Repositório DSpace :: Navegando por Autor "Andrade, Carolina Horta"

Navegando por Autor "Andrade, Carolina Horta"

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4D-QSAR: perspectives in drug design
(2010) Andrade, Carolina Horta; Pasqualoto, Kerly Fernanda Mesquita; Ferreira, Elizabeth Igne; Hopfinger, Anton J.
Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. The quantitative structure–activity relationship (QSAR) formalisms are among the most important strategies that can be applied for the successful design new molecules. This review provides a comprehensive review on the evolution and current status of 4D-QSAR, highlighting present challenges and new opportunities in drug design.
Antitrypanosomal activity of acetogenins isolated from the seeds of is associated with alterations in both plasma membrane electric potential and mitochondrial membrane potentia
(2019) Oliveira, Emerson Alves de; Brito, Ivanildo Afonso de; Lima, Marta Lopes; Silva, Maiara Maria Romanelli; Moreira Filho, José Teófilo; Neves, Bruno Junior; Andrade, Carolina Horta; Sartorelli, Patricia; Cardoso, André Gustavo Tempone; Silva, Thais Alves da Costa; Lago, João Henrique Ghilardi
As part of a drug discovery program aimed at the identification of anti-Trypanosoma cruzi metabolites from Brazilian flora, four acetogenins (1–4) were isolated from the seeds of Porcelia macrocarpa and were identified by NMR spectroscopy and HRESIMS. The new compounds 1 and 2 displayed activity against the trypomastigote (IC50 = 0.4 and 3.6 μM) and amastigote (IC50 = 23.0 and 27.7 μM) forms. The structurally related known compound 3 showed less potency to the amastigotes, with an IC50 value of 58 μM, while the known compound 4 was inactive. To evaluate the potential mechanisms for parasite death, parameters were evaluated by fluorometric assays: (i) plasma membrane permeability, (ii) plasma membrane electric potential (ΔΨp), (iii) reactive oxygen species production, and (iv) mitochondrial membrane potential (ΔΨm). The results obtained indicated that compounds 1 and 2 depolarize plasma membranes, affecting ΔΨp and ΔΨm and contributing to the observed cellular damage and disturbing the bioenergetic system. In silico studies of pharmacokinetics and toxicity (ADMET) properties predicted that all compounds were nonmutagenic, noncarcinogenic, nongenotoxic, and weak hERG blockers. Additionally, none of the isolated acetogenins 1–4 were predicted as pan-assay interference compounds.
Automated framework for developing predictive machine learning models for data-driven drug discovery
(2021) Neves, Bruno Junior; Moreira Filho, José Teófilo; Silva, Arthur de Carvalho e; Borba, Joyce Villa Verde Bastos; Mottin, Melina; Alves, Vinicius de Medeiro; Braga, Rodolpho de Campos; Muratov, Eugene; Andrade, Carolina Horta
The increasing availability of extensive collections of chemical compounds associated with experimental data provides an opportunity to build predictive quantitative structure-activity relationship (QSAR) models using machine learning (ML) algorithms. These models can promote data-driven decisions and have the potential to speed up the drug discovery process and reduce their failure rates. However, many essential aspects of data preparation and modeling are not available in any standalone program. Here, we developed an automated framework for the curation of chemogenomics data and to develop QSAR models for virtual screening using the open-source KoNstanz Information MinEr (KNIME) program. The workflow includes four modules: (i) dataset preparation and curation; (ii) chemical space analysis and structure-activity relationships (SAR) rules; (iii) modeling; and (iv) virtual screening (VS). As case studies, we applied these workflows to four datasets associated with different endpoints. The implemented protocol can efficiently curate chemical and biological data in public databases and generates robust QSAR models. We provide scientists a simple and guided cheminformatics workbench following the best practices widely accepted by the community, in which scientists can adapt to solve their research problems. The workflows are freely available for download at GitHub and LabMol web portals.
