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).

The limitations of traditional toxicity testing characterized by high-cost animal models with low-throughput readouts, inconsistent responses, ethical issues, and extrapolability to humans call for alternative strategies for chemical risk assessment. A new strategy using in vitro human cell-based assays has been designed to identify key toxicity pathways and molecular mechanisms leading to the prediction of an in vivo response. The emergence of quantitative high-throughput screening (qHTS) technology has proved to be an efficient way to decompose complex toxicological end points to specific pathways of targeted organs. In addition, qHTS has made a significant impact on computational toxicology in two aspects. First, the ease of mechanism of action identification brought about by in vitro assays has enhanced the simplicity and effectiveness of machine learning, and second, the high-throughput nature and high reproducibility of qHTS have greatly improved the data quality and increased the quantity of training datasets available for predictive model construction. In this review, the benefits of qHTS routinely used in the US Tox21 program will be highlighted. Quantitative structure-activity relationships models built on traditional in vivo data and new qHTS data will be compared and analyzed. In conjunction with the transition from the pilot phase to the production phase of the Tox21 program, more qHTS data will be made available that will enrich the data pool for predictive toxicology. It is perceivable that new in silico toxicity models based on high-quality qHTS data will achieve unprecedented reliability and robustness, thus becoming a valuable tool for risk assessment and drug discovery.

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.