Title:Investigation on Quantitative Structure-Activity Relationships of 1,3,4-Oxadiazole Derivatives as Potential Telomerase Inhibitors
VOLUME: 17 ISSUE: 1
Author(s):Marco Tutone*, Beatrice Pecoraro and Anna M. Almerico
Affiliation:Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche (STEBICEF) Universita degli Studi di Palermo, via Archirafi 28, 90123-Palermo, Department of Clinical and Pharmaceutical Sciences, School of Life and Medical Sciences, University of Hertfordshire, College Lane, Hatfield, Hertfordshire AL10 9AB, Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche (STEBICEF) Universita degli Studi di Palermo, via Archirafi 28, 90123-Palermo
Keywords:1, 3, 4-oxadiazoles, anticancer activity, Telomerase inhibitors, QSAR, 2D descriptors, MLR.
Abstract:
Background: Telomerase, a reverse transcriptase, maintains telomere and chromosomes
integrity of dividing cells, while it is inactivated in most somatic cells. In tumor cells, telomerase is
highly activated, and works in order to maintain the length of telomeres causing immortality, hence it
could be considered as a potential marker to tumorigenesis.A series of 1,3,4-oxadiazole derivatives
showed significant broad-spectrum anticancer activity against different cell lines, and demonstrated
telomerase inhibition.
Methods: This series of 24 N-benzylidene-2-((5-(pyridine-4-yl)-1,3,4-oxadiazol-2yl)thio)acetohydrazide
derivatives as telomerase inhibitors has been considered to carry out QSAR studies. The endpoint
to build QSAR models is determined by the IC50 values for telomerase inhibition, i.e., the
concentration (μM) of inhibitor that produces 50% inhibition. These values were converted to pIC50 (-
log IC50) values. We used the most common and transparent method, where models are described by
clearly expressed mathematical equations: Multiple Linear Regression (MLR) by Ordinary Least
Squares (OLS).
Results: Validated models with high correlation coefficients were developed. The Multiple Linear
Regression (MLR) models, by Ordinary Least Squares (OLS), showed good robustness and predictive
capability, according to the Multi-Criteria Decision Making (MCDM = 0.8352), a technique that
simultaneously enhances the performances of a certain number of criteria. The descriptors selected for
the models, such as electrotopological state (E-state) descriptors, and extended topochemical atom
(ETA) descriptors, showed the relevant chemical information contributing to the activity of these
compounds.
Conclusion: The results obtained in this study make sure about the identification of potential hits as
prospective telomerase inhibitors.