Pharmacophore mapping of flavone derivatives for aromatase inhibition

Aromatase, which catalyses the final step in the steroidogenesis pathway of estrogen, has been target for the design of inhibitor in the treatment of hormone dependent breast cancer for postmenopausal women. The extensive SAR studies performed in the last 30 years to search for potent, selective and less toxic compounds, have led to the development of second and third generation of non-steroidal aromatase inhibitors (AI). Besides the development of synthetic compounds, several naturally occurring and synthetic flavonoids, which are ubiquitous natural phenolic compounds and mediate the host of biological activities, are found to demonstrate inhibitory effects on aromatase. The present study explores the pharmacophores, i.e., the structural requirements of flavones (Fig. 1) for inhibition of aromatase activity, using quantitative structure activity relationship (QSAR) and space modeling approaches. The classical QSAR studies generate the model (R (2) = 0.924, Q (2) = 0.895, s = 0.233) that shows the importance of aromatic rings A and C, along with substitutional requirements in meta and para positions of ring C for the activity. 3D QSAR of Comparative Molecular Field Analysis (CoMFA, R (2) = 0.996, [Formula: see text]) and Comparative Molecular Similarity Analysis (CoMSIA, R (2) = 0.992, [Formula: see text]) studies show contour maps of steric and hydrophobic properties and contribution of acceptor and donor of the molecule, suggesting the presence of steric hindrance due to ring C and R'-substituent, bulky hydrophobic substitution in ring A, along with acceptors at positions 11, and alpha and gamma of imidazole ring, and donor in ring C favor the inhibitory activity. Further space modeling (CATALYST) study (R = 0.941, Delta( cost ) = 96.96, rmsd = 0.876) adjudge the presence of hydrogen bond acceptor (keto functional group), hydrophobic (ring A) and aromatic rings (steric hindrance) along with critical distance among features are important for the inhibitory activity.     

Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection

(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data.     

Cathepsin D inhibitors as potential therapeutics for breast cancer treatment: Molecular docking and bioevaluation against triple-negative and triple-positive breast cancers

The main aim of this study was to discover small molecule inhibitors against Cathepsin D (CatD) (EC.3.4.23.5), a clinically proven prognostic marker for breast cancer, and to explore the mechanisms by which CatD could be a useful therapeutic target for triple-positive and triple-negative breast cancers (TPBC & TNBC). The crystal structure of CatD at 2.5 Å resolution (PDB: 1LYB), which was complexed with Pepstatin A, was selected for computer-aided molecular modeling. The methods used in our study were pharmacophore modeling and molecular docking. Virtual screening was performed to identify small molecules from an in-house database and a large commercial chemical library. Cytotoxicity studies were performed on human normal cell line HEK293T and growth inhibition studies on breast adenocarcinoma cell lines, namely MCF-7, MDA-MB-231, SK-BR-3, and MDA-MB-468. Furthermore, RT-PCR analysis, in vitro enzyme assay, and cell cycle analysis ascertained the validity of the selected molecules. A set of 28 molecules was subjected to an in vitro fluorescence-based inhibitory activity assay, and among them six molecules exhibited \(>\)50 % inhibition at \(25\,\upmu \hbox {M}\). These molecules also exhibited good growth inhibition against TPBC and TNBC cancer types. Among them, molecules 1 and 17 showed single-digit micromolar \(\hbox {GI}_{50}\) values against MCF-7 and MDA-MB-231 cell lines.     

Activity landscape analysis of novel 5$$\upalpha $$-reductase inhibitors

Inhibitors of the enzyme 5\(\upalpha \)-reductase (5aR) are promising therapeutic agents for the treatment of benign prostatic hyperplasia (BPH) and prostate cancer. The lack of structural data of the enzyme 5aR prompts the application of ligand-based approaches to systematically explore the activity landscape of 5aR inhibitors. As part of an effort to develop inhibitors of this enzyme for the treatment of BPH, herein we discuss a chemoinformatic-based analysis of the activity landscape of a novel set of 53 novel pregnane and androstene compounds. It was found that, in general, for each pair of compounds in the set, as the structure similarity of the compounds increases the corresponding potency difference decreases. These results are in agreement with an overall smooth activity landscape. However, two potent activity cliff generators were identified pointing to specific small structural changes that have a large impact on the inhibition of 5aR.     

In silico design of peptidic inhibitors targeting estrogen receptor alpha dimer interface

Human estrogen receptor alpha (ERα), which acts as a biomarker and as a therapeutic target for breast cancers, is activated by agonist ligands and co-activator proteins. Selective estrogen receptor modulators (SERM) act as antagonists in specific tissues and tamoxifen, a SERM, has served as a drug for decades for ERα-positive breast cancers. However, the ligand-selective and tissue-specific response of ERα biological activity and the resistance to tamoxifen treatment in advanced stages of ERα-positive breast cancers underscores the need to find a ligand-independent inhibitor for ERα. Here we present a ligand-independent approach of inhibiting ERα transactivation targeting its dimerization-a key process of ERα biological activity. Using in silico techniques, we first elucidated the hydrogen bond interactions involved in dimerization and identified three interfacial sequence motifs, where sequence I (DKITD) and sequence II (QQQHQRLAQ) of one monomer form hydrogen bonding with sequence II and sequence I of the second monomer, respectively, and sequence III (LSHIRHMSNK) hydrogen bonds with the same from the second monomer. Studying the structural stability and the binding affinity of the peptides derived from these sequence motifs, we found that an extended and ARG mutated version (LQQQHQQLAQ) of sequence II can act as a suitable template for designing peptidic inhibitors. It provides additional structural stability and interacts more strongly with ERα dimer interface groove formed by helices 9 and 10/11 and prevent ERα dimerization. Our result provides a novel therapeutic designing pipeline for ligand-independent inhibition of ERα.     

Inverse vibration problem for un-damped 3-dimensional multi-story shear building models

Various researchers have contributed to the identification of the mass and stiffness matrices of two dimensional (2-D) shear building structural models for a given set of vibratory frequencies. The suggested methods are based on the specific characteristics of the Jacobi matrices, i.e., symmetric, tri-diagonal and semi-positive definite matrices. However, in case of three dimensional (3-D) structural models, those methods are no longer applicable, since their stiffness matrices are not tri-diagonal. In this paper the inverse problem for a special class of vibratory structural systems, i.e., 3-D shear building models, is investigated. A practical algorithm is proposed for solving the inverse eigenvalue problem for un-damped, 3-D shear buildings. The problem is addressed in two steps. First, using the target frequencies, a so-called normalized eigenvector matrix, which is a banded matrix containing the information related to the frequencies and mode shapes of the target structural system, is determined. In this regard, similar to the solution of inverse problem for 2-D shear building structural models in which an auxiliary structure is constructed by adding constraints (or springs) to the original system, three auxiliary structures are proposed to solve the problem for 3-D cases. In the second step, the normalized eigenvector matrix is utilized to obtain the normalized stiffness matrix; in turn, this matrix is decomposed into the stiffness and mass matrices of the system. Finally, a numerical example is presented to demonstrate the efficiency of the proposed algorithm in determining the mass and stiffness matrices of a 3-D structural model for a given set of target vibrational frequencies.     

Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths

Radiomic features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As medical centers transition from 1.5 T to 3.0 T magnetic resonance (MR) imaging, it is beneficial to identify potentially robust radiomic features across field strengths because images acquired at different field strengths could be used in machine learning models. Dynamic contrast-enhanced MR images of benign breast lesions and hormone receptor positive/HER2-negative (HR+/HER2-) breast cancers were acquired retrospectively, yielding 612 unique cases: 150 and 99 benign lesions imaged at 1.5 T and 3.0 T, and 223 and 140 HR+/HER2- cancerous lesions imaged at 1.5 T and 3.0 T, respectively. In addition, an independent set of seven lesions imaged at both field strengths, three benign lesions and four HR+/HER2- cancers, was analyzed separately. Lesions were automatically segmented using a 4D fuzzy c-means method; thirty-eight radiomic features were extracted. Feature value distributions were compared by cancer status and imaging field strength using the Kolmogorov-Smirnov test. Features that did not demonstrate a statistically significant difference were considered to be potentially robust. The area under the receiver operating characteristic curve (AUC), for the task of classifying lesions as benign or HR+/HER2- cancer, was determined for each feature at each field strength. Three features were found to be both potentially robust across field strength and of high classification performance, i.e., AUCs statistically greater than 0.5 in the classification task: one shape feature (irregularity), one texture feature (sum average) and one enhancement variance kinetics features (enhancement variance increasing rate). In the demonstration set of lesions imaged at both field strengths, two of the three potentially robust features showed qualitative agreement across field strength. These findings may contribute to the development of computer-aided diagnosis models that are robust across field strength for this classification task.     

EGFRisopred: a machine learning-based classification model for identifying isoform-specific inhibitors against EGFR and HER2

The EGFR kinase pathway is one of the most frequently activated signaling pathways in human cancers. EGFR and HER2 are the two significant members of this pathway, which are attractive drug targets of clinical relevance in lung and breast cancer. Therefore, identifying EGFR- and HER2-specific inhibitors is one of the important challenges in cancer drug discovery. To address this issue, a dataset of 519 compounds having inhibitory activity against both the isoforms, i.e., EGFR and HER2, was collected from the literature and developed a knowledge-based computational classification model for predicting the specificity of a molecule for an isoform (EGFR/HER2) with precision. A total of seventy-two classification models using nine fingerprint types, four classifiers (IBK, NB, SMO and RF) and two different datasets (EGFR and HER2 isoform specific) were developed. It was observed that the models developed using random forest and IBK performed better for EGFR- and HER2-specific datasets, respectively. Scaffold and functional group analysis led to the identification of prevalent core and fragments in each of the datasets. The accuracy of the selected best performing models was also evaluated using the decoy dataset. We have also developed an application EGFRisopred, which integrates the best performing models and permits the user to predict the specificity of a compound as an EGFR-/HER2-specific anticancer agent. It is expected that the tool’s availability as a free utility will allow researchers to identify new inhibitors against these targets important in cancer.

Graphic abstract

     

Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors

Aminopeptidase N (APN) inhibitors have been reported to be effective in treating of life threatening diseases including cancer. Validated ligand- and structure-based pharmacophore mapping approaches were combined with Bayesian modeling and recursive partitioning to identify structural and physicochemical requirements for highly active APN inhibitors. Based on the assumption that ligand- and structure-based pharmacophore models are complementary, the efficacy of ‘multiple pharmacophore screening’ for filtering true positive virtual hits was investigated. These multiple pharmacophore screening methods were utilized to search novel virtual hits for APN inhibition. The number of hits was refined and reduced by recursive partitioning, drug-likeliness, pharmacokinetic property prediction, and comparative molecular-docking studies. Four compounds were proposed as the potential virtual hits for APN enzyme inhibition.     

Diversity through semisynthesis: the chemistry and biological activity of semisynthetic epothilone derivatives

Epothilones are myxobacterial natural products that inhibit human cancer cell growth through the stabilization of cellular microtubules (i.e., a “taxol-like” mechanism of action). They have proven to be highly productive lead structures for anticancer drug discovery, with at least seven epothilone-type agents having entered clinical trials in humans over the last several years. SAR studies on epothilones have included a large number of fully synthetic analogs and semisynthetic derivatives. Previous reviews on the chemistry and biology of epothilones have mostly focused on analogs that were obtained by de novo chemical synthesis. In contrast, the current review provides a comprehensive overview on the chemical transformations that have been investigated for the major epothilones A and B as starting materials, and it discusses the biological activity of the resulting products. Many semisynthetic epothilone derivatives have been found to exhibit potent effects on human cancer cell growth and several of these have been advanced to the stage of clinical development. This includes the epothilone B lactam ixabepilone (Ixempra^®, which has been approved by the FDA for the treatment of advanced and metastatic breast cancer.     

Support Vector Regression Model for Millimeter Wave Transitions

In this paper, we introduce a new method, support vector regression (SVR) method, to model millimeter wave transitions. SVR is based on the structural risk minimization (SRM) principle, which leads to good generalization ability for regression problem. The SVR model can be electromagnetically developed with a set of training data and testing data which produced by the electromagnetic simulation. Two Ka-band millimeter wave transitions, i.e., waveguide to microstrip transition and coaxial to waveguide adapter, are used as examples to validate the method. Experimental results show that the developed SVR models have a good predictive ability, and they are useful for interactive CAD of millimeter wave transitions.     

The main factors influencing the O vacancy formation on the Ir doped ceria surface: A DFT+U study

The effects of Ir doping on the oxygen vacancy formation energy have been investigated using the DFT+U method, i.e., first-principles density functional theory calculations with the inclusion of the on-site Coulomb interaction. The main factors influencing the reducibility of Ir-doped ceria are studied carefully. It is found that, although the Ir doping induces gap states (MIGS) as do other noble metals (Pd, Pt, Rh), the structural relaxation (instead of the electronic structure relaxation) is the main factor responsible for the decrease of the oxygen vacancy formation energy, i.e., the Ir doping makes structural distortions much more exothermic for the reduced ceria.     