BeeToxAI: an artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees
(2021) Moreira Filho, José Teófilo; Braga, Rodolpho de Campos; Lemos, Jade Milhomem; Alves, Vinicius de Medeiros; Borba, Joyce Villa Verde Bastos; Costa, Wesley dos Santos; Kleinstreuer, Nicole; Muratov, Eugene; Andrade, Carolina Horta; Neves, Bruno Junior
An innovative artificial intelligence-based web app (BeeToxAI) for assessing the acute toxicity of chemicals to Apis mellifera. Initially, we developed and externally validated QSAR models for classification (external set accu racy ∼91%) through the combination of Random Forest and molecular fingerprints to predict the potential for chemicals to cause acute contact toxicity and acute oral toxicity to honey bees. Then, we developed and exter nally validated regression QSAR models (𝑅2 = 0.75) using Feedforward Neural Networks (FNNs). Afterward, the best models were implemented in the publicly available BeeToxAI web app (http://beetoxai.labmol.com.br/). The outputs of BeeToxAI are: toxicity predictions with estimated confidence, applicability domain estimation, and color-coded maps of relative structure fragment contributions to toxicity. As an additional assessment of BeeToxAI performance, we collected an external set of pesticides with known bee toxicity that were not included in our modeling dataset. BeeToxAI classification models were able to predict four out of five pesticides correctly. The acute contact toxicity model correctly predicted all of the eight pesticides. Here we demonstrate that Bee ToxAI can be used as a rapid new approach methodology for predicting acute toxicity of chemicals in honey bees.
Butenolides from Nectandra oppositifolia (Lauraceae) displayed anti-Trypanosoma cruzi activity via deregulation of mitochondria
(2019) Conserva, Geanne Alexsandra Alves; Silva, Thais Alves da Costa; Silva, Maiara Maria Romanelli; Antar, Guilherme de Medeiros; Neves, Bruno Junior; Andrade, Carolina Horta; Cardoso, André Gustavo Tempone; Lago, João Henrique Ghilardi
Background From a previous screening of Brazilian biodiversity for antitrypanosomal activity, the n-hexane extract from twigs of Nectandra oppositifolia (Lauraceae) demonstrated in vitro activity against Trypanosoma cruzi. Purpose To perform the isolation and chemical characterization of bioactive compounds from n-hexane extract from twigs of N. oppositifolia and evaluate their therapeutical potential as well as to elucidate their mechanism of action against T. cruzi. Methods/Study design Bioactivity-guided fractionation of the n-hexane extract from twigs of N. oppositifolia afforded three related butenolides: isolinderanolide D (1), isolinderanolide E (2) and secosubamolide A (3). These compounds were evaluated in vitro against T. cruzi (trypomastigote and amastigote forms) and against NCTC (L929) cells for mammalian cytotoxicity. Additionally, phenotypic analyzes of compounds-treated parasites were performed: alterations in the plasma membrane permeability, plasma membrane electric potential (ΔΨp), mitochondrial membrane potential (ΔΨm) and induction of ROS. Results Compounds 1–3 were effective against T. cruzi, with IC50 values of 12.9, 29.9 and 12.5 µM for trypomastigotes and 25.3, 10.1 and 12.3 µM for intracellular amastigotes. Furthermore, it was observed alteration in the mitochondrial membrane potential (ΔΨm) of parasites treated with butenolides 1–3. These compounds caused no alteration to the parasite plasma membrane, and the deregulation of the mitochondria might be an early event to cell death. In addition, in silico studies showed that all butenolides were predicted to be non-mutagenic, non-carcinogenic, non hERG blockers, with acceptable human intestinal absorption, low inhibitory promiscuity with the main five CYP isoforms, and with high metabolic stability. Otherwise, tested butenolides showed unfavorable blood-brain barrier penetration (BBB+). Conclusion Our results demonstrated the anti-T. cruzi effects of compounds 1–3 isolated from N. oppositifolia and indicated that the lethal effect of these compounds in trypomastigotes of T. cruzi could be associated to the alteration in the mitochondrial membrane potential (ΔΨm).
