Publications by authors named "Vinícius Gonçalves Maltarollo"

25 Publications

  • Page 1 of 1

Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking.

Mol Divers 2021 Aug 30;25(3):1301-1314. Epub 2021 Jun 30.

Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.

Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models-decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure-activity relationship model (SAR).
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http://dx.doi.org/10.1007/s11030-021-10261-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241884PMC
August 2021

Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti- activity.

J Biomol Struct Dyn 2021 Jun 14:1-12. Epub 2021 Jun 14.

Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.

is a fungus responsible for infections in humans with a significant number of cases in immunosuppressed patients, mainly in underdeveloped countries. In this context, the thiazolylhydrazones are a promising class of compounds with activity against . The understanding of the structure-activity relationship of these derivatives could lead to the design of robust compounds that could be promising drug candidates for fungal infections. Specifically, modern techniques such as 4D-QSAR and machine learning methods were employed in this work to generate two QSAR models (one 2D and one 4D) with high predictive power (r for the test set equals to 0.934 and 0.831, respectively), and one random forest classification model was reported with Matthews correlation coefficient equals to 1 and 0.62 for internal and external validations, respectively. The physicochemical interpretation of selected models, indicated the importance of aliphatic substituents at the hydrazone moiety to antifungal activity, corroborating experimental data.Communicated by Ramaswamy H. Sarma.
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http://dx.doi.org/10.1080/07391102.2021.1935321DOI Listing
June 2021

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade.

Expert Opin Drug Discov 2021 09 7;16(9):961-975. Epub 2021 May 7.

Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.

: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.
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http://dx.doi.org/10.1080/17460441.2021.1918098DOI Listing
September 2021

Ligand Accessibility Insights to the Dengue Virus NS3-NS2B Protease Assessed by Long-Timescale Molecular Dynamics Simulations.

ChemMedChem 2021 Aug 18;16(16):2524-2534. Epub 2021 May 18.

Department of Medical Oncology and Pneumology, University Hospital of Tübingen, Otfried-Müller-Strasse 14, 72076, Tübingen, Germany.

Dengue is a tropical disease caused by the dengue virus (DENV), with an estimate of 300 million new cases every year. Due to the limited vaccine efficiency and absence of effective antiviral treatment, new drug candidates are urgently needed. DENV NS3-NS2B protease complex is essential for viral post-translational processing and maturation, and this enzyme has been extensively studied as a relevant drug target. Crystal structures often underestimate NS3-NS2B flexibility, whereas they can adopt different conformational states depending on the bound substrate. We conducted molecular dynamics simulations (∼30 μs) with a non- and covalently bound inhibitor to understand the conformational changes in the DENV-3 NS3-NS2B complex. Our results show that the open-closing movement of the protease exposes multiple druggable subpockets that can be investigated in later drug discovery efforts.
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http://dx.doi.org/10.1002/cmdc.202100246DOI Listing
August 2021

Cytotoxic Activity of Triterpenoids from Cheiloclinium cognatum Branches against Chronic and Acute Leukemia Cell Lines.

Chem Biodivers 2020 Dec 23;17(12):e2000773. Epub 2020 Nov 23.

Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Avenida Presidente Antônio Carlos, 6627, Pampulha, 31270-901, Belo Horizonte-MG, Brasil.

Cheiloclinium cognatum (Miers) A.C.Sm. is an endemic species of Brazilian Cerrado that belongs to Celastraceae family. The phytochemical study of C. cognatum branches led to the identification of ten triterpenoids (TPs), 3β-acyloxyurs-12-ene (1), friedelin (2), β-friedelinol (3), glut-5-en-3β-ol (4), α-amyrin (5), β-amyrin (6), β-sitosterol (7), canophyllol (8), 29-hydroxyfriedelan-3-one (9) and friedelane-3β,29-diol (10). TPs 4, 5 and 6 are described for the first Cheiloclinium genus and TPs 8 and 9 were isolated in expressive amounts. Their cytotoxic activities were evaluated against THP-1 and K562 leukemia cell lines. TPs 3 and 5 were the most active, exhibiting lower or similar IC against both cell lines when compared to the controls. Their mechanisms of action were investigated suggesting an intrinsic mitochondrial pathway of apoptosis evidenced by up-regulation of BAK mRNA expression. Chemometric studies indicated that their activities may be related to their molecular size and shape as well as electronic interactions of C-3 hydroxy group with molecular targets.
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http://dx.doi.org/10.1002/cbdv.202000773DOI Listing
December 2020

The application of machine learning techniques to innovative antibacterial discovery and development.

