Discovery of Dual Target Inhibitors against Cyclooxygenases and Leukotriene A4 Hydrolyase

Discovery of a novel dual functional phenylpyrazole-styryl hybrid that induces apoptotic and autophagic cell death in bladder cancer cells

Unpleasant side effects and resistance development remained the Achilles heel of chemotherapy. Since low tumor-selectivity and monotonous effect of chemotherapy are closely related to such bottleneck, targeting tumor-selective multi-functional anticancer agents may be an ideal strategy in the search of new safer drugs. Herein, we report the discovery of compound 21, a nitro-substituted 1,5-diphenyl-3-styryl-1H-pyrazole that possesses dual functional characteristics. The 2D- and 3D-culture-based studies revealed that 21 not only could induce ROS-independent apoptotic and EGFR/AKT/mTOR-mediated autophagic cell deaths in EJ28 cells simultaneously but also has the ability in inducing cell death at both proliferating and quiescent zones of EJ28 spheroids. The molecular modelling analysis showed that 21 possesses EGFR targeting capability as it forms stable interactions in the EGFR active site. Together with its good safety profile in the zebrafish-based model, the present study showed that 21 is promising and may lead to the discovery of tumor-selective multi-functional anti-cancer agents.

Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure-activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.

LigBuilder V3: A Multi-Target de novo Drug Design Approach

With the rapid development of systems-based pharmacology and poly-pharmacology, method development for rational design of multi-target drugs has becoming urgent. In this paper, we present the first de novo multi-target drug design program LigBuilder V3, which can be used to design ligands to target multiple receptors, multiple binding sites of one receptor, or various conformations of one receptor. LigBuilder V3 is generally applicable in de novo multi-target drug design and optimization, especially for the design of concise ligands for protein targets with large difference in binding sites. To demonstrate the utility of LigBuilder V3, we have used it to design dual-functional inhibitors targeting HIV protease and HIV reverse transcriptase with three different strategy, including multi-target de novo design, multi-target growing, and multi-target linking. The designed compounds were computational validated by MM/GBSA binding free energy estimation as highly potential multi-target inhibitors for both HIV protease and HIV reverse transcriptase. The LigBuilder V3 program can be downloaded at “http://www.pkumdl.cn/ligbuilder3/”.

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods

This work reports the classification study conducted on the biggest COX-2 inhibitor data set so far. Using 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF), to develop 12 classification models. The best SVM and RF models resulted in MCC values of 0.73 and 0.72, respectively. The 2925 COX-2 inhibitors were reduced to a data set of 1630 molecules by removing intermediately active inhibitors, and 12 new classification models were constructed, yielding MCC values above 0.72. The best MCC value of the external test set was predicted to be 0.68 by the RF model using ECFP_4 fingerprints. Moreover, the 2925 COX-2 inhibitors were clustered into eight subsets, and the structural features of each subset were investigated. We identified substructures important for activity including halogen, carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms. The models developed in this study could serve as useful tools for compound screening prior to lab tests.

Nimesulide analogues: From anti-inflammatory to antitumor agents

Nimesulide is a nonsteroidal anti-inflammatory drug possessing analgesic and antipyretic properties. This drug is considered a selective cyclooxygenase-2 (COX-2) inhibitor and, more recently, has been associated to antitumor activity. Thus, numerous works have been developed to modify the nimesulide skeleton aiming to develop new and more potent and selective COX-2 inhibitors as well as potential anticancer agents. This review intends to provide an overview on analogues of nimesulide, including the general synthetic approaches used for their preparation and structural diversification and their main anti-inflammatory and/or antitumor properties.

Activation of Glutathione Peroxidase 4 as a Novel Anti-inflammatory Strategy

The anti-oxidative enzyme, glutathione peroxidase 4 (GPX4), helps to promote inflammation resolution by eliminating oxidative species produced by the arachidonic acid (AA) metabolic network. Up-regulating its activity has been proposed as a promising strategy for inflammation intervention. In the present study, we aimed to study the effect of GPX4 activator on the AA metabolic network and inflammation related pathways. Using combined computational and experimental screen, we identified a novel compound that can activate the enzyme activity of GPX4 by more than two folds. We further assessed its potential in a series of cellular assays where GPX4 was demonstrated to play a regulatory role. We are able to show that GPX4 activation suppressed inflammatory conditions such as oxidation of AA and NF-κB pathway activation. We further demonstrated that this GPX4 activator can decrease the intracellular ROS level and suppress ferroptosis. Our study suggests that GPX4 activators can be developed as anti-inflammatory or cyto-protective agent in lipid-peroxidation-mediated diseases.

SAR and QSAR models of cyclooxygenase-1 (COX-1) inhibitors

Cyclooxygenase-1 (COX-1) is one isoform of COX, and it is a main target of nonsteroidal anti-inflammatory drugs (NSAIDs). It is important to develop efficient and selective COX-1 inhibitors. In this work, 12 classification models for 1530 cyclooxygenase-1 (COX-1) inhibitors were built by support vector machine (SVM), decision tree (DT) and random forest (RF) methods. The best classification model (model 1A) was built by SVM with MACCS fingerprints. The classification accuracies for the training and test sets were 99.67% and 97.39%, respectively. The Matthews correlation coefficient (MCC) of the test set was 0.94. We also divided the 1530 COX-1 inhibitors into nine subsets according to their different scaffolds using Kohonen's self-organizing map (SOM). In addition, six quantitative structure-activity relationship (QSAR) models for 181 COX-1 inhibitors whose IC₅₀ were measured by enzyme immunoassay were built by multiple linear regression (MLR) and SVM. The best QSAR model (model 5A) was built by SVM with CORINA Symphony descriptors. The correlation coefficients of the training and test sets are 0.93 and 0.84, respectively. The models built in this study can be obtained from the authors.

Novel Allosteric Activators for Ferroptosis Regulator Glutathione Peroxidase 4

Glutathione peroxidase 4 (GPX4) is essential for cell membrane repair, inflammation suppression, and ferroptosis inhibition. GPX4 upregulation provides unique drug discovery opportunities for inflammation and ferroptosis-related diseases. However, rational design of protein activators is challenging. Until now, no compound has been reported to activate the enzyme activity of GPX4. Here, we identified a potential allosteric site in GPX4 and successfully found eight GPX4 activators using a novel computational strategy and experimental studies. Compound 1 from the virtual screen increased GPX4 activity, suppressed ferroptosis, reduced pro-inflammatory lipid mediator production, and inhibited NF-κB pathway activation. Further chemical synthesis and structure-activity relationship studies revealed seven more activators. The strongest compound, 1d4, increased GPX4 activity to 150% at 20 μM in the cell-free assay and 61 μM in cell extracts. Therefore, we demonstrated that GPX4 can be directly activated using chemical compounds to suppress ferroptosis and inflammation. Meanwhile, the discovery of GPX4 activators verified the possibility of rational design of allosteric activators.

Multiple target-centric strategy to tame inflammation

Single-target inhibition is an unsatisfactory therapeutic option to treat multifactorial pathologies, brought into limelight 'paradox of inflammation' beside dearth of innovation, rationalizes a shift toward 'multiple-target' design concept in anti-inflammatory research field. To improvise, two platform strategies, drugs mixture or multitarget drugs, are plausible. Dual cyclooxygenase/lipoxygenase inhibitor 'licofelone' developed after the backfire of rofecoxib due to safety concerns has fetched first light of triumph of the latter strategy. As hitting multiple targets in restraint is perhaps more viable strategy rather than single target, this review, outlines the most germane multiple target agents of synthetic and natural origin placing clear advantage in favors of multitarget strategy as real therapeutic solution for inflammation.

Synthesis and characterization of boron fenbufen and its F-18 labeled homolog for boron neutron capture therapy of COX-2 overexpressed cholangiocarcinoma

Boron neutron capture therapy (BNCT) is a binary therapy that employs neutron irradiation on the boron agents to release high-energy helium and alpha particles to kill cancer cells. An optimal response to BNCT depends critically on the time point of maximal ¹⁰B accumulation and highest tumor to normal ratio (T/N) for performing the neutron irradiation. The aggressive cholangiocarcinoma (CCA) representing a liver cancer that overexpresses COX-2 enzyme is aimed to be targeted by COX-2 selective boron carrier, fenbufen boronopinacol (FBPin). Two main works were performed including: 1) chemical synthesis of FBPin as the boron carrier and 2) radiochemical labeling with F-18 to provide the radiofluoro congener, m-[¹⁸F]fluorofenbufen ester boronopinacol (m-[¹⁸F]FFBPin), to assess the binding affinity, cellular accumulation level and distribution profile in CCA rats. FBPin was prepared from bromofenbufen via 3 steps with 82% yield. The binding assay employed [¹⁸F]FFBPin to compete FBPin for binding to COX-1 (IC₅₀=0.91±0.68μM) and COX-2 (IC₅₀=0.33±0.24μM). [¹⁸F]FFBPin-derived 60-min dynamic PET scans predict the ¹⁰B-accumulation of 0.8-1.2ppm in liver and 1.2-1.8ppm in tumor and tumor to normal ratio=1.38±0.12. BNCT was performed 40-55min post intravenous administration of FBPin (20-30mg) in the CCA rats. CCA rats treated with BNCT display more tumor reduction than that by NCT with respect of 2-[¹⁸F]fluoro-2-deoxy glucose uptake in the tumor region of interest, 20.83±3.00% (n=12) vs. 12.83±3.79% (n=10), P=0.05. The visualizing agent [¹⁸F]FFBPin resembles FBPin to generate the time-dependent boron concentration profile. Optimal neutron irradiation period is thus determinable for BNCT. A boron-substituted agent based on COX-2-binding features has been prepared. The moderate COX-2/COX-1 selectivity index of 2.78 allows a fair tumor selectivity index of 1.38 with a mild cardiovascular effect. The therapeutic effect from FBPin with BNCT warrants a proper COX-2 targeting of boron NSAIDs.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

Antinociceptive activity of a synthetic oxopyrrolidine-based compound, ASH21374, and determination of its possible mechanisms

This study was carried out to determine the antinociceptive activity of a novel synthetic oxopyrrolidine-based compound, (2R,3R,4S)-ethyl 4-hydroxy-1,2-dimethyl-5-oxopyrrolidine-3-carboxylate (ASH21374), and to elucidate the involvement of the opioid, vanilloid, glutamate, and nitric oxide - cyclic guanosine monophosphate (NO/cGMP) systems in modulating the observed antinociception. ASH21374, in the doses of 2, 10, and 100 mg/kg body mass, was administered orally to mice 60 mins prior to exposure to various antinociceptive assays. From the results obtained, ASH21374 exhibited significant (P < 0.05) antinociceptive activity in the abdominal constriction, hot-plate, and formalin tests that was comparable with 100 mg/kg acetylsalicylic acid or 5 mg/kg morphine, respectively. ASH21374 also attenuated capsaicin- and glutamate-induced paw licking. Pre-treatment with 5 mg/kg naloxone significantly (P < 0.05) inhibited the activity in all assays, while pretreatment with 10 mg/kg β-funaltraxamine, 1 mg/kg naltrindole, or 1 mg/kg nor-binaltorphimine significantly (P < 0.05) reversed the activity in the abdominal constriction test. l-Arginine, N(G)-nitro-l-arginine methyl esters (l-NAME), methylene blue, and their combinations, failed to inhibit the ASH21374 antinociceptive activity. In conclusion, ASH21374 demonstrated antinociceptive activities on the peripheral and central nervous systems, mediated through the activation of opioid receptors, inhibition of the glutamatergic system, and attenuation of vanilloid-mediated nociceptive transmission. Further studies have been planned to determine the pharmacological potential of ASH21374.