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1.
bioRxiv ; 2022 Mar 16.
Artigo em Inglês | MEDLINE | ID: mdl-35313579

RESUMO

The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for an acute treatment for the disease. We investigate whether compounds that bind the human ACE2 protein can interrupt SARS-CoV-2 replication without damaging ACE2’s natural enzymatic function. Initial compounds were screened for binding to ACE2 but little interruption of ACE2 enzymatic activity. This set of compounds was extended by application of quantitative structure-activity analysis, which resulted in 512 virtual hits for further confirmatory screening. A subsequent SARS-CoV-2 replication assay revealed that five of these compounds inhibit SARS-CoV-2 replication in human cells. Further effort is required to completely determine the antiviral mechanism of these compounds, but they serve as a strong starting point for both development of acute treatments for COVID-19 and research into the mechanism of infection.

2.
Environ Health Perspect ; 130(2): 27012, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-35192406

RESUMO

BACKGROUND: Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points. METHODS: We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets. RESULTS: In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays. CONCLUSIONS: We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.


Assuntos
Alternativas aos Testes com Animais , Simulação por Computador , Substâncias Perigosas , Animais , Cosméticos/toxicidade , Substâncias Perigosas/toxicidade , Praguicidas/toxicidade , Preparações Farmacêuticas , Relação Quantitativa Estrutura-Atividade
3.
Drug Discov Today ; 2022 Feb 16.
Artigo em Inglês | MEDLINE | ID: mdl-35182735

RESUMO

Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) â†’ intermediate event(s) â†’ clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.

4.
Drug Discov Today ; 27(2): 490-502, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-34718207

RESUMO

The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.


Assuntos
Inteligência Artificial , Doenças Raras , Descoberta de Drogas/métodos , Humanos , Bases de Conhecimento , Aprendizado de Máquina , Doenças Raras/tratamento farmacológico
5.
Curr Top Med Chem ; 21(21): 1943-1974, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34544342

RESUMO

BACKGROUND: Neglected diseases require special attention when looking for new therapeutic alternatives, as these are diseases of extreme complexity and severity that affect populations belonging to lower social classes who lack access to basic rights, such as sanitation. INTRODUCTION: Among the alternatives available for obtaining new drugs is Medicinal Chemistry, which is responsible for the discovery, identification, invention, and preparation of prototypes. In this perspective, the present work aims to make a bibliographic review on the recent studies of Medicinal Chemistry applied to neglected diseases. METHODS: A literature review was carried out by searching the "Web of Sciences" database, including recent articles published on the Neglected Drug Design. RESULTS: In general, it was noticed that the most studied neglected diseases corresponded to Chagas disease and leishmaniasis, with studies on organic synthesis, optimization of structures, and molecular hybrids being the most used strategies. It is also worth mentioning the growing number of computationally developed studies, providing speed and optimization of costs in the procurement process. CONCLUSION: The CADD approach and organic synthesis studies, when applied in the area of Medicinal Chemistry, have proven to be important in the research and discovery of drugs for Neglected Diseases, both in terms of planning the experimental methodology used to obtain it and in the selection of compounds with higher activity potential.


Assuntos
Química Farmacêutica , Desenho de Fármacos , Doenças Negligenciadas/tratamento farmacológico , Doença de Chagas/tratamento farmacológico , Humanos , Leishmaniose/tratamento farmacológico
6.
Chem Soc Rev ; 50(16): 9121-9151, 2021 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-34212944

RESUMO

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.


Assuntos
COVID-19/tratamento farmacológico , Simulação por Computador , Desenho de Fármacos , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos , Antivirais/uso terapêutico , COVID-19/virologia , Ensaios Clínicos como Assunto , Humanos , Pandemias , SARS-CoV-2/efeitos dos fármacos
7.
Altern Lab Anim ; 49(3): 73-82, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-34233495

RESUMO

New Approach Methodologies (NAMs) that employ artificial intelligence (AI) for predicting adverse effects of chemicals have generated optimistic expectations as alternatives to animal testing. However, the major underappreciated challenge in developing robust and predictive AI models is the impact of the quality of the input data on the model accuracy. Indeed, poor data reproducibility and quality have been frequently cited as factors contributing to the crisis in biomedical research, as well as similar shortcomings in the fields of toxicology and chemistry. In this article, we review the most recent efforts to improve confidence in the robustness of toxicological data and investigate the impact that data curation has on the confidence in model predictions. We also present two case studies demonstrating the effect of data curation on the performance of AI models for predicting skin sensitisation and skin irritation. We show that, whereas models generated with uncurated data had a 7-24% higher correct classification rate (CCR), the perceived performance was, in fact, inflated owing to the high number of duplicates in the training set. We assert that data curation is a critical step in building computational models, to help ensure that reliable predictions of chemical toxicity are achieved through use of the models.