Expert Opin Drug Discov 2020 10 17;15(10):1165-1180. Epub 2020 Jun 17.

Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil.

Introduction: After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (. construction of regression/classification models and ranking/virtual screening of compounds).

Areas Covered: In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years.

Expert Opinion: The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.
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http://dx.doi.org/10.1080/17460441.2020.1776696DOI Listing
October 2020

Virtual screening of antibacterial compounds by similarity search of Enoyl-ACP reductase (FabI) inhibitors.

Future Med Chem 2020 01 15;12(1):51-68. Epub 2019 Nov 15.

Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Av. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, MG, Brazil 31270-901.

Antibiotic resistance is an alarming issue, as multidrug-resistant bacteria are growing worldwide, hence the decrease of therapeutic potential of available antibiotic arsenal. Among these bacteria, was pointed by the WHO in the pathogens list to be prioritized in drug development. We report the use of chemical similarity models for the virtual screening of new antibacterial with structural similarity to known inhibitors of FabI. The potential inhibitors were experimentally evaluated for antibacterial activity and membrane disrupting capabilities. These models led to the finding of four new compounds with antibacterial activity, one of which having antimicrobial activity already reported in the literature.
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http://dx.doi.org/10.4155/fmc-2019-0158DOI Listing
January 2020

Ligand- and Structure-Based Approaches of Escherichia coli FabI Inhibition by Triclosan Derivatives: From Chemical Similarity to Protein Dynamics Influence.

ChemMedChem 2019 12 7;14(23):1995-2004. Epub 2019 Nov 7.

Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Pampulha, Belo Horizonte, MG, 31270-901, Brazil.

Enoyl-acyl carrier protein reductase (FabI) is the limiting step to complete the elongation cycle in type II fatty acid synthase (FAS) systems and is a relevant target for antibacterial drugs. E. coli FabI has been employed as a model to develop new inhibitors against FAS, especially triclosan and diphenyl ether derivatives. Chemical similarity models (CSM) were used to understand which features were relevant for FabI inhibition. Exhaustive screening of different CSM parameter combinations featured chemical groups, such as the hydroxy group, as relevant to distinguish between active/decoy compounds. Those chemical features can interact with the catalytic Tyr156. Further molecular dynamics simulation of FabI revealed the ionization state as a relevant for ligand stability. Also, our models point the balance between potency and the occupancy of the hydrophobic pocket. This work discusses the strengths and weak points of each technique, highlighting the importance of complementarity among approaches to elucidate EcFabI inhibitor's binding mode and offers insights for future drug discovery.
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http://dx.doi.org/10.1002/cmdc.201900415DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6916556PMC
December 2019

Pharmacological and physicochemical profile of arylacetamides as tools against human cancers.

Toxicol Appl Pharmacol 2019 10 26;380:114692. Epub 2019 Jul 26.

Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil.

