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Gold@mesoporous silica nanocarriers for the effective delivery of antibiotics and by-passing of β-lactam resistance
Publication . Marcelo, Gonçalo A.; Duarte, Maria Paula; Oliveira, Elisabete; LAQV@REQUIMTE; MEtRICS - Centro de Engenharia Mecânica e Sustentabilidade de Recursos; DCTB - Departamento de Ciências e Tecnologia da Biomassa (ex-GDEH); Springer
Current antibiotics effectiveness relies on higher doses and administration frequency, which are responsible for the growth of antimicrobial resistance (AMR). AMR is one of the major threatening issues of the century with last-line antibiotics already failing. To overcome such problems associated with bacterial infections, nanoparticles combined with antibiotics emerged as a promising strategy. In this work, nanocarriers comprising of gold–silica core–shell mesoporous nanoparticles (Au@MNs) and silica mesoporous nanoparticles (MNs) were synthesized, loaded with amoxicillin (Amox) and ofloxacin and investigated regarding its antibacterial activity towards S. aureus, methicillin-resistant S. aureus (MRSA), E. coli and P. aeruginosa. Both nanocarriers showed a beneficial role in the effective delivery of amoxicillin against MRSA and the well-known β-lactam resistant P. aeruginosa. Reductions of 10-fold (Amox@MNs) and 20-fold (Amox@Au@MNs) in the amount of antibiotic to treat P. aeruginosa; and a reduction of 20-fold (Amox@MNs) towards MRSA allied to a full reversion of resistance, strongly supports the promising potential of these nanocarriers to tackle antibiotics resistance.
Antioxidant potential of the bio-based fucose-rich polysaccharide fucopol supports its use in oxidative stress-inducing systems
Publication . Guerreiro, Bruno M.; Silva, Jorge Carvalho; Lima, João Carlos; Reis, Maria A. M.; Freitas, Filomena; DQ - Departamento de Química; UCIBIO - Applied Molecular Biosciences Unit; LAQV@REQUIMTE; DF – Departamento de Física; CENIMAT-i3N - Centro de Investigação de Materiais (Lab. Associado I3N); MDPI - Multidisciplinary Digital Publishing Institute
Reactive oxygen species (ROS) are dangerous sources of macromolecular damage. While most derive from mitochondrial oxidative phosphorylation, their production can be triggered by ex-ogenous stresses, surpassing the extinction capacity of intrinsic antioxidant defense systems of cells. Here, we report the antioxidant activity of FucoPol, a fucose-rich polyanionic polysaccharide produced by Enterobacter A47, containing ca. 17 wt% of negatively charged residues in its struc-ture. Ferric reducing antioxidant power (FRAP) assays coupled to Hill binding kinetics fitting have shown FucoPol can neutralize ferricyanide and Fe3+-TPTZ species at an EC50 of 896 and 602 µg/mL, respectively, with positive binding cooperativity (2.52 ≤ H ≤ 4.85). This reducing power is greater than most polysaccharides reported. Moreover, an optimal 0.25% w/v FucoPol concentration shown previously to be cryo-and photoprotective was also demonstrated to protect Vero cells against H2O2-induced acute exposure not only by attenuating metabolic viability decay, but also by accentuating post-stress proliferation capacity, whilst preserving cell morphology. These results on antioxidant activity provide evidence for the biopolymer’s ability to prevent positive feedback cascades of the radical-producing Fenton reaction. Ultimately, FucoPol provides a biotechnological alternative for implementation in cryopreservation, food supplementation, and photoprotective sunscreen formula design, as all fields benefit from an antioxidant functionality.
In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
Publication . Cruz, Sara; Gomes, Sofia E.; Borralho, Pedro M.; Rodrigues, Cecília M. P.; Gaudêncio, Susana P.; Pereira, Florbela; LAQV@REQUIMTE; DQ - Departamento de Química; UCIBIO - Applied Molecular Biosciences Unit; DCV - Departamento de Ciências da Vida; MDPI - Multidisciplinary Digital Publishing Institute
To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure–activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC50). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R2 of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (1H and13C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets.
A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
Publication . Dias, Tiago; Gaudêncio, Susana P.; Pereira, Florbela; UCIBIO - Applied Molecular Biosciences Unit; LAQV@REQUIMTE; DQ - Departamento de Química; MDPI - Multidisciplinary Digital Publishing Institute
The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R 2 of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data ( 1 H and 13 C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.
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Fundação para a Ciência e a Tecnologia
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5876
Número da atribuição
147218