Discovery of a potent and selective inhibitor of histone lysine demethylase KDM4D.

Histone lysine demethylase 4D (KDM4D) plays an important role in the regulation of tumorigenesis, progression and drug resistance and has been considered a potential target for cancer treatment. However, there is still a lack of potent and selective KDM4D inhibitors. In this investigation, we report a new class of KDM4D inhibitors containing the 2-(aryl(pyrrolidine-1-yl)methyl)phenol scaffold, identified through AlphaLisa-based screening, structural optimization, and structure-activity relationship analyses. Among these inhibitors, 24s was the most potent, with an IC50 value of 0.023 ± 0.004 µM. This compound exhibited more than 1500-fold selectivity towards KDM4D versus KDM4A as well as other JMJD subfamily members, indicating good selectivity for KDM4D. Kinetic analysis indicated that 24s did not occupy the 2-oxoglutarate binding pocket. In an in vitro assay, 24s significantly suppressed the proliferation and migration of colorectal cancer (CRC) cells. Overall, this study has identified a good tool compound to explore the biological function of KDM4D and a good lead compound for drug discovery targeting KDM4D.

Discovery of selective BPTF bromodomain inhibitors by screening and structure-based optimization.

Bromodomain and PHD finger containing transcription factor (BPTF) is a multidomain protein that regulates the transcription of chromatin and is related to many cancers. Herein, we report the screening-based discovery of Cpd1, a compound with micromolar affinity to the BPTF bromodomain. Through structure-guided optimization, we synthesized a variety of new inhibitors. Among these compounds, Cpd8 and Cpd10 were highly potent and selective inhibitors, with KD values of 428 nM and 655 nM in ITC assays, respectively. The high activity was explained by the cocrystal structure of Cpd8 in complex with the BPTF bromodomain protein. Cpd8 and Cpd10 were able to stabilize the BPTF bromodomain protein in cells in a cellular thermal shift assay (CETSA). Cpd8 downregulated c-MYC expression in A549 cells. All experiments prove that these two compounds are potential BPTF inhibitors.

Preclinical pharmacodynamic evaluation of a new Src/FOSL1 inhibitor, LY-1816, in pancreatic ductal adenocarcinoma.

Despite tremendous efforts, the clinical prognosis of pancreatic ductal adenocarcinoma (PDAC) remains disappointing. There is an urgent need to develop more effective treatment strategies to improve the prognosis of patients with PDAC. In this study, we evaluate the anti-PDAC effects of LY-1816, a new multikinase inhibitor developed by us. In in vitro assays, LY-1816 showed significant inhibitory effects on the proliferation, migration, and invasion of human PDAC cells, and induced PDAC cell apoptosis. Western blot analysis revealed that LY-1816 markedly suppressed the Src signaling, and downregulated the expression of FOSL1; FOSL1 is an oncogene vulnerability in KRAS-driven pancreatic cancer. In in vivo models of PDAC xenografts (Aspc-1 and Bxpc-3), LY-1816 showed more potent antitumor activity than dasatinib and gemcitabine. Moreover, mice treated with LY-1816 showed a much more significant survival advantage in a metastatic model of PDAC compared with those treated with vehicle, dasatinib, or gemcitabine. These results provide effective support for the subsequent clinical evaluation of LY-1816 in the treatment of PDAC.

Discovery of pyrazolo[1,5-a]pyrimidine-3-carbonitrile derivatives as a new class of histone lysine demethylase 4D (KDM4D) inhibitors.

Herein we report the discovery of a series of new small molecule inhibitors of histone lysine demethylase 4D (KDM4D). Molecular docking was first performed to screen for new KDM4D inhibitors from various chemical databases. Two hit compounds were retrieved. Further structural optimization and structure-activity relationship (SAR) analysis were carried out to the more selective one, compound 2, which led to the discovery of several new KDM4D inhibitors. Among them, compound 10r is the most potent one with an IC50 value of 0.41±0.03µM against KDM4D. Overall, compound 10r could be taken as a good lead compound for further studies.