Arylacetamides are widely used as synthetic intermediates to obtain medicinal substances. This work evaluated in vitro antiproliferative activity of ten 2-Chloro-N-arylacetamides on human normal and cancer cells and detailed in vivo toxicological and anticancer investigations. Initially, cytotoxic colorimetric assays were performed using tumor lines, peripheral blood mononuclear cells (PBMC) and erythrocytes. Compounds 2, 3 and 4 were tested for acute toxicity (50, 150 and 300 mg/kg) and for subacute antitumoral capacity in HCT-116 colon carcinoma-bearing xenograft mice for 15 days at 25 mg/kg/day. Most compounds revealed cytotoxic action on tumor lines and PBMC, but activity on human erythrocytes were not detected. Molecular dipole moment, lipophilicity and electronic constant of aryl substituents had effects upon in vitro antiproliferative capacity. More common in vivo acute behavioral signals with compounds 2, 3 and 4 were muscle relaxation, reduction of spontaneous locomotor activity and number of entries in closed arms and increased number of falls andtime spent in open arms, suggesting diazepam-like anxiolytic properties. Decrease of grabbing strength and overall activity were common, but palpebral ptosis and deaths occurred at 300 mg/kg only. Compounds 2 and 3 reduced colon carcinoma growth (21.2 and 27.5%, respectively, p < 0.05) without causing apparent signals of organ-specific toxicity after subacute exposure. The structural chemical simplicity of arylacetamides make them cost-effective alternatives and justifies further improvements to enhance activity, selectivity and the development of pharmaceutical formulations.
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http://dx.doi.org/10.1016/j.taap.2019.114692DOI Listing
October 2019

HQSAR and random forest-based QSAR models for anti-T. vaginalis activities of nitroimidazoles derivatives.

J Mol Graph Model 2019 07 19;90:180-191. Epub 2019 Apr 19.

Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Av. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, MG, 31270-901, Brazil. Electronic address:

Trichomonas vaginalis is the causative agent of trichomoniasis, a highly prevalent sexually transmitted infection worldwide. Nitroimidazole drugs, such as metronidazole and tinidazole, are the only recommended treatment, but cases of resistance represent at least 5%. In case of resistance or therapeutic failure, posology with higher doses is used, culminating in the increase of the toxic effects of the treatment. In this context, the development of new drugs becomes an eminent necessity. Hologram quantitative structure-activity relationship (HQSAR) models using nitroimidazole derivatives were generated to discover the relationship between the different chemical structures and the activity against cells and the selectivity against susceptible and resistant strains. One model of each strain was chosen for interpretation, both showed good internal coefficient (q values: 0.607 for susceptible strain and 0.646 for resistant strain subsets) and great values in other internal and external validations metrics. From the contribution of fragments to HQSAR models, several differences between the most and least potent compounds were found: 5-nitroimidazole contributes positively while 4-nitroimidazole negatively. QSAR models employing random forest (RF-QSAR) machine learning technique were also built and a robust model was obtained from resistant strain activity prediction (q equals to 0.618). The constructed HQSAR and RF-QSAR models were employed to predict the activity of three newly planned nitroimidazole derivatives in the design of new drugs candidates against T. vaginalis strains.
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http://dx.doi.org/10.1016/j.jmgm.2019.04.007DOI Listing
July 2019

QSAR studies on the human sirtuin 2 inhibition by non-covalent 7,5,2-anilinobenzamide derivatives.

J Biomol Struct Dyn 2020 02 21;38(2):354-363. Epub 2019 Feb 21.

Department of Pharmacy, Faculty of Pharmaceutical Sciences, University of Sao Paulo (USP), Sao Paulo, SP, Brazil.

Sirtuin 2 is a key enzyme in gene expression regulation that is often associated with tumor proliferation control and therefore is a relevant anticancer drug target. Anilinobenzamide derivatives have been discussed as selective sirtuin 2 inhibitors and can be developed further. In the present study, hologram and three-dimensional quantitative structure-activity relationship (HQSAR and 3D-QSAR) analyses were employed for determining structural contributions of a compound series containing human sirtuin-2-selective inhibitors that were then correlated with structural data from the literature. The final QSAR models were robust and predictive according to statistical validation ( and values higher than 0.85 and 0.75, respectively) and could be employed further to generate fragment contribution and contour maps. 3D-QSAR models together with information about the chemical properties of sirtuin 2 inhibitors can be useful for designing novel bioactive ligands.Communicated by Ramaswamy H. Sarma.
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http://dx.doi.org/10.1080/07391102.2019.1574603DOI Listing
February 2020

Advances with support vector machines for novel drug discovery.

Expert Opin Drug Discov 2019 01 29;14(1):23-33. Epub 2018 Nov 29.

c Escola de Artes, Ciências e Humanidades , Universidade de São Paulo (USP) , São Paulo , Brazil.