Chalcones as a basis for computer-aided drug design: innovative approaches to tackle malaria
(2019) Lima, Marilia Nunes do Nascimento; Neves, Bruno Junior; Cassiano, Gustavo Capatti; Gomes, Marcelo do Nascimento; Tomaz, Kaira Cristina Peralis; Ferreira, Letícia Tiburcio; Tavella, Tatyana Almeida; Paim, Juliana Calit; Bargieri, Daniel Youssef; Muratov, Eugene N.; Costa, Fabio Trindade Maranhão; Andrade, Carolina Horta
Aim: Computer-aided drug design approaches were applied to identify chalcones with antiplasmodial activity. Methodology: The virtual screening was performed as follows: structural standardization of in-house database of chalcones; identification of potential Plasmodium falciparum protein targets for the chalcones; homology modeling of the predicted P. falciparum targets; molecular docking studies; and in vitro experimental validation. Results: Using these models, we prioritized 16 chalcones with potential antiplasmodial activity, for further experimental evaluation. Among them, LabMol-86 and LabMol-87 showed potent in vitro antiplasmodial activity against P. falciparum, while LabMol-63 and LabMol-73 were potent inhibitors of Plasmodium berghei progression into mosquito stages. Conclusion: Our results encourage the exploration of chalcones in hit-to-lead optimization studies for tackling malaria.
Cyclic voltammetry and computational chemistry studies on the evaluation of the redox behavior of parabens and other analogues
(2012) Gil, Eric de Souza; Andrade, Carolina Horta; Barbosa, Núsia Luísa; Campos, Braga Rodolpho de; Serrano, Silvia Helena Pires
Parabens are antimicrobial preservatives widely used in pharmaceutical, cosmetic and food industries. The alkyl chain connected to the ester group defines some important physicochemical characteristics of these compounds, including the partition coefficient and redox properties. The voltammetric and computational analyses were carried out in order to evaluate the redox behavior of these compounds and other phenolic analogues. A strong correlation between chemical substituents inductive effects of parabens with redox potentials was observed. Using cyclic voltammetry and glassy carbon working electrode, only one irreversible anodic peak was observed around 0.8 V for methylparaben (MP), ethylparaben (EP), propylparaben (PP), butylparaben (BP), benzylparaben (BzP) and p-substituted phenolic analogues. The electrodonating inductive effect of alkyl groups was demonstrated by the anodic oxidation potential shift to lower values as the carbon number increases and, therefore the parabens (and other phenolic analogues) oxidation processes to the quinonoidic forms showed great dependence on the substituent pattern.
Dealing with frequent hitters in drug discovery: a multidisciplinary view on the issue of filtering compounds on biological screenings
(2019) Dantas, Rafael Ferreira; Evangelista, Tereza Cristina Santos; Neves, Bruno Junior; Senger, Mário Roberto; Andrade, Carolina Horta; Ferreira, Sabrina Baptista; Silva Junior, Floriano Paes
Introduction: The timely identification biologically active chemicals, in disease relevant screening assays, is a major endeavor in drug discovery. The existence of frequent hitters (FHs) in non-related assays poses a formidable challenge in terms of whether to consider these molecules as chemical gold or promiscuous non-selective reactive trash (also known as PAINS – pan assay interference compounds). Areas covered: In this review, the authors bring together expertize in synthetic chemistry, cheminformatics and biochemistry, three key areas for dealing with FHs. They discuss synthetic methods facilitating preparation of chemically diverse molecular libraries, while favoring activity in the biological space. They also survey and discuss recent computational advances in the prediction of PAINS from chemical structures. Finally, they review experimental approaches for the validation of the biological activity of screening hits and discuss alternatives for exploiting promiscuity and chemical reactivity. Expert opinion: It’s essential to develop more efficient computational methods to reliably recognize PAINS in distinct molecular environments. Accordingly, advances in synthetic chemistry hold the promise to provide a better quality of chemical matter for drug discovery. Medicinal chemists should be more open to screening for hits showing biologically complex mechanisms of action rather than discarding molecules that may prove valuable as innovative disease treatments.
