Pesquisa

PHYTOTOXIC

qua, 11 set 2019

Publicado por

Authors:

Biasetto, Carolina R. 1 ; Somensi, Andressa 1 ; Abdalla, Viviane de C. P. 2 ; de Abreu, Lucas M. 3 ; Gualtieri, Sonia C. J. 2 ; Pfenning, Ludwig H. 4 ; Bolzani, Vanderlan S. 1 ; Araujo, Angela R. 1


Abstract:

The upscaling of Xylaria cubensis, an endophyte of Eugenia brasiliensis (Myrtaceae), in PDB medium led to the isolation of known compounds including cytochalasin D (7) and cytochalasin C (8), which exhibited relatively higher phytotoxic activity in all the concentrations tested compared to the commercial herbicide GOAL (R). Besides the aforementioned metabolites, one dikctopiperazinc (DKP) and two isocoumarins were isolated and two DKPs were also identified in the mixture. The structures were determined by 1D and 2D H-1 NMR, MS analyses and were compared with the literature.


1   Departamento de Química Orgânica, Instituto de Química, Universidade Estadual Paulista, 14800-900 Araraquara – SP, Brasil

2   Departamento de Botânica, Universidade Federal de São Carlos, 13565-905 São Carlos – SP, Brasil

3   Departamento de Fitopatologia, Universidade Federal de Viçosa, 36570-000 Viçosa – MG, Brasil

4   Departamento de Fitopatologia, Universidade Federal de Lavras, 37200-000 Lavras – MG, Brasil


Link to article:   http://quimicanova.sbq.org.br/imagebank/pdf/AR20180500.pdf

 

ADMET modeling approaches 27ago19

ter, 27 ago 2019

Publicado por

Authors:

Ferreira, Leonardo L.G 1 ;  Andricopulo, Adriano D. 1


Abstract:

In silico prediction of ADMET is an important component of pharmaceutical R&D. Last year, the FDA approved 59 new molecular entities, with small molecules comprising 64% of the therapies approved in 2018. Estimation of pharmacokinetic properties in the early phases of drug discovery has been central to guiding hit-to-lead and lead-optimization efforts. Given the outstanding complexity of the current R&D model, drug discovery players have intensely pursued molecular modeling strategies to identify patterns in ADMET data and convert them into knowledge. The field has advanced alongside the progress of chemoinformatics, which has evolved from traditional chemometrics to advanced machine learning methods.


1   Laboratory of Medicinal and Computational Chemistry, Center for Research and Innovation in Biodiversity and Drug Discovery, Sao Carlos Institute of Physics, University of  Sao Paulo, Av. Joao Dagnone 1100, 13563-120, Sao Carlos, SP, Brazil


Link to article:   https://www.sciencedirect.com/science/article/pii/S1359644618303301?via%3Dihub