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Journal ArticleDOI

Quantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries

TL;DR: This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.
Abstract: Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.
Citations
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Journal ArticleDOI
TL;DR: In conclusion, HDACs have shown desirable effects on breast cancer, especially when they are used in combination with other anticancer agents, and more multicenter and randomized Phase III studies are expected to be conducted pushing promising new therapies closer to the market.

41 citations

Journal ArticleDOI
TL;DR: Detailed investigation on the estimation of absorption, distribution, metabolism, excretion, and toxicity (ADMET) suggested that compounds 4g, 6c, and 6g, while showing potent HDAC2 inhibitory activity and cytotoxicity, also potentially displayed ADMET characteristics desirable to be expected as promising anticancer drug candidates.
Abstract: In our search for novel histone deacetylases inhibitors, we have designed and synthesized a series of novel hydroxamic acids and N-hydroxybenzamides incorporating quinazoline heterocycles (4a - 4i, 6a - 6i). Bioevaluation showed that these quinazoline-based hydroxamic acids and N-hydroxybenzamides were potently cytotoxic against three human cancer cell lines (SW620, colon; PC-3, prostate; NCI-H23, lung). In term of cytotoxicity, several compounds, e.g., 4g, 4c, 4g - 4i, 6c, and 6h, displayed from 5- up to 10-fold higher potency than SAHA (suberoylanilidehydroxamic acid, vorinostat). The compounds were also generally comparable to SAHA in inhibiting HDACs with IC50 values in sub-micromolar range. Some compounds, e.g., 4g, 6c, 6e, and 6h, were even more potent HDAC inhibitors compared to SAHA in HeLa extract assay. Docking studies demonstrated that the compounds tightly bound to HDAC2 at the active binding site with binding affinities higher than that of SAHA. Detailed investigation on the estimation of absorption, distribution, metabolism, excretion, and toxicity (ADMET) suggested that compounds 4g, 6c, and 6g, while showing potent HDAC2 inhibitory activity and cytotoxicity, also potentially displayed ADMET characteristics desirable to be expected as promising anticancer drug candidates.

19 citations

Journal ArticleDOI
TL;DR: Pyrrole-2,3-dicarboxylate derivatives synthesized in this study significantly inhibited the growth of HepG2 cells in a dose-dependent manner and may be proven to be novel therapeutic candidates to cure cancer.

19 citations

Journal ArticleDOI
TL;DR: This work introduces a novel approach for epigenetic quantitative structure–activity relationship (QSAR) modelling using conformal prediction and discusses the development of models for 11 sets of inhibitors of histone deacetylases, which are one of the major epigenetic target families that have been screened.
Abstract: The growing interest in epigenetic probes and drug discovery, as revealed by several epigenetic drugs in clinical use or in the lineup of the drug development pipeline, is boosting the generation of screening data. In order to maximize the use of structure-activity relationships there is a clear need to develop robust and accurate models to understand the underlying structure-activity relationship. Similarly, accurate models should be able to guide the rational screening of compound libraries. Herein we introduce a novel approach for epigenetic quantitative structure-activity relationship (QSAR) modelling using conformal prediction. As a case study, we discuss the development of models for 11 sets of inhibitors of histone deacetylases (HDACs), which are one of the major epigenetic target families that have been screened. It was found that all derived models, for every HDAC endpoint and all three significance levels, are valid with respect to predictions for the external test sets as well as the internal validation of the corresponding training sets. Furthermore, the efficiencies for the predictions are above 80% for most data sets and above 90% for four data sets at different significant levels. The findings of this work encourage prospective applications of conformal prediction for other epigenetic target data sets.

13 citations

References
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Journal ArticleDOI
TL;DR: A linear Quantitative Structure–Activity Relationship (QSAR) is developed in this work for modeling and predicting HDAC inhibition by 5-pyridin- 2-yl-thiophene-2-hydroxamic acids and the physical meaning of the selected descriptors is discussed in detail.
Abstract: A linear Quantitative Structure-Activity Relationship (QSAR) is developed in this work for modeling and predicting HDAC inhibition by 5-pyridin-2-yl-thiophene-2-hydroxamic acids. In particular, a five-variable model is produced by using the Multiple Linear Regression (MLR) technique and the Elimination Selection-Stepwise Regression Method (ES-SWR) on a database that consists of 58 recently discovered 5-pyridin-2-yl-thiophene-2-hydroxamic acids and 69 descriptors. The physical meaning of the selected descriptors is discussed in detail. The validity of the proposed MLR model is established using the following techniques: cross validation, validation through an external test set and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. Based on the produced model, an in silico-screening study explores novel structural patterns and suggests new potent lead compounds.