Introduction: Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds. Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges. Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.
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http://dx.doi.org/10.1080/17460441.2019.1549033DOI Listing
January 2019

In vitro and in silico studies of antioxidant activity of 2-thiazolylhydrazone derivatives.

J Mol Graph Model 2019 01 12;86:106-112. Epub 2018 Oct 12.

Department of Pharmaceutical Products, Pharmacy Faculty, Federal University of Minas Gerais, 6627 Antônio Carlos AVE, 31270-901, Belo Horizonte, Minas Gerais, Brazil. Electronic address:

The antioxidant potential of a series of thiazolylhydrazone derivatives was investigated using three different methods namely DPPH, ABTS and FRAP assays. In general, the tested compounds showed higher or comparable activity to that of curcumin, used as positive control. Chemometric analyses demonstrated that the presence of hydrazone moiety is required for the activity of this class of compounds. From these results, compound 4 was identified as the most promising molecule and was then selected for further studies. The antiproliferative effect of compound 4 was evaluated, being active in three (T47D, MDA-MB-231 and SKMEL) of the six cancer cell lines tested, with IC values ranging from 15.9 to 31.3 μM. Compound 4 exhibited no detectable cytotoxic effect on peripheral blood mononuclear cells (PBMC) when tested at a concentration of 100 μM, demonstrating good selectivity. From these results, it is possible to infer that there is a correlation between antioxidant capacity and anticancer effects.
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http://dx.doi.org/10.1016/j.jmgm.2018.10.007DOI Listing
January 2019

Targeting the Plasmodium falciparum plasmepsin V by ligand-based virtual screening.

Chem Biol Drug Des 2019 03 1;93(3):300-312. Epub 2018 Nov 1.

Unit for Drug Discovery, Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil.

Malaria is a devastating disease depending only on chemotherapy as treatment. However, medication is losing efficacy, and therefore, there is an urgent need for the discovery of novel pharmaceutics. Recently, plasmepsin V, an aspartic protease anchored in the endoplasmaic reticulum, was demonstrated as responsible for the trafficking of parasite-derived proteins to the erythrocytic surface and further validated as a drug target. In this sense, ligand-based virtual screening has been applied to design inhibitors that target plasmepsin V of P. falciparum (PMV). After screening 5.5 million compounds, four novel plasmepsin inhibitors have been identified which were subsequently analyzed for the potency at the cellular level. Since PMV is membrane-anchored, the verification in vivo by using transgenic PMV overexpressing P. falciparum cells has been performed in order to evaluate drug efficacy. Two lead compounds, revealing IC values were 44.2 and 19.1 μm, have been identified targeting plasmepsin V in vivo and do not significantly affect the cell viability of human cells up to 300 μm. We herein report the use of the consensus of individual virtual screening as a new technique to design new ligands, and we propose two new lead compounds as novel protease inhibitors to target malaria.
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http://dx.doi.org/10.1111/cbdd.13416DOI Listing
March 2019

Synthesis, molecular modeling studies and evaluation of antifungal activity of a novel series of thiazole derivatives.

Eur J Med Chem 2018 May 31;151:248-260. Epub 2018 Mar 31.

Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil. Electronic address:

In the search for new antifungal agents, a novel series of fifteen hydrazine-thiazole derivatives was synthesized and assayed in vitro against six clinically important Candida and Cryptococcus species and Paracoccidioides brasiliensis. Eight compounds showed promising antifungal activity with minimum inhibitory concentration (MIC) values ranging from 0.45 to 31.2 μM, some of them being equally or more active than the drug fluconazole and amphotericin B. Active compounds were additionally tested for toxicity against human embryonic kidney (HEK-293) cells and none of them exhibited significant cytotoxicity, indicating high selectivity. Molecular modeling studies results corroborated experimental SAR results, suggesting their use in the design of new antifungal agents.
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http://dx.doi.org/10.1016/j.ejmech.2018.03.083DOI Listing
May 2018

Molecular cloning and characterization of pirarucu (Arapaima gigas) follicle-stimulating hormone and luteinizing hormone β-subunit cDNAs.

