of new MMP-2 inhibitors.

Multivariate SAR/QSAR of 3-aryl-4-hydroxyquinolin-2(1H)-one derivatives as type I fatty acid synthase (FAS) inhibitors.

Two multivariate studies, a PCA-SAR and a PLS-QSAR, of 3-aryl-4-hydroxyquinolin-2(1H)-one derivatives described as type I fatty acid synthase (FAS) inhibitors, are presented in this work. The variable selection was performed with the Fisher's weight and Ordered Predictors Selection (OPS) algorithm, respectively. In the PCA, a separation between active and inactive compounds was obtained by six descriptors (topological and geometrical). The PLS model presented five descriptors and two Latent Variables. Leave-N-out cross validation and y-randomization test showed that the model presented robustness and no chance correlation, respectively, and the descriptors indicated that the FAS inhibition depends on electronic distribution of the investigated compounds. The model obtained in this study may provide a guidance for proposition of new FAS inhibitors.

[Quality of over-the-counter medicines: a study with dipyrone brands commercialized in a drugstore in Cascavel city (Paraná, Brazil)]. / Qualidade de medicamentos isentos de prescrição: um estudo com marcas de dipirona comercializadas em uma drogaria de Cascavel (PR, Brasil).

Dipyrone is an antipyretic and analgesic medicine very used by the Brazilian population. The administration is considered safe even in pregnant women, nurseling and children, but is forbidden in some countries, as supposedly causes agranulocytosis and aplastic anemia. In 2001, National Health Surveillance Agency (Anvisa) approved the commercialization of this medicine in Brazil. However, it does not matter the safeness in the use of a medicine advances, if it does not have quality. Based on this quality, this work was elaborated, that analyzes seven samples of commercialized different marks of dipyrone oral solution in pharmaceutical establishment in the Cascavel city, Paraná, Brazil. The results demonstrate that the quality control of similar drugs must be improved as were the ones that presented quality deviations.

Multivariate QSAR study of 4,5-dihydroxypyrimidine carboxamides as HIV-1 integrase inhibitors.

A multivariate QSAR study of thirty-three 4,5-dihydroxypyrimidine carboxamides as HIV-1 integrase (HIV-1 IN) inhibitors was performed employing Ordered Predictors Selection (OPS) algorithm and PLS regression for variable selection and model construction, respectively. Four descriptors were chosen and a reasonable model (n=30; R(2)=0.68; SEC=0.57; PRESS(cal)=8.72; F((2,27))=28.97; Q(2)(LOO)=0.58; SEV=0.62; PRESS(val)=11.62; R(2)(pred)=0.87; SEP=0.29; ARE(pred)=4.37%; k=0.99; k'=1.01; |r(2)(0)-r(2)(0)'|=-0.18) was built with two latent variables (59.54% of the information). Leave-N-out (LNO) and Y-randomization methods confirmed the model robustness. The descriptors indicated that the HIV-1 IN inhibition depends on the electronic distribution of the investigated compounds. The interpretation of the model is related to the most accepted mechanism of action.