Quantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries
28 Feb 2017-Sar and Qsar in Environmental Research (SAR QSAR Environ Res)-Vol. 28, Iss: 3, pp 199-220
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.
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Journal Article•
9,185 citations
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
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
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
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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TL;DR: The present results demonstrate that these novel 3-substituted-2-oxoindoline-based N-hydroxypropenamides are potential for further development as anticancer agents.
Abstract: Histone deacetylases (HDAC) are currently a group of validated targets for anticancer drug discovery and development. In our research program to find novel small molecules targeting these enzymes, we designed and synthesized two series of 3-hydroxyimino-2-oxoindoline- and 3- methoxyimino-2-oxoindoline-based N-hydroxypropenamides (3a-g, 6a-g). The results show that these propenamides potently inhibited HDAC2 with IC 50 values in sub-micromolar range, approximately 10-fold lower than that of SAHA (also known as suberoylanilohydroxamic acid). Evaluation of cytotoxicity of these compounds in three human cancer cell lines revealed that most of the synthesized compounds were up to 5-fold more cytotoxic than SAHA. Docking studies showed that the compounds bound to HDAC2 at the binding site with higher binding affinities compared to SAHA. Our present results demonstrate that these novel 3-substituted-2-oxoindoline-based N-hydroxypropenamides are potential for further development as anticancer agents.
20 citations
TL;DR: The well-expressed inhibitor activity of 36 indole amide hydroxamic acids to Histone Deacetylases was analyzed by CODESSA PRO software and the Best Multiple Linear Regression algorithm, to provide a reliable 2D-QSAR model from a set of more than 800 descriptors.
Abstract: The well-expressed inhibitor activity of 36 indole amide hydroxamic acids to Histone Deacetylases (HDACs) was analyzed by CODESSA PRO software and the Best Multiple Linear Regression (BLMR) algorithm, to provide a reliable 2D-QSAR model from a set of more than 800 descriptors. Concurrently, Chem-X (version 1994) software was used to develop a corresponding 3D-QSAR model; the steric and electrostatic interactions between a fictitious probe atom “H+” and a set of aligned molecules were evaluated using the CoMFA approach as implemented in Chem-X. A Partial Least Squares (PLS) procedure generated the principal components needed to build a 3D-QSAR model. The Weighted Least Squares (WLS) method generated an R2 map of the regions around the molecules important for steric and electrostatic interactions. Enzyme–inhibitor docking calculations were carried out to combine and compare with the results obtained by CoMFA. New potentially active structures were proposed.
19 citations
01 Jan 2007
TL;DR: It is demonstrated that validated, predictive models could be used successfully as a means of virtual screening of chemical libraries for the effective discovery of novel biologically active compounds.
Abstract: This chapter focuses on modern and developing trends in quantitative structure–activity relationship (QSAR) modeling. This research methodology is based on two strongly interrelated components, i.e., molecular ‘descriptors’ and statistical ‘data modeling’ techniques that are used to correlate descriptors with the ‘target property’ such as biological activity. The QSAR modeling field has experienced significant changes over its more than 40 years in existence. Its complexity has increased both with respect to molecular descriptors and data modeling techniques with the relatively recent emphasis of the latter methods on machine learning approaches. Some of these novel methods are reviewed in the chapter briefly but the real emphasis is placed on ‘model validation’ that has not been given sufficient attention in the past. We discuss general internal and external validation approaches that can be used by QSAR practitioners irrespective of the specific modeling approach and propose general QSAR modeling workflow. We emphasize that only extensively validated models characterized by their ‘applicability domains’ could be considered reliable and predictive. In the last section of the chapter, we demonstrate that validated, predictive models could be used successfully as a means of virtual screening of chemical libraries for the effective discovery of novel biologically active compounds.
19 citations
"Quantitative structure–activity rel..." refers background or methods in this paper
...It is believed that training these samples could significantly worsen the model statistics and they should be separated from the training set [56]....
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...There are two outlier types, namely, leverage (or structural) and activity outliers [56]....
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...A widely used approach for the structural AD of statistical QSAR models is based on the leverage (h) calculated from the diagonal values of the hat matrix of the molecular descriptors [56]....
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Journal Article•
TL;DR: A three-dimensional quantitative structure-activity relationship (3D-QSAR) study performed using genetic function approximation (GFA) for this class of molecules indicated that molecular shape analysis (MSA).
Abstract: Histone deacetylases (HDACs) play a critical role in gene transcription and are implicated in cancer therapy and other diseases. Inhibitors of HDACs induce cell differentiation and suppress cell proliferation in the tumor cells. Although many such inhibitors have been designed and synthesized, but selective inhibitors for HDAC isoforms are lacking. Various hydroxamic acid analogues have been reported as HDAC inhibitors. Here, we report a three-dimensional quantitative structure-activity relationship (3D-QSAR) study performed using genetic function approximation (GFA) for this class of molecules. QSAR models were generated using a training set of 39 molecules and the predictive ability of final model was assessed using a test set of 17 molecules. The internal consistency of the final QSAR model was 0.712 and showed good external predictivity of 0.585. The results of the present QSAR study indicated that molecular shape analysis (MSA), thermodynamic and structural descriptors are important for inhibition of HDACs.
18 citations
TL;DR: The NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery and highly predictive models were used for database virtual screening.
Abstract: Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure–activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Final...
17 citations
"Quantitative structure–activity rel..." refers methods in this paper
...Several developed models were further applied for virtually screening chemical libraries, such as ZINC, PubChem, Maybridge, NCI, Asinex databases, and so on [24,30,34,35]....
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