QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

Gomes, Marcelo N.; Braga, Rodolpho C.; Grzelak, Edyta M.; Neves, Bruno J.; Muratov, Eugene; Ma, Rui; Klein, Larry L.; Cho, Sang Hyun; Oliveira, Guilherme R.; Franzblau, Scott G.; Andrade, Carolina Horta

doi:10.1016/j.ejmech.2017.05.026

Cited by 107 publications

(49 citation statements)

References 86 publications

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“…The results of the activity test indicated that compounds 1a , 1b , 1c , 1d , and 1e (Figure ) possessed minimal inhibitory concentration (MIC) of <0.33 μg/ml against rifampin‐ and isoniazid‐resistant mycobacteria. Among these compounds, compound 1b displayed the highest activity against rifampin‐resistant and isoniazid‐resistant tuberculosis bacilli and had MIC values of 0.02 and <0.01 μg/ml, respectively (Gomes et al, ).…”

Section: Five‐membered Ring Compoundsmentioning

confidence: 99%

Opportunities and challenges of using five‐membered ring compounds as promising antitubercular agents

Wang

et al. 2020

Drug Development Research

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Tuberculosis (TB), a chronic infectious disease, is one of the greatest risks to human beings and 10 million people were diagnosed with TB and 1.6 million died from this disease in 2017. In addition, with the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB), the TB situation has become even worse, which has aggravated the mortality and spread of this disease.To overcome this problem, research into novel antituberculosis agents with enhanced activities against MDR-TB, reduced toxicity, and shortened duration of therapy is of great importance. Fortunately, many novel potential anti-TB drug candidates with five-membered rings, which are most likely to be effective against sensitive and resistant strains, have recently entered clinical trials. Different five-membered rings such as furans, pyranoses, thiazoles, pyrazolines, imidazoles, oxazolidinone, thiazolidins, isoxazoles, triazoles, oxadiazoles, thiadiazoles, and tetrazoles have been designed, prepared, and evaluated for their antimycobacterial activity against Mycobacterium tuberculosis. In this article, we highlight the recent advances made in the discovery of novel five-membered ring compounds and focus on their antitubercular activities, toxicity, structure-activity relationships, and mechanisms of action. K E Y W O R D Santi-TB drug, antitubercular activities, five-membered ring, MDR-TB, tuberculosis

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Section: Five‐membered Ring Compoundsmentioning

confidence: 99%

Opportunities and challenges of using five‐membered ring compounds as promising antitubercular agents

Wang

et al. 2020

Drug Development Research

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“…The availability of large datasets of chemical compounds with at least one biological property measured [25,26], associated with thousands of molecular descriptors paired with the popularization of in silico approaches resulted in the widespread use of QSAR for a diverse array of biological properties relevant to drug discovery [27][28][29][30][31][32]. However, dealing with big datasets has posed a challenge to model biological properties using classical machine learning algorithms [33,34].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-driven research for drug discovery: Tackling Malaria

Neves

Braga²,

Alves

et al. 2020

PLoS Comput Biol

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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 (EC 50 <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. Author summaryMalaria is a serious infectious disease caused by parasites of the genus Plasmodium. The recommended treatment is a combination of antimalarial drugs. However, the rise of parasites resistant to the current antimalarial drugs means that new therapeutics are continually required. To meet this challenge, we developed and applied models using deep learning, a powerful artificial intelligence method, supported by experimental validation PLOS Computational Biology | https://doi.to identify new drug candidates against malaria. We used the developed computational models to prioritize novel, active, and nontoxic compounds from virtual chemical libraries for experimental evaluation. Then, the predicted antimalarial compounds were experimentally validated in assays on Plasmodium falciparum culture. This allowed us to discover two new potential antimalarial candidates. The use of computational approaches is an attractive route to expedite the discovery of new therapeutics, especially to infectious tropical diseases, as it can reduce time and development costs. Future directions include in vivo studies on animal models.Deep Learning-driven research for tackling Malaria PLOS Computational Biology | https://doi.

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“…The goal of this study was to identify novel antileishmanial compounds among 32 previously synthesized chalcones and chalcone-like compounds 26 . The general workflow is shown in Figure 1.…”

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confidence: 99%

“…Amphotericin B exhibits CC 50 of 9.8 μM and SI of 5.2; therefore, LabMol-65 exhibited SI higher than the control and Labmol-73 showed selectivity index (SI) similar to or higher than the control. All active compounds were also tested against Vero cells 26 (Table 3). LabMol-65 and Labmol-73 demonstrated SI’s in the range 55–243 and 4–10, respectively, that make them promising anti-leishmanial agents.…”

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confidence: 99%

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Computer-aided discovery of two novel chalcone-like compounds active and selective against Leishmania infantum

Gomes

Alcântara

Neves

et al. 2017

Bioorganic & Medicinal Chemistry Letters

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Leishmaniasis are infectious diseases caused by parasites of genus Leishmania that affects 12 million people in 98 countries mainly in Africa, Asia, and Latin America. Effective treatments for this disease are urgently needed. In this study, we present a computer-aided approach to investigate a set of 32 recently synthesized chalcones and chalcone-like compounds to act as anti-leishmanial agents. As a result, nine most promising compounds and three potentially inactive compounds were experimentally evaluated against Leishmania infantum amastigotes and mammalian cells. Four compounds exhibited EC50 in the range of 6.2–10.98 μM. In addition, two compounds, LabMol-65 and LabMol-73, exhibited cytotoxicity in macrophages >50 μM that resulted in better selectivity than Amphotericin B. These two compounds also demonstrated low cytotoxicity and high selectivity towards Vero cells. The results of target fishing followed by homology modeling and docking suggest that these chalcone compounds could act in Leishmania because of their interaction with cysteine proteases, such as procathepsin L. Finally, we have provided structural recommendations for designing of new anti-leishmanial chalcones.

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QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

Cited by 107 publications

References 86 publications

Opportunities and challenges of using five‐membered ring compounds as promising antitubercular agents

Opportunities and challenges of using five‐membered ring compounds as promising antitubercular agents

Deep Learning-driven research for drug discovery: Tackling Malaria

Computer-aided discovery of two novel chalcone-like compounds active and selective against Leishmania infantum

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