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Showing 2601 to 2604 of 5186 entries
Published on March 1, 2020
Exploration of the Molecular Mechanism of FUZI (Aconiti Lateralis Radix Praeparata) in Allergic Rhinitis Treatment Based on Network Pharmacology.
Abstract: FUZI (Aconiti Lateralis Radix Praeparata) is a traditional Chinese medicine herb used extensively for nourishing yang (regarded as the positive, male universal force), which is critical in treatment of allergic rhinitis. In this paper, FUZI was explored based on network pharmacology. The active components of FUZI were screened out, its protein targets were assessed, and the protein interaction network map was built with the differential protein of allergic rhinitis, as an attempt to determine the critical targets of FUZI for treating allergic rhinitis. Subsequently, DAVID was employed to explore the biological function and pathway enrichment to determine the biological pathway of FUZI for treating allergic rhinitis. As suggested by the results, FUZI is likely to affect the inhibition of inflammation and the regulation of immunity, probably reducing the incidence of allergic rhinitis, or alleviating nasal discomfort attributed to allergic inflammation. The targets and pathways of FUZI for treating allergic rhinitis assessed by network pharmacology provided a direction for our subsequent studies and may be a novel therapeutic target.
Published in February 2020
SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association.
Abstract: Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed many computational models to reveal miRNA-SM associations due to the huge cost of experimental methods. Considering the shortcomings of the previous models, such as the prediction accuracy of some models is low or some cannot be applied for new SMs (miRNAs), we developed a novel model named Symmetric Nonnegative Matrix Factorization for Small Molecule-MiRNA Association prediction (SNMFSMMA). Different from some models directly applying the integrated similarities, SNMFSMMA first performed matrix decomposition on the integrated similarity matrixes, and calculated the Kronecker product of the new integrated similarity matrixes to obtain the SM-miRNA pair similarity. Further, we applied regularized least square to obtain the mapping function of the SM-miRNA pairs to the associated probabilities by minimizing the objective function. On the basis of Dataset 1 and 2 extracted from SM2miR v1.0 database, we implemented global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold cross-validation to evaluate the prediction performance. Finally, the AUC values obtained by SNMFSMMA in these validation reached 0.9711 (0.8895), 0.9698 (0.8884), 0.8329 (0.7651) and 0.9644 +/- 0.0035 (0.8814 +/- 0.0033) based on Dataset 1 (Dataset 2), respectively. In the first case study, 5 of the top 10 associations predicted were confirmed. In the second, 7 and 8 of the top 10 predicted miRNAs related with 5-FU and 5-Aza-2'-deoxycytidine were confirmed. These results demonstrated the reliable predictive power of SNMFSMMA.
Published in February 2020
Cell death and survival due to cytotoxic exposure modelled as a two-state Ising system.
Abstract: Cancer chemotherapy agents are assessed for their therapeutic utility primarily by their ability to cause apoptosis of cancer cells and their potency is given by an IC50 value. Chemotherapy uses both target-specific and systemic-action drugs and drug combinations to treat cancer. It is important to judiciously choose a drug type, its dosage and schedule for optimized drug selection and administration. Consequently, the precise mathematical formulation of cancer cells' response to chemotherapy may assist in the selection process. In this paper, we propose a mathematical description of the cancer cell response to chemotherapeutic agent exposure based on a time-tested physical model of two-state multiple-component systems near criticality. We describe the Ising model methodology and apply it to a diverse panel