Data mining of NCI's anticancer screening database reveals mitochondrial complex I inhibitors cytotoxic to leukemia cell lines

Wear Scar Similarities between Retrieved and Simulator-Tested Polyethylene TKR Components: An Artificial Neural Network Approach

The aim of this study was to determine how representative wear scars of simulator-tested polyethylene (PE) inserts compare with retrieved PE inserts from total knee replacement (TKR). By means of a nonparametric self-organizing feature map (SOFM), wear scar images of 21 postmortem- and 54 revision-retrieved components were compared with six simulator-tested components that were tested either in displacement or in load control according to ISO protocols. The SOFM network was then trained with the wear scar images of postmortem-retrieved components since those are considered well-functioning at the time of retrieval. Based on this training process, eleven clusters were established, suggesting considerable variability among wear scars despite an uncomplicated loading history inside their hosts. The remaining components (revision-retrieved and simulator-tested) were then assigned to these established clusters. Six out of five simulator components were clustered together, suggesting that the network was able to identify similarities in loading history. However, the simulator-tested components ended up in a cluster at the fringe of the map containing only 10.8% of retrieved components. This may suggest that current ISO testing protocols were not fully representative of this TKR population, and protocols that better resemble patients' gait after TKR containing activities other than walking may be warranted.

Ceramide-tamoxifen regimen targets bioenergetic elements in acute myelogenous leukemia

The objective of our study was to determine the mechanism of action of the short-chain ceramide analog, C6-ceramide, and the breast cancer drug, tamoxifen, which we show coactively depress viability and induce apoptosis in human acute myelogenous leukemia cells. Exposure to the C6-ceramide-tamoxifen combination elicited decreases in mitochondrial membrane potential and complex I respiration, increases in reactive oxygen species (ROS), and release of mitochondrial proapoptotic proteins. Decreases in ATP levels, reduced glycolytic capacity, and reduced expression of inhibitors of apoptosis proteins also resulted. Cytotoxicity of the drug combination was mitigated by exposure to antioxidant. Cells metabolized C6-ceramide by glycosylation and hydrolysis, the latter leading to increases in long-chain ceramides. Tamoxifen potently blocked glycosylation of C6-ceramide and long-chain ceramides. N-desmethyltamoxifen, a poor antiestrogen and the major tamoxifen metabolite in humans, was also effective with C6-ceramide, indicating that traditional antiestrogen pathways are not involved in cellular responses. We conclude that cell death is driven by mitochondrial targeting and ROS generation and that tamoxifen enhances the ceramide effect by blocking its metabolism. As depletion of ATP and targeting the "Warburg effect" represent dynamic metabolic insult, this ceramide-containing combination may be of utility in the treatment of leukemia and other cancers.

Computational Search for Possible Mechanisms of 4-Thiazolidinones Anticancer Activity: The Power of Visualization

Public databases of NCI-60 tumor cell line screen results and measurements of molecular targets in the NCI-60 panel give the opportunity to assign possible anticancer mechanism to compounds with positive outcome from antitumor assay. Here, the novel protocol of NCI databases mining where inferences are based on the visualization is presented and utilized with the aim to identify putative biological routes of 4-thiazolidinones anticancer effect. As a result, highly potent 4-thiazolidinone-pyrazoline-isatin conjugates show the similarity of activity patterns with puromycin and CBU-028 and their pattern is also highly correlated with fraction of methylated CpG sites in CD34, AF5q31 and SYK. Several compounds from this group show strong negative correlation with fraction of methylated CpG sites in HOXA5. Thiopyrano[2,3-d][1,3]thiazol-2-ones bearing naphtoquinone fragment were found to possess the same activity pattern as fusarubin does. But none of the studied 4-thiazolidinone derivatives has activity fingerprint similar to standard anticancer agents. The obtained results bring medicinal chemistry closer to the understanding of basic nature of 4-thiazolidinones effect on cancer cells.

Integrating constitutive gene expression and chemoactivity: mining the NCI60 anticancer screen

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound’s tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/andhttp://www.ncbi.nlm.nih.gov/projects/geo (GPL96, GSE32474).

Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis

Background

Public resources of chemical compound are in a rapid growth both in quantity and the types of data-representation. To comprehensively understand the relationship between the intrinsic features of chemical compounds and protein targets is an essential task to evaluate potential protein-binding function for virtual drug screening. In previous studies, correlations were proposed between bioactivity profiles and target networks, especially when chemical structures were similar. With the lack of effective quantitative methods to uncover such correlation, it is demanding and necessary for us to integrate the information from multiple data sources to produce an comprehensive assessment of the similarity between small molecules, as well as quantitatively uncover the relationship between compounds and their targets by such integrated schema.

Results

In this study a multi-view based clustering algorithm was introduced to quantitatively integrate compound similarity from both bioactivity profiles and structural fingerprints. Firstly, a hierarchy clustering was performed with the fused similarity on 37 compounds curated from PubChem. Compared to clustering in a single view, the overall common target number within fused classes has been improved by using the integrated similarity, which indicated that the present multi-view based clustering is more efficient by successfully identifying clusters with its members sharing more number of common targets. Analysis in certain classes reveals that mutual complement of the two views for compound description helps to discover missing similar compound when only single view was applied. Then, a large-scale drug virtual screen was performed on 1267 compounds curated from Connectivity Map (CMap) dataset based on the fused similarity, which obtained a better ranking result compared to that of single-view. These comprehensive tests indicated that by combining different data representations; an improved assessment of target-specific compound similarity can be achieved.

Conclusions

Our study presented an efficient, extendable and quantitative computational model for integration of different compound representations, and expected to provide new clues to improve the virtual drug screening from various pharmacological properties. Scripts, supplementary materials and data used in this study are publicly available at http://lifecenter.sgst.cn/fusion/.

Mitochondrial genome-knockout cells demonstrate a dual mechanism of action for the electron transport complex I inhibitor mycothiazole

Mycothiazole, a polyketide metabolite isolated from the marine sponge Cacospongia mycofijiensis, is a potent inhibitor of metabolic activity and mitochondrial electron transport chain complex I in sensitive cells, but other cells are relatively insensitive to the drug. Sensitive cell lines (IC(50) 0.36-13.8 nM) include HeLa, P815, RAW 264.7, MDCK, HeLa S3, 143B, 4T1, B16, and CD4/CD8 T cells. Insensitive cell lines (IC(50) 12.2-26.5 μM) include HL-60, LN18, and Jurkat. Thus, there is a 34,000-fold difference in sensitivity between HeLa and HL-60 cells. Some sensitive cell lines show a biphasic response, suggesting more than one mechanism of action. Mitochondrial genome-knockout ρ(0) cell lines are insensitive to mycothiazole, supporting a conditional mitochondrial site of action. Mycothiazole is cytostatic rather than cytotoxic in sensitive cells, has a long lag period of about 12 h, and unlike the complex I inhibitor, rotenone, does not cause G(2)/M cell cycle arrest. Mycothiazole decreases, rather than increases the levels of reactive oxygen species after 24 h. It is concluded that the cytostatic inhibitory effects of mycothiazole on mitochondrial electron transport function in sensitive cell lines may depend on a pre-activation step that is absent in insensitive cell lines with intact mitochondria, and that a second lower-affinity cytotoxic target may also be involved in the metabolic and growth inhibition of cells.

Adaptation of high-throughput screening in drug discovery-toxicological screening tests

High-throughput screening (HTS) is one of the newest techniques used in drug design and may be applied in biological and chemical sciences. This method, due to utilization of robots, detectors and software that regulate the whole process, enables a series of analyses of chemical compounds to be conducted in a short time and the affinity of biological structures which is often related to toxicity to be defined. Since 2008 we have implemented the automation of this technique and as a consequence, the possibility to examine 100,000 compounds per day. The HTS method is more frequently utilized in conjunction with analytical techniques such as NMR or coupled methods e.g., LC-MS/MS. Series of studies enable the establishment of the rate of affinity for targets or the level of toxicity. Moreover, researches are conducted concerning conjugation of nanoparticles with drugs and the determination of the toxicity of such structures. For these purposes there are frequently used cell lines. Due to the miniaturization of all systems, it is possible to examine the compound's toxicity having only 1-3 mg of this compound. Determination of cytotoxicity in this way leads to a significant decrease in the expenditure and to a reduction in the length of the study.

Self-Organizing Maps for In Silico Screening and Data Visualization

Self-organizing maps, which are unsupervised artificial neural networks, have become a very useful tool in a wide area of disciplines, including medicinal chemistry. Here, we will focus on two applications of self-organizing maps: the use of self-organizing maps for in silico screening and for clustering and visualisation of large datasets. Additionally, the importance of parameter selection is discussed and some modifications to the original algorithm are summarised.

Chemogenomic analysis identifies Macbecin II as a compound specific for SMAD4-negative colon cancer cells

The tumor suppressor gene, SMAD4, is mutated in approximately 30% of colon cancers. To identify compounds with enhanced potency on cells with a SMAD4-negative context, we combined genomic and cheminformatic analyses of publicly available data relating to the colon cancer cell lines within the NCI60 panel. Two groups of cell lines were identified with either wild-type or negative SMAD4 status. A cheminformatic analysis of the NCI60 screening data was carried out, which led to the identification of 14 compounds that preferentially inhibited cell growth of the SMAD4-negative cell lines. Using cell viability assays, the effect of these compounds was validated on four colon cancer cell lines: HCT-116 and HCT-15 (SMAD4-expressing), and HT-29 and COLO-205 (SMAD4-negative). Our data identified Macbecin II, a hydroquinone ansamycin antibiotic, as having increased potency in the SMAD4-negative cells compared to SMAD4 wild-type cells. In addition, we showed that silencing of SMAD4 using siRNA in HCT-116 enhanced Macbecin II potency. Our results demonstrate that Macbecin II is specifically active in colon cancer cells having a SMAD4-negative background and thus is a potential candidate for further investigation in a drug discovery perspective.

Understanding mechanisms of toxicity: insights from drug discovery research

Toxicology continues to rely heavily on use of animal testing for prediction of potential for toxicity in humans. Where mechanisms of toxicity have been elucidated, for example endocrine disruption by xenoestrogens binding to the estrogen receptor, in vitro assays have been developed as surrogate assays for toxicity prediction. This mechanistic information can be combined with other data such as exposure levels to inform a risk assessment for the chemical. However, there remains a paucity of such mechanistic assays due at least in part to lack of methods to determine specific mechanisms of toxicity for many toxicants. A means to address this deficiency lies in utilization of a vast repertoire of tools developed by the drug discovery industry for interrogating the bioactivity of chemicals. This review describes the application of high-throughput screening assays as experimental tools for profiling chemicals for potential for toxicity and understanding underlying mechanisms. The accessibility of broad panels of assays covering an array of protein families permits evaluation of chemicals for their ability to directly modulate many potential targets of toxicity. In addition, advances in cell-based screening have yielded tools capable of reporting the effects of chemicals on numerous critical cell signaling pathways and cell health parameters. Novel, more complex cellular systems are being used to model mammalian tissues and the consequences of compound treatment. Finally, high-throughput technology is being applied to model organism screens to understand mechanisms of toxicity. However, a number of formidable challenges to these methods remain to be overcome before they are widely applicable. Integration of successful approaches will contribute towards building a systems approach to toxicology that will provide mechanistic understanding of the effects of chemicals on biological systems and aid in rationale risk assessments.