Arginine Methyltransferase 1 in the Nucleus Accumbens Regulates Behavioral Effects of Cocaine.

Recent evidence suggests that histone modifications play a role in the behavioral effects of cocaine in rodent models. Histone arginine is known to be methylated by protein arginine N-methyltransferases (PRMTs). Evidence shows that PRMT1 contributes to >90% of cellular PRMT activity, which regulates histone H4 arginine 3 asymmetric dimethylation (H4R3me2a). Though histone arginine methylation represents a chemical modification that is relatively stable compared with other histone alterations, it is less well studied in the setting of addiction. Here, we demonstrate that repeated noncontingent cocaine injections increase PRMT1 activity in the nucleus accumbens (NAc) of C57BL/6 mice. We, subsequently, identify a selective inhibitor of PRMT1, SKLB-639, and show that systemic injections of the drug decrease cocaine-induced conditioned place preference to levels observed with genetic knockdown of PRMT1. NAc-specific downregulation of PRMT1 leads to hypomethylation of H4R3me2a, and hypoacetylation of histone H3 lysine 9 and 14. We also found that H4R3me2a is upregulated in NAc after repeated cocaine administration, and that H4R3me2a upregulation in turn controls the expression of Cdk5 and CaMKII. Additionally, the suppression of PRMT1 in NAc with lentiviral-short hairpin PMRT1 decreases levels of CaMKII and Cdk5 in the cocaine-treated group, demonstrating that PRMT1 affects the ability of cocaine to induce CaMKII and Cdk5 in NAc. Notably, increased H4R3me2a by repeated cocaine injections is relatively long-lived, as increased expression was observed for up to 7 d after the last cocaine injection. These results show the role of PRMT1 in the behavioral effects of cocaine. SIGNIFICANCE STATEMENT: This work demonstrated that repeated cocaine injections led to an increase of protein arginine N-methyltransferase (PRMT1) in nucleus accumbens (NAc). We then identified a selective inhibitor of PRMT1 (SKLB-639), which inhibited cocaine-induced conditioned place preference (CPP). Additionally, genetic downregulation of PRMT1 in NAc also attenuated cocaine-caused CPP and locomotion activity, which was associated with decreased expression of histone H4 arginine 3 asymmetric demethylation (H4R3me2a) and hypoacetylation of histone H3 lysine 9 and 14 (acH3K9/K14). This study also showed that H4R3me2a controlled transcriptions of Cdk5 and CaMKII, and that PRMT1 negatively affected the ability of cocaine to induce CaMKII and Cdk5 in NAc. Notably, increased H4R3me2a by repeated cocaine injection was relatively long-lived as increased expression was observed up to 7 d after withdrawal from cocaine. Together, this study suggests that PRMT1 inhibition may serve as a potential therapeutic strategy for cocaine addiction.

TS-Chemscore, a Target-Specific Scoring Function, Significantly Improves the Performance of Scoring in Virtual Screening.

Most of the scoring functions currently used in structure-based drug design belong to 'universal' scoring functions, which often give a poor correlation between the calculated scores and experimental binding affinities. In this investigation, we proposed a simple strategy to construct target-specific scoring functions based on known 'universal' scoring functions. This strategy was applied to Chemscore, a widely used empirical scoring function, which led to a new scoring function, termed TS-Chemscore. TS-Chemscore was validated on 14 protein targets, which cover a wide range of biological target categories. The results showed that TS-Chemscore significantly improved the correlation between the calculated scores and experimental binding affinities compared with the original Chemscore. TS-Chemscore was then applied in virtual screening to retrieve novel JAK3 and YopH inhibitors. Top 30 compounds for each target were selected for experimental validation. Six active compounds for JAK3 and four for YopH were obtained. These compounds were out of the lists of top 30 compounds sorted by Chemscore. Collectively, TS-Chemscore established in this study showed a better performance in virtual screening than its counterpart Chemscore.

