Rational design of novel sirtuin 1 activators via structure-activity insights from application of QSAR modeling

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Reny Pratiwi
Veda Prachayasittikul
Supaluk Prachayasittikul
Chanin Nantasenamat


Sirtuin 1 (SIRT1) enzyme regulates major cell activities, and its activation offers lucrative therapeutic potentials for aging diseases including Alzheimer’s disease (AD). Regarding the global aging society, continual attention has been given to various chemical scaffolds as a source for the discovery of novel SIRT1 activators since the discovery of the pioneer activator, resveratrol. Understanding structure-activity relationship (SAR) is essential for screening, designing as well as improving the properties of drugs. In this study, an in silico approach based on quantitative structure-activity relationship (QSAR) modeling, was employed for understanding the SAR of currently available SIRT1 fused-aromatic activators (i.e., imidazothiazole, oxazolopyridine, and azabenzimidazole analogs). Three QSAR models constructed using multiple linear regression (MLR) provided good predictive performance (R2LOOCV = 0.729 - 0.863 and RMSELOOCV = 0.165 - 0.325). An additional novel set of 181 structurally modified compounds were rationally designed according to key descriptors deduced from the QSAR findings and their SIRT1 activities were predicted using the constructed models. In overview, the study provides insightful SAR findings of currently available SIRT1 activators that would be useful for guiding the rational design, screening, and development of further potent SIRT1 activators for managing age-related clinical conditions. A series of promising compounds as well as important scaffolds and molecular properties for potent SIRT1 activator were highlighted. This study demonstrated the efficacious role of QSAR-driven structural modification for the rational design of novel leads.

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Pratiwi, R., Prachayasittikul, V., Prachayasittikul, S., & Nantasenamat, C. (2019). Rational design of novel sirtuin 1 activators via structure-activity insights from application of QSAR modeling. EXCLI Journal, 18, 207-222. https://doi.org/10.17179/excli2019-1274
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