PLoS One 2017 28;12(8):e0183545. Epub 2017 Aug 28.

Biotechnology Department, IPEN-CNEN/SP, Cidade Universitária, São Paulo, SP, Brazil.

The common gonadotrophic hormone α-subunit (GTHα) has been previously isolated by our research group from A. gigas pituitaries; in the present work the cDNA sequences encoding FSHβ and LHβ subunits have also been isolated from the same species of fish. The FSH β-subunit consists of 126 amino acids with a putative 18 amino acid signal peptide and a 108 amino acid mature peptide, while the LH β-subunit consists of 141 amino acids with a putative 24 amino acid amino acid signal peptide and a 117 amino acid mature peptide. The highest identity, based on the amino acid sequences, was found with the order of Anguilliformes (61%) for FSHβ and of Cypriniformes (76%) for LHβ, followed by Siluriformes, 53% for FSHβ and 75% for LHβ. Interestingly, the identity with the corresponding human amino acid sequences was still remarkable: 45.1% for FSHβ and 51.4% for LHβ. Three dimensional models of ag-FSH and ag-LH, generated by using the crystal structures of h-FSH and h-LH as the respective templates and carried out via comparative modeling and molecular dynamics simulations, suggested the presence of the so-called "seat-belt", favored by a disulfide bond formed between the 3rd and 12th cysteine in both β-subunits. The sequences found will be used for the biotechnological synthesis of A. gigas gonadotrophic hormones (ag-FSH and ag-LH). In a first approach, to ascertain that the cloned transcripts allow the expression of the heterodimeric hormones, ag-FSH has been synthesized in human embryonic kidney 293 (HEK293) cells, preliminarily purified and characterized.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183545PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5573580PMC
October 2017

Advances and Challenges in Drug Design of PPARδ Ligands.

Curr Drug Targets 2018 ;19(2):144-154

Center of Human and Natural Sciences, Federal University of ABC (UFABC) - Santo Andre, SP, Brazil.

Background: Peroxisome proliferator-activated receptors (PPAR) are nuclear receptors activated by endogenous fatty acids and prostaglandins that are classified into three types: α, γ and δ, which have different functions and tissue distribution. PPAR modulators have been exploited to the treatment of important metabolic diseases, such as type 2 diabetes mellitus and metabolic syndrome, which are considered relevant epidemic diseases currently. Along the last decades, several studies have reported structural differences between the three PPAR subtypes associated with the discovery of selective ligands, dual and pan-agonists. Nowadays, there are several approved drugs that activate PPARα (fibrates) and PPARγ (glitazones), but up to now there is none clinically used drug targeting PPARδ. Additionally, several side-effects associated with the use of PPARα and γ agonists are reported by regulatory agencies, which do not indicate anymore their use as first-line drugs.

Objective: A significant new market has grown in the last years, focusing on the development of new PPARδ agonists as drug candidates to treat metabolic diseases and, in this sense, this study proposes to review the structural requirements to achieve selective PPARδ activation, as well to discuss the most relevant agonists in clinical trials, providing information on the current phase in the drug discovery and design targeting PPARδ.

Conclusion: Several PPARδ ligands with high potency were reported in the literature and were designed or discovered by a combination of experimental and computational approaches. Furthermore, the reported importance of pockets and individual residues at PPARδ binding site as well as the importance of substituent and some physicochemical properties that could help to design of new classes of agonists.
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http://dx.doi.org/10.2174/1389450118666170414113159DOI Listing
May 2019

MD simulations and multivariate studies for modeling the antileishmanial activity of peptides.

Chem Biol Drug Des 2017 Oct 8;90(4):501-510. Epub 2017 Apr 8.

Departamento de Física, Universidade Estadual Paulista, São José do Rio Preto, SP, Brazil.

