Synthesis and cytotoxicity of novel 6,8,9-trisubstituted purine analogs against liver cancer cells.

A series of novel 6-(substituted phenyl piperazine)-8-(4-substituted phenyl)-9-cyclopentyl purines, 10-51, were synthesized by a four-step synthesis, achieving an overall yield of about 43 %. The reaction conditions were effectively optimized, and the final products were obtained with high purity and yield in all synthesis steps. The synthesized nucleobases were evaluated for their in vitro cytotoxic activities on selected human cancer cell lines (HUH7 (liver), HCT116 (colon), and MCF7 (breast)) using the Sulforhodamine B (SRB) assay. Among these analogs, compounds bearing 4-trifluoromethyl phenyl (19, 20 and 21), 4-methoxy phenyl (27) and 4-fluoro phenyl (34) substitutions at C-8 of purine were the most potent, and they were also analyzed in drug-resistance and drug-sensitive hepatocellular cancer cell (HCC) panels. Compound 19 displayed remarkable anticancer activities (IC50 = 2.9-9.3 µM) against Huh7, FOCUS, SNU475, SNU182, HepG2, and Hep3B cells compared to the positive control, Fludarabine. Additionally, the pharmacological properties and toxicity profiles of the molecules were investigated computationally by the Swiss-ADME and Pro-Tox II online tools, respectively. Results showed that our compounds have favorable physicochemical characteristics for oral bioavailability and do not reveal any toxicity endpoints such as carcinogenicity, immunotoxicity, mutagenicity, or cytotoxicity.

SLPred: a multi-view subcellular localization prediction tool for multi-location human proteins.

SUMMARY: Accurate prediction of the subcellular locations (SLs) of proteins is a critical topic in protein science. In this study, we present SLPred, an ensemble-based multi-view and multi-label protein subcellular localization prediction tool. For a query protein sequence, SLPred provides predictions for nine main SLs using independent machine-learning models trained for each location. We used UniProtKB/Swiss-Prot human protein entries and their curated SL annotations as our source data. We connected all disjoint terms in the UniProt SL hierarchy based on the corresponding term relationships in the cellular component category of Gene Ontology and constructed a training dataset that is both reliable and large scale using the re-organized hierarchy. We tested SLPred on multiple benchmarking datasets including our-in house sets and compared its performance against six state-of-the-art methods. Results indicated that SLPred outperforms other tools in the majority of cases. AVAILABILITY AND IMPLEMENTATION: SLPred is available both as an open-access and user-friendly web-server (https://slpred.kansil.org) and a stand-alone tool (https://github.com/kansil/SLPred). All datasets used in this study are also available at https://slpred.kansil.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations.

Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.

Novel Indole-Pyrazole Hybrids as Potential Tubulin-Targeting Agents; Synthesis, antiproliferative evaluation, and molecular modeling studies.

Structurally diverse indole-3-pyrazole-5-carboxamide analogues (10-29) were designed, synthesized, and evaluated for their antiproliferative activity against three cancer cell lines (Huh7, MCF-7, and HCT116) using the sulforhodamine B assay. Some of the derivatives showed anticancer activities equal to or better than sorafenib against cancer cell lines. Compounds 18 showed potent activity against the hepatocellular cancer (HCC) cell lines, with IC50 values in the range 0.6-2.9 µM. Compound 18 also exhibited moderate inhibitory activity against tubulin polymerization (IC50 = 19 µM). Flow cytometric analysis of cultured cells treated with 18 also demonstrated that the compound caused cell cycle arrest at the G2/M phase in both Huh7 and Mahlavu cells and induced apoptotic cell death in HCC cells. Docking simulations were performed to determine possible modes of interaction between 18 and the colchicine site of tubulin and quantum mechanical calculations were performed to observe the electronic nature of 18 and to support docking results.

Protein domain-based prediction of drug/compound-target interactions and experimental validation on LIM kinases.

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins' structure/function, and bias in system training datasets. Here, we propose a new method "DRUIDom" (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound-target pairs (~2.9M data points), and used as training data for calculating parameters of compound-domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound-protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound-domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.

The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

iBioProVis: interactive visualization and analysis of compound bioactivity space.

SUMMARY: iBioProVis is an interactive tool for visual analysis of the compound bioactivity space in the context of target proteins, drugs and drug candidate compounds. iBioProVis tool takes target protein identifiers and, optionally, compound SMILES as input, and uses the state-of-the-art non-linear dimensionality reduction method t-Distributed Stochastic Neighbor Embedding (t-SNE) to plot the distribution of compounds embedded in a 2D map, based on the similarity of structural properties of compounds and in the context of compounds' cognate targets. Similar compounds, which are embedded to proximate points on the 2D map, may bind the same or similar target proteins. Thus, iBioProVis can be used to easily observe the structural distribution of one or two target proteins' known ligands on the 2D compound space, and to infer new binders to the same protein, or to infer new potential target(s) for a compound of interest, based on this distribution. Principal component analysis (PCA) projection of the input compounds is also provided, Hence the user can interactively observe the same compound or a group of selected compounds which is projected by both PCA and embedded by t-SNE. iBioProVis also provides detailed information about drugs and drug candidate compounds through cross-references to widely used and well-known databases, in the form of linked table views. Two use-case studies were demonstrated, one being on angiotensin-converting enzyme 2 (ACE2) protein which is Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike protein receptor. ACE2 binding compounds and seven antiviral drugs were closely embedded in which two of them have been under clinical trial for Coronavirus disease 19 (COVID-19). AVAILABILITY AND IMPLEMENTATION: iBioProVis and its carefully filtered dataset are available at https://ibpv.kansil.org/ for public use. CONTACT: vatalay@metu.edu.tr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Induction of Apoptosis in Hepatocellular Carcinoma Cell Lines by Novel Indolylacrylamide Derivatives: Synthesis and Biological Evaluation.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer and one of the leading causes of cancer associated death worldwide. This is due to the highly resistant nature of this malignancy and the lack of effective treatment options for advanced stage HCC patients. The hyperactivity of PI3K/Akt and Ras/Raf/MEK/ERK signaling pathways contribute to the cancer progression, survival, motility, and resistance mechanisms, and the interaction of these two pathways are responsible for the regulation of cancer cell growth and development. Therefore, it is vital to design and develop novel therapeutic options for HCC treatment targeting these hyperactive pathways. For this purpose, novel series of trans-indole-3-ylacrylamide derivatives originated from the lead compound, 3-(1H-indole-3-yl)-N-(3,4,5-trimethoxyphenyl)acrylamide, have been synthesized and analyzed for their bioactivity on cancer cells along with the lead compound. Based on the initial screening, the most potent compounds were selected to elucidate their effects on cellular signaling activity of HCC cell lines. Cell cycle analysis, immunofluorescence, and Western blot analysis revealed that lead compound and (E)-N-(4-tert-butylphenyl)-3-(1H-indole-3-yl)acrylamide induced cell cycle arrest at the G2/M phase, enhanced chromatin condensation and PARP-cleavage, addressing induction of apoptotic cell death. Additionally, these compounds decreased the activity of ERK signaling pathway, where phosphorylated ERK1/2 and c-Jun protein levels diminished significantly. Relevant to these findings, the lead compound was able to inhibit tubulin polymerization as well. To conclude, the novel trans-indole-3-ylacrylamide derivatives inhibit one of the critical pathways associated with HCC which results in cell cycle arrest and apoptosis in HCC cell lines.

TGF-ß Promotes Metabolic Reprogramming in Lung Fibroblasts via mTORC1-dependent ATF4 Activation.

Idiopathic pulmonary fibrosis is a fatal interstitial lung disease characterized by the TGF-ß (transforming growth factor-ß)-dependent differentiation of lung fibroblasts into myofibroblasts, which leads to excessive deposition of collagen proteins and progressive scarring. We have previously shown that synthesis of collagen by myofibroblasts requires de novo synthesis of glycine, the most abundant amino acid found in collagen protein. TGF-ß upregulates the expression of the enzymes of the de novo serine-glycine synthesis pathway in lung fibroblasts; however, the transcriptional and signaling regulators of this pathway remain incompletely understood. Here, we demonstrate that TGF-ß promotes accumulation of ATF4 (activating transcription factor 4), which is required for increased expression of the serine-glycine synthesis pathway enzymes in response to TGF-ß. We found that induction of the integrated stress response (ISR) contributes to TGF-ß-induced ATF4 activity; however, the primary driver of ATF4 downstream of TGF-ß is activation of mTORC1 (mTOR Complex 1). TGF-ß activates the PI3K-Akt-mTOR pathway, and inhibition of PI3K prevents activation of downstream signaling and induction of ATF4. Using a panel of mTOR inhibitors, we found that ATF4 activation is dependent on mTORC1, independent of mTORC2. Rapamycin, which incompletely and allosterically inhibits mTORC1, had no effect on TGF-ß-mediated induction of ATF4; however, Rapalink-1, which specifically targets the kinase domain of mTORC1, completely inhibited ATF4 induction and metabolic reprogramming downstream of TGF-ß. Our results provide insight into the mechanisms of metabolic reprogramming in myofibroblasts and clarify contradictory published findings on the role of mTOR inhibition in myofibroblast differentiation.

Identification of small-molecule urea derivatives as novel NAMPT inhibitors via pharmacophore-based virtual screening.

Nicotinamide phosphoribosyltransferase (NAMPT) catalyzes the condensation of nicotinamide (NAM) with 5-phosphoribosyl-1-prophosphate (PRPP) to yield nicotinamide mononucleotide (NMN), a rate limiting enzyme in a mammalian salvage pathway of nicotinamide adenine dinucleotide (NAD+) synthesis. Recently, intracellular NAD+ has received substantial attention due to the recent discovery that several enzymes including poly(ADP-ribose) polymerases (PARPs), mono(ADP-ribose) transferases (ARTs), and sirtuins (SIRTs), use NAD+ as a substrate, suggesting that intracellular NAD+ level may regulate cytokine production, metabolism, and aging through these enzymes. NAMPT is found to be upregulated in various types of cancer, and given its importance in the NAD+ salvage pathway, NAMPT is considered as an attractive target for the development of new cancer therapies. In this study, the reported NAMPT inhibitors bearing amide, cyanoguanidine, and urea scaffolds were used to generate pharmacophore models and pharmacophore-based virtual screening studies were performed against ZINC database. Following the filtering steps, ten hits were identified and evaluated for their in vitro NAMPT inhibitory effects. Compounds GF4 (NAMPT IC50 = 2.15 ± 0.22 µM) and GF8 (NAMPT IC50 = 7.31 ± 1.59 µM) were identified as new urea-typed inhibitors of NAMPT which also displayed cytotoxic activities against human HepG2 hepatocellular carcinoma cell line with IC50 values of 15.20 ± 1.28 and 24.28 ± 6.74 µM, respectively.

ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature.

BACKGROUND: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers. RESULTS: In ECPred, each EC number constituted an individual class and therefore, had an independent learning model. Enzyme vs. non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach exploiting the tree structure of the EC nomenclature. ECPred provides predictions for 858 EC numbers in total including 6 main classes, 55 subclass classes, 163 sub-subclass classes and 634 substrate classes. The proposed method is tested and compared with the state-of-the-art enzyme function prediction tools by using independent temporal hold-out and no-Pfam datasets constructed during this study. CONCLUSIONS: ECPred is presented both as a stand-alone and a web based tool to provide probabilistic enzymatic function predictions (at all five levels of EC) for uncharacterized protein sequences. Also, the datasets of this study will be a valuable resource for future benchmarking studies. ECPred is available for download, together with all of the datasets used in this study, at: https://github.com/cansyl/ECPred . ECPred webserver can be accessed through http://cansyl.metu.edu.tr/ECPred.html .

Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants.

Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and a database of GO term predictions for proteomes of several organisms in UniProt Knowledgebase (UniProtKB). UniGOPred provides function predictions for 514 molecular function (MF), 2909 biological process (BP), and 438 cellular component (CC) GO terms for each protein sequence. UniGOPred covers nearly the whole functionality spectrum in Gene Ontology system and it can predict both generic and specific GO terms. UniGOPred was run on CAFA2 challenge target protein sequences and it is categorized within the top 10 best performing methods for the molecular function category. In addition, the performance of UniGOPred is higher compared to the baseline BLAST classifier in all categories of GO. UniGOPred predictions are compared with UniProtKB/TrEMBL database annotations as well. Furthermore, the proposed tool's ability to predict negatively associated GO terms that defines the functions that a protein does not possess, is discussed. UniGOPred annotations were also validated by case studies on PTEN protein variants experimentally and on CHD8 protein variants with literature. UniGOPred protein functional annotation system is available as an open access tool at http://cansyl.metu.edu.tr/UniGOPred.html.

Object-Oriented Segmentation of Cell Nuclei in Fluorescence Microscopy Images.

Cell nucleus segmentation remains an open and challenging problem especially to segment nuclei in cell clumps. Splitting a cell clump would be straightforward if the gradients of boundary pixels in-between the nuclei were always higher than the others. However, imperfections may exist: inhomogeneities of pixel intensities in a nucleus may cause to define spurious boundaries whereas insufficient pixel intensity differences at the border of overlapping nuclei may cause to miss some true boundary pixels. In contrast, these imperfections are typically observed at the pixel-level, causing local changes in pixel values without changing the semantics on a large scale. In response to these issues, this article introduces a new nucleus segmentation method that relies on using gradient information not at the pixel level but at the object level. To this end, it proposes to decompose an image into smaller homogeneous subregions, define edge-objects at four different orientations to encode the gradient information at the object level, and devise a merging algorithm, in which the edge-objects vote for subregion pairs along their orientations and the pairs are iteratively merged if they get sufficient votes from multiple orientations. Our experiments on fluorescence microscopy images reveal that this high-level representation and the design of a merging algorithm using edge-objects (gradients at the object level) improve the segmentation results.