57 citations

Journal ArticleDOI
TL;DR: Docking simulations and three-dimensional quantitative structure-activity relationship analyses were conducted on a series of indole amide analogues as potent histone deacetylase inhibitors to provide insight into inhibitor-HDAC interactions at the molecular level.

56 citations

Journal ArticleDOI
TL;DR: Comparative molecular field analysis and comparative molecular similarity indices analysis were employed to study three-dimensional quantitative structure-activity relationships (3D-QSARs) and exhibited good external predictivity as compared to that of CoMSIA models.
Abstract: The histone deacetylase enzyme has increasingly become an attractive target for developing novel anticancer drugs. Hydroxamates are a new class of anticancer agents reported to act by selective inhibition of the histone deacetylase (HDAC) enzyme. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed to study three-dimensional quantitative structure-activity relationships (3D-QSARs). QSAR models were derived from a training set of 40 molecules. An external test set consisting of 17 molecules was used to validate the CoMFA and CoMSIA models. All molecules were superimposed on the template structure by atom-based, multifit and the SYBYL QSAR rigid body field fit alignments. The statistical quality of the QSAR models was assessed using the parameters r(2)(conv), r(2)(cv) and r(2)(pred). In addition to steric and electronic fields, ClogP was also taken as descriptor to account for lipophilicity. The resulting models exhibited a good conventional r(2)(conv) and cross-validated r(2)(cv) values up to 0.910 and 0.502 for CoMFA and 0.987 and 0.534 for CoMSIA. Robust cross-validation by 2 groups was performed 25 times to eliminate chance correlation. The CoMFA models exhibited good external predictivity as compared to that of CoMSIA models. These 3D-QSAR models are very useful for design of novel HDAC inhibitors.

55 citations

Journal ArticleDOI
TL;DR: The herein-described compounds and method of synthesis will provide invaluable tools to investigate the molecular mechanism responsible for the reported selectively improved antileukemic activity.
Abstract: A series of SAHA cap derivatives was designed and prepared in good-to-excellent yields that varied from 49% to 95%. These derivatives were evaluated for their antiproliferative activity in several human cancer cell lines. Antiproliferative activity was observed for concentrations varying from 0.12 to >100 μM, and a molecular modeling approach of selected SAHA derivatives, based on available structural information of human HDAC8 in complex with SAHA, was performed. Strikingly, two compounds displayed up to 10-fold improved antileukemic activity with respect to SAHA; however, these compounds displayed antiproliferative activity similar to SAHA when assayed against solid tumor-derived cell lines. A 10-fold improvement in the leukemic vs peripheral blood mononuclear cell therapeutic ratio, with no evident in vivo toxicity toward blood cells, was also observed. The herein-described compounds and method of synthesis will provide invaluable tools to investigate the molecular mechanism responsible for the reporte...

54 citations


"Quantitative structure–activity rel..." refers background in this paper

  • ...-CF3) have been widely explored in designing novel HDAC inhibitors, including capping group substitution-based SAHA derivatives and 5-substituted phenyl benzamides, to name a few [66,67]....

    [...]

Journal ArticleDOI
TL;DR: A comprehensive quantitative structure-activity relationship (QSAR) study of HDACIs in the hope of identifying the structural determinants for anticancer activity by using various QSAR and classification methods.
Abstract: Histone deacetylases (HDACs) play a critical role in gene transcription and have become a novel target for the discovery of drugs against cancer and other diseases. During the past several years there have been extensive efforts in the identification and optimization of histone deacetylase inhibitors (HDACIs) as novel anticancer drugs. Here we report a comprehensive quantitative structure-activity relationship (QSAR) study of HDACIs in the hope of identifying the structural determinants for anticancer activity. We have identified, collected, and verified the structural and biological activity data for 124 compounds from various literature sources and performed an extensive QSAR study on this comprehensive data set by using various QSAR and classification methods. A highly predictive QSAR model with R(2) of 0.76 and leave-one-out cross-validated R(2) of 0.73 was obtained. The overall rate of cross-validated correct prediction of the classification model is around 92%. The QSAR and classification models provided direct guidance to our internal programs of identifying and optimizing HDAC inhibitors. Limitations of the models were also discussed.

49 citations