Antifungal activity of bis-azasqualenes, inhibitors of oxidosqualene cyclase

Conformational features of 4-(N)-squalenoyl-gemcitabine in solution: a combined NMR and molecular dynamics investigation

Folded conformations of 4-(N)-squalenoyl-gemcitabine in solution are obtained from a comparison of experimental and theoretical J-couplings.

Self-assembled squalenoyl-cytarabine nanostructures as a potent nanomedicine for treatment of leukemic diseases

Background

In this investigation, the antileukemic activity of a new nanomedicine based on the conjugation of 1,1′,2-tris-nor-squalenic acid with cytarabine (Ara-C) was evaluated.

Methods

Squalenoyl-Ara-C conjugate (Sq-Ara-C) self-assembled nanosystems were obtained by the nanoprecipitation method and characterized in vitro and in vivo.

Results

This new nanomedicine, which had a mean diameter of approximately 150 nm, improved the in vitro antitumoral activity of Ara-C in different cancer cell lines (L1210, K562, and MCF-7). Sq-Ara-C nanomedicine allowed reduction of the IC₅₀ value with respect to the free drug and was also active against drug-resistant leukemic cells (L1210R). A noticeable increase in the survival rate of mice with aggressive metastatic L1210R leukemia was observed after treatment with Sq-Ara-C (50 mg/kg) as compared with the free active compound (100 mg/kg). Finally, evaluation of the biodistribution and pharmacokinetic profiles of the drug demonstrated that these nanoaggregates preferentially localized to the liver and spleen, and protected the drug from physiological metabolism.

Conclusion

Squalenoylation of cytarabine offers several pharmacological benefits both in vitro and in vivo.

Compound prioritization methods increase rates of chemical probe discovery in model organisms

Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided.