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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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: 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: Results show that HDAC1 and HDAC2 function in the DNA-damage response by promoting DSB repair and thus provide important insights into the radio-sensitizing effects of HDAC inhibitors that are being developed as cancer therapies.
Abstract: DNA double-strand break (DSB) repair occurs within chromatin and can be modulated by chromatin-modifying enzymes. Here we identify the related human histone deacetylases HDAC1 and HDAC2 as two participants in the DNA-damage response. We show that acetylation of histone H3 Lys56 (H3K56) was regulated by HDAC1 and HDAC2 and that HDAC1 and HDAC2 were rapidly recruited to DNA-damage sites to promote hypoacetylation of H3K56. Furthermore, HDAC1- and 2-depleted cells were hypersensitive to DNA-damaging agents and showed sustained DNA-damage signaling, phenotypes that reflect defective DSB repair, particularly by nonhomologous end-joining (NHEJ). Collectively, these results show that HDAC1 and HDAC2 function in the DNA-damage response by promoting DSB repair and thus provide important insights into the radio-sensitizing effects of HDAC inhibitors that are being developed as cancer therapies.
580 citations
TL;DR: Four major structural classes of HDAC inhibitors that are in clinical trials and different computer modeling tools available for their structural modifications are summarized as a guide to discover additional HDAC inhibitor-based therapies with greater therapeutic utility.
Abstract: Histone dacetylases (HDACs) are a group of enzymes that remove acetyl groups from histones and regulate expression of tumor suppressor genes. They are implicated in many human diseases, especially cancer, making them a promising therapeutic target for treatment of the latter by developing a wide variety of inhibitors. HDAC inhibitors interfere with HDAC activity and regulate biological events, such as cell cycle, differentiation and apoptosis in cancer cells. As a result, HDAC inhibitor-based therapies have gained much attention for cancer treatment. To date, the FDA has approved three HDAC inhibitors for cutaneous/peripheral T-cell lymphoma and many more HDAC inhibitors are in different stages of clinical development for the treatment of hematological malignancies as well as solid tumors. In the intensifying efforts to discover new, hopefully more therapeutically efficacious HDAC inhibitors, molecular modeling-based rational drug design has played an important role in identifying potential inhibitors that vary in molecular structures and properties. In this review, we summarize four major structural classes of HDAC inhibitors that are in clinical trials and different computer modeling tools available for their structural modifications as a guide to discover additional HDAC inhibitors with greater therapeutic utility.
569 citations
"Quantitative structure–activity rel..." refers background in this paper
...The success of three HDACIs, vorinostat (SAHA), romidepsin (FK228) and belinostat, which were approved from 2006 to 2014 by the US FDA for the treatment of cutaneous and peripheral T-cell lymphoma, reveals the future direction of HDACIs for cancer therapy [8]....
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...HDAC6 inhibition has been widely investigated by many authors, and there is one HDAC6selective inhibitor (Rocilinostat) currently undergoing phase II clinical trial [8,72]....
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TL;DR: This work focuses on machine-learning techniques within the context of ligand-based VS (LBVS), providing a detailed view of the current state of the art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances.
542 citations
"Quantitative structure–activity rel..." refers background in this paper
...While many machine learning (ML) approaches have been successfully applied in real-world problems [36,37], only a few types of ML have been explored for studying structure–activity relationships of HDACIs....
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TL;DR: Analysis of the physicochemical properties of a large database of hits and corresponding leads identified in the past decade shows that this undesirable phenomenon can be traced back to the nature of high-throughput screening hits and hit-to-lead optimization practices.
Abstract: By analysing the physicochemical properties of a database of hits and leads, Keseru and Makara conclude that high-throughput screening, as well as hit-to-lead optimization practices in general, are responsible for the unfavourable physicochemical profile of recent leads and clinical candidates. Major adjustments may be needed to enable a balanced lead-evolution process and reduce the likelihood of high compound-related attrition in clinical trials.
540 citations
"Quantitative structure–activity rel..." refers background in this paper
...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: This tutorial review focuses on the recent progress toward the identification of class- selective and isoform-selective HDAC inhibitors, suggesting that subtle differences in the active sites of the HDAC isoforms can be exploited to dictate selectivity.
Abstract: Histone deacetylase (HDAC) proteins are transcription regulators linked to cancer. As a result, multiple small molecule HDAC inhibitors are in various phases of clinical trials as anti-cancer drugs. The majority of HDAC inhibitors non-selectively influence the activities of eleven human HDAC isoforms, which are divided into distinct classes. This tutorial review focuses on the recent progress toward the identification of class-selective and isoform-selective HDAC inhibitors. The emerging trends suggest that subtle differences in the active sites of the HDAC isoforms can be exploited to dictate selectivity.
339 citations
"Quantitative structure–activity rel..." refers background in this paper
...On the other hand, although there are many HDACIs currently marketed and undergoing clinical trials, most of them are non-isoform-selective inhibitors (the so-called pan-inhibitors) [70]....
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