FEN1 has key roles in Okazaki fragment maturation during replication, long patch base excision repair, rescue of stalled replication forks, maintenance of telomere stability and apoptosis. FEN1 may be dysregulated in breast and ovarian cancers and have clinicopathological significance in patients. We comprehensively investigated FEN1 mRNA expression in multiple cohorts of breast cancer [training set (128), test set (249), external validation (1952)]. FEN1 protein expression was evaluated in 568 oestrogen receptor (ER) negative breast cancers, 894 ER positive breast cancers and 156 ovarian epithelial cancers. FEN1 mRNA overexpression was highly significantly associated with high grade (p = 4.89 × 10(-57)), high mitotic index (p = 5.25 × 10(-28)), pleomorphism (p = 6.31 × 10(-19)), ER negative (p = 9.02 × 10(-35)), PR negative (p = 9.24 × 10(-24)), triple negative phenotype (p = 6.67 × 10(-21)), PAM50.Her2 (p = 5.19 × 10(-13)), PAM50. Basal (p = 2.7 × 10(-41)), PAM50.LumB (p = 1.56 × 10(-26)), integrative molecular cluster 1 (intClust.1) (p = 7.47 × 10(-12)), intClust.5 (p = 4.05 × 10(-12)) and intClust. 10 (p = 7.59 × 10(-38)) breast cancers. FEN1 mRNA overexpression is associated with poor breast cancer specific survival in univariate (p = 4.4 × 10(-16)) and multivariate analysis (p = 9.19 × 10(-7)). At the protein level, in ER positive tumours, FEN1 overexpression remains significantly linked to high grade, high mitotic index and pleomorphism (ps < 0.01). In ER negative tumours, high FEN1 is significantly associated with pleomorphism, tumour type, lymphovascular invasion, triple negative phenotype, EGFR and HER2 expression (ps < 0.05). In ER positive as well as in ER negative tumours, FEN1 protein overexpression is associated with poor survival in univariate and multivariate analysis (ps < 0.01). In ovarian epithelial cancers, similarly, FEN1 overexpression is associated with high grade, high stage and poor survival (ps < 0.05). We conclude that FEN1 is a promising biomarker in breast and ovarian epithelial cancer.

Domiphen bromide and didecyl dimethylammonium bromide were widely used environmental chemicals with potent activity on blockade of human ether-a-go-go related gene (HERG) channels. But the mechanism of their action is not clear. The kinetics of block of HERG channels by domiphen bromide and didecyl dimethylammonium bromide was studied in order to characterize the inhibition of HERG currents by these quaternary ammonium compounds (QACs). Domiphen bromide and didecyl dimethylammonium bromide inhibited HERG channel currents in a dose-dependent manner with IC50 values of 9nM and 5nM, respectively. Block of HERG channel by domiphen bromide and didecyl dimethylammonium bromide was voltage-dependent and use-dependent. Domiphen bromide and didecyl dimethylammonium bromide caused substantial negative shift of the activation curves, accelerated activated process, but had no effects on the deactivation and reactivation processes. The docking models implied that these two compounds bound to PAS domain of HERG channels and inhibited its function. Our data demonstrated that domiphen bromide and didecyl dimethylammonium bromide blocked the HERG channel with a preference for the activated channel state.

Anti-cancer topoisomerase I (Top1) inhibitors (camptothecin and its derivatives irinotecan and topotecan, and indenoisoquinolines) induce lethal DNA lesions by stabilizing Top1-DNA cleavage complex (Top1cc). These lesions are repaired by parallel repair pathways including the tyrosyl-DNA phosphodiesterase 1 (TDP1)-related pathway and homologous recombination. As TDP1-deficient cells in vertebrates are hypersensitive to Top1 inhibitors, small molecules inhibiting TDP1 should augment the cytotoxicity of Top1 inhibitors. We developed a cell-based high-throughput screening assay for the discovery of inhibitors for human TDP1 using a TDP1-deficient chicken DT40 cell line (TDP1-/-) complemented with human TDP1 (hTDP1). Any compounds showing a synergistic effect with the Top1 inhibitor camptothecin (CPT) in hTDP1 cells should either be a TDP1-related pathway inhibitor or an inhibitor of alternate repair pathways for Top1cc. We screened the 400,000-compound Small Molecule Library Repository (SMLR, NIH Molecular Libraries) against hTDP1 cells in the absence or presence of CPT. After confirmation in a secondary screen using both hTDP1 and TDP1-/- cells in the absence or presence of CPT, five compounds were confirmed as potential TDP1 pathway inhibitors. All five compounds showed synergistic effect with CPT in hTDP1 cells, but not in TDP1-/- cells, indicating that the compounds inhibited a TDP1-related repair pathway. Yet, in vitro gel-based assay revealed that the five compounds did not inhibit TDP1 catalytic activity directly. We tested the compounds for their ability to inhibit poly(ADP-ribose)polymerase (PARP) because PARP inhibitors are known to potentiate the cytotoxicity of CPT by inhibiting the recruitment of TDP1 to Top1cc. Accordingly, we found that the five compounds inhibit catalytic activity of PARP by ELISA and Western blotting. We identified the most potent compound (Cpd1) that offers characteristic close to veliparib, a leading clinical PARP inhibitor. Cpd1 may represent a new scaffold for the development of PARP inhibitors.