Deep learning-driven research for drug discovery: tackling malaria
(2020) Neves, Bruno Junior; Braga, Rodolpho de Campos; Alves, Vinícius de Medeiros; Lima, Marília Nunes do Nascimento; Cassiano, Gustavo Capatti; Muratov, Eugene; Costa, Fabio Trindade Maranhão; Andrade, Carolina Horta
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.
Dihydroquinoline derivative as a potential anticancer agent: synthesis, crystal structure, and molecular modeling studies
(2021) Vaz, Wesley Fonseca; Custodio, Jean Marcos Ferreira; D´Oliveira, Giulio Demetrius Creazzo; Neves, Bruno Junior; Carvalho Júnior, Paulo de Sousa; Moreira Filho, José Teófilo; Andrade, Carolina Horta; Noda Pérez, Caridad; Lacerda, Elisângela de Paula Silveira; Napolitano, Hamilton Barbosa
Cancer is one of the leading causes of death worldwide and requires intense and growing research investments from the public and private sectors. This is expected to lead to the development of new medicines. A determining factor in this process is the structural understanding of molecules with potential anticancer properties. Since the major compounds used in cancer therapies fail to encompass every spectrum of this disease, there is a clear need to research new molecules for this purpose. As it follows, we have studied the class of quinolinones that seem effective for such therapy. This paper describes the structural elucidation of a novel dihydroquinoline by single-crystal X-ray diffraction and spectroscopy characterization. Topology studies were carried through Hirshfeld surfaces analysis and molecular electrostatic potential map; electronic stability was evaluated from the calculated energy of frontier molecular orbitals. Additionally, in silico studies by molecular docking indicated that this dihydroquinoline could act as an anticancer agent due to their higher binding affinity with human aldehyde dehydrogenase 1A1 (ALDH 1A1). Tests in vitro were performed for VERO (normal human skin keratinocytes), B16F10 (mouse melanoma), and MDA-MB-231 (metastatic breast adenocarcinoma), and the results certified that compound as a potential anticancer agent.
Drug repurposing for paracoccidioidomycosis through a computational chemogenomics framework
(2019) Oliveira, Amanda Alves de; Neves, Bruno Junior; Soares, Célia Maria de Almeida; Andrade, Carolina Horta; Pereira, Maristela
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.
Estudos de QSAR-3D para uma série de análogos à timidina com atividade tuberculostática
(2011) Bueno, Renata Vieira; Braga, Rodolpho Campos; Toledo, Ney Ramos; Andrade, Carolina Horta
Tuberculosis (TB) is a chronic infectious disease with high epidemiological rates. The emergence of multidrug and extensively drug-resistant TB strains, as well as the long-term treatment and the side effects, become urgent the need for the development of new anti-tuberculosis drugs. Furthermore, the search for new targets is also necessary; as the current antimycobacterial drugs have just a small number of enzymes related to essential functions of the mycobacteria as targets. TMPKmt is an attractive target for the design of new antituberculosis agents since this enzyme is essential for DNA replication. In this work, we used the 3D-QSAR, by applying the Comparative Molecular Field Analysis (CoMFA), in order to elucidate the structural requirements relevant to the biological activity and to generate 3D-QSAR models for predicting the biological activity of compounds not yet synthesized, considering quantitative biological data and the interaction with TMPKmt of 106 thymidine analogues obtained from the literature. Robust and significant statistical models were obtained, confirming the importance of groups with higher electronic density and less bulky near ring 2. The studies herein developed with thymidine analogues through rational drug design lead to important data towards the search of new candidates as new anti-tuberculosis drugs.
