ß-Funaltrexamine Displayed Anti-inflammatory and Neuroprotective Effects in Cells and Rat Model of Stroke.

Chronic treatment involving opioids exacerbates both the risk and severity of ischemic stroke. We have provided experimental evidence showing the anti-inflammatory and neuroprotective effects of the µ opioid receptor antagonist ß-funaltrexamine for neurodegenerative diseases in rat neuron/glia cultures and a rat model of cerebral Ischemia/Reperfusion (I/R) injury. Independent of in vitro Lipopolysaccharide (LPS)/interferon (IFN-γ)-stimulated neuron/glia cultures and in vivo cerebral I/R injury in Sprague-Dawley rats, ß-funaltrexamine downregulated neuroinflammation and ameliorated neuronal degeneration. Alterations in microglia polarization favoring the classical activation state occurred in LPS/IFN-γ-stimulated neuron/glia cultures and cerebral I/R-injured cortical brains. ß-funaltrexamine shifted the polarization of microglia towards the anti-inflammatory phenotype, as evidenced by decreased nitric oxide, tumor necrosis factor-α, interleukin-1ß, and prostaglandin E2, along with increased CD163 and arginase 1. Mechanistic studies showed that the suppression of microglia pro-inflammatory polarization by ß-funaltrexamine was accompanied by the reduction of NF-κB, AP-1, cyclic AMP response element-binding protein, along with signal transducers and activators of transcription transcriptional activities and associated upstream activators. The effects of ß-funaltrexamine are closely linked with its action on neuroinflammation by switching microglia polarization from pro-inflammatory towards anti-inflammatory phenotypes. These findings provide new insights into the anti-inflammatory and neuroprotective mechanisms of ß-funaltrexamine in combating neurodegenerative diseases, such as stroke.

Development of 3D-QSAR combination approach for discovering and analysing neuraminidase inhibitors in silico.

Zanamivir and Oseltamivir are both sialic acid analog inhibitors of Neuraminidase (NA), which is an important target in influenza A virus treatment. Quantitative Structure-Activity Relationships (QSAR) is a common computational method for correlating the structural properties of compounds (or inhibitors) with their biological activities. The pharmcophore model easily and quickly recognises related inhibitors and also fits the binding site interaction features of a protein structure. The Comparative Molecular Similarity Index Analysis (CoMSIA) model easily optimises molecular structures and describes the limit range of molecule weights. This study proposes a combination approach that integrates these two models based on the same training set inhibitors in order to screen and optimize NA inhibitor candidates during drug design.

Development of a human dihydroorotate dehydrogenase (hDHODH) pharma-similarity index approach with scaffold-hopping strategy for the design of novel potential inhibitors.

Human dihydroorotate dehydrogenase (hDHODH) is a class-2 dihydroorotate dehydrogenase. Because it is extensively used by proliferating cells, its inhibition in autoimmune and inflammatory diseases, cancers, and multiple sclerosis is of substantial clinical importance. In this study, we had two aims. The first was to develop an hDHODH pharma-similarity index approach (PhSIA) using integrated molecular dynamics calculations, pharmacophore hypothesis, and comparative molecular similarity index analysis (CoMSIA) contour information techniques. The approach, for the discovery and design of novel inhibitors, was based on 25 diverse known hDHODH inhibitors. Three statistical methods were used to verify the performance of hDHODH PhSIA. Fischer's cross-validation test provided a 98% confidence level and the goodness of hit (GH) test score was 0.61. The q(2), r(2), and predictive r(2) values were 0.55, 0.97, and 0.92, respectively, for a partial least squares validation method. In our approach, each diverse inhibitor structure could easily be aligned with contour information, and common substructures were unnecessary. For our second aim, we used the proposed approach to design 13 novel hDHODH inhibitors using a scaffold-hopping strategy. Chemical features of the approach were divided into two groups, and the Vitas-M Laboratory fragment was used to create de novo inhibitors. This approach provides a useful tool for the discovery and design of potential inhibitors of hDHODH, and does not require docking analysis; thus, our method can assist medicinal chemists in their efforts to identify novel inhibitors.

Clinical relevance and genotypes of circulating noroviruses in northern Taiwan, 2006-2011.

The incidence of noroviral gastroenteritis has increased dramatically in recent years, and norovirus (NoV) genogroup II.4 (GII.4) is associated with outbreaks worldwide. The NoV genotypes and their clinical relevance in children hospitalized with acute gastroenteritis between 2006 and 2011 in northern Taiwan were evaluated in this study. NoV sequences were amplified from 47 clinical specimens and phylogenetic analysis was performed. Based on noroviral capsid protein (VP1) and RNA dependent RNA polymerase (RdRp) phylogeny, circulating NoV could be divided into GII.2, GII.3, GII.12, and GII.4 and GII.16, GII.12, GII.g, and GII.4; respectively. The GII.4 subtype was predominant and could be divided further into the 2004 (Hunter), 2006b, and 2010 (New Orleans) subtypes. Regarding clinical manifestations, convulsive disorder occurred only in cases caused by NoV GII.4 2006b. Patients affected by NoV GII.4 2006b presented with a higher frequency of diarrhea (P = 0.0204), longer duration of diarrhea (P = 0.0215), more frequent hypoglycemia (P = 0.038), and electrolyte imbalance (P = 0.0487) than acute gastroenteritis caused by NoV GII.4 2010. Structural analysis showed that the amino acid changes in viral VP1 between GII.4 2006b and 2010 subtype were located mainly in the protruding domain 2 (P2 domain). In conclusion, the NoV GII.4 variants 2006b and 2010 were the main causes of acute gastroenteritis in hospitalized children in northern Taiwan during 2006-2011. The clinical presentations and structural changes in VP1 of the two NoV GII.4 variants should be evaluated in the future.

Evolutionarily conserved residues at an oligomerization interface of the influenza A virus neuraminidase are essential for viral survival.

Neuraminidase (NA) is a homotetramer viral surface glycoprotein that is essential for virus release during influenza virus infections. Previous studies have not explored why influenza NA forms a tetramer when the bacterial monomer NA already exhibits excellent NA enzymatic activity levels. In this study, we focused on 28 highly conserved residues among all NA subtypes, identifying 21 of 28 positions as crucial residues for viral survival by using reverse genetics. Maintaining NA enzymatic activity levels is critical and numerous conserved residues were located at the oligomerization interface; however, these mutations did not affect NA enzymatic activity levels or NA cellular localization, but rather affected the stability of NA oligomerization, suggesting that the oligomerization of NA is essential for viral viability. An increased understanding of the biological functions of NA, in particular NA oligomerization, could facilitate an alternative design for antivirals to combat influenza virus infections.

Design checkpoint kinase 2 inhibitors by pharmacophore modeling and virtual screening techniques.

Damage to DNA is caused by ionizing radiation, genotoxic chemicals or collapsed replication forks. When DNA is damaged or cells fail to respond, a mutation that is associated with breast or ovarian cancer may occur. Mammalian cells control and stabilize the genome using a cell cycle checkpoint to prevent damage to DNA or to repair damaged DNA. Checkpoint kinase 2 (Chk2) is one of the important kinases, which strongly affects DNA-damage and plays an important role in the response to the breakage of DNA double-strands and related lesions. Therefore, this study concerns Chk2. Its purpose is to find potential inhibitors using the pharmacophore hypotheses (PhModels) and virtual screening techniques. PhModels can identify inhibitors with high biological activities and virtual screening techniques are used to screen the database of the National Cancer Institute (NCI) to retrieve compounds that exhibit all of the pharmacophoric features of potential inhibitors with high interaction energy. Ten PhModels were generated using the HypoGen best algorithm. The established PhModel, Hypo01, was evaluated by performing a cost function analysis of its correlation coefficient (r), root mean square deviation (RMSD), cost difference, and configuration cost, with the values 0.955, 1.28, 192.51, and 16.07, respectively. The result of Fischer's cross-validation test for the Hypo01 model yielded a 95% confidence level, and the correlation coefficient of the testing set (rtest) had a best value of 0.81. The potential inhibitors were then chosen from the NCI database by Hypo01 model screening and molecular docking using the cdocker docking program. Finally, the selected compounds exhibited the identified pharmacophoric features and had a high interaction energy between the ligand and the receptor. Eighty-three potential inhibitors for Chk2 are retrieved for further study.

Design of novel FLT-3 inhibitors based on dual-layer 3D-QSAR model and fragment-based compounds in silico.

FMS-like tyrosine kinase 3 (FLT-3) is strongly correlated with acute myeloid leukemia, but no FLT-3-inhibitor cocomplex structure is available to assist the design of therapeutic inhibitors. Hence, we propose a dual-layer 3D-QSAR model for FLT-3 that integrates the pharmacophore, CoMFA, and CoMSIA. We then coupled the model with the fragment-based design strategy to identify novel FLT-3 inhibitors. In the first layer, the previously established model, Hypo02, was evaluated in terms of its correlation coefficient (r), RMS, cost difference, and configuration cost, with values of 0.930, 1.24, 106.45, and 16.44, respectively. Moreover, Fischer's cross-validation test of data generated by Hypo02 yielded a 98% confidence level, and the validation of the testing set yielded a best r value of 0.87. The features of Hypo02 were separated into two parts and then used to screen the MiniMaybridge fragment compound database. Nine novel FLT-3 inhibitors were generated in this layer. In the second layer, Hypo02 was subjected to an alignment rule to generate CoMFA- and CoMSIA-based models, for which the partial least-squares validation method was utilized. The values of q(2), r(2), and predictive r(2) were 0.58, 0.98, and 0.76, respectively, derived from the CoMFA model with steric and electrostatic fields. The CoMSIA model with five different fields yielded values of 0.54, 0.97, and 0.76 for q(2), r(2), and predictive r(2), respectively. The CoMFA and CoMSIA models were used to constrain 3D structures of the nine novel FLT-3 inhibitors. This dual-layer 3D-QSAR model constitutes a valuable tool to easily and quickly screen and optimize novel potential FLT-3 inhibitors for the treatment of acute myeloid leukemia.

Pharmacophore modeling and virtual screening to identify potential RET kinase inhibitors.

Chemical features based 3D pharmacophore model for REarranged during Transfection (RET) tyrosine kinase were developed by using a training set of 26 structurally diverse known RET inhibitors. The best pharmacophore hypothesis, which identified inhibitors with an associated correlation coefficient of 0.90 between their experimental and estimated anti-RET values, contained one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic, and one ring aromatic features. The model was further validated by a testing set, Fischer's randomization test, and goodness of hit (GH) test. We applied this pharmacophore model to screen NCI database for potential RET inhibitors. The hits were docked to RET with GOLD and CDOCKER after filtering by Lipinski's rules. Ultimately, 24 molecules were selected as potential RET inhibitors for further investigation.

Development of novel 3D-QSAR combination approach for screening and optimizing B-Raf inhibitors in silico.

B-Raf is a member of the RAF family of serine/threonine kinases: it mediates cell division, differentiation, and apoptosis signals through the RAS-RAF-MAPK pathway. Thus, B-Raf is of keen interest in cancer therapy, such as melanoma. In this study, we propose the first combination approach to integrate the pharmacophore (PhModel), CoMFA, and CoMSIA models for B-Raf, and this approach could be used for screening and optimizing potential B-Raf inhibitors in silico. Ten PhModels were generated based on the HypoGen BEST algorithm with the flexible fit method and diverse inhibitor structures. Each PhModel was designated to the alignment rule and screening interface for CoMFA and CoMSIA models. Therefore, CoMFA and CoMSIA models could align and recognize diverse inhibitor structures. We used two quality validation methods to test the predication accuracy of these combination models. In the previously proposed combination approaches, they have a common factor in that the number of training set inhibitors is greater than that of testing set inhibitors. In our study, the 189 known diverse series B-Raf inhibitors, which are 7-fold the number of training set inhibitors, were used as a testing set in the partial least-squares validation. The best validation results were made by the CoMFA09 and CoMSIA09 models based on the Hypo09 alignment model. The predictive r(2)(pred) values of 0.56 and 0.56 were derived from the CoMFA09 and CoMSIA09 models, respectively. The CoMFA09 and CoMSIA09 models also had a satisfied predication accuracy of 77.78% and 80%, and the goodness of hit test score of 0.675 and 0.699, respectively. These results indicate that our combination approach could effectively identify diverse B-Raf inhibitors and predict the activity.