Protein ubiquitination and deubiquitination are central to the control of a large number of cellular pathways and signaling networks in eukaryotes. Although the essential roles of ubiquitination have been established in the eukaryotic DNA damage response, the deubiquitination process remains poorly defined. Chemical probes that perturb the activity of deubiquitinases (DUBs) are needed to characterize the cellular function of deubiquitination. Here we report ML323 (2), a highly potent inhibitor of the USP1-UAF1 deubiquitinase complex with excellent selectivity against human DUBs, deSUMOylase, deneddylase and unrelated proteases. Using ML323, we interrogated deubiquitination in the cellular response to UV- and cisplatin-induced DNA damage and revealed new insights into the requirement of deubiquitination in the DNA translesion synthesis and Fanconi anemia pathways. Moreover, ML323 potentiates cisplatin cytotoxicity in non-small cell lung cancer and osteosarcoma cells. Our findings point to USP1-UAF1 as a key regulator of the DNA damage response and a target for overcoming resistance to the platinum-based anticancer drugs.

Plasmodium falciparum resistance to artemisinin derivatives, the first-line antimalarial drug, drives the search for new classes of chemotherapeutic agents. Current discovery is primarily directed against the intracellular forms of the parasite. However, late schizont-infected red blood cells (RBCs) may still rupture and cause disease by sequestration; consequently targeting invasion may reduce disease severity. Merozoite invasion of RBCs requires interaction between two parasite proteins AMA1 and RON2. Here we identify the first inhibitor of this interaction that also blocks merozoite invasion in genetically distinct parasites by screening a library of over 21,000 compounds. We demonstrate that this inhibition is mediated by the small molecule binding to AMA1 and blocking the formation of AMA1-RON complex. Electron microscopy confirms that the inhibitor prevents junction formation, a critical step in invasion that results from AMA1-RON2 binding. This study uncovers a strategy that will allow for highly effective combination therapies alongside existing antimalarial drugs.

Both pharmacophore models of the human ether-à-go-go-related gene (hERG) channel blockers and phospholipidosis (PLD) inducers contain a hydrophobic moiety and a hydrophilic motif/positively charged center, so it is interesting to investigate the overlap between the ligand chemical spaces of both targets. We have assayed over 4000 non-redundant drug-like compounds for both their hERG inhibitory activity and PLD inducing potential in a quantitative high throughput screening (qHTS) format. Seventy-seven percent of PLD inducing compounds identified from the screening were also found to be hERG channel blockers, and 96.9% of the dually active compounds were positively charged. Among the 48 compounds that induced PLD without inhibiting hERG channel, 24 compounds (50.0%) carried steroidal structures. According to our results, hERG channel blockers and PLD inducers share a large chemical space. In addition, a positively charged hERG channel blocker will most likely induce PLD, while a steroid PLD inducer is less likely a hERG channel blocker.

Yes1 kinase has been implicated as a potential therapeutic target in a number of cancers including melanomas, breast cancers, and rhabdomyosarcomas. Described here is the development of a robust and miniaturized biochemical assay for Yes1 kinase that was applied in a high throughput screen (HTS) of kinase-focused small molecule libraries. The HTS provided 144 (17% hit rate) small molecule compounds with IC₅₀ values in the sub-micromolar range. Three of the most potent Yes1 inhibitors were then examined in a cell-based assay for inhibition of cell survival in rhabdomyosarcoma cell lines. Homology models of Yes1 were generated in active and inactive conformations, and docking of inhibitors supports binding to the active conformation (DFG-in) of Yes1. This is the first report of a large high throughput enzymatic activity screen for identification of Yes1 kinase inhibitors, thereby elucidating the polypharmacology of a variety of small molecules and clinical candidates.

Discovery of cancer genes through interrogation of genomic dosage is one of the major approaches in cancer research. In this study, we report that phosphodiesterase subtype 4D (PDE4D) gene was homozygously deleted in 198 cases of 5,569 primary solid tumors (3.56%), with most being internal microdeletions. Unexpectedly, the microdeletions did not result in loss of their gene products. Screening PDE4D expression in 11 different types of primary tumor samples (n = 165) with immunohistochemistry staining revealed that its protein levels were up-regulated compared with corresponding nontransformed tissues. Importantly, depletion of endogenous PDE4D with three independent shRNAs caused apoptosis and growth inhibition in multiple types of cancer cells, including breast, lung, ovary, endometrium, gastric, and melanoma, which could be rescued by reexpression of PDE4D. We further showed that antitumor events triggered by PDE4D suppression were lineage-dependently associated with Bcl-2 interacting mediator of cell death (BIM) induction and microphthalmia-associated transcription factor (MITF) down-regulation. Furthermore, ectopic expression of the PDE4D short isoform, PDE4D2, enhanced the proliferation of cancer cells both in vitro and in vivo. Moreover, treatment of cancer cells with a unique specific PDE4D inhibitor, 26B, triggered massive cell death and growth retardation. Notably, these antineoplastic effects induced by either shRNAs or small molecule occurred preferentially in cancer cells but not in nonmalignant epithelial cells. These results suggest that although targeted by genomic homozygous microdeletions, PDE4D functions as a tumor-promoting factor and represents a unique targetable enzyme of cancer cells.

The human cytochrome P450 (CYP) enzyme family is involved in the biotransformation of many xenobiotics. As part of the U.S. Tox21 Phase I effort, we profiled the CYP activity of approximately three thousand compounds, primarily those of environmental concern, against human CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4 isoforms in a quantitative high throughput screening (qHTS) format. In order to evaluate the extent to which computational models built from a drug-like library screened in these five CYP assays under the same conditions can accurately predict the outcome of an environmental compound library, five support vector machines (SVM) models built from over 17,000 drug-like compounds were challenged to predict the CYP activities of the Tox21 compound collection. Although a large fraction of the test compounds fall outside of the applicability domain (AD) of the models, as measured by k-nearest neighbor (k-NN) similarities, the predictions were largely accurate for CYP1A2, CYP2C9, and CYP3A4 ioszymes with area under the receiver operator characteristic curves (AUC-ROC) ranging between 0.82 and 0.84. The lower predictive power of the CYP2C19 model (AUC-ROC = 0.76) is caused by experimental errors and that of the CYP2D6 model (AUC-ROC = 0.76) can be rescued by rebalancing the training data. Our results demonstrate that decomposing molecules into atom types enhanced the coverage of the AD and that computational models built from drug-like molecules can be used to predict the ability of non-drug like compounds to interact with these CYPs.

Drug-induced phospholipidosis (PLD), characterized by an intracellular accumulation of phospholipids and formation of concentric lamellar bodies, has raised concerns in the drug discovery community, due to its potential adverse effects. To evaluate the PLD induction potential, 4,161 nonredundant drug-like molecules from the National Institutes of Health Chemical Genomics Center (NCGC) Pharmaceutical Collection (NPC), the Library of Pharmacologically Active Compounds (LOPAC), and the Tocris Biosciences collection were screened in a quantitative high-throughput screening (qHTS) format. The potential of drug-lipid complex formation can be linked directly to the structures of drug molecules, and many PLD inducing drugs were found to share common structural features. Support vector machine (SVM) models were constructed by using customized atom types or Molecular Operating Environment (MOE) 2D descriptors as structural descriptors. Either the compounds from LOPAC or randomly selected from the entire data set were used as the training set. The impact of training data with biased structural features and the impact of molecule descriptors emphasizing whole-molecule properties or detailed functional groups at the atom level on model performance were analyzed and discussed. Rebalancing strategies were applied to improve the predictive power of the SVM models. Using the undersampling method, the consensus model using one-third of the compounds randomly selected from the data set as the training set achieved high accuracy of 0.90 in predicting the remaining two-thirds of the compounds constituting the test set, as measured by the area under the receiver operator characteristic curve (AUC-ROC).