Identificação da desvenlafaxina, o principal metabólito da Vvnlafaxina, em cápsulas de liberação prolongada
(Ricardo Menegatti, 2010-03) Carneiro, Wilsione Jose; Andrade, Carolina Horta; Braga, Rodolpho Campos; Oliveira, Valéria de
In this work, we describe the identification of desvenlafaxine in extended release capsules of venlafaxine (VEN) in acid degradation studies. We developed a stability indicating reverse-phase HPLC method and validated for the analysis of VEN in pharmaceutical formulation. The HPLC method was linear over the range of 0.45-1.05 mg/ml (r2=0.999). The RSD values for intra- and inter-day precision studies showed good results (RSD < 2%) and accuracy was greater than 99%. The degradation studies in acidic media for 24 h showed two additional peaks, which were further identified by ESI-MS/MS as the desvenlafaxine and the dehydration product of venlafaxine. Furthermore, desvenlafaxine is the major active metabolite of venlafaxine and has recently been approved for treatment of major depressive disorder.
Identification of desvenlafaxine, the major active metabolite of venlafaxine, in extended-release capsules
(Ricardo Menegatti, 2010-03) Carneiro, Wilsione Jose; Andrade, Carolina Horta; Braga, Rodolpho Campos; Oliveira, Valéria de
In silico repositioning of new drugs against Schistosoma mansoni
(Ruy de Souza Lino Junior, 2018-09) Bezerra, José Clecildo Barreto; Arantes, Morgana Elias; Andrade, Carolina Horta; Silva, Lourival Almeida; Neves, Bruno Junior
Schistosomiasis is a neglected tropical disease caused by parasites of the genus Schistosoma. In Brazil only Schistosoma mansoni causes this disease. The World Health Organization estimated in 2012 approximately 249 million people at risk of acquiring this disease around the world. The main strategy to control this disease is praziquantel treatment of individuals living in endemic areas. The drug praziquantel is used on a large scale in the treatment of schistosomiasis and currently there are reported cases of resistance, indicating the need to discover new drugs. In silico drug repositioning is a time and cost reducing strategy in the search for anti-Schistosoma agents. This work used bioinformatic tools to identify potential schistosomicidal drugs. A list was compiled of S. mansoni potential targets that are part of essential processes in the database TDR and the targets that are part of the tegument were obtained in the scientific literature. The file with S. mansoni targets contained 1,376 targets, and of these only 61 targets associated with 399 drugs had homology with drug targets. After removal of duplicate drugs, drugs found in previous studies and after the analysis of the conservation of the binding site, only 28 S. mansoni targets associated with 102 drugs had 60% or more of the active site conserved. Some of the drugs had activity and are interesting to validate this study such as: artemether, lumefantrine, meloxicam. Among the drugs found 18 drugs were selected to be tested in prospective experimental assays according to the following criteria: low toxicity in vivo, off-patent status, and logP <5.0.
In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni
(2015-01) Neves, Bruno Junior; Braga, Rodolpho de Campos; Bezerra, José Clecildo Barreto; Cravo, Pedro Vitor Lemos; Andrade, Carolina Horta
Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes.
In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni
(2015) Neves, Bruno Junior; Braga, Rodolpho de Campos; Bezerra, José Clecildo Barreto; Cravo, Pedro Vitor Lemos; Andrade, Carolina Horta
Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes.
In silico strategies to support fragment-to-lead optimization in drug discovery
(2020) Souza Neto, Lauro Ribeiro de; Moreira Filho, José Teófilo; Neves, Bruno Junior; Riveros Maidana, Rocío Lucía Beatriz; Guimarães, Ana Carolina Ramos; Furnham, Nicholas; Andrade, Carolina Horta; Silva Junior, Floriano Paes
Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET—absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.
