tCFA15, a trimethyl cyclohexenonic long-chain fatty alcohol, affects neural stem fate and differentiation by modulating Notch1 activity.

We have investigated the effects of tCFA15, a non-peptidic compound, on the differentiation of neural stem cell-derived neurospheres, and have found that tCFA15 promotes their differentiation into neurons and reduces their differentiation into astrocytes, in a dose-dependent manner. This response is reminiscent of that resulting from the loss-of-function of Notch signaling after inactivation of the Delta-like 1 (Dll1) gene. Further analysis of the expression of genes from the Notch pathway by reverse transcriptase-PCR revealed that tCFA15 treatment results in a consistent decrease in the level of Notch1 mRNA. We have confirmed this result in other cell lines and propose that it reflects a general effect of the tCFA15 molecule. We discuss the implications of this finding with respect to regulation of Notch activity in neural stem cells, and the possible use of tCFA15 as a therapeutic tool for various pathologies that result from impairment of Notch signaling.

QSAR Modelling of CYP3A4 Inhibition as a Screening Tool in the Context of DrugDrug Interaction Studies.

Drugdrug interaction potential (DDI), especially cytochrome P450 (CYP) 3A4 inhibition potential, is one of the most important parameters to be optimized before preclinical and clinical pharmaceutical development as regard to the number of marketed drug metabolized mainly by this CYP and potentially co-administered with the future drug. The present study aims to develop in silico models for CYP3A4 inhibition prediction to help medicinal chemists during the discovery phase and even before the synthesis of new chemical entities (NCEs), focusing on NCEs devoid of any inhibitory potential toward this CYP. In order to find a relevant relationship between CYP3A4 inhibition and chemical features of the screened compounds, we applied a genetic-algorithm-based QSAR exploratory tool SQS (Stochastic QSAR Sampler) in combination with different description approaches comprising alignment-independent Volsurf descriptors, ISIDA fragments and Topological Fuzzy Pharmacophore Triplets. The experimental data used to build models were extracted from an in-house database. We derived a model with good prediction ability that was confirmed on both newly synthesized compound and public dataset retrieved from Pubchem database. This model is a promising efficient tool for filtering out potentially problematic compounds.