Assuntos
Alternativas aos Testes com Animais , Inteligência Artificial , Animais , Simulação por Computador , Confiabilidade dos Dados , Reprodutibilidade dos Testes
8.
J Hazard Mater ; 419: 126438, 2021 10 05.
Artigo em Inglês | MEDLINE | ID: mdl-34182425

RESUMO

Organic chemicals identified in raw landfill leachate (LL) and their transformation products (TPs), formed during Fenton treatment, were analyzed for chemical safety following REACH guidelines. The raw LL was located in the metropolitan region of Campina Grande, in northeast Brazil. We elucidated 197 unique chemical structures, including 154 compounds that were present in raw LL and 82 compounds that were detected in the treated LL, totaling 39 persistent compounds and 43 TPs. In silico models were developed to identify and prioritize the potential level of hazard/risk these compounds pose to the environment and society. The models revealed that the Fenton process improved the biodegradability of TPs. Still, a slight increase in ecotoxicological effects was observed among the compounds in treated LL compared with those present in raw LL. No differences were observed for aryl hydrocarbon receptor (AhR) and antioxidant response element (ARE) mutagenicity. Similar behavior among both raw and treated LL samples was observed for biodegradability; Tetrahymena pyriformis, Daphnia magna, Pimephales promelas and ARE, AhR, and Ames mutagenicity. Overall, our results suggest that raw and treated LL samples have similar activity profiles for all endpoints other than biodegradability.


Assuntos
Segurança Química , Poluentes Químicos da Água , Peróxido de Hidrogênio , Compostos Orgânicos , Oxirredução , Poluentes Químicos da Água/análise , Poluentes Químicos da Água/toxicidade
9.
Struct Chem ; 32(4): 1365-1392, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34177203

RESUMO

We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.

10.
J Chem Inf Model ; 61(6): 2516-2522, 2021 06 28.
Artigo em Inglês | MEDLINE | ID: mdl-34014674

RESUMO

Natural products and their secondary metabolites are promising starting points for the development of drug prototypes and new drugs, as many current treatments for numerous diseases are directly or indirectly related to such compounds. State-of-the-art, curated, integrated, and frequently updated databases of secondary metabolites are thus highly relevant to drug discovery. The SistematX Web Portal, introduced in 2018, is undergoing development to address this need and documents crucial information about plant secondary metabolites, including the exact location of the species from which the compounds were isolated. SistematX also allows registered users to log in to the data management area and gain access to administrative pages. This study reports recent updates and modifications to the SistematX Web Portal, including a batch download option, the generation and visualization of 1H and 13C nuclear magnetic resonance spectra, and the calculation of physicochemical (drug-like and lead-like) properties and biological activity profiles. The SistematX Web Portal is freely available at http://sistematx.ufpb.br.


Assuntos
Produtos Biológicos , Bases de Dados Factuais , Descoberta de Drogas , Espectroscopia de Ressonância Magnética , Plantas
12.
J Chem Inf Model ; 61(3): 1033-1036, 2021 03 22.
Artigo em Inglês | MEDLINE | ID: mdl-33667090

RESUMO

Many laboratories working in the field of drug discovery use the ZINC database to identify and then acquire commercially available chemicals. However, finding the best deal for a given compound is often time-intensive and laborious, as the process involves searching for all vendors selling the desired compound, comparing prices, and interacting with the preferred vendor. To streamline this process, we have developed ZINC Express, a web application that simplifies the online purchase of chemicals annotated in the ZINC database. For any compound with a known ZINC ID, ZINC Express finds a list of vendors offering that compound and for each such vendor returns the available package quantities, the price of each package, and the price per milligram along with a link to that vendor. We expect that ZINC Express will be of use to both computational and experimental researchers. ZINC Express is freely accessible online at https://zincexpress.mml.unc.edu/.