Leishmaniasis, a protozoan-caused disease, requires alternative treatments with minimized side-effects and less prone to resistance development. Antimicrobial peptides represent a possible choice to be developed. We report on the prospection of structural parameters of 23 helical antimicrobial and leishmanicidal peptides as a tool for modeling and predicting the activity of new peptides. This investigation is based on molecular dynamic simulations (MD) in mimetic membrane environment, as most of these peptides share the feature of interacting with phospholipid bilayers. To overcome the lack of experimental data on peptides' structures, we started simulations from designed 100% α-helices. This procedure was validated through comparisons with NMR data and the determination of the structure of Decoralin-amide. From physicochemical features and MD results, descriptors were raised and statistically related to the minimum inhibitory concentration against Leishmania by the multivariate data analysis technique. This statistical procedure confirmed five descriptors combined by different loadings in five principal components. The leishmanicidal activity depends on peptides' charge, backbone solvation, volume, and solvent-accessible surface area. The generated model possesses good predictability (q  = 0.715, r  = 0.898) and is indicative for the most and the least active peptides. This is a novel theoretical path for structure-activity studies combining computational methods that identify and prioritize the promising peptide candidates.
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http://dx.doi.org/10.1111/cbdd.12970DOI Listing
October 2017

Studies of Staphylococcus aureus FabI inhibitors: fragment-based approach based on holographic structure-activity relationship analyses.

Future Med Chem 2017 Jan 27;9(2):135-151. Epub 2017 Jan 27.

Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, MG 31270-901, Brazil.

Aim: FabI is a key enzyme in the fatty acid metabolism of Gram-positive bacteria such as Staphylococcus aureus and is an established drug target for known antibiotics such as triclosan. However, due to increasing antibacterial resistance, there is an urgent demand for new drug discovery. Recently, aminopyridine derivatives have been proposed as promising competitive inhibitors of FabI.

Methods: In the present study, holographic structure-activity relationship (HQSAR) analyses were employed for determining structural contributions of a series containing 105 FabI inhibitors.

Results & Conclusion: The final HQSAR model was robust and predictive according to statistical validation (q and r equal to 0.696 and 0.854, respectively) and could be further employed to generate fragment contribution maps. Then, final HQSAR model together with FabI active site information can be useful for designing novel bioactive ligands.
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http://dx.doi.org/10.4155/fmc-2016-0179DOI Listing
January 2017

Studies of Staphylococcus aureus FabI inhibitors: fragment-based approach based on holographic structure-activity relationship analyses.

Future Med Chem 2017 02 27;9(2):135-151. Epub 2017 Jan 27.

Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, MG 31270-901, Brazil.

Aim: FabI is a key enzyme in the fatty acid metabolism of Gram-positive bacteria such as Staphylococcus aureus and is an established drug target for known antibiotics such as triclosan. However, due to increasing antibacterial resistance, there is an urgent demand for new drug discovery. Recently, aminopyridine derivatives have been proposed as promising competitive inhibitors of FabI.

Methods: In the present study, holographic structure-activity relationship (HQSAR) analyses were employed for determining structural contributions of a series containing 105 FabI inhibitors.

Results & Conclusion: The final HQSAR model was robust and predictive according to statistical validation (q and r equal to 0.696 and 0.854, respectively) and could be further employed to generate fragment contribution maps. Then, final HQSAR model together with FabI active site information can be useful for designing novel bioactive ligands.
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http://dx.doi.org/10.4155/fmc-2016-0179DOI Listing
February 2017

Use of machine learning approaches for novel drug discovery.

Expert Opin Drug Discov 2016 ;11(3):225-39

a Centro de Ciências Naturais e Humanas , Universidade Federal do ABC , São Paulo , Brazil.

Introduction: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.

Areas Covered: This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.

Expert Opinion: Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.
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http://dx.doi.org/10.1517/17460441.2016.1146250DOI Listing
October 2016

Molecular docking studies and 2D analyses of DPP-4 inhibitors as candidates in the treatment of diabetes.

Mol Biosyst 2015 Nov;11(11):3188-93

Center for Natural and Human Sciences, Federal University of ABC, 09210-170, Santo André, SP, Brazil.

