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: 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.
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References
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TL;DR: The QSAR analysis presented here is an attempt to organize the knowledge on the HDACIs with the purpose of designing new chemical entities with enhanced inhibitory potencies and to study the mechanism of action of the compounds.
Abstract: Histone deacetylase (HDAC) inhibition is a recent, clinically validated therapeutic strategy for cancer treatment. HDAC inhibitors (HDACIs) block angiogenesis, arrest cell growth, and lead to differentiation and apoptosis in tumor cells. In this article, a survey of published quantitative structure-activity relationships (QSARs) studies are presented and discussed in the hope of identifying the structural determinants for anticancer activity. Secondly a two-dimensional QSAR study was carried out on biological results derived from various types of HDACIs and from different assays using the C-QSAR program of Biobyte. The QSAR analysis presented here is an attempt to organize the knowledge on the HDACIs with the purpose of designing new chemical entities with enhanced inhibitory potencies and to study the mechanism of action of the compounds. This study revealed that lipophilicity is one of the most important determinants of activity. Additionally, steric factors such as the overall molar refractivity (CMR), molar volume (MgVol), the substituent's molar refractivity (MR) (linear or parabola), or the sterimol parameters B(1) and L are important. Electronic parameters indicated as σ(p), are found to be present only in one case.
77 citations
TL;DR: It is suggested that the present methodology can be used in the design of large libraries of compounds with appropriate values of permeability and to perform virtual screening in the early stages of drug development.
Abstract: In the present study, 21 validated QSAR models that discriminate compounds with high Caco-2 permeability (Papp ≥8×10(-6) cm/s) from those with moderate-poor permeability (Papp <8×10(-6) cm/s) were developed on a novel large dataset of 674 compounds. 20 DRAGON descriptor families were used. The global accuracies of obtained models were ranking between 78-82 %. A general model combining all types of molecular descriptors was developed and it classified correctly 81.56 % and 83.94 % for training and test sets, respectively. An external set of 10 compounds was predicted and 80 % was correctly assessed by in vitro Caco-2 assays. The potential use of the final model was evaluated by a virtual screening of a human intestinal absorption database of 269 compounds. The model predicted 121 compounds with high Caco-2 permeability and the 90 % of them had high values of human intestinal absorption (HIA≥80). This study provides the most comprehensive database of Caco-2 permeability and evidenced the utility of the combined methodology (in silico+in vitro) in the prediction of Caco-2 permeability. It suggests that the present methodology can be used in the design of large libraries of compounds with appropriate values of permeability and to perform virtual screening in the early stages of drug development.
73 citations
"Quantitative structure–activity rel..." refers background or methods in this paper
...Detailed descriptions of the mathematical basis of this method can be found elsewhere [50,51]....
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...However, it has been discussed that this removal must be done with care, because such compounds may hold unique information that makes the model more precise [51]....
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TL;DR: The kappa index of agreement has comprehensively been used to assess the overall accuracy of the model's predictive power and the models are statistically significant and can be used as a rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries.
Abstract: QSAR models for a diverse set of compounds for cytochrome P450 1A2 inhibition have been produced using 4 statistical approaches; partial least squares (PLS), multiple linear regression (MLR), classification and regression trees (CART), and bayesian neural networks (BNN). The models complement one another and have identified the following descriptors as important features for CYP1A2 inhibition; lipophilicity, aromaticity, charge, and the HOMO/LUMO energies. Furthermore all models are global and have been used to predict a diverse independent set of compounds. For the first time in the field of QSAR, the κ index of agreement has comprehensively been used to assess the overall accuracy of the model's predictive power. The models are statistically significant and can be used as a rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries.
70 citations
"Quantitative structure–activity rel..." refers background in this paper
...4 is considered to be predictive for ML methods [57]....
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Book•
31 Jul 2010TL;DR: Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques provides an overview of current research in machine learning and applications to chemoinformatics tasks.
Abstract: Chemoinformatics is a scientific area that endeavours to study and solve complex chemical problems using computational techniques and methods. Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques provides an overview of current research in machine learning and applications to chemoinformatics tasks. As a timely compendium of research, this book offers perspectives on key elements that are crucial for complex study and investigation.
70 citations
"Quantitative structure–activity rel..." refers background in this paper
...The benchmarks of all these approaches can be widely found in the literature [52,53]....
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TL;DR: A 3D chemical-feature-based QSAR pharmacophore model was developed and was well consistent with the structure-functional requirements for the binding of the HDAC inhibitors and showed that the type and spatial location of chemical features encoded in the pharmacophores are in full agreement with the enzyme-inhibitor interaction pattern identified from molecular docking.
Abstract: Histone deacetylases (HDACs) enzyme plays a significant role in transcriptional regulation by modifying the core histones of the nucleosome. It has emerged as an important therapeutic target for the treatment of cancer and other diseases. Inhibitors of HDACs become a new class of anticancer agents and have provoked much interest amongst pharmacologists and cancer researchers. To facilitate the discovery of specific HDACs inhibitors, a 3D chemical-feature-based QSAR pharmacophore model was developed and was well consistent with the structure-functional requirements for the binding of the HDAC inhibitors. Using this model, the interactions between the benzamide MS-275 and HDAC were explored. The result showed that the type and spatial location of chemical features encoded in the pharmacophore are in full agreement with the enzyme-inhibitor interaction pattern identified from molecular docking.
60 citations