Assuntos
Comércio , Descoberta de Drogas , Bases de Dados Factuais , Zinco
13.
J Chem Inf Model ; 61(2): 653-663, 2021 02 22.
Artigo em Inglês | MEDLINE | ID: mdl-33533614

RESUMO

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.


Assuntos
Aprendizado Profundo , Consenso , Bases de Dados Factuais , Redes Neurais de Computação
14.
Curr Med Chem ; 28(27): 5498-5526, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33550959

RESUMO

BACKGROUND: Natural products are useful agents for the discovery of new lead- compounds and effective drugs to combat coronaviruses (CoV). OBJECTIVE: The present work provides an overview of natural substances, plant extracts, and essential oils as potential anti-SARS-CoV agents. In addition, this work evaluates their drug-like properties which are essential in the selection of compounds in order to accelerate the drug development process. METHODS: The search was carried out using PubMed, ScienceDirect and SciFinder. Articles addressing plant-based natural products as potential SARS-CoV or SARS-CoV-2 agents within the last seventeen years were analyzed and selected. The descriptors for Chemometrics analysis were obtained in alvaDesc and the principal component analysis (PCA) was carried out in SIMCA version 13.0. RESULTS: Based on in vitro assays and computational analyses, this review covers twentynine medicinal plant species and more than 300 isolated substances as potential anti-coronavirus agents. Among them, flavonoids and terpenes are the most promising compound classes. In silico analyses of drug-like properties corroborate these findings and indicate promising candidates for in vitro and in vivo studies to validate their activity. CONCLUSION: This paper highlights the role of ethnopharmacology in drug discovery and suggests the use of integrative (in silico/ in vitro) and chemocentric approaches to strengthen current studies and guide future research in the field of antiviral agents.


Assuntos
Produtos Biológicos , COVID-19 , Plantas Medicinais , Antivirais/farmacologia , Antivirais/uso terapêutico , Produtos Biológicos/farmacologia , Humanos , SARS-CoV-2
15.
Mini Rev Med Chem ; 21(14): 1865-1887, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33438538

RESUMO

BACKGROUND: Organocalcogens are a class of organic compounds obtained by the synthesis experiments to include S, Se, or Te. Among the elements that comprise this class, Se is characterized as an essential mineral and nutrient for humans. Se has been widely studied in many aspects. Organic synthesis of organoselenides is used for obtaining new potential drug candidates and may be highly beneficial from the use of computational approaches to reduce time and cost of the experiments. Thus, the goal of our study is to evaluate the computational approaches used in the organoselenides research from 1999 to 2019. METHODS: A literature review was performed by searching the database "Web of Sciences". RESULTS: Most of the theoretical studies included structural elucidation or structure-property analysis. We also found research regarding molecular docking approaches and Quantitative Structure-Activity Relationship (QSAR) studies. CONCLUSIONS: Computational studies have been widely applied to organoselenides. They demonstrated promising results and resulted in reduced the cost of research, increased efficacy, and, ultimately, novel organoselenides with desired properties.


Assuntos
Compostos Organosselênicos/química , Desenho de Fármacos , Humanos , Ligantes , Simulação de Acoplamento Molecular , Monoaminoxidase/química , Monoaminoxidase/metabolismo , Neoplasias/tratamento farmacológico , Neoplasias/patologia , Compostos Organosselênicos/metabolismo , Compostos Organosselênicos/uso terapêutico , Relação Quantitativa Estrutura-Atividade
16.
Mol Inform ; 40(1): e2000113, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33405340

RESUMO

The main protease (Mpro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) Mpro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC50 of 8.9 µM), proglumetacin (tested twice independently, with AC50 of 8.9 µM and 12.5 µM), and sufugolix (AC50 12.6 µM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).


