Novel Inhibitors of Human Histone Deacetylase (HDAC) Identified by QSAR Modeling of Known Inhibitors, Virtual Screening, and Experimental Validation

Abstract

Inhibitors of histone deacetylases (HDACIs) have emerged as a new class of drugs for the treatment of human cancers and other diseases because of their effects on cell growth, differentiation, and apoptosis. In this study we have developed several quantitative structure-activity relationship (QSAR) models for 59 chemically diverse histone deacetylase class 1 (HDAC1) inhibitors. The variable selection k nearest neighbor (kNN) and support vector machines (SVM) QSAR modeling approaches using both MolconnZ and MOE chemical descriptors generated from two-dimensional rendering of compounds as chemical graphs have been employed. We have relied on a rigorous model development workflow including the division of the data set into training, test, and external sets and extensive internal and external validation. Highly predictive QSAR models were generated with leave-one-out cross-validated (LOO-CV) q2 and external R2 values as high as 0.80 and 0.87, respectively, using the kNN/MolconnZ approach and 0.93 and 0.87, respectively, using the SVM/MolconnZ approach. All validated QSAR models were employed concurrently for virtual screening (VS) of an in-house compound collection including 9.5 million molecules compiled from the ZINC7.0 database, the World Drug Index (WDI) database, the ASINEX Synergy libraries, and other commercial databases. VS resulted in 45 structurally unique consensus hits that were considered novel putative HDAC1 inhibitors. These computational hits had several novel structural features that were not present in the original data set. Four computational hits with novel scaffolds were tested experimentally, and three of them were confirmed active against HDAC1, with IC50 values for the most active compound of 1.00 microM. The fourth compound was later identified to be a selective inhibitor of HDAC6, a Class II HDAC. Moreover, two of the confirmed hits are marketed drugs, which could potentially facilitate their further development as anticancer agents. This study illustrates the power of the combined QSAR-VS method as a general approach for the effective identification of structurally novel bioactive compounds.

Best Practices for QSAR Model Development, Validation, and Exploitation

“…Examples include anticonvulsants, [53] HIV-1 reverse transcriptase inhibitors, [59] D1 antagonists, [60] antitumor compounds, [61] beta-lactamase inhibitors, [62] Human Histone Deacetylase (HDAC) inhibitors, [63] and geranylgeranyltransferase-I inhibitors. [64] Thus, models resulting from predictive QSAR workflow could be used to prioritize the selection of chemicals for the experimental validation.…”

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

“…All HDACi are distributed in 702 compound families (method for deriving compound families described in our earlier publication [40,41] and their structural diversity index is 0.506, which is comparable to that of the structurally diverse estrogen receptor agonist dataset. [46] Therefore, our collected HDACi are fairly diverse in structures and physicochemical properties, and they are significantly higher in numbers than the 40-200 compounds used in developing ligand-based HDACi prediction tools reported in the literatures (QSAR, [9][10][11][12][13] 3D-QSAR, [14][15][16][17][18][19] and pharmacophore [20] ). Among the 1488 HDACi and 84 weak HDACi, there are 1268 HDACi, 70 weak HDACi published before 2008, and 220 HDACi, 14 weak HDACi published since 2008.…”

“…[1,2] Efforts have been directed at expanded search of the chemical space, rational modification of linker and cap groups, and the introduction of pro-drugs. [1,2] Some of these efforts have been facilitated by the use of such virtual screening (VS) tools as ligand-based QSAR, [9][10][11][12][13] 3D-QSAR, [14][15][16][17][18][19] and pharmacophore, [20] and structure-based molecular docking. [21][22][23][24][25][26] The applicability domains of these ligand-based methods in some cases are restricted [27,28] by limited diversity (< 200 compounds in most cases) [29][30][31] or structural types (e.g.…”

Cap-Modified Hydroxamate Analogues as Histone Deacetylases Inhibitors and Antitumor Agents

“…14,15 The chemical structures of HDACi reported so far include a zinc-binding motif, a hydrophobic cavity-binding linker, and a surface recognition cap which can produce specific interactions with external surface of the protein, leading to enhancing HDACi activity. [16][17][18][19] As part of our efforts to discover novel HDACi and further enhance our understanding of the roles of specific interactions between the enzyme outer rim and inhibitor cap groups in HDAC inhibitory activity, we designed a series of potent SAHA-liked cap-modified hydroxamate analogues (Fig. 2).…”