Melting of Bi sublattice in nanosized BiFeO3 Perovskite by resonant X-ray diffraction

Free-standing BiFeO3 perovskite particles with a size ranging from polycrystalline bulk down to 5?nm have been studied by high-energy resonant (Bi K edge) x-ray diffraction coupled to differential atomic pair distribution function analysis. Nanosized BiFeO3 particles are found to exhibit extra, i.e., beyond the usual thermal, structural disorder that increases progressively with diminishing their size. In particles of size smaller than approximately 18?nm the disorder destroys the structural coherence of the Bi?sublattice and disturbs that of the Fe-based sublattice in the perovskite structure, substantially affecting the magnetoelectric properties it carries. The new structural information helps better understand the unusual behavior of perovskites structured at the nanoscale.     

Emergence of the self-similar property in gene expression dynamics

Many theoretical models have recently been proposed to understand the structure of cellular systems composed of various types of elements (e.g., proteins, metabolites and genes) and their interactions. However, the cell is a highly dynamic system with thousands of functional elements fluctuating across temporal states. Therefore, structural analysis alone is not sufficient to reproduce the cell's observed behavior.In this article, we analyze the gene expression dynamics (i.e., how the amount of mRNA molecules in cell fluctuate in time) by using a new constructive approach, which reveals a symmetry embedded in gene expression fluctuations and characterizes the dynamical equation of gene expression (i.e., a specific stochastic differential equation). First, by using experimental data of human and yeast gene expression time series, we found a symmetry in short-time transition probability from time t to time t+1. We call it self-similarity symmetry (i.e., the gene expression short-time fluctuations contain a repeating pattern of smaller and smaller parts that are like the whole, but different in size). Secondly, we reconstruct the global behavior of the observed distribution of gene expression (i.e., scaling-law) and the local behavior of the power-law tail of this distribution. This approach may represent a step forward toward an integrated image of the basic elements of the whole cell.     

Brouwer-Zadeh (Fuzzy-intuitionistic) posets for unsharp quantum mechanics

The partial ordered structure which plays for unsharp quantum mechanics the same role of orthomodular lattices for ordinary quantum mechanics is introduced. Differently from the unsharp case, in which one can identify quantum propositions (i.e., Hilbert space subspaces) with yes-no devices (i.e., orthogonal projections) they are tested by, in the unsharp case this identification is broken down: every quantum generalized proposition (i.e., pair of mutually orthogonal subspaces) is tested by many different yes-no devices (i.e., Hilbert space effects). The set of all quantum effects has a structure of Brouwer-Zadeh poset, canonically embeddable in a (minimal) Brouwer-Zadeh lattice, whereas the set of all quantum generalized propositions has a structure of Brouwer-Zadeh complete lattice.A Brouwer-Zadeh poset is defined as a partially ordered structure equipped with two nonusual orthocomplementations: a regular degenerate (Zadeh or fuzzy-like) one and a weak (Brouwer or intuitionistic-like) one linked by an interconnection rule. Using these two orthocomplementations it is possible to introduce the two modal-like operators of necessity and possibility.     

Design of potential anti-tumor PARP-1 inhibitors by QSAR and molecular modeling studies

Molecular Diversity - Poly ADP-ribose polymerase-1 (PARP-1) inhibitors have been recognized as new agents for the treatment of patients with breast cancer type 1 (BRCA1) disorders. The quantitative...     

Anticancer Activity of Copper Complex of (4R)-(-)-2-Thioxo-4-thiazolidinecarboxylic Acid and 3-Rhodaninepropionic Acid on Prostate and Breast Cancer Cells by Fluorescent Microscopic Imaging

Copper complexes with strong anticancer activity are promising new drugs for treatment of patients with metastatic cancer. Copper 8-hydroxyquinoline-2-carboxaldehyde-thiosemicarbazide (CuHQTS) and copper 8-hydroxyquinoline-2-carboxaldehyde-4,4-dimethyl-3-thiosemicarbazide (CuHQDMTS) were found to have strong anticancer activity against cisplatin-resistant neuroblastoma cells and prostate cancer cells. This study aimed to synthesize and characterize two new anticancer copper complexes, copper complex of (4R)-(?)-2-Thioxo-4-thiazolidinecarboxylic acid (CuTTDC), and copper complex of 3-Rhodaninepropionic acid-copper complex (CuRDPA). Cell growth inhibition and cytotoxicity of CuTTDC and CuRDPA on prostate and breast cancer cells were evaluated with Cell Counting Kits-8 (CCK8) assay and fluorescent microscopic imaging respectively. Strong anticancer activity of CuTTDC and CuRDPA was demonstrated by growth inhibition and cytotoxicity of prostate and breast cancer cells treated with these two copper complexes, supporting further investigation of potential use of these two new anticancer complexes for treatment of prostate and breast cancer metastasis.