Assuntos
Antivirais , COVID-19 , Proteases 3C de Coronavírus , Reposicionamento de Medicamentos , Imidazóis/química , Ácidos Indolacéticos/química , Simulação de Acoplamento Molecular , Inibidores de Proteases , SARS-CoV-2/enzimologia , Sulfóxidos/química , Antivirais/química , Antivirais/uso terapêutico , COVID-19/tratamento farmacológico , COVID-19/enzimologia , Domínio Catalítico , Proteases 3C de Coronavírus/antagonistas & inibidores , Proteases 3C de Coronavírus/química , Humanos , Imidazóis/uso terapêutico , Ácidos Indolacéticos/uso terapêutico , Inibidores de Proteases/química , Inibidores de Proteases/uso terapêutico , Relação Quantitativa Estrutura-Atividade , Sulfóxidos/uso terapêutico
17.
Chem Res Toxicol ; 34(2): 258-267, 2021 02 15.
Artigo em Inglês | MEDLINE | ID: mdl-32673477

RESUMO

Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure-activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.


Assuntos
Cosméticos/efeitos adversos , Testes Cutâneos , Pele/efeitos dos fármacos , Animais , Teorema de Bayes , Cosméticos/química , Humanos , Camundongos , Relação Quantitativa Estrutura-Atividade
18.
Food Chem Toxicol ; 147: 111899, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33279675

RESUMO

Pesticides are used to control and combat insects and pests in the agricultural sector, households, and public health programs. The frequent and disorderly use of these pesticides may lead to variety of undesired effects. Therefore, natural products have many advantages over to synthetic compounds to be used as insecticides. The goal of this study was to find natural products with insecticidal potential against Musca domestica and Mythimna separata. To achieve this goal, we developed predictive QSAR models using MuDRA, PLS, and RF approaches and performed virtual screening of 117 natural products. As a result of QSAR modeling, we formulated the recommendations regarding physico-chemical characteristics for promising compounds active against Musca domestica and Mythimna separata. Homology models were successfully built for both species and molecular docking of QSAR hits vs known insecticides allowed us to prioritize twenty-two compounds against Musca domestica and six against Mythimna separata. Our results suggest that pimarane diterpenes, abietanes diterpenes, dimeric diterpenes and scopadulane diterpenes obtained from aerial parts of species of the genus Calceolaria (Calceolariaceae: Scrophulariaceae) can be considered as potential insecticidal.


Assuntos
Dípteros/efeitos dos fármacos , Diterpenos/química , Diterpenos/farmacologia , Inseticidas/farmacologia , Animais , Desenho de Fármacos , Moscas Domésticas/efeitos dos fármacos , Modelos Biológicos , Estrutura Molecular , Relação Quantitativa Estrutura-Atividade , Scrophulariaceae/química , Sensibilidade e Especificidade
19.
Mol Ther ; 29(2): 873-885, 2021 02 03.
Artigo em Inglês | MEDLINE | ID: mdl-33333292

RESUMO

Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the US Food and Drug Administration (FDA)-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat COVID-19.


Assuntos
Antivirais/uso terapêutico , COVID-19/tratamento farmacológico , SARS-CoV-2/efeitos dos fármacos , Monofosfato de Adenosina/análogos & derivados , Monofosfato de Adenosina/uso terapêutico , Alanina/análogos & derivados , Alanina/uso terapêutico , Combinação de Medicamentos , Sinergismo Farmacológico , Humanos , Hidroxicloroquina/uso terapêutico
20.
ChemMedChem ; 16(8): 1234-1245, 2021 04 20.
Artigo em Inglês | MEDLINE | ID: mdl-33336460

RESUMO

Leishmaniasis is a complex disease caused by over 20 Leishmania species that primarily affects populations with poor socioeconomic conditions. Currently available drugs for treating leishmaniasis include amphotericin B, paromomycin, and pentavalent antimonials, which have been associated with several limitations, such as low efficacy, the development of drug resistance, and high toxicity. Natural products are an interesting source of new drug candidates. The Asteraceae family includes more than 23 000 species worldwide. Secondary metabolites that can be found in species from this family have been widely explored as potential new treatments for leishmaniasis. Recently, computational tools have become more popular in medicinal chemistry to establish experimental designs, identify new drugs, and compare the molecular structures and activities of novel compounds. Herein, we review various studies that have used computational tools to examine various compounds identified in the Asteraceae family in the search for potential drug candidates against Leishmania.


Assuntos
Asteraceae/química , Leishmaniose/tratamento farmacológico , Tripanossomicidas/farmacologia , Animais , Humanos , Leishmania/efeitos dos fármacos , Aprendizado de Máquina , Metabolômica , Simulação de Acoplamento Molecular
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