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

Abstract: Breast cancer, a heterogeneous disease, is the most frequently diagnosed cancer and the second leading cause of cancer-related death among women worldwide. Recently, epigenetic abnormalities have emerged as an important hallmark of cancer development and progression. Given that histone deacetylases (HDACs) are crucial to chromatin remodeling and epigenetics, their inhibitors have become promising potential anticancer drugs for research. Here we reviewed the mechanism and classification of histone deacetylases (HDACs), association between HDACs and breast cancer, classification and structure-activity relationship (SAR) of HDACIs, pharmacokinetic and toxicological properties of the HDACIs, and registered clinical studies for breast cancer treatment. In conclusion, HDACIs have shown desirable effects on breast cancer, especially when they are used in combination with other anticancer agents. In the coming future, more multicenter and randomized Phase III studies are expected to be conducted pushing promising new therapies closer to the market. In addition, the design and synthesis of novel HDACIs are also needed.

Abstract: Novel anti-hepatic carcinoma drugs are of great therapeutic significance in the treatment of different types of hepatic cancers. Since last decade, there has been a progressive improvement in computational drug designing strategies. Human topoisomerase-II (Topo-II) and human platelet derived growth factor receptor-α (PDGFR-α) have been identified as main enzymes involved in hepatic carcinoma. In the current work, we assessed novel pyrrole-2,3-dicarboxylate derivatives as potential anti-cancer agents using docking, virtual screening and experimental analyses. Pyrrole-2,3-dicarboxylate derivatives which we have evaluated in this study were synthesized and characterized by UV, IR, ESI-MS, 1H and 13C NMR spectroscopic techniques. Structural validation was done using quantum chemical calculations using Density Function Theory (DFT) employing B3LYP method and 6-311++G(d,p) basis set. Potential energy distribution (PED) for normal vibrational modes was computed by VEDA4. The HOMO and LUMO analysis was carried out to determine the charge transfer within the molecule. The synthesized compounds were tested in-silico and in-vitro for anti-cancer activity; Docking studies were performed against topo-II and PDGFR-α. By utilizing ligand based pharmacophore generation approach and virtual screening against control drugs (Doxorubicin Hydrochloride and Rituximab) fifty-one novel molecules have been proposed that displayed highest binding affinities, least binding energies and effective druglikeness. The docking analyses revealed that Met782, Val785, Asn786, Gly813, Lys814 and Ile43 were important interacting residues for topo-II and Glu556, Ile557, Arg558, Arg560, Glu789 and Arg817 for PDGFR-α receptor-ligand interaction. Absorption, distribution, metabolism, excretion and toxicological (ADMET) calculations predicted drugs to have improved pharmacokinetic properties. The compounds may be proven to be novel therapeutic candidates to cure cancer. The anti-hepatic carcinoma activity of compounds 1and 29 was evaluated against human liver carcinoma HepG2 cells using MTT assay, nuclear fragmentation and ROS generation analysis. The result of MTT assay revealed that these synthesized compounds significantly inhibited the growth of HepG2 cells in a dose-dependent manner. In addition to this, increment in condensed apoptotic nuclei and augmentation of intracellular reactive oxygen species (ROS) at higher doses of tested compounds showed apoptotic cell death of HepG2 cells. In brief, the cytotoxicity data revealed that compounds 1 and 29 possessed potent anti-hepatic carcinoma activities against HepG2 cells.

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.

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.

Abstract: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect difference...

Abstract: tic regression, and it concerns studying the effect of covariates on the risk of disease. The chapter includes generalized estimating equations (GEE’s) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC. As a prelude to the following chapter on repeated-measures data, Chapter 5 presents time series analysis. The material on repeated-measures analysis uses linear additive models with GEE’s and PROC MIXED in SAS for linear mixed-effects models. Chapter 7 is about survival data analysis. All computing throughout the book is done using SAS procedures.

Abstract: The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index.