Combining Ligand-Based Pharmacophore Modeling, Quantitative Structure−Activity Relationship Analysis and in Silico Screening for the Discovery of New Potent Hormone Sensitive Lipase Inhibitors

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Abstract

Hormone sensitive lipase (HSL) has been recently implicated in diabetes and obesity, prompting attempts to discover new HSL inhibitors. Toward this end, we explored the pharmacophoric space of HSL inhibitors using four diverse sets of compounds. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of yielding a self-consistent and predictive quantitative structure-activity relationship (QSAR) (r = 0.822, n = 99, F = 11.1, r LOO (2) = 0.521, r PRESS (2) against 23 external test inhibitors = 0.522). Interestingly, two pharmacophoric models emerged in the QSAR equation suggesting at least two binding modes. These pharmacophores were employed to screen the National Cancer Institute (NCI) list of compounds and our in-house built database of established drugs and agrochemicals. Active hits included the safe herbicidal agent bifenox (IC 50 = 0.43 microM) and the nonsteroidal anti-inflammatory naproxen (IC 50 = 1.20 microM). Our active hits undermined the traditional believe that HSL inhibitors should possess covalent bond-forming groups.

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