In silico-driven identification of novel molluscicides effective against Biomphalaria glabrata (Say, 1818)
(2020) Santos, Daniela Braz dos; Moreira Filho, José Teófilo; Melo, Amanda de Oliveira; Lemes, Josiel Araújo; Rocha, Thiago Lopes; Andrade, Carolina Horta; Neves, Bruno Junior; Bezerra, José Clecildo Barreto
Schistosomiasis control in endemic areas depends on several factors, including mass drug delivery programs and interrupting the transmission of disease by controlling the intermediate host snails in the freshwater ecosystem using molluscicides. However, the use of the gold standard molluscicide, i.e., niclosamide, has been considered problematic due to its high cost, toxicity for aquatic organisms, and the emergence of niclosamide-resistant snail populations. In this work, we report the in silico driven identification of novel naphthoquinone compounds with high molluscicidal activity against Biomphalaria glabrata. For this purpose, we developed statistically robust and validated shape-based and machine learning models using B. glabrata bioassay compounds data. Using these models, we prioritized fourteen naphthoquinone compounds for further in vivo testing against adult, newly-hatched, and embryo of B. glabrata snails. Among them, compounds 3, 5, 6, 7, and 12 were the best candidates, presenting moderate potency against adult snails (LC50: 28.98–102.24 μM) and high potency (LC50: 14.52–0.45 μM) against newly-hatched snails and embryos. To summarize, the in silico approach explored here allowed us to discover five new molluscicidal candidates for prospective field studies.
Integração de abordagens computacionais para identificar novos inibidores da proteína NS5 do vírus Zika
(Universidade Federal de Goiás, 2020-12-14) Ramos, Paulo Ricardo Pimenta da Silva; Andrade, Carolina Horta; Andrade, Carolina Horta; Borba, Joyce Villa Verde Bastos; Neves, Bruno Junior
The Zika virus (ZIKV), which caused recent outbreaks and epidemics in 2007, 2014 and 2015, is transmitted to humans mainly through the bite of female Aedes aegypti mosquitoes. In addition, transmission through blood, intercourse and lactation has been described in the literature. ZIKV infection has several consequences, mainly neurological serious, so far, there are no antivirals to fight infection or vaccines to prevent infection. Non-structural protein 5 (NS5), composed by the methyltransferase (MTase) and RNA polymerase (RdRP) domains, plays an essential role in the synthesis and stability of viral RNA, in addition to inhibiting the host's immune system, being a promising target for development of new antiviral. In this work, we perform the integration of computational approaches as quantitative structure activity relationships (QSAR) based on machine learning methods, molecular docking and search for similarity to identify new inhibitors of the NS5 protein from ZIKV. Due to the availability of data in the literature and the high sequential similarity between the NS5 sites of the dengue virus (DENV) and ZIKV, we initially searched for inhibitors of DENV NS5 protein in the PubChem and ChEMBL databases, to guide the screening of new NS5 inhibitors from ZIKV. 145 DENV NS5 inhibitors have been found in literature. We performed the virtual screening of these compounds using ZIKV phenotypic QSAR models. We also carry out molecular docking studies in the binding sites of the MTase and RdRP domains of ZIKV NS5. A total of 32 compounds were prioritized at this stage. Subsequently, a similarity search as performed in the commercial eMolecules database and the QSAR and docking steps were performed with the similar compounds. This analysis resulted in 4,953 similar compounds that also passed through the QSAR and docking filters. After this step, 176 compounds were selected as promising inhibitors of ZIKV NS5. We also submitted These compounds to Bayesian machine learning models to predict ZIKV activity and cytotoxicity, resulting in 44 compounds predicted to be active and non-cytotoxic to mammalian cells. These compounds were then submitted to the nAPOLI software for analysis of ligand-protein interactions. This step allowed the final selection of 14 promising compounds that will be acquired and experimentally validated, through ZIKV NS5 enzymatic assays and celular assays, that will be performed with collaborators.

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