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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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.
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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.
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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.
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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.
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References
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TL;DR: A series of 5-substitutedphenyl-1,3,4-thiadiazole-based hydroxamic acids as analogues of SAHA were designed and synthesized and found to possess potent anticancer cytotoxicity and HDAC inhibition effects.
Abstract: Since the first histone deacetylase (HDAC) inhibitor (Zolinza®, widely known as suberoylanilide hydroxamic acid; SAHA) was approved by the Food and Drug Administration for the treatment of T-cell lymphoma in 2006, the search for newer HDAC inhibitors has attracted a great deal of interest of medicinal chemists worldwide. As a continuity of our ongoing research in this area, we designed and synthesized a series of 5-substitutedphenyl-1,3,4-thiadiazole-based hydroxamic acids as analogues of SAHA and evaluated their biological activities. A number of compounds in this series, for example, N(1)-hydroxy-N(8)-(5-(2-chlorophenyl)-1,3,4-thiadiazol-2-yl)octandiamide (5b), N(1)-hydroxy-N(8)-(5-(3-chlorophenyl-1,3,4-thiadiazol-2-yl)octandiamide (5c) and N(1)-hydroxy-N(8)-(5-(4-chlorophenyl)-1,3,4-thiadiazol-2-yl)octandiamide (5d), were found to possess potent anticancer cytotoxicity and HDAC inhibition effects. Compounds 5b-d were generally two- to five-fold more potent in terms of cytotoxicity compared to SAHA against five cancer cell lines tested. Docking studies revealed that these hydroxamic acid displayed higher affinities than SAHA toward HDAC8.
30 citations
TL;DR: A two-step modeling approach was employed to study the selectivity and activity of histone deacetylase inhibitors and resulted in 8 and 13 structurally unique consensus hits that were considered novel putative HDAC1 and HDAC6 inhibitors, respectively.
30 citations
TL;DR: This work explored the use of support vector machines (SVM) combined with the newly developed putative non‐inhibitor generation method as a virtual screening tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false‐hit rates similar to HTS.
Abstract: Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.
29 citations
"Quantitative structure–activity rel..." refers background or 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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...From these data sets compounds with inconclusive inhibitory potency were excluded (IC50 >100 μM, inhibition <50%, Ki >10 μM or inhibition activity reported as “inactive”) [24,44,45]....
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TL;DR: One pharmacophore model and three quantitative structure–activity relationship models were developed on a series of benzimidazole and imidazoles inhibitors of histone deacetylase 2, and good predictive models were obtained using comparative molecular field analysis, comparative molecular similarity indices analysis, and Topomer comparative molecularField analysis.
Abstract: One pharmacophore model and three quantitativestructure–activity relationship models were devel-oped on a series of benzimidazole and imidazoleinhibitors of histone deacetylase 2. The goodnessof hit score value of the best pharmacophoremodel was 0.756, which indicated that it is reliableto be used for virtual screening. The built pharma-cophore model was used to search the NCI data-base. The hit compounds were subjected tomolecular docking. The results showed that 25compounds had high scores and strong interac-tions with histone deacetylase 2. In three-dimen-sional quantitative structure–activity relationshipstudies, good predictive models were obtainedusing comparative molecular field analysis, com-parative molecular similarity indices analysis, andTopomer comparative molecular field analysis.Some putative active compounds were proposedbased on compound no. 41. Twenty-six compoundshad high scores and good interactions when theywere docking into histone deacetylase 2.Key words: histone deacetylase 2, pharmacophore, quantitativestructure–activity relationship, topomer comparative molecular fieldanalysis, virtual screeningReceived 6 September 2011, revised 25 December 2011 and acceptedfor publication 7 January 2012
24 citations
TL;DR: Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.
Abstract: Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR) modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC50 values and experimental energy scores with extensive validations. The QSAR model having an external R2 value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10) were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9) were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.
20 citations