Dipeptidyl peptidase-4 (DPP-4) is an important biological target related to the treatment of diabetes as DPP-4 inhibitors can lead to an increase in the insulin levels and a prolonged activity of glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP), being effective in glycemic control. Thus, this study analyses the main molecular interactions between DPP-4 and a series of bioactive ligands. The methodology used here employed molecular modeling methods, such as HQSAR (Hologram Quantitative Structure-Activity) analyses and molecular docking, with the aim of understanding the main structural features of the compound series that are essential for the biological activity. Analyses of the main interactions in the active site of DPP-4, in particular, the contribution of the hydroxyl coordination between Tyr547 and Ser630 by the water molecule, which is described in the literature as important for the coordinated interactions in the active site, were performed. Significant correlation coefficients of the best 2D model (r(2) = 0.942 and q(2) = 0.836) were obtained, indicating the predictive power of this model for untested compounds. Therefore, the final model constructed in this study, along with the information from the contribution maps, could be useful in the design of novel DPP-4 ligands with improved activity.
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http://dx.doi.org/10.1039/c5mb00493dDOI Listing
November 2015

Convergent QSAR studies on a series of NK₃ receptor antagonists for schizophrenia treatment.

J Enzyme Inhib Med Chem 2016 9;31(2):283-94. Epub 2015 Apr 9.

a Department of Pharmacy, Faculty of Pharmaceutical Sciences , University of São Paulo , SP , Brazil .

The dopamine hypothesis states that decreased dopaminergic neurotransmission reduces schizophrenia symptoms. Neurokinin-3 receptor (NK3) antagonists reduce dopamine release and have shown positive effects in pre-clinical and clinical trials. We employed 2D and 3D-QSAR analysis on a series of 40 non-peptide NK3 antagonists. Multivariate statistical analysis, PCA and HCA, were performed to rational training/test set splitting and PLS regression was employed to construct all QSAR models. We constructed one highly predictive CoMFA model (q(2)= 0.810 and r(2)= 0.929) and acceptable HQSAR and CoMSIA models (HQSAR q(2)= 0.644 and r(2)= 0.910; CoMSIA q(2)= 0.691, r(2)= 0.911). The three different techniques provided convergent physicochemical results. All models indicate cyclopropane, piperidine and di-chloro-phenyl ring attached to cyclopropane ring and also the amide group attached to the piperidine ring could play an important role in ligand-receptor interactions. These findings may contribute to develop potential NK3 receptor antagonists for schizophrenia.
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http://dx.doi.org/10.3109/14756366.2015.1021250DOI Listing
November 2017

Applying machine learning techniques for ADME-Tox prediction: a review.

Expert Opin Drug Metab Toxicol 2015 Feb 2;11(2):259-71. Epub 2014 Dec 2.

Federal University of ABC (UFABC), Centre for Natural Sciences and Humanities , Santa Adélia Street, 166, Bangu, Santo André -SP , Brazil.

Introduction: Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.

Areas Covered: This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.

Expert Opinion: ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure-activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.
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http://dx.doi.org/10.1517/17425255.2015.980814DOI Listing
February 2015

Molecular features for antitrypanosomal activity of thiosemicarbazones revealed by OPS-PLS QSAR studies.

Med Chem 2012 Nov;8(6):1045-56

Instituto de Química de São Carlos - USP, São Carlos, SP, Brazil.

A quantitative structure-activity relationship analysis was employed to explore the relationship between the molecular structure of thiosemicarbazone analogues and the inhibition of the cysteine protease cruzain, a validated target for Chagas' disease treatment. A data set containing 53 thiosemicarbazone derivatives was used to produce a quantitative model for activity prediction of unknown compounds. Several electronic descriptors were obtained through DFT calculations, along with a large amount of Dragon descriptors. The ordered predictor selection (OPS) algorithm was employed to select the most relevant descriptors to perform PLS regressions. With this procedure, significant correlation coefficients (r(2) = 0.85, q(2) = 0.78) were achieved. Furthermore, predicted values for an external test set are in good agreement with the experimental results, indicating the potential of the model for untested compounds. Additional validation tests were carried out, indicating that a robust and reliable model was obtained to be used in the design of new thiosemicarbazones with improved cruzain inhibition potential.
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http://dx.doi.org/10.2174/1573406411208061045DOI Listing
November 2012
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