Hongmao Sun

A selective USP1–UAF1 inhibitor links deubiquitination to DNA damage responses

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.

Disrupting malaria parasite AMA1–RON2 interaction with a small molecule prevents erythrocyte invasion

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. Invasion of host erythrocytes is an essential step in the life cycle of P. falciparum. Srinivasan et al.demonstrate that small-molecule inhibitors can block the entry of the parasite into erythrocytes, highlighting the potential of invasion inhibitors as antimalarials.

Paradigm Shift in Toxicity Testing and Modeling

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.

Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data.

The human cytochrome P450 (CYP450) isozymes are the most important enzymes in the body to metabolize many endogenous and exogenous substances including environmental toxins and therapeutic drugs. Any unnecessary interactions between a small molecule and CYP450 isozymes may raise a potential to disarm the integrity of the protection. Accurately predicting the potential interactions between a small molecule and CYP450 isozymes is highly desirable for assessing the metabolic stability and toxicity of the molecule. The National Institutes of Health Chemical Genomics Center (NCGC) has screened a collection of over 17,000 compounds against the five major isozymes of CYP450 (1A2, 2C9, 2C19, 2D6, and 3A4) in a quantitative high throughput screening (qHTS) format. In this study, we developed support vector classification (SVC) models for these five isozymes using a set of customized generic atom types. The CYP450 data sets were randomly split into equal-sized training and test sets. The optimized SVC models exhibited high predictive power against the test sets for all five CYP450 isozymes with accuracies of 0.93, 0.89, 0.89, 0.85, and 0.87 for 1A2, 2C9, 2C19, 2D6, and 3A4, respectively, as measured by the area under the receiver operating characteristic (ROC) curves. The important atom types and features extracted from the five models are consistent with the structural preferences for different CYP450 substrates reported in the literature. We also identified novel features with significant discerning power to separate CYP450 actives from inactives. These models can be useful in prioritizing compounds in a drug discovery pipeline or recognizing the toxic potential of environmental chemicals.

High-throughput matrix screening identifies synergistic and antagonistic antimalarial drug combinations

Drug resistance in Plasmodium parasites is a constant threat. Novel therapeutics, especially new drug combinations, must be identified at a faster rate. In response to the urgent need for new antimalarial drug combinations we screened a large collection of approved and investigational drugs, tested 13,910 drug pairs, and identified many promising antimalarial drug combinations. The activity of known antimalarial drug regimens was confirmed and a myriad of new classes of positively interacting drug pairings were discovered. Network and clustering analyses reinforced established mechanistic relationships for known drug combinations and identified several novel mechanistic hypotheses. From eleven screens comprising >4,600 combinations per parasite strain (including duplicates) we further investigated interactions between approved antimalarials, calcium homeostasis modulators, and inhibitors of phosphatidylinositide 3-kinases (PI3K) and the mammalian target of rapamycin (mTOR). These studies highlight important targets and pathways and provide promising leads for clinically actionable antimalarial therapy.

Genomic and protein expression analysis reveals flap endonuclease 1 (FEN1) as a key biomarker in breast and ovarian cancer.

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.

Structure based model for the prediction of phospholipidosis induction potential of small molecules.

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).

Synthesis and Structure–Activity Relationship Studies of N-Benzyl-2-phenylpyrimidin-4-amine Derivatives as Potent USP1/UAF1 Deubiquitinase Inhibitors with Anticancer Activity against Nonsmall Cell Lung Cancer

Deregulation of ubiquitin conjugation or deconjugation has been implicated in the pathogenesis of many human diseases including cancer. The deubiquitinating enzyme USP1 (ubiquitin-specific protease 1), in association with UAF1 (USP1-associated factor 1), is a known regulator of DNA damage response and has been shown as a promising anticancer target. To further evaluate USP1/UAF1 as a therapeutic target, we conducted a quantitative high throughput screen of >400000 compounds and subsequent medicinal chemistry optimization of small molecules that inhibit the deubiquitinating activity of USP1/UAF1. Ultimately, these efforts led to the identification of ML323 (70) and related N-benzyl-2-phenylpyrimidin-4-amine derivatives, which possess nanomolar USP1/UAF1 inhibitory potency. Moreover, we demonstrate a strong correlation between compound IC50 values for USP1/UAF1 inhibition and activity in nonsmall cell lung cancer cells, specifically increased monoubiquitinated PCNA (Ub-PCNA) levels and decreased cell survival. Our results establish the druggability of the USP1/UAF1 deubiquitinase complex and its potential as a molecular target for anticancer therapies.

Identification of potent Yes1 kinase inhibitors using a library screening approach.

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.

Are hERG channel blockers also phospholipidosis inducers

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.