A preclinical evaluation of SKLB261, a multikinase inhibitor of EGFR/Src/VEGFR2, as a therapeutic agent against pancreatic cancer.

The clinical prognosis of pancreatic cancer remains rather disappointing despite tremendous efforts in exploring medical treatments in the past two decades. Development of more effective treatment strategies is still desperately needed to improve outcomes in patients with pancreatic cancer. SKLB261 is a multikinase inhibitor obtained recently through a lead optimization. In this investigation, we shall evaluate its anti-pancreatic cancer effects both in vitro and in vivo. SKLB261 is a multikinase inhibitor potently inhibiting EGFR, Src, and VEGFR2 kinases. It could significantly inhibit cell proliferation, migration, and invasion, and induce apoptosis in cellular assays of human pancreatic cancer cells that are sensitive or resistant to dasatinib and/or gemcitabine. Western blot analysis showed that SKLB261 inhibited the activation of EGFR and Src kinases as well as their downstream signaling proteins, including FAK, ERK, and STAT3. SKLB261 also showed potent antiangiogenic effects in transgenic zebrafish models. In vivo, SKLB261 displayed more potent antitumor activities than dasatinib, gemcitabine, or erlotinib in pancreatic cancer xenografts, including BxPC-3, PANC-1, AsPC-1, and HPAC. Furthermore, mice receiving SKLB261 therapy showed significant survival advantage compared with vehicle-treated and gemcitabine-treated groups in an experimental metastasis model of pancreatic cancer. These data, together with the good pharmacokinetic properties and low toxicity of this compound, provide a rationale for the ongoing clinical evaluation of SKLB261 in the treatment of pancreatic cancer.

Virtual screening in small molecule discovery for epigenetic targets.

Epigenetic modifications are critical mechanisms that regulate many biological processes and establish normal cellular phenotypes. Aberrant epigenetic modifications are frequently linked to the development and maintenance of several diseases including cancer, inflammation and metabolic diseases and so on. The key proteins that mediate epigenetic modifications have been thus recognized as potential therapeutic targets for these diseases. Consequently, discovery of small molecule inhibitors for epigenetic targets has received considerable attention in recent years. Here, virtual screening methods and their applications in the discovery of epigenetic target inhibitors are the focus of this review. Newly emerging approaches or strategies including rescoring methods, docking pose filtering methods, machine learning methods and 3D molecular similarity methods were also underlined. They are expected to be employed for identifying novel inhibitors targeting epigenetic regulation more efficiently.

A preclinical evaluation of a novel multikinase inhibitor, SKLB-329, as a therapeutic agent against hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a serious life-threatening malignant disease of liver. Molecular targeted therapies are considered a promising strategy for the treatment of HCC. Sorafenib is the first, and so far the only targeted drug approved by the US Food and Drug Administration (FDA) for clinical therapy of HCC. Despite being effective in some HCC patients, some demerits of sorafenib in the treatment of HCC, such as modest survival benefits, and drug resistance, have also been reported, which highlights the unmet medical need among patients with HCC. Here, we report a novel multikinase inhibitor discovered by us, SKLB-329, which potently inhibits angiogenesis-related kinases including VEGFR1/2/3, and FGFR2, and the Src kinase. SKLB-329 significantly inhibited endothelial cell growth, migration, invasion and tube formation. It showed potent anti-angiogenic activity in a transgenic zebrafish model. Moreover, SKLB-329 could efficiently restrain the proliferation of HCC cells through down-regulation of Src-mediated FAK and Stat3 activity. In vivo, oral administration of SKLB-329 considerably suppressed the tumor growth in HCC xenograft models (HepG2 and SMMC7721) in a dose-dependent manner. In all of the in vitro and in vivo assays of this investigation, sorafenib was used as a positive control, and in most assays SKLB-329 exhibited a higher potency compared with the positive control. In addition, SKLB-329 also bears favorable pharmacokinetic properties. Collectively, the results of preclinical studies presented here demonstrate that SKLB-329 is a promising drug candidate for HCC treatment.

Discovery and structure-activity relationships study of novel thieno[2,3-b]pyridine analogues as hepatitis C virus inhibitors.

Current treatment for hepatitis C is barely satisfactory, there is an urgent need to develop novel agents for combating hepatitis C virus infection. This study discovered a new class of thieno[2,3-b]pyridine derivatives as HCV inhibitors. First, a hit compound characterized by a thienopyridine core was identified in a cell-based screening of our privileged small molecule library. And then, structure activity relationship study of the hit compound led to the discovery of several potent compounds without obvious cytotoxicity in vitro (12c, EC50=3.3µM, SI >30.3, 12b, EC50=3.5µM, SI >28.6, 10l, EC50=3.9µM, SI >25.6, 12o, EC50=4.5µM, SI >22.2, respectively). Although the mechanism of them had not been clearly elucidated, our preliminary optimization of this class of compounds had provided us a start point to develop new anti-HCV agents.

Retrieving novel C5aR antagonists using a hybrid ligand-based virtual screening protocol based on SVM classification and pharmacophore models.

C5aR antagonists have been thought as potential immune mediators in various inflammatory and autoimmune diseases, and discovery of C5aR antagonists has attracted much attention in recent years. The discovery of C5aR antagonists was usually achieved through high-throughput screening, which usually suffered a high cost and a low success rate. Currently, the fast developing computer-aided virtual screening (VS) methods provide economic and rapid approaches to the lead discovery. In this account, we proposed a hybrid ligand-based VS protocol that is based on support vector machine (SVM) classification and pharmacophore models for retrieving novel C5aR antagonists. Performance evaluation of this hybrid VS protocol in virtual screening against a large independent test set, T-CHEM, showed that the hybrid VS approach significantly increased the hit rate and enrichment factor compared with the individual SVM classification model-based VS and pharmacophore model-based VS, as well as molecular docking-based VS in that the receptor structure was created by homology modeling. The hybrid VS approach was then used to screen several large chemical libraries including PubChem, Specs, and Enamine. Finally, a total of 20 compounds were selected from the top ranking hits, and shifted to the subsequent in vitro and in vivo studies, which results will be reported in the near future.

Receptor-based pharmacophore and pharmacophore key descriptors for virtual screening and QSAR modeling.

The intuitive nature of the pharmacophore concept has made it widely accepted by the medicinal chemistry community, evidenced by the past 3 decades of development and application of computerized pharmacophore modeling tools. On the other hand, shape complementarity has been recognized as a critical factor in molecular recognition between drugs and their receptors. Recent development of fast and accurate shape comparison tools has facilitated the wide spread use of shape matching technologies in drug discovery. However, pharmacophore and shape technologies, if used separately, often lead to high false positive rate. Thus, integrating pharmacophore matching and shape matching technologies into one program has the potential to reduce the false positive rates in virtual screening. Other issues of current pharmacophore technologies include sometimes high false negative rate and non-quantitative prediction. In this article, we first focus on a recently implemented method (Shape4) that combines receptor based shape matching and pharmacophore comparison in a single algorithm to create shape pharmacophore models for virtual screening. We also examine a recent example that utilizes multi-complex information to develop receptor-based pharmacophore models that promises to reduce false negative rate. Finally, we review several methods that employ receptor-based pharmacophore map and pharmacophore key descriptors for QSAR modeling. We conclude by emphasizing the concept of receptor-based shape pharmacophore and its roles in future drug discovery.

Pharmacophore modeling and hybrid virtual screening for the discovery of novel IκB kinase 2 (IKK2) inhibitors.

IKK2 (IκB kinase 2) inhibitors have been identified as potential drug candidates in the treatment of various immune/inflammatory disorders as well as cancer. So far more than one hundred small molecule inhibitors against IKK2 have been reported publicly. In this investigation, pharmacophore modeling was carried out to clarify the essential structure-activity relationship for the known IKK2 inhibitors. One of the established pharmacophore hypotheses, namely Hypo8, which has the best prediction ability to an external test data set, was suggested as a template for virtual screening. Evaluation of the performances of Hypo8 and a hybrid method (Hypo81docking) in virtual screening indicated that the use of the hybrid virtual screening considerably increased the hit rate and enrichment factor. The hybrid method was therefore adopted for screening several commercially available chemical databases, including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD), for novel potent IKK2 inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. Finally some of the final hit compounds were selected and suggested for further experimental investigations.

Rapid and accurate assessment of seizure liability of drugs by using an optimal support vector machine method.

Drug-induced seizures are a serious adverse effect and assessment of seizure risk usually takes place at the late stage of drug discovery process, which does not allow sufficient time to reduce the risk by chemical modification. Thus early identification of chemicals with seizure liability using rapid and cheaper approaches would be preferable. In this study, an optimal support vector machine (SVM) modeling method has been employed to develop a prediction model of seizure liability of chemicals. A set of 680 compounds were used to train the SVM model. The established SVM model was then validated by an independent test set comprising 175 compounds, which gave a prediction accuracy of 86.9%. Further, the SVM-based prediction model of seizure liability was compared with various preclinical seizure assays, including in vitro rat hippocampal brain slice, in vivo zebrafish larvae assay, mouse spontaneous seizure model, and mouse EEG model. In terms of predictability, the SVM model was ranked just behind the mouse EEG model, but better than the rat brain slice and zebrafish models. Nevertheless, the SVM model has considerable advantages compared with the preclinical seizure assays in speed and cost. In summary, the SVM-based prediction model of seizure liability established here offers potential as a cheaper, rapid and accurate assessment of seizure liability of drugs, which could be used in the seizure risk assessment at the early stage of drug discovery. The prediction model is freely available online at http://www.sklb.scu.edu.cn/lab/yangsy/download/ADMET/seizure_pred.tar.

Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking.

In this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based VS (PB-VS), and docking-based VS (DB-VS) methods. In this approach, the three VS methods are applied in an increasing order of complexity so that the first filter (SB-VS) is fast and simple, while successive ones (PB-VS and DB-VS) are more time-consuming but are applied only to a small subset of the entire database. Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor. This approach was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database. From the final hits, 47 compounds were selected for further in vitro Pim-1 inhibitory assay, and 15 compounds show nanomolar level or low micromolar inhibition potency against Pim-1. In particular, four of them were found to have new scaffolds which have potential for the chemical development of Pim-1 inhibitors.

Synthesis and biological evaluation of oleanolic acid derivatives as inhibitors of protein tyrosine phosphatase 1B.

Protein tyrosine phosphatase 1B (PTP1B) is a negative regulator in the process of insulin signaling and a promising drug target for diabetes and obesity. Derivatives of oleanolic acid were synthesized and evaluated as PTP1B inhibitors. Several derivatives exhibited moderate to good inhibitory activities against PTP1B, with 25f displaying the most promising inhibition (IC(50) = 3.12 µM). Structure-activity relationship analyses of these derivatives demonstrated that the integrity of the A ring and 12-ene moieties was important in the retention of PTP1B enzyme inhibitory activities. In addition, hydrophilic and acidic groups as well as the distance between the oleanene and acid moieties were associated with PTP1B inhibitory activities. Possible binding modes of 25f were explored by molecular docking simulations.

Discovery of (Z)-5-(4-methoxybenzylidene)thiazolidine-2,4-dione, a readily available and orally active glitazone for the treatment of concanavalin A-induced acute liver injury of BALB/c mice.

A large amount of evidence suggests that monocytes/macrophages infiltration is implicated in a variety of inflammatory diseases including acute liver injury. Monocyte chemoattractant protein 1 (MCP-1) plays a crucial role in the process of macrophages recruitment. We herein presented a small-molecule library and a feasible quick screening method of evaluating potency of inhibition of chemotaxis of RAW264.7 cells stimulated by MCP-1. Fifty-three small molecules were synthesized and screened, and four compounds (2g, 2h, 4f, and 6h) showed inhibitory effects with IC(50) values range from 0.72 to 20.47 microM, with compound 4f being the most efficient. Further in vivo studies demonstrated that oral administration of 2g, 2h, 4f, or 6h decreases, most significantly for 4f, the serum levels of alanine aminotransaminase (ALT) and asparate aminotransaminase (AST) in ConA-induced acute livery injury BALB/c mice. Histopathological evaluation liver sections confirmed 4f as a potent, orally active compound for hepatoprotective effects against ConA-induced acute liver injury in BALB/c mice.

[The feasibility of application of reverse docking method to the selectivity studies of protein kinase inhibitors].

This investigation is to explore the feasibility of applying reverse docking method to the selectivity studies of protein kinase inhibitors. Firstly, a database that consists of 422 protein kinase structures was established through collecting the reported crystal structures or homology modeling. Then a reverse docking based method of protein kinase target screening was established, followed by the optimization of related parameters and scoring functions. Finally, seven typical selective kinase inhibitors were used to test the established method. The results show that the selective targets of these inhibitors have relatively high scoring function values (ranking in the first 35% of the tested kinase targets according to the scoring function values). This implies that the reverse docking method can be applied to the virtual screening of kinase targets and further to the selectivity studies of protein kinase inhibitors.

A specific pharmacophore model of Aurora B kinase inhibitors and virtual screening studies based on it.

In this study, 3D-pharmacophore models of Aurora B kinase inhibitors have been developed by using HipHop and HypoGen modules in Catalyst software package. The best pharmacophore model, Hypo1, which has the highest correlation coefficient (0.9911), consists of one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic aliphatic moiety and one ring aromatic feature. Hypo1 was validated by test set and cross-validation methods. And the specificity of Hypo1 to Aurora B inhibitors was examined with the use of selective inhibitors against Aurora B and its paralogue Aurora A. The results clearly indicate that Hypo1 can differentiate selective inhibitors of Aurora B from those of Aurora A, and the ring aromatic feature likely plays some important roles for the specificity of Hypo1. Then Hypo1 was used as a 3D query to screen several databases including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD) for identifying new inhibitors of Aurora B. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking studies to refine the retrieved hits, and some compounds selected from the top ranked hits have been suggested for further experimental assay studies.

In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach.

Drug-induced mitochondrial toxicity has become one of the key reasons for which some drugs fail to enter market or are withdrawn from market. Thus early identification of new chemical entities that injure mitochondrial function grows to be very necessary to produce safer drugs and directly reduce attrition rate in later stages of drug development. In this study, support vector machine (SVM) method combined with genetic algorithm (GA) for feature selection and conjugate gradient method (CG) for parameter optimization (GA-CG-SVM), has been employed to develop prediction model of mitochondrial toxicity. We firstly collected 288 compounds, including 171 MT+ and 117 MT-, from different literature resources. Then these compounds were randomly separated into a training set (253 compounds) and a test set (35 compounds). The overall prediction accuracy for the training set by means of 5-fold cross-validation is 84.59%. Further, the SVM model was evaluated by using the independent test set. The overall prediction accuracy for the test set is 77.14%. These clearly indicate that the mitochondrial toxicity is predictable. Meanwhile impacts of the feature selection and SVM parameter optimization on the quality of SVM model were also examined and discussed. The results implicate the potential of the proposed GA-CG-SVM in facilitating the prediction of mitochondrial toxicity.