Discovery of Putative Dual Inhibitor of Tubulin and EGFR by Phenotypic Approach on LASSBio-1586 Homologs.
Gisele BarbosaLuis Gabriel Valdivieso GelvesCaroline Marques Xavier CostaLucas Silva FrancoJoão Alberto Lins de LimaCristiane Aparecida-SilvaJohn Douglas TeixeiraClaudia Dos Santos MermelsteinEliezer Jesus BarreiroLidia Moreira LimaPublished in: Pharmaceuticals (Basel, Switzerland) (2022)
Combretastatin A-4 (CA-4, 1 ) is an antimicrotubule agent used as a prototype for the design of several synthetic analogues with anti-tubulin activity, such as LASSBio-1586 ( 2 ). A series of branched and unbranched homologs of the lead-compound 2, and vinyl, ethinyl and benzyl analogues, were designed and synthesized. A comparison between the cytotoxic effect of these homologs and 2 on different human tumor cell lines was performed from a cell viability study using MTT with 48 h and 72 h incubations. In general, the compounds were less potent than CA-4, showing CC 50 values ranging from 0.030 μM to 7.53 μM (MTT at 72 h) and 0.096 μM to 8.768 μM (MTT at 48 h). The antimitotic effect of the target compounds was demonstrated by cell cycle analysis through flow cytometry, and the cellular mechanism of cytotoxicity was determined by immunofluorescence. While the benzyl homolog 10 (LASSBio-2070) was shown to be a microtubule stabilizer, the lead-compound 2 (LASSBio-1586) and the methylated homolog 3 (LASSBio-1735) had microtubule destabilizing behavior. Molecular docking studies were performed on tubulin protein to investigate their binding mode on colchicine and taxane domain. Surprisingly, the benzyl homolog 10 was able to modulate EGFR phosphorylate activity in a phenotypic model. These data suggest LASSBio-2070 ( 10 ) as a putative dual inhibitor of tubulin and EGFR. Its binding mode with EGFR was determined by molecular docking and may be useful in lead-optimization initiatives.
Keyphrases
- molecular docking
- small cell lung cancer
- epidermal growth factor receptor
- cell cycle
- tyrosine kinase
- flow cytometry
- molecular dynamics simulations
- cell proliferation
- endothelial cells
- small molecule
- quality improvement
- electronic health record
- high throughput
- anti inflammatory
- big data
- protein protein
- machine learning
- deep learning
- pluripotent stem cells
- transcription factor
- single cell