GNUV201, a novel human/mouse cross-reactive and low pH-selective anti-PD-1 monoclonal antibody for cancer immunotherapy.

BACKGROUND: Several PD-1 antibodies approved as anti-cancer therapies work by blocking the interaction of PD-1 with its ligand PD-L1, thus restoring anti-cancer T cell activities. These PD-1 antibodies lack inter-species cross-reactivity, necessitating surrogate antibodies for preclinical studies, which may limit the predictability and translatability of the studies. RESULTS: To overcome this limitation, we have developed an inter-species cross-reactive PD-1 antibody, GNUV201, by utilizing an enhanced diversity mouse platform (SHINE MOUSE™). GNUV201 equally binds to human PD-1 and mouse PD-1, equally inhibits the binding of human PD-1/PD-L1 and mouse PD-1/PD-L1, and effectively suppresses tumor growth in syngeneic mouse models. The epitope of GNUV201 mapped to the "FG loop" of hPD-1, distinct from those of Keytruda® ("C'D loop") and Opdivo® (N-term). Notably, the structural feature where the protruding epitope loop fits into GNUV201's binding pocket supports the enhanced binding affinity due to slower dissociation (8.7 times slower than Keytruda®). Furthermore, GNUV201 shows a stronger binding affinity at pH 6.0 (5.6 times strong than at pH 7.4), which mimics the hypoxic and acidic tumor microenvironment (TME). This phenomenon is not observed with marketed antibodies (Keytruda®, Opdivo®), implying that GNUV201 achieves more selective binding to and better occupancy on PD-1 in the TME. CONCLUSIONS: In summary, GNUV201 exhibited enhanced affinity for PD-1 with slow dissociation and preferential binding in TME-mimicking low pH. Human/monkey/mouse inter-species cross-reactivity of GNUV201 could enable more predictable and translatable efficacy and toxicity preclinical studies. These results suggest that GNUV201 could be an ideal antibody candidate for anti-cancer drug development.

GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation.

In this paper, we propose a new model for conditional video generation (GammaGAN). Generally, it is challenging to generate a plausible video from a single image with a class label as a condition. Traditional methods based on conditional generative adversarial networks (cGANs) often encounter difficulties in effectively utilizing a class label, typically by concatenating a class label to the input or hidden layer. In contrast, the proposed GammaGAN adopts the projection method to effectively utilize a class label and proposes scaling class embeddings and normalizing outputs. Concretely, our proposed architecture consists of two streams: a class embedding stream and a data stream. In the class embedding stream, class embeddings are scaled to effectively emphasize class-specific differences. Meanwhile, the outputs in the data stream are normalized. Our normalization technique balances the outputs of both streams, ensuring a balance between the importance of feature vectors and class embeddings during training. This results in enhanced video quality. We evaluated the proposed method using the MUG facial expression dataset, which consists of six facial expressions. Compared with the prior conditional video generation model, ImaGINator, our model yielded relative improvements of 1.61%, 1.66%, and 0.36% in terms of PSNR, SSIM, and LPIPS, respectively. These results suggest potential for further advancements in conditional video generation.

Image Recommendation System Based on Environmental and Human Face Information.

With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user's current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user's current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system's ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users' emotional responses, resulting in a positive mood change for most users. Moreover, the system's scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system's ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human-computer interaction, psychology, and social sciences.

Molecular basis of PD-1 blockade by dostarlimab, the FDA-approved antibody for cancer immunotherapy.

Targeting of programmed cell death 1 (PD-1) with monoclonal antibodies to block the interaction with its ligand PD-L1 has been successful in immunotherapy of multiple types of cancer, and their mechanism involves the restoration of the T-cell immune response. April 2021, the US FDA approved dostarlimab, a therapeutic antibody against PD-1, for the treatment of endometrial cancer. Here, we report the crystal structure of the extracellular domain of PD-1 in complex with the dostarlimab Fab at the resolution of 1.53 Å. Although the interaction between PD-1 and dostarlimab involves mainly the residues within the heavy chain of dostarlimab, the steric occlusion of PD-L1 binding is primarily contributed by the light chain. Dostarlimab induces conformational rearrangements of the BC, C'D and FG loops of PD-1 to achieve a high affinity. Significantly, the residue R86 within the C'D loop of PD-1 plays a critical role for dostarlimab binding by occupying the concave surface on the heavy chain via multiple interactions. This high-resolution structure can provide helpful information for designing improved anti-PD-1 biologics or effective combination strategies for cancer immunotherapy.

Efficient Multi-Scale Stereo-Matching Network Using Adaptive Cost Volume Filtering.

While recent deep learning-based stereo-matching networks have shown outstanding advances, there are still some unsolved challenges. First, most state-of-the-art stereo models employ 3D convolutions for 4D cost volume aggregation, which limit the deployment of networks for resource-limited mobile environments owing to heavy consumption of computation and memory. Although there are some efficient networks, most of them still require a heavy computational cost to incorporate them to mobile computing devices in real-time. Second, most stereo networks indirectly supervise cost volumes through disparity regression loss by using the softargmax function. This causes problems in ambiguous regions, such as the boundaries of objects, because there are many possibilities for unreasonable cost distributions which result in overfitting problem. A few works deal with this problem by generating artificial cost distribution using only the ground truth disparity value that is insufficient to fully regularize the cost volume. To address these problems, we first propose an efficient multi-scale sequential feature fusion network (MSFFNet). Specifically, we connect multi-scale SFF modules in parallel with a cross-scale fusion function to generate a set of cost volumes with different scales. These cost volumes are then effectively combined using the proposed interlaced concatenation method. Second, we propose an adaptive cost-volume-filtering (ACVF) loss function that directly supervises our estimated cost volume. The proposed ACVF loss directly adds constraints to the cost volume using the probability distribution generated from the ground truth disparity map and that estimated from the teacher network which achieves higher accuracy. Results of several experiments using representative datasets for stereo matching show that our proposed method is more efficient than previous methods. Our network architecture consumes fewer parameters and generates reasonable disparity maps with faster speed compared with the existing state-of-the art stereo models. Concretely, our network achieves 1.01 EPE with runtime of 42 ms, 2.92M parameters, and 97.96G FLOPs on the Scene Flow test set. Compared with PSMNet, our method is 89% faster and 7% more accurate with 45% fewer parameters.

Semantic Segmentation Using Pixel-Wise Adaptive Label Smoothing via Self-Knowledge Distillation for Limited Labeling Data.

To achieve high performance, most deep convolutional neural networks (DCNNs) require a significant amount of training data with ground truth labels. However, creating ground-truth labels for semantic segmentation requires more time, human effort, and cost compared with other tasks such as classification and object detection, because the ground-truth label of every pixel in an image is required. Hence, it is practically demanding to train DCNNs using a limited amount of training data for semantic segmentation. Generally, training DCNNs using a limited amount of data is problematic as it easily results in a decrease in the accuracy of the networks because of overfitting to the training data. Here, we propose a new regularization method called pixel-wise adaptive label smoothing (PALS) via self-knowledge distillation to stably train semantic segmentation networks in a practical situation, in which only a limited amount of training data is available. To mitigate the problem caused by limited training data, our method fully utilizes the internal statistics of pixels within an input image. Consequently, the proposed method generates a pixel-wise aggregated probability distribution using a similarity matrix that encodes the affinities between all pairs of pixels. To further increase the accuracy, we add one-hot encoded distributions with ground-truth labels to these aggregated distributions, and obtain our final soft labels. We demonstrate the effectiveness of our method for the Cityscapes dataset and the Pascal VOC2012 dataset using limited amounts of training data, such as 10%, 30%, 50%, and 100%. Based on various quantitative and qualitative comparisons, our method demonstrates more accurate results compared with previous methods. Specifically, for the Cityscapes test set, our method achieved mIoU improvements of 0.076%, 1.848%, 1.137%, and 1.063% for 10%, 30%, 50%, and 100% training data, respectively, compared with the method of the cross-entropy loss using one-hot encoding with ground truth labels.

Recent Advances in Antibody Therapeutics.

Antibody-based therapeutics have achieved unprecedented success in treating various diseases, including cancers, immune disorders, and infectious diseases [...].

Crystal structure of CD38 in complex with daratumumab, a first-in-class anti-CD38 antibody drug for treating multiple myeloma.

Multiple myeloma is a blood cancer characterized by the plasma cell malignancy in the bone marrow, resulting in the destruction of bone tissue. Recently, the US FDA approved two antibody drugs for the treatment of multiple myeloma, daratumumab and isatuximab, targeting CD38, a type II transmembrane glycoprotein highly expressed in plasma cells and multiple myeloma cells. Here, we report the crystal structure of CD38 in complex with the Fab fragment of daratumumab, providing its exact epitope on CD38 and the structural insights into the mechanism of action of the antibody drug. Daratumumab binds to a specific discontinuous region on CD38 that includes residues located opposite to the active site of CD38. All the six complementarity determining regions of daratumumab are involved in the CD38 interaction. The epitopes of daratumumab and isatuximab do not overlap at all and their bindings to CD38 induce different structural changes within the CD38 protein. This structural study can facilitate the design of improved biologics or effective combination therapies for the treatment of multiple myeloma.

High-resolution structure of the vWF A1 domain in complex with caplacizumab, the first nanobody-based medicine for treating acquired TTP.

von Willebrand factor (vWF) is a huge oligomeric glycoprotein involved in blood homeostasis. However, this protein is also implicated in acquired thrombotic thrombocytopenic purpura (TTP). The blocking of its binding with platelets has been recognized as an attractive therapeutic strategy for treating acquired TTP. Caplacizumab, a bivalent single-domain antibody (VHH), is the first FDA-approved nanobody drug against vWF for the treatment of acquired TTP. Here, we describe the crystal structure of the A1 domain of vWF in complex with the caplacizumab nanobody at the resolution of 1.60 Å. This structure elucidates the precise epitope and binding mode of caplacizumab. Unexpectedly, caplacizumab binds to the bottom face of the vWF A1 domain and does not create any steric clash with platelet-receptor glycoprotein Ib (GPIb) bound to vWF. However, its binding can stabilize the different conformation within the N-terminus and α1ß2 loop from the GPIb bound structure, suggesting that the mechanisms of caplacizumab would not be the direct competition of GPIb binding to vWF A1 domain but the conformational arrestment of vWF in an inappropriate state to platelet adhesion. This high-resolution structure would provide helpful information for the design of improved anti-vWF therapeutics for the treatment of acquired TTP.

Crystal structure of PD-1 in complex with an antibody-drug tislelizumab used in tumor immune checkpoint therapy.

Blocking of the interaction between Programmed cell death 1 (PD-1) and its ligand PD-L1 by monoclonal antibodies has elicited unprecedented therapeutic benefits and achieved a major breakthrough in immunotherapy of multiple types of tumors. Here, we determined the crystal structure of PD-1 in complex with the Fab fragment of tislelizumab. This monoclonal antibody was approved in December 2019 by the China National Medical Product Administration for Hodgkin's lymphoma and is under multiple clinical trials in China and the US. While the three complementarity determining regions (CDRs) in the light chain are involved in the target interaction, only CDR3 within the heavy chain interacts with PD-1. Tislelizumab binds the front ß-sheet of PD-1 in a very similar way as PD-L1 binds to PD-1, thereby blocking the PD-1/PD-L1 interaction with a higher affinity. A comparative analysis of PD-1 interactions with therapeutic antibodies targeting PD-1 provides a better understanding of the blockade mechanism of PD-1/PD-L1 interaction in addition to useful information for the improvement of therapeutic antibodies capable of diminishing checkpoint signaling for cancer immunotherapy.

Knowledge Distillation for Semantic Segmentation Using Channel and Spatial Correlations and Adaptive Cross Entropy.

In this paper, we propose an efficient knowledge distillation method to train light networks using heavy networks for semantic segmentation. Most semantic segmentation networks that exhibit good accuracy are based on computationally expensive networks. These networks are not suitable for mobile applications using vision sensors, because computational resources are limited in these environments. In this view, knowledge distillation, which transfers knowledge from heavy networks acting as teachers to light networks as students, is suitable methodology. Although previous knowledge distillation approaches have been proven to improve the performance of student networks, most methods have some limitations. First, they tend to use only the spatial correlation of feature maps and ignore the relational information of their channels. Second, they can transfer false knowledge when the results of the teacher networks are not perfect. To address these two problems, we propose two loss functions: a channel and spatial correlation (CSC) loss function and an adaptive cross entropy (ACE) loss function. The former computes the full relationship of both the channel and spatial information in the feature map, and the latter adaptively exploits one-hot encodings using the ground truth labels and the probability maps predicted by the teacher network. To evaluate our method, we conduct experiments on scene parsing datasets: Cityscapes and Camvid. Our method presents significantly better performance than previous methods.

Molecular Interactions of Antibody Drugs Targeting PD-1, PD-L1, and CTLA-4 in Immuno-Oncology.

Cancer cells can evade immune surveillance through the molecular interactions of immune checkpoint proteins, including programmed death 1 (PD-1), PD-L1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4). Since 2011, the FDA-approved antibody drugs ipilimumab (Yervoy®), nivolumab (Opdivo®), pembrolizumab (Keytruda®), cemiplimab (Libtayo®), atezolizumab (Tecentriq®), durvalumab (Imfinzi®), and avelumab (Bavencio®), which block the immune checkpoint proteins, have brought about a significant breakthrough in the treatment of a wide range of cancers, as they can induce durable therapeutic responses. In recent years, crystal structures of the antibodies against PD-1, PD-L1, and CTLA-4 have been reported. In this review, we describe the latest structural studies of these monoclonal antibodies and their interactions with the immune checkpoint proteins. A comprehensive analysis of the interactions of these immune checkpoint blockers can provide a better understanding of their therapeutic mechanisms of action. The accumulation of these structural studies would provide a basis that is essential for the rational design of next-generation therapies in immuno-oncology.

Structural Biology of the TNFα Antagonists Used in the Treatment of Rheumatoid Arthritis.

The binding of the tumor necrosis factor α (TNFα) to its cognate receptor initiates many immune and inflammatory processes. The drugs, etanercept (Enbrel®), infliximab (Remicade®), adalimumab (Humira®), certolizumab-pegol (Cimzia®), and golimumab (Simponi®), are anti-TNFα agents. These drugs block TNFα from interacting with its receptors and have enabled the development of breakthrough therapies for the treatment of several autoimmune inflammatory diseases, including rheumatoid arthritis, Crohn's disease, and psoriatic arthritis. In this review, we describe the latest works on the structural characterization of TNFα-TNFα antagonist interactions related to their therapeutic efficacy at the atomic level. A comprehensive comparison of the interactions of the TNFα blockers would provide a better understanding of the molecular mechanisms by which they neutralize TNFα. In addition, an enhanced understanding of the higher order complex structures and quinary structures of the TNFα antagonists can support the development of better biologics with the improved pharmacokinetic properties. Accumulation of these structural studies can provide a basis for the improvement of therapeutic agents against TNFα for the treatment of rheumatoid arthritis and other autoimmune inflammatory diseases in which TNFα plays an important role in pathogenesis.

Osajin Inhibits Solar UV-Induced Cyclooxygenase-2 Expression Through Direct Inhibition of RSK2.

Solar ultraviolet light (sUV) has been shown to promote the development of skin disorders including inflammation, photoaging, and skin carcinogenesis. Osajin is the major bioactive isoflavone present in the fruit of Maclura pomifera, commonly referred to as the Osage orange. In this study, we observed that osajin inhibited sUV-induced cyclooxygenase (COX)-2 protein expression in both HaCaT and JB6 cells. COX-2 is a major mediator of skin inflammation. sUV activated the transcription factors nuclear factor-κB and activator protein-1 which, in turn, induces COX-2 expression. Osajin inhibited transactivation of these transcription factors. We identified RSK2 as an inhibitory target of osajin by screening against 68 kinases related to inflammation. Osajin binds with RSK2 directly in an ATP-competitive manner. Computer modeling simulated a plausible binding orientation between osajin and RSK2. Osajin inhibited sUV-induced phosphorylation of histone H3, a substrate of RSK2. However, sUV-induced phosphorylation of extracellular signal-regulated kinases, p38 kinase, c-Jun N-terminal kinase and Akt, which are signaling factors upstream of RSK2, was unchanged in the presence of osajin. The anti-inflammatory effects and molecular mechanism of osajin suggest that it may have utility as a functional food for skin health and cosmetic ingredient. J. Cell. Biochem. 118: 4080-4087, 2017. © 2017 Wiley Periodicals, Inc.

Molecular Basis for the Neutralization of Tumor Necrosis Factor α by Certolizumab Pegol in the Treatment of Inflammatory Autoimmune Diseases.

Monoclonal antibodies against TNFα, including infliximab, adalimumab, golimumab, and certolizumab pegol, are widely used for the treatment of the inflammatory diseases such as rheumatoid arthritis and inflammatory bowel disease. Recently, the crystal structures of TNFα, in complex with the Fab fragments of infliximab and adalimumab, have revealed the molecular mechanisms of these antibody drugs. Here, we report the crystal structure of TNFα in complex with the Fab fragment of certolizumab pegol to clarify the precise antigen-antibody interactions and the structural basis for the neutralization of TNFα by this therapeutic antibody. The structural analysis and the mutagenesis study revealed that the epitope is limited to a single protomer of the TNFα trimer. Additionally, the DE loop and the GH loop of TNFα play critical roles in the interaction with certolizumab, suggesting that this drug exerts its effects by partially occupying the receptor binding site of TNFα. In addition, a conformational change of the DE loop was induced by certolizumab binding, thereby interrupting the TNFα-receptor interaction. A comprehensive comparison of the interactions of TNFα blockers with TNFα revealed the epitope diversity on the surface of TNFα, providing a better understanding of the molecular mechanism of TNFα blockers. The accumulation of these structural studies can provide a basis for the improvement of therapeutic antibodies against TNFα.

Dehydroglyasperin C suppresses TPA-induced cell transformation through direct inhibition of MKK4 and PI3K.

Bioactive natural compounds from plant-derived sources have received substantial interest due to their potential therapeutic and preventive effects toward various human diseases. Licorice (Glycyrrhiza), a frequently-used component in traditional oriental medicines, has been incorporated into recipes not only to enhance taste, but also to treat various conditions including inflammation, chronic fatigue syndrome, and even cancer. Dehydroglyasperin C (DGC) is a major isoflavone found in the root of licorice. In the present study, we investigated the cancer chemopreventive effect of DGC and the underlying molecular mechanisms involved, by analyzing its effects on 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced neoplastic cell transformation and cyclooxygenase (COX)-2 expression in JB6 P+ mouse epidermal cells. DGC treatment attenuated TPA-induced activator protein-1 (AP-1) and nuclear factor-κB (NF-κB) transcriptional activation, two major regulators of TPA-induced cell transformation, and COX-2 expression. TPA-induced phosphorylation of p38, JNK1/2 and Akt was also suppressed by DGC. Kinase assay data revealed that DGC inhibited the kinase activity of MKK4 and PI3K and this outcome was due to direct physical binding with DGC. Notably, DGC bound directly to MKK4 and PI3K in an ATP-competitive manner. Taken together, these results suggest that DGC exhibits cancer chemopreventive potential via its inhibitory effect on TPA-induced neoplastic cell transformation and COX-2 modulation through regulation of the MKK4 and PI3K pathways.

Structure of the catalytic phosphatase domain of MTMR8: implications for dimerization, membrane association and reversible oxidation.

Myotubularin-related proteins are a large family of phosphoinositide phosphatases; their activity, stability and subcellular localization are regulated by dimeric interactions with other members of the family. Here, the crystal structure of the phosphatase domain of MTMR8 is reported. Conformational deviation of the two loops that mediate interaction with the PH-GRAM domain suggests that the PH-GRAM domain interacts differently with the phosphatase domain of each MTMR member. The protein exists as a dimer with twofold symmetry, providing insight into a novel mode of dimerization mediated by the phosphatase domain. Structural comparison and mutation studies suggest that Lys255 of MTMR8 interacts with the substrate diacylglycerol moiety, similar to Lys333 of MTMR2, although the positions of these residues are different. The catalytic activity of the MTMR8 phosphatase domain is inhibited by oxidation and is reversibly reactivated by reduction, suggesting the presence of an oxidation-protective intermediate other than a disulfide bond owing to the absence of a cysteine within a disulfide-bond distance from Cys338.

Licochalcone A, a polyphenol present in licorice, suppresses UV-induced COX-2 expression by targeting PI3K, MEK1, and B-Raf.

Licorice is a traditional botanical medicine, and has historically been commonly prescribed in Asia to treat various diseases. Glycyrrhizin (Gc), a triterpene compound, is the most abundant phytochemical constituent of licorice. However, high intake or long-term consumption of Gc has been associated with a number of side effects, including hypertension. However, the presence of alternative bioactive compounds in licorice with anti-carcinogenic effects has long been suspected. Licochalcone A (LicoA) is a prominent member of the chalcone family and can be isolated from licorice root. To date, there have been no reported studies on the suppressive effect of LicoA against solar ultraviolet (sUV)-induced cyclooxygenase (COX)-2 expression and the potential molecular mechanisms involved. Here, we show that LicoA, a major chalcone compound of licorice, effectively inhibits sUV-induced COX-2 expression and prostaglandin E2 PGE2 generation through the inhibition of activator protein 1 AP-1 transcriptional activity, with an effect that is notably more potent than Gc. Western blotting analysis shows that LicoA suppresses sUV-induced phosphorylation of Akt/ mammalian target of rapamycin (mTOR) and extracellular signal-regulated kinases (ERK)1/2/p90 ribosomal protein S6 kinase (RSK) in HaCaT cells. Moreover, LicoA directly suppresses the activity of phosphoinositide 3-kinase (PI3K), mitogen-activated protein kinase kinase (MEK)1, and B-Raf, but not Raf-1 in cell-free assays, indicating that PI3K, MEK1, and B-Raf are direct molecular targets of LicoA. We also found that LicoA binds to PI3K and B-Raf in an ATP-competitive manner, although LicoA does not appear to compete with ATP for binding with MEK1. Collectively, these results provide insight into the biological action of LicoA, which may have potential for development as a skin cancer chemopreventive agent.

Anti-inflammatory activity of rhamnetin and a model of its binding to c-Jun NH2-terminal kinase 1 and p38 MAPK.

Rhamnetin (1), a commonly occurring plant O-methylated flavonoid, possesses antioxidant properties. To address the potential therapeutic efficacy of 1, its anti-inflammatory activity and mode of action in mouse macrophage-derived RAW264.7 cells stimulated with lipopolysaccharide (LPS) or interferon (IFN)-γ were investigated. Rhamnetin (1) suppressed mouse tumor necrosis factor (mTNF)-α, mouse macrophage inflammatory protein (mMIP)-1, and mMIP-2 cytokine production in LPS-stimulated macrophages. A nontoxic dose of 1 suppressed nitric oxide production. It was found that the anti-inflammatory effects of 1 are mediated by actions on the p38 mitogen-activated protein kinase (MAPK), extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and cyclooxygenase (COX)-2 pathways in LPS- or IFN-γ-stimulated RAW264.7 cells. It was determined that 1 binds to human JNK1 (9.7 × 10(8) M(-1)) and p38 MAPK (2.31 × 10(7) M(-1)) with good affinity. The binding model showed interactions with the 3'- and 4'-hydroxy groups of the B-ring and the 5-hydroxy group of the A-ring of 1. Further, 1 exerted an anti-inflammatory effect, reducing the levels of pro-inflammatory cytokines and mediators.

Cytotoxic activity of 3,6-dihydroxyflavone in human cervical cancer cells and its therapeutic effect on c-Jun N-terminal kinase inhibition.

Previously we have shown that 3,6-dihydroxyflavone (3,6-DHF) is a potent agonist of the human peroxisome proliferator-activated receptor (hPPAR) with cytotoxic effects on human cervical cancer cells. To date, the mechanisms by which 3,6-DHF exerts its antitumor effects on cervical cells have not been clearly defined. Here, we demonstrated that 3,6-DHF exhibits a novel antitumor activity against HeLa cells with IC50 values of 25 µM and 9.8 µM after 24 h and 48 h, respectively. We also showed that the anticancer effects of 3,6-DHF are mediated via the toll-like receptor (TLR) 4/CD14, p38 mitogen-activated protein kinase (MAPK), Jun-N terminal kinase (JNK), extracellular-signaling regulated kinase (ERK), and cyclooxygenase (COX)-2 pathways in lipopolysaccharide (LPS)-stimulated RAW264.7 cells. We found that 3,6-DHF showed a similar IC50 (113 nM) value to that of the JNK inhibitor, SP600125 (IC50 = 118 nM) in a JNK1 kinase assay. Binding studies revealed that 3,6-DHF had a strong binding affinity to JNK1 (1.996 × 105 M-1) and that the 6-OH and the carbonyl oxygen of the C ring of 3,6-DHF participated in hydrogen bonding interactions with the carbonyl oxygen and the amide proton of Met111, respectively. Therefore, 3,6-DHF may be a candidate inhibitor of JNKs, with potent anticancer effects.