Synthesis of novel 6-substituted amino-9-(ß-d-ribofuranosyl)purine analogs and their bioactivities on human epithelial cancer cells.

New nucleoside derivatives with nitrogen substitution at the C-6 position were prepared and screened initially for their in vitro anticancer bioactivity against human epithelial cancer cells (liver Huh7, colon HCT116, breast MCF7) by the NCI-sulforhodamine B assay. N6-(4-trifluoromethylphenyl)piperazine analog (27) exhibited promising cytotoxic activity. The compound 27 was more cytotoxic (IC50 = 1-4 µM) than 5-FU, fludarabine on Huh7, HCT116 and MCF7 cell lines. The most potent nucleosides (11, 13, 16, 18, 19, 21, 27, 28) were further screened for their cytotoxicity in hepatocellular cancer cell lines. The compound 27 demonstrated the highest cytotoxic activity against Huh7, Mahlavu and FOCUS cells (IC50 = 1, 3 and 1 µM respectively). Physicochemical properties, drug-likeness, and drug score profiles of the molecules showed that they are estimated to be orally bioavailable. The results pointed that the novel derivatives would be potential drug candidates.

Synthesis and cellular bioactivities of novel isoxazole derivatives incorporating an arylpiperazine moiety as anticancer agents.

In our endeavour towards the development of effective anticancer therapeutics, a novel series of isoxazole-piperazine hybrids were synthesized and evaluated for their cytotoxic activities against human liver (Huh7 and Mahlavu) and breast (MCF-7) cancer cell lines. Within series, compounds 5l-o showed the most potent cytotoxicity on all cell lines with IC50 values in the range of 0.3-3.7 µM. To explore the mechanistic aspects fundamental to the observed activity, further biological studies with 5m and 5o in liver cancer cells were carried out. We have demonstrated that 5m and 5o induce oxidative stress in PTEN adequate Huh7 and PTEN deficient Mahlavu human liver cancer cells leading to apoptosis and cell cycle arrest at different phases. Further analysis of the proteins involved in apoptosis and cell cycle revealed that 5m and 5o caused an inhibition of cell survival pathway through Akt hyperphosphorylation and apoptosis and cell cycle arrest through p53 protein activation.

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

Synthesis of Some Substituted 6-Phenyl Purine Analogues and Their Biological Evaluation as Cytotoxic Agents.

A series of 6-(4-substituted phenyl)-9-(tetrahydropyran-2-yl)purines 3-9, 6-(4-substituted phenyl)purines 10-16, 9-((4-substituted phenyl)sulfonyl)-6-(4-substituted phenyl)purines 17-32 were prepared and screened initially for their in vitro anticancer activity against selected human cancer cells (liver Huh7, colon HCT116, breast MCF7). 6-(4-Phenoxyphenyl) purine analogues 9, 16, 30-32, had potent cytotoxic activities. The most active purine derivatives 5-9, 14, 16, 18, 28-32 were further screened for their cytotoxic activity in hepatocellular cancer cells. 6-(4-Phenoxyphenyl)-9-(tetrahydropyran-2-yl)-9H-purine (9) had better cytotoxic activity (IC50 5.4 µM) than the well-known nucleobase analogue 5-FU and known nucleoside drug fludarabine on Huh7 cells. The structure-activity relationship studies reported that the substitution at C-6 positions in purine nucleus with the 4-phenoxyphenyl group is responsible for the anti-cancer activity.

Iterative h-minima-based marker-controlled watershed for cell nucleus segmentation.

Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. © 2016 International Society for Advancement of Cytometry.

Novel triazolothiadiazines act as potent anticancer agents in liver cancer cells through Akt and ASK-1 proteins.

Newly designed triazolothiadiazines incorporating with structural motifs of nonsteroidal analgesic anti-inflammatory drugs were synthesized and screened for their bioactivity against epithelial cancer cells. Compounds with bioactivities less then ∼5µM (IC50) were further analyzed and showed to induce apoptotic cell death and SubG1 cell cycle arrest in liver cancer cells. Among this group, two compounds (1g and 1h) were then studied to identify the mechanism of action. These molecules triggered oxidative stress induced apoptosis through ASK-1 protein activation and Akt protein inhibition as demonstrated by downstream targets such as GSK3ß, ß-catenin and cyclin D1. QSAR and molecular docking models provide insight into the mechanism of inhibition and indicate the optimal direction of future synthetic efforts. Furthermore, molecular docking results were confirmed with in vitro COX bioactivity studies. This study demonstrates that the novel triazolothiadiazine derivatives are promising drug candidates for epithelial cancers, especially liver cancer.