AnoChem: Prediction of chemical structural abnormalities based on machine learning models.

De novo drug design aims to rationally discover novel and potent compounds while reducing experimental costs during the drug development stage. Despite the numerous generative models that have been developed, few successful cases of drug design utilizing generative models have been reported. One of the most common challenges is designing compounds that are not synthesizable or realistic. Therefore, methods capable of accurately assessing the chemical structures proposed by generative models for drug design are needed. In this study, we present AnoChem, a computational framework based on deep learning designed to assess the likelihood of a generated molecule being real. AnoChem achieves an area under the receiver operating characteristic curve score of 0.900 for distinguishing between real and generated molecules. We utilized AnoChem to evaluate and compare the performances of several generative models, using other metrics, namely SAscore and Fréschet ChemNet distance (FCD). AnoChem demonstrates a strong correlation with these metrics, validating its effectiveness as a reliable tool for assessing generative models. The source code for AnoChem is available at https://github.com/CSB-L/AnoChem.

A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration.

The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3ß), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.

MOTIVATION: Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS: Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION: Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

PredPS: Attention-based graph neural network for predicting stability of compounds in human plasma.

Stability of compounds in the human plasma is crucial for maintaining sufficient systemic drug exposure and considered an essential factor in the early stages of drug discovery and development. The rapid degradation of compounds in the plasma can result in poor in vivo efficacy. Currently, there are no open-source software programs for predicting human plasma stability. In this study, we developed an attention-based graph neural network, PredPS to predict the plasma stability of compounds in human plasma using in-house and open-source datasets. The PredPS outperformed the two machine learning and two deep learning algorithms that were used for comparison indicating its stability-predicting efficiency. PredPS achieved an area under the receiver operating characteristic curve of 90.1%, accuracy of 83.5%, sensitivity of 82.3%, and specificity of 84.6% when evaluated using 5-fold cross-validation. In the early stages of drug discovery, PredPS could be a helpful method for predicting the human plasma stability of compounds. Saving time and money can be accomplished by adopting an in silico-based plasma stability prediction model at the high-throughput screening stage. The source code for PredPS is available at https://bitbucket.org/krict-ai/predps and the PredPS web server is available at https://predps.netlify.app.

Regorafenib prevents the development of emphysema in a murine elastase model.

Emphysema is a chronic obstructive lung disease characterized by inflammation and enlargement of the air spaces. Regorafenib, a potential senomorphic drug, exhibited a therapeutic effect in porcine pancreatic elastase (PPE)-induced emphysema in mice. In the current study we examined the preventive role of regorafenib in development of emphysema. Lung function tests and morphometry showed that oral administration of regorafenib (5 mg/kg/day) for seven days after instillation of PPE resulted in attenuation of emphysema. Mechanistically, regorafenib reduced the recruitment of inflammatory cells, particularly macrophages and neutrophils, in bronchoalveolar lavage fluid. In agreement with these findings, measurements using a cytokine array and ELISA showed that expression of inflammatory mediators including interleukin (IL)-1ß, IL-6, and CXCL1/KC, and tissue inhibitor of matrix metalloprotease-1 (TIMP-1), was downregulated. The results of immunohistochemical analysis confirmed that expression of IL-6, CXCL1/KC, and TIMP-1 was reduced in the lung parenchyma. Collectively, the results support the preventive role of regorafenib in development of emphysema in mice and provide mechanistic insights into prevention strategies. [BMB Reports 2023; 56(8): 439-444].

Development of a Particle Filter-Based Path Tracking Algorithm of Autonomous Trucks with a Single Steering and Driving Module Using a Monocular Camera.

Recently, in various fields, research into the path tracking of autonomous vehicles and automated guided vehicles has been conducted to improve worker safety, convenience, and work efficiency. For path tracking of various systems applied to autonomous driving technology, it is necessary to recognize the surrounding environment, determine technology accordingly, and develop control methods. Various sensors and artificial-intelligence-based perception methods have limitations in that they must learn a large amount of data. Therefore, a particle-filter-based path tracking algorithm using a monocular camera was used for the recognition of target RGB. The path tracking errors were calculated and a linear-quadratic-regulator-based desired steering angle were derived. The autonomous trucks were steered and driven using a pulse-width-modulation-based steering and driving motor. Based on an autonomous truck with a single steering and driving module, it was verified that the path tracking could be used in three evaluation scenarios. To compare the LQR-based path tracking control performance proposed in this paper, an elliptical path tracking scenario using a conventional sliding mode control with robust control performance was performed. The results show that the RMS of the lateral preview error of the SMC was approximately 18% larger than that of the LQR-based method.

Defining regorafenib as a senomorphic drug: therapeutic potential in the age-related lung disease emphysema.

Senescence, a hallmark of aging, is a factor in age-related diseases (ARDs). Therefore, targeting senescence is widely regarded as a practicable method for modulating the effects of aging and ARDs. Here, we report the identification of regorafenib, an inhibitor of multiple receptor tyrosine kinases, as a senescence-attenuating drug. We identified regorafenib by screening an FDA-approved drug library. Treatment with regorafenib at a sublethal dose resulted in effective attenuation of the phenotypes of ßPIX knockdown- and doxorubicin-induced senescence and replicative senescence in IMR-90 cells; cell cycle arrest, and increased SA-ß-Gal staining and senescence-associated secretory phenotypes, particularly increasing the secretion of interleukin 6 (IL-6) and IL-8. Consistent with this result, slower progression of ßPIX depletion-induced senescence was observed in the lungs of mice after treatment with regorafenib. Mechanistically, the results of proteomics analysis in diverse types of senescence indicated that growth differentiation factor 15 and plasminogen activator inhibitor-1 are shared targets of regorafenib. Analysis of arrays for phospho-receptors and kinases identified several receptor tyrosine kinases, including platelet-derived growth factor receptor α and discoidin domain receptor 2, as additional targets of regorafenib and revealed AKT/mTOR, ERK/RSK, and JAK/STAT3 signaling as the major effector pathways. Finally, treatment with regorafenib resulted in attenuation of senescence and amelioration of porcine pancreatic elastase-induced emphysema in mice. Based on these results, regorafenib can be defined as a novel senomorphic drug, suggesting its therapeutic potential in pulmonary emphysema.

PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models.

BACKGROUND: Acute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats. METHODS: PredAOT is based on multiple random forest models for the accurate prediction of acute oral toxicity. A total of 6226 and 6238 compounds evaluated in mice and rats, respectively, were used to train the models. RESULTS: PredAOT has the advantage of predicting acute oral toxicity in mice and rats simultaneously, and its prediction performance is similar to or better than that of existing tools. CONCLUSION: PredAOT will be a useful tool for the quick and accurate prediction of the acute oral toxicity of small compounds in mice and rats during drug development.

CDI Exerts Anti-Tumor Effects by Blocking the FoxM1-DNA Interaction.

The Forkhead box protein M1 (FoxM1) is an appealing target for anti-cancer therapeutics as this cell proliferation-associated transcription factor is overexpressed in most human cancers. FoxM1 is involved in tumor invasion, angiogenesis, and metastasis. To discover novel inhibitors that disrupt the FoxM1-DNA interaction, we identified CDI, a small molecule that inhibits the FoxM1-DNA interaction. CDI was identified through an assay based on the time-resolved fluorescence energy transfer response of a labeled consensus oligonucleotide that was bound to a recombinant FoxM1-dsDNA binding domain (FoxM1-DBD) protein and exhibited potent inhibitory activity against FoxM1-DNA interaction. CDI suppressed cell proliferation and induced apoptosis in MDA-MB-231 cells obtained from a breast cancer patient. Furthermore, it decreased not only the mRNA and protein expression of FoxM1 but also that of downstream targets such as CDC25b. Additionally, global transcript profiling of MDA-MB-231 cells by RNA-Seq showed that CDI decreases the expression of FoxM1-regulated genes. The docking and MD simulation results indicated that CDI likely binds to the DNA interaction site of FoxM1-DBD and inhibits the function of FoxM1-DBD. These results of CDI being a possible effective inhibitor of FoxM1-DNA interaction will encourage its usage in pharmaceutical applications.

Identification and New Indication of Melanin-Concentrating Hormone Receptor 1 (MCHR1) Antagonist Derived from Machine Learning and Transcriptome-Based Drug Repositioning Approaches.

Melanin-concentrating hormone receptor 1 (MCHR1) has been a target for appetite suppressants, which are helpful in treating obesity. However, it is challenging to develop an MCHR1 antagonist because its binding site is similar to that of the human Ether-à-go-go-Related Gene (hERG) channel, whose inhibition may cause cardiotoxicity. Most drugs developed as MCHR1 antagonists have failed in clinical development due to cardiotoxicity caused by hERG inhibition. Machine learning-based prediction models can overcome these difficulties and provide new opportunities for drug discovery. In this study, we identified KRX-104130 with potent MCHR1 antagonistic activity and no cardiotoxicity through virtual screening using two MCHR1 binding affinity prediction models and an hERG-induced cardiotoxicity prediction model. In addition, we explored other possibilities for expanding the new indications for KRX-104130 using a transcriptome-based drug repositioning approach. KRX-104130 increased the expression of low-density lipoprotein receptor (LDLR), which induced cholesterol reduction in the gene expression analysis. This was confirmed by comparison with gene expression in a nonalcoholic steatohepatitis (NASH) patient group. In a NASH mouse model, the administration of KRX-104130 showed a protective effect by reducing hepatic lipid accumulation, liver injury, and histopathological changes, indicating a promising prospect for the therapeutic effect of NASH as a new indication for MCHR1 antagonists.

PredMS: a random forest model for predicting metabolic stability of drug candidates in human liver microsomes.

MOTIVATION: Poor metabolic stability leads to drug development failure. Therefore, it is essential to evaluate the metabolic stability of small compounds for successful drug discovery and development. However, evaluating metabolic stability in vitro and in vivo is expensive, time-consuming and laborious. In addition, only a few free software programs are available for metabolic stability data and prediction. Therefore, in this study, we aimed to develop a prediction model that predicts the metabolic stability of small compounds. RESULTS: We developed a computational model, PredMS, which predicts the metabolic stability of small compounds as stable or unstable in human liver microsomes. PredMS is based on a random forest model using an in-house database of metabolic stability data of 1917 compounds. To validate the prediction performance of PredMS, we generated external test data of 61 compounds. PredMS achieved an accuracy of 0.74, Matthew's correlation coefficient of 0.48, sensitivity of 0.70, specificity of 0.86, positive predictive value of 0.94 and negative predictive value of 0.46 on the external test dataset. PredMS will be a useful tool to predict the metabolic stability of small compounds in the early stages of drug discovery and development. AVAILABILITY AND IMPLEMENTATION: The source code for PredMS is available at https://bitbucket.org/krictai/predms, and the PredMS web server is available at https://predms.netlify.app. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

KR-31831 improves survival and protects hematopoietic cells and radiosensitive tissues against radiation-induced injuries in mice.

This study explored the radioprotective effects and possible underlying mechanisms of KR-31831 against radiation-induced injury in a mouse model. KR-31831 (30 and 50 mg/kg) was administered to mice 24 h and 30 min before exposure to a single lethal or sublethal dose of whole-body irradiation (WBI) (7 or 4 Gy, respectively). These animals were then evaluated for changes in mortality, various hematological and biochemical parameters, and histological features in response to these treatments. In addition, RNA sequencing was used to profile the radiation-induced transcriptomic response in the bone marrow cells. The results showed that KR-31831 dose-dependently prolonged the 30-day survival period and prevented damage to radiation-sensitive organs, such as the intestine and testis, in response to WBI. Damage to the hematopoietic system was also notably improved in the KR-31831-treated mice, as evidenced by an increase in bone marrow and peripheral blood cells, as well as recovery of the histopathological characteristics of the bone marrow. These protective effects were achieved, at least in part, via the suppression of radiation-induced increases in apoptotic cell death and erythropoietin levels in the plasma. Furthermore, the gene expression profiles of the bone marrow cells of the WBI-treated mice suggested that KR-31831 upregulates the expression of the genes involved in regulating apoptosis and modulating the immune response, both of which are required for protecting the bone marrow. These results suggest the potential therapeutic efficacy of KR-31831 for protection against radiation-induced injury.

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles.

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.

Development of a FOXM1-DBD Binding Assay for High-Throughput Screening Using TR-FRET Assay.

Electrophoretic mobility shift assay (EMSA) technology has been widely employed for the analysis of transcription factors such as Forkhead box protein M1 (FOXM1). However, the application of high-throughput screening (HTS) in performing, such analyses are limited as it uses time consuming electrophoresis procedure and radioisotopes. In this study, we developed a FOXM1-DNA binding domain (DBD) binding assay based on time-resolved fluorescence energy transfer (TR-FRET) that enables HTS for the inhibitors of FOXM1-DNA interaction. This assay was robust, highly reproducible and could be easily miniaturized into 384-well plate format. The signal-to-background (S/B) ratio and Z' factor were calculated as 7.46 and 0.74, respectively, via a series of optimization of the assay conditions. A pilot library screening of 1019 natural compounds was performed using the FOXM1-DBD binding assay. Five hit compounds, namely, AC1LXM, BRN5, gangaleoidin, leoidin, and roemerine were identified as the inhibitors of FOXM1. In a cell viability assay, it was demonstrated that cell proliferation of FOXM1 overexpressed cell lines was suppressed in cell lines such as MDA-MB-231 and MCF-7 by five hit compounds. These results indicate that developed FOXM1-DBD binding assay can be applied to highly efficiency HTS of compound libraries.

Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach.

Drug repositioning research using transcriptome data has recently attracted attention. In this study, we attempted to identify new target proteins of the urotensin-II receptor antagonist, KR-37524 (4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)piperazine-1-carboxamide dihydrochloride), using a transcriptome-based drug repositioning approach. To do this, we obtained KR-37524-induced gene expression profile changes in four cell lines (A375, A549, MCF7, and PC3), and compared them with the approved drug-induced gene expression profile changes available in the LINCS L1000 database to identify approved drugs with similar gene expression profile changes. Here, the similarity between the two gene expression profile changes was calculated using the connectivity score. We then selected proteins that are known targets of the top three approved drugs with the highest connectivity score in each cell line (12 drugs in total) as potential targets of KR-37524. Seven potential target proteins were experimentally confirmed using an in vitro binding assay. Through this analysis, we identified that neurologically regulated serotonin transporter proteins are new target proteins of KR-37524. These results indicate that the transcriptome-based drug repositioning approach can be used to identify new target proteins of a given compound, and we provide a standalone software developed in this study that will serve as a useful tool for drug repositioning.

Kinesin-1-dependent transport of the ßPIX/GIT complex in neuronal cells.

Proper targeting of the ßPAK-interacting exchange factor (ßPIX)/G protein-coupled receptor kinase-interacting target protein (GIT) complex into distinct cellular compartments is essential for its diverse functions including neurite extension and synaptogenesis. However, the mechanism for translocation of this complex is still unknown. In the present study, we reported that the conventional kinesin, called kinesin-1, can transport the ßPIX/GIT complex. Additionally, ßPIX bind to KIF5A, a neuronal isoform of kinesin-1 heavy chain, but not KIF1 and KIF3. Mapping analysis revealed that the tail of KIF5s and LZ domain of ßPIX were the respective binding domains. Silencing KIF5A or the expression of a variety of mutant forms of KIF5A inhibited ßPIX targeting the neurite tips in PC12 cells. Furthermore, truncated mutants of ßPIX without LZ domain did not interact with KIF5A, and were unable to target the neurite tips in PC12 cells. These results defined kinesin-1 as a motor protein of ßPIX, and may provide new insights into ßPIX/GIT complex-dependent neuronal pathophysiology. [BMB Reports 2021; 54(7): 380-385].

LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM.

MOTIVATION: Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery. RESULTS: A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models. AVAILABILITYAND IMPLEMENTATION: The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.

KR-39038, a Novel GRK5 Inhibitor, Attenuates Cardiac Hypertrophy and Improves Cardiac Function in Heart Failure.

G protein-coupled receptor kinase 5 (GRK5) has been considered as a potential target for the treatment of heart failure as it has been reported to be an important regulator of pathological cardiac hypertrophy. To discover novel scaffolds that selectively inhibit GRK5, we have identified a novel small molecule inhibitor of GRK5, KR-39038 [7-((3-((4-((3-aminopropyl)amino)butyl)amino)propyl) amino)-2-(2-chlorophenyl)-6-fluoroquinazolin-4(3H)-one]. KR-39038 exhibited potent inhibitory activity (IC50 value=0.02 µM) against GRK5 and significantly inhibited angiotensin II-induced cellular hypertrophy and HDAC5 phosphorylation in neonatal cardiomyocytes. In the pressure overload-induced cardiac hypertrophy mouse model, the daily oral administration of KR-39038 (30 mg/kg) for 14 days showed a 43% reduction in the left ventricular weight. Besides, KR-39038 treatment (10 and 30 mg/kg/ day, p.o.) showed significant preservation of cardiac function and attenuation of myocardial remodeling in a rat model of chronic heart failure following coronary artery ligation. These results suggest that potent GRK5 inhibitor could effectively attenuate both cardiac hypertrophy and dysfunction in experimental heart failure, and KR-39038 may be useful as an effective GRK5 inhibitor for pharmaceutical applications.

DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity.

MOTIVATION: Blockade of the human ether-à-go-go-related gene (hERG) channel by small compounds causes a prolonged QT interval that can lead to severe cardiotoxicity and is a major cause of the many failures in drug development. Thus, evaluating the hERG-blocking activity of small compounds is important for successful drug development. To this end, various computational prediction tools have been developed, but their prediction performances in terms of sensitivity and negative predictive value (NPV) need to be improved to reduce false negative predictions. RESULTS: We propose a computational framework, DeepHIT, which predicts hERG blockers and non-blockers for input compounds. For the development of DeepHIT, we generated a large-scale gold-standard dataset, which includes 6632 hERG blockers and 7808 hERG non-blockers. DeepHIT is designed to contain three deep learning models to improve sensitivity and NPV, which, in turn, produce fewer false negative predictions. DeepHIT outperforms currently available tools in terms of accuracy (0.773), MCC (0.476), sensitivity (0.833) and NPV (0.643) on an external test dataset. We also developed an in silico chemical transformation module that generates virtual compounds from a seed compound, based on the known chemical transformation patterns. As a proof-of-concept study, we identified novel urotensin II receptor (UT) antagonists without hERG-blocking activity derived from a seed compound of a previously reported UT antagonist (KR-36676) with a strong hERG-blocking activity. In summary, DeepHIT will serve as a useful tool to predict hERG-induced cardiotoxicity of small compounds in the early stages of drug discovery and development. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/krictai/deephit and https://bitbucket.org/krictai/chemtrans. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Thiazolidine-2,4-dione-based irreversible allosteric IKK-ß kinase inhibitors: Optimization into in vivo active anti-inflammatory agents.

Selective kinase inhibitors development is a cumbersome task because of ATP binding sites similarities across kinases. On contrast, irreversible allosteric covalent inhibition offers opportunity to develop novel selective kinase inhibitors. Previously, we reported thiazolidine-2,4-dione lead compounds eliciting in vitro irreversible allosteric inhibition of IKK-ß. Herein, we address optimization into in vivo active anti-inflammatory agents. We successfully developed potent IKK-ß inhibitors with the most potent compound eliciting IC50 = 0.20 µM. Cellular assay of a set of active compounds using bacterial endotoxin lipopolysaccharide (LPS)-stimulated macrophages elucidated significant in vitro anti-inflammatory activity. In vitro evaluation of microsomal and plasma stabilities showed that the promising compound 7a is more stable than compound 7p. Finally, in vivo evaluation of 7a, which has been conducted in a model of LPS-induced septic shock in mice, showed its ability to protect mice against septic shock induced mortality. Accordingly, this study presents compound 7a as a novel potential irreversible allosteric covalent inhibitor of IKK-ß with verified in vitro and in vivo anti-inflammatory activity.