KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states.

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

A simple and sensitive differential digestion method to analyze adeno-associated virus residual host cell proteins by LC-MS.

Many methods using liquid chromatography-mass spectrometry (LC-MS) have been established for identifying residual host cell proteins (HCPs) to aid in the process development and quality control of therapeutic proteins. However, the use of MS-based techniques for adeno-associated virus (AAV) is still in its infancy, with few methods reported and minimal information available on potentially problematic HCPs. In this study, we developed a highly sensitive and effective differential digestion method to profile residual HCPs in AAV. Unlike direct digestion, which completely digests both AAV and HCPs, our differential digestion method takes advantage of AAV's unique characteristics to maintain the integrity of AAV while preferentially digesting HCPs under denaturing and reducing conditions. This differential digestion method requires only several micrograms of sample and significantly enhances the identification of HCPs. Furthermore, this method can be applied to all five different AAV serotypes for comprehensive HCP profiling. Our work fills a gap in AAV HCP analysis by providing a sensitive and robust strategy for detecting, monitoring, and measuring HCPs.

Tasquinimod enhances the sensitivity of ovarian cancer cells to cisplatin by regulating the Nur77-Bcl-2 apoptotic pathway.

BACKGROUND: Resistance to cisplatin (DDP) in ovarian cancer therapy has been a major clinical barrier. Drug-resistant cancers have been shown to downregulate the proapoptotic protein B-cell lymphoma-2 (Bcl-2) to inhibit apoptosis. Therefore, we explored whether tasquinimod could modulate resistance to DDP through apoptotic pathways. OBJECTIVES: We aimed to explore the relationship between tasquinimod, Nur77-Bcl-2 apoptosis pathway and sensitivity of the ovarian carcinoma cell line SKOV3 and the DDP-resistant strain SKOV3/DDP cells to DDP. MATERIAL AND METHODS: First, SKOV3 and SKOV3/DDP cells were treated with 2 µg/mL DDP or 40 µM tasquinimod. Western blot and quantitative real-time polymerase chain reaction (qPCR) were then used to analyze the expression of histone deacetylase 4 (HDAC4), Nur77, Bcl-2 (BH3 domain-specific), and caspase-3. Flow cytometry, scratch-wound assay and immunofluorescence were used to detect apoptosis, migration rate, and related expression of Nur77 and Bcl-2 (BH3 domain-specific). Subsequently, 5×107 SKOV3 or SKOV3/DDP cells cultured with 2 µg/mL DDP were injected into 4-week-old female BALB/c nude mice. Then, the mice were administered 4 mg/kg DDP and 50 mg/kg tasquinimod every 3 days. Finally, the changes in tumor diameter and weight were measured. RESULTS: After treatment of SKOV3 and SKOV3/DDP cells with tasquinimod, cell migration and HDAC4 expression levels were significantly reduced, while Nur77 expression was increased. Tasquinimod treatment enhanced the expression of Nur77 and caspase-3, and cells transfected with si-Nur77 showed the opposite result. Transfection of si-Nur77 reduced the expression of caspase-3 and Nur77 in the SKOV3/DDP cells that were treated with both DDP and tasquinimod. After injection of SKOV3/DDP cells into the mice, the tumor diameter, mass and in vivo HDAC4 level were significantly decreased by tasquinimod. Meanwhile, the levels of Nur77 and Bcl-2 (BH3 domain-specific) were increased. CONCLUSIONS: Tasquinimod upregulated the Nur77/Bcl-2 pathway to induce apoptosis in SKOV3/DDP cells and enhanced the anti-tumor effect of DDP in SKOV3/DDP xenografts. Therefore, tasquinimod can be expected to find clinical applications in enhancing DDP resistance.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale.

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Deep Deterministic Policy Gradient With Compatible Critic Network.

Deep deterministic policy gradient (DDPG) is a powerful reinforcement learning algorithm for large-scale continuous controls. DDPG runs the back-propagation from the state-action value function to the actor network's parameters directly, which raises a big challenge for the compatibility of the critic network. This compatibility emphasizes that the policy evaluation is compatible with the policy improvement. As proved in deterministic policy gradient, the compatible function guarantees the convergence ability but restricts the form of the critic network tightly. The complexities and limitations of the compatible function impede its development in DDPG. This article introduces neural networks' similarity indices with gradients to measure the compatibility concretely. Represented as kernel matrices, we consider the actor network's and the critic network's training dataset, trained parameters, and gradients. With the sketching trick, the calculation time of the similarity index decreases hugely. The centered kernel alignment index and the normalized Bures similarity index provide us with consistent compatibility scores empirically. Moreover, we demonstrate the necessity of the compatible critic network in DDPG from three aspects: 1) analyzing the policy improvement/evaluation steps; 2) conducting the theoretic analysis; and 3) showing the experimental results. Following our research, we remodel the compatible function with an energy function model, enabling it suitable to the sizeable state-action space problem. The critic network has higher compatibility scores and better performance by introducing the policy change information into the critic-network optimization process. Besides, based on our experiment observations, we propose a light-computation overestimation solution. To prove our algorithm's performance and validate the compatibility of the critic network, we compare our algorithm with six state-of-the-art algorithms using seven PyBullet robotics environments.

WRKY transcription factor family in lettuce plant (Lactuca sativa): Genome-wide characterization, chromosome location, phylogeny structures, and expression patterns.

WRKY transcription factors (TF) have been identified in many plant species and play critical roles in multiple stages of growth and development and under various stress conditions. As one of the most popular vegetable crops, asparagus lettuce has important medicinal and nutritional value. However, study of WRKY TFs family in asparagus lettuce is limited. With the lettuce (Lactuca sativa L.) genome publication, we identified 76 WRKY TFs and analyzed structural characteristics, phylogenetic relationships, chromosomal distribution, interaction network, and expression profiles. The 76 LsWRKY TFs were phylogenetically classified as Groups I, II (IIa-IIe), and III. Cis element analysis revealed complex regulatory relationships of LsWRKY genes in response to different biological progresses. Interaction network analysis indicated that LsWRKY TFs could interact with other proteins, such as SIB (sigma factor binding protein), WRKY TFs, and MPK. The WRKYIII subfamily genes showed different expression patterns during the progress of asparagus lettuce stem enlargement. According to qRT-PCR analysis, abiotic stresses (drought, salt, low temperature, and high temperature) and phytohormone treatment could induce specific LsWRKYIII gene expression. These results will provide systematic and comprehensive information on LsWRKY TFs and lay the foundation for further clarification of the regulatory mechanism of LsWRKY, especially LsWRKYIII TFs, involved in stress response and the progress of plant growth and development.

Host cell protein identification in monoclonal antibody high molecular weight species.

High molecular weight (HMW) species are product-related variants that may impact therapeutic product safety and efficacy. Therefore, HMW species and aggregates are considered critical quality attributes and their levels should be closely monitored and controlled during drug development, commercial manufacturing, and shelf-life storage period for therapeutic monoclonal antibody drug products. Various biophysical and analytical methods have been developed to characterize the HMW species to understand their mechanisms of formation and assess potential product risk. However, host cell protein (HCP) analysis has seldom been conducted to characterize the impurities in aggregates. In this work, HCP analysis of enriched HMW species and drug substance (DS) from five different monoclonal antibodies (mAbs) was performed. More HCPs are identified in the enriched HMW than in the DS, thus demonstrating a potential interaction between HCPs and HMW. Certain HCPs, including commonly detected HCPs and problematic HCPs, were enriched in HMW fractions. Especially, the most abundant HCP from mAb1, CC motif chemokine, was 46 times more abundant in enriched HMW than DS. The enriched HMW was further fractionated into enriched dimers and enriched very HMW (vHMW) fractions. The CC motif chemokine was found to interact mainly with mAb1 dimer species rather than vHMW fraction. Removing the HMW species from mAb1 significantly decreased the CC motif chemokine level in the final mAb1 DS. Our findings demonstrate that some HCPs are more preferentially bound to HMW species and this finding may provide a new opportunity for removing HCPs in downstream purification steps.

E2SGAN: EEG-to-SEEG translation with generative adversarial networks.

High-quality brain signal data recorded by Stereoelectroencephalography (SEEG) electrodes provide clinicians with clear guidance for presurgical assessments for epilepsy surgeries. SEEG, however, is limited to selected patients with epilepsy due to its invasive procedure. In this work, a brain signal synthesis framework is presented to synthesize SEEG signals from non-invasive EEG signals. First, a strategy to determine the matching relation between EEG and SEEG channels is presented by considering both signal correlation and spatial distance. Second, the EEG-to-SEEG generative adversarial network (E2SGAN) is proposed to precisely synthesize SEEG data from the simultaneous EEG data. Although the widely adopted magnitude spectra has proved to be informative in EEG tasks, it leaves much to be desired in the setting of signal synthesis. To this end, instantaneous frequency spectra is introduced to further represent the alignment of the signal. Correlative spectral attention (CSA) is proposed to enhance the discriminator of E2SGAN by capturing the correlation between each pair of EEG and SEEG frequencies. The weighted patch prediction (WPP) technique is devised to ensure robust temporal results. Comparison experiments on real-patient data demonstrate that E2SGAN outperforms baseline methods in both temporal and frequency domains. The perturbation experiment reveals that the synthesized results have the potential to capture abnormal discharges in epileptic patients before seizures.

An improved method of using two-dimensional model to evaluate the carrying capacity of regional water resource in Inner Mongolia of China.

The evaluation of regional water resource carrying capacity has been repeatedly conducted to provide a scientific basis for the local water resource management and the sustainable development, in particular in the water-limited regions. However, the definition of regional water resource carrying capacity and its evaluation method are still arguable. Through a case study of Inner Mongolia, located in the arid and semi-arid northern China, this paper developed an improved method to calculate regional water resource carrying capacity by the combination of the water supply-demand analysis and the S-shaped curve threshold analysis. The spatial and temporal patterns of the regional water resource carrying capacity in Inner Mongolia during 2000-2019 was evaluated at three scales, namely the province scale, the basin scale and the city scale. The results showed that the average regional water resource carrying capacity of the whole province was 0.25 (the full mark is 1.00); at the basin scale, the Yellow River Basin had the lowest regional water resource carrying capacity (0.17) among all the basins, showing that the utilization of the water resources was unreasonable; at the city scale, the average regional water resource carrying capacities in Hulunbuir and Xilingol were both over 0.25, while those in Alxa, BayanNur and Wuhai were below 0.1; Hulunbuir had 25.48 billion m3 water surplus, while BayanNur suffered from an average water deficit of 4.51 billion m3 from 2000 to 2019. This paper has provided a reasonable way to measure the regional water resource carrying capacity using an improved method by incorporating S-shaped curve threshold analysis, which may have a wider application for the clustering and optimization of regional water management. In addition, the spatial and temporal patterns of regional water carrying capacity are beneficial for policymakers in the implementation of the effective water usage.

Discovery of novel brain-penetrant GluN2B NMDAR antagonists via pharmacophore-merging strategy as anti-stroke therapeutic agents.

In this work, a novel structural series of brain-penetrant GluN2B NMDAR antagonists were designed, synthesized and biologically evaluated as anti-stroke therapeutic agents via merging the structures of NBP and known GluN2B ligands. Approximately half of them exhibited superior neuroprotective activity to NBP against NMDA-induced neurotoxicity in hippocampal neurons at 10 µM, and compound 45e and 45f exerted equipotent activity to ifenprodil, an approved GluN2B- selective NMDAR antagonist. In particular, 45e, with the most potent neuroprotective activity throughout this series, displayed dramatically enhanced activity (Ki = 3.26 nM) compared to ifenprodil (Ki = 14.80 nM) in Radioligand Competitive Binding Assay, and remarkable inhibition (IC50 = 79.32 nM) against GluN1/GluN2B receptor-mediated current in Patch Clamp Assay. Meanwhile, 45e and its enantiomers exhibited low inhibition rate against the current mediated by other investigated receptors at the concentration of 10 µM, indicating their favorable selectivity for GluN1/GluN2B. In the rat model of middle cerebral artery ischemia (MCAO), 45e exerted comparable therapeutic efficacy to ifenprodil at the same dosage. In addition to the attractive in vitro and in vivo potency, 45e displayed a favorable bioavailability (F = 63.37%) and an excellent brain exposure. In further repeated dose toxicity experiments, compound 45e demonstrated an acceptable safety profile. With the above merits, 45e is worthy of further functional investigation as a novel anti-stroke therapeutic agent.

Host cell protein profiling of commercial therapeutic protein drugs as a benchmark for monoclonal antibody-based therapeutic protein development.

Therapeutic proteins including monoclonal antibodies (mAbs) are usually produced in engineered host cell lines that also produce thousands of endogenous proteins at varying levels. A critical aspect of the development of biotherapeutics manufacturing processes is the removal of these host cell proteins (HCP) to appropriate levels in order to minimize risk to patient safety and drug efficacy. During the development process and associated analytical characterization, mass spectrometry (MS) has become an increasingly popular tool for HCP analysis due to its ability to provide both relative abundance and identity of individual HCP and because the method does not rely on polyclonal antibodies, which are used in enzyme-linked immunosorbent assays. In this study, HCP from 29 commercially marketed mAb and mAb-based therapeutics were profiled using liquid chromatography (LC)-MS/MS with the identification and relative quantification of 79 individual HCP in total. Excluding an outlier drug, the relative levels of individual HCP determined in the approved therapeutics were generally low, with an average of 20 ppm (µmol HCP/mol drug) measured by LC-MS/MS, and only a few (<7 in average) HCP were identified in each drug analyzed. From this analysis, we also gained knowledge about which HCP are frequently identified in mAb-based products and their typical levels relative to the drugs for the identified individual HCP. In addition, we examined HCP composition from antibodies produced in house and found our current development process brings HCP to levels that are consistent with marketed drugs. Finally, we described a specific case to demonstrate how the HCP information from commercially marketed drugs could inform future HCP analyses.

Toward unbiased identification and comparative quantification of host cell protein impurities by automated iterative LC-MS/MS (HCP-AIMS) for therapeutic protein development.

Host cell proteins (HCPs) are process-related impurities expressed by the host cells used for production of therapeutic proteins. Although an extensive purification process removes most of the HCPs, residual HCPs are commonly present in protein therapeutics. If not well-controlled, certain HCPs may be present in the product, impacting drug stability, and potentially affecting product safety leading to safety risks for patients. Therefore, as a critical quality attribute, the levels of HCPs must be closely monitored during drug development and determined in the final drug substance at release. Liquid chromatography-mass spectrometry (LC-MS) as an orthogonal approach to traditional ELISA for HCP analysis has shown tremendous value in drug process development and analytical characterization by providing additional critical information, such as protein identity and relative abundance of individual HCPs. To meet the challenges in HCP analysis during drug development, especially downstream process development, which entails fast turnaround time and robustness while identifying high level of HCPs and their clearance trend for further purification development, we have developed HCP-automated iterative MS (HCP-AIMS): it is a simple, automated and robust HCP analysis workflow with deep and unbiased identification and relative quantification capability. This HCP-AIMS approach only requires easy direct digestion of the samples without enrichment or pre- treatment. With the fully automated precursor ion exclusion in MS/MS mode, low abundance HCP peptides could be selected for MS/MS analysis in iterative replicates, and therefore, the identification of HCPs at low abundance can be achieved. Using an in-house mAb with various levels of spiked-in HCPs as well as the NIST mAb, we were able to achieve unbiased identification and quantitation of HCPs as low as 10 ppm level. Furthermore, robustness of the HCP-AIMS approach was also confirmed for the feasibility of large-scale and high-throughput analysis.

Continuous Model Adaptation Using Online Meta-Learning for Smart Grid Application.

The rapid development of deep learning algorithms provides us an opportunity to better understand the complexity in engineering systems, such as the smart grid. Most of the existing data-driven predictive models are trained using historical data and fixed during the execution stage, which cannot adapt well to real-time data. In this research, we propose a novel online meta-learning (OML) algorithm to continuously adapt pretrained base-learner through efficiently digesting real-time data to adaptively control the base-learner parameters using meta-optimizer. The simulation results show that: 1) both ML and OML can perform significantly better than online base learning. 2) OML can perform better than ML and online base learning when the training data are limited, or the training and real-time data have very different time-variant patterns.

Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways.

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases' lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

Unravelling the importance of the eukaryotic and bacterial communities and their relationship with Legionella spp. ecology in cooling towers: a complex network.

BACKGROUND: Cooling towers are a major source of large community-associated outbreaks of Legionnaires' disease, a severe pneumonia. This disease is contracted when inhaling aerosols that are contaminated with bacteria from the genus Legionella, most importantly Legionella pneumophila. How cooling towers support the growth of this bacterium is still not well understood. As Legionella species are intracellular parasites of protozoa, it is assumed that protozoan community in cooling towers play an important role in Legionella ecology and outbreaks. However, the exact mechanism of how the eukaryotic community contributes to Legionella ecology is still unclear. Therefore, we used 18S rRNA gene amplicon sequencing to characterize the eukaryotic communities of 18 different cooling towers. The data from the eukaryotic community was then analysed with the bacterial community of the same towers in order to understand how each community could affect Legionella spp. ecology in cooling towers. RESULTS: We identified several microbial groups in the cooling tower ecosystem associated with Legionella spp. that suggest the presence of a microbial loop in these systems. Dissolved organic carbon was shown to be a major factor in shaping the eukaryotic community and may be an important factor for Legionella ecology. Network analysis, based on co-occurrence, revealed that Legionella was correlated with a number of different organisms. Out of these, the bacterial genus Brevundimonas and the ciliate class Oligohymenophorea were shown, through in vitro experiments, to stimulate the growth of L. pneumophila through direct and indirect mechanisms. CONCLUSION: Our results suggest that Legionella ecology depends on the host community, including ciliates and on several groups of organisms that contribute to its survival and growth in the cooling tower ecosystem. These findings further support the idea that some cooling tower microbiomes may promote the survival and growth of Legionella better than others. Video Abstract.

Impact of temperature on Legionella pneumophila, its protozoan host cells, and the microbial diversity of the biofilm community of a pilot cooling tower.

Legionella pneumophila is a waterborne bacterium known for causing Legionnaires' Disease, a severe pneumonia. Cooling towers are a major source of outbreaks, since they provide ideal conditions for L. pneumophila growth and produce aerosols. In such systems, L. pneumophila typically grow inside protozoan hosts. Several abiotic factors such as water temperature, pipe material and disinfection regime affect the colonization of cooling towers by L. pneumophila. The local physical and biological factors promoting the growth of L. pneumophila in water systems and its spatial distribution are not well understood. Therefore, we built a lab-scale cooling tower to study the dynamics of L. pneumophila colonization in relationship to the resident microbiota and spatial distribution. The pilot was filled with water from an operating cooling tower harboring low levels of L. pneumophila. It was seeded with Vermamoeba vermiformis, a natural host of L. pneumophila, and then inoculated with L. pneumophila. After 92 days of operation, the pilot was disassembled, the water was collected, and biofilm was extracted from the pipes. The microbiome was studied using 16S rRNA and 18S rRNA genes amplicon sequencing. The communities of the water and of the biofilm were highly dissimilar. The relative abundance of Legionella in water samples reached up to 11% whereas abundance in the biofilm was extremely low (≤0.5%). In contrast, the host cells were mainly present in the biofilm. This suggests that L. pneumophila grows in host cells associated with biofilm and is then released back into the water following host cell lysis. In addition, water temperature shaped the bacterial and eukaryotic community of the biofilm, indicating that different parts of the systems may have different effects on Legionella growth.

Discovery of talmapimod analogues as polypharmacological anti-inflammatory agents.

Twenty novel talmapimod analogues were designed, synthesised and evaluated for the in vivo anti-inflammatory activities. Among them, compound 6n, the most potent one, was selected for exploring the mechanisms underlying its anti-inflammatory efficacy. In RAW264.7 cells, it effectively suppressed lipopolysaccharides-induced (LPS-induced) expressions of iNOS and COX-2. As illustrated by the western blot analysis, 6n downregulated both the NF-κB signalling and p38 MAPK phosphorylation. Further enzymatic assay identified 6n as a potent inhibitor against both p38α MAPK (IC50=1.95 µM) and COX-2 (IC50=0.036 µM). By virtue of the concomitant inhibition of p38α MAPK, its upstream effector, and COX-2, along with its capability to downregulate NF-κB and MAPK-signalling pathways, 6n, a polypharmacological anti-inflammatory agent, deserves further development as a novel anti-inflammatory drug.

Design, synthesis, and biological evaluation of tetrahydroisoquinoline-based diaryl urea derivatives for suppressing VEGFR-2 signaling.

A novel structural series of tetrahydroisoquinoline-based compounds that incorporate the diaryl urea moiety was designed, synthesized, and biologically evaluated as suppressors of VEFGR-2 signaling. As a consequence, compounds 9k and 9s exhibited comparable or superior cytotoxic activity to that of gefitinib against the tested three cell lines, including A549, MCF-7, and PC-3. Importantly, both of them downregulated the expression of VEGFR-2, and inhibited VEGFR-2 phosphorylation at the concentration of 0.5 or 1.0 µmol/l. Besides, they suppressed human umbilical vein endothelial cell tube formation at the concentration of 4.0 µmol/l. Considering their capability of down-regulating VEGFR-2 expression and inhibiting VEGFR-2 phosphorylation, 9k and 9s may serve as suppressors of angiogenesis for further investigation.

Discovery of traditional Chinese medicine monomers and their synthetic intermediates, analogs or derivatives for battling P-gp-mediated multi-drug resistance.

P-glycoprotein (P-gp)-mediated multi-drug resistance (MDR) is a well-documented and predominant phenotype hampering patients' response to cancer chemotherapy. Although the past several decades have witnessed the development of three generations of P-gp inhibitors, they have not lived up to the high expectations owing to their drawbacks, as exemplified by limited efficacy, drug-drug interactions (DDIs) and severe untoward reactions. The discovery of artemisinin is a testimony of the importance of traditional Chinese medicine (TCM) in innovative drug discovery. In search for a new generation of chemo-sensitizers, P-gp modulators originated from TCM have attracted increasing concern in the research community. In addition to identify TCM monomers or their synthetic intermediates as P-gp modulators, massive medicinal chemistry efforts have been made in discovering promising structural analogs and derivatives of them. Among these, compounds with dual role both as P-gp inhibitor and cytotoxic agent have continuously emerged. Hence, in this article, we will mainly enumerate the representative work conducted in the discovery of TCM monomers and their synthetic intermediates, analogs or derivatives as reversers of P-gp-mediated MDR.

Design, synthesis chalcone derivatives as AdipoR agonist for type 2 diabetes.

Two structurally novel series of chalcone derivatives were designed and synthesized as potential agents against type 2 diabetes. As a result of the antidiabetic biological evaluation in streptozotocin (STZ)-induced type 2 diabetes animal model, 13e, 13g, and 19f showed more significant reduction in serum Glu, TG, TC levels by contrast to the positive control AdipoRon. In addition to upregulating the expression of AdipoR1 and AdipoR2, 13e and 19f treatment also increased expression of AMPK and PPAR-α. Taken together, these results suggested that 13e and 19f might be a promising compound for type 2 diabetes treatment.