Hepatic metabolism of artemisinin drugs--I. Drug metabolism in rat liver microsomes.

1. In this communication, metabolism of the semisynthetic antimalarial drugs of the artemisinin class (beta-arteether, beta-artelinic acid and dihydroartemisinin) in rat liver microsomes, is reported. 2. Dihydroartemisinin was the major early metabolite of arteether (57%) and artelinic acid (80%); in addition, arteether was hydroxylated in the positions 9 alpha- and 2 alpha- of the molecule. 3. Dihydroartemisinin was further metabolized by extensive hydroxylation of its molecule; we were able to identify four hydroxylated derivatives of DQHS, but not the exact positions of the hydroxyl groups. 4. The rates of NADPH-supported metabolism of arteether, artelinic acid and dihydroartemisinin in rat liver microsomes were: 4.0, 2.5 and 1.3 nmol/min/mg of microsomal protein, respectively. 5. The apparent affinity constants of arteether and artelinic acid for the microsomal metabolizing system, calculated from the rates of product formation, were 0.54 mM and 0.33 mM (for arteether) and 0.11 mM (for artelinic acid), respectively. The appearance of two affinity constants indicated that arteether was metabolized by two different isoenzymes of cytochrome P-450 in rat liver microsomes.     

Acid-sensing ion channel 3 mediates peripheral anti-hyperalgesia effects of acupuncture in mice inflammatory pain

Background  

Peripheral tissue inflammation initiates hyperalgesia accompanied by tissue acidosis, nociceptor activation, and inflammation mediators. Recent studies have suggested a significantly increased expression of acid-sensing ion channel 3 (ASIC3) in both carrageenan- and complete Freund's adjuvant (CFA)-induced inflammation. This study tested the hypothesis that acupuncture is curative for mechanical hyperalgesia induced by peripheral inflammation.     

Protein structure similarity from principle component correlation analysis

Background  

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.     

Mast cells and inflammation

Mast cells are well known for their role in allergic and anaphylactic reactions, as well as their involvement in acquired and innate immunity. Increasing evidence now implicates mast cells in inflammatory diseases where they are activated by non-allergic triggers, such as neuropeptides and cytokines, often exerting synergistic effects as in the case of IL-33 and neurotensin. Mast cells can also release pro-inflammatory mediators selectively without degranulation. In particular, IL-1 induces selective release of IL-6, while corticotropin-releasing hormone secreted under stress induces the release of vascular endothelial growth factor. Many inflammatory diseases involve mast cells in cross-talk with T cells, such as atopic dermatitis, psoriasis and multiple sclerosis, which all worsen by stress. How mast cell differential responses are regulated is still unresolved. Preliminary evidence suggests that mitochondrial function and dynamics control mast cell degranulation, but not selective release. Recent findings also indicate that mast cells have immunomodulatory properties. Understanding selective release of mediators could explain how mast cells participate in numerous diverse biologic processes, and how they exert both immunostimulatory and immunosuppressive actions. Unraveling selective mast cell secretion could also help develop unique mast cell inhibitors with novel therapeutic applications. This article is part of a Special Issue entitled: Mast cells in inflammation.     

Quercetin is more effective than cromolyn in blocking human mast cell cytokine release and inhibits contact dermatitis and photosensitivity in humans

Mast cells are immune cells critical in the pathogenesis of allergic, but also inflammatory and autoimmune diseases through release of many pro-inflammatory cytokines such as IL-8 and TNF. Contact dermatitis and photosensitivity are skin conditions that involve non-immune triggers such as substance P (SP), and do not respond to conventional treatment. Inhibition of mast cell cytokine release could be effective therapy for such diseases. Unfortunately, disodium cromoglycate (cromolyn), the only compound marketed as a mast cell "stabilizer", is not particularly effective in blocking human mast cells. Instead, flavonoids are potent anti-oxidant and anti-inflammatory compounds with mast cell inhibitory actions. Here, we first compared the flavonoid quercetin (Que) and cromolyn on cultured human mast cells. Que and cromolyn (100 μM) can effectively inhibit secretion of histamine and PGD(2). Que and cromolyn also inhibit histamine, leukotrienes and PGD(2) from primary human cord blood-derived cultured mast cells (hCBMCs) stimulated by IgE/Anti-IgE. However, Que is more effective than cromolyn in inhibiting IL-8 and TNF release from LAD2 mast cells stimulated by SP. Moreover, Que reduces IL-6 release from hCBMCs in a dose-dependent manner. Que inhibits cytosolic calcium level increase and NF-kappa B activation. Interestingly, Que is effective prophylactically, while cromolyn must be added together with the trigger or it rapidly loses its effect. In two pilot, open-label, clinical trials, Que significantly decreased contact dermatitis and photosensitivity, skin conditions that do not respond to conventional treatment. In summary, Que is a promising candidate as an effective mast cell inhibitor for allergic and inflammatory diseases, especially in formulations that permit more sufficient oral absorption.     

Conditional random pattern model for copy number aberration detection

Background  

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.     

Polyamines spermidine and spermine as modulators of calcium-dependent immune processes

The polyamines spermidine and spermine are ubiquitous in animal cells and have been shown to bind to cell membranes. At least two types of immune processes, secretion of active substances during inflammation and lymphocyte activation in cellular immunity, involve cell membranes and calcium ions. Although these processes are activated by polyvalent substances, spermidine and spermine appear to inhibit them. This action of polyamines is discussed in the context of regulating both cell membrane fluidity and calcium fluxes.     

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Background

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Results

Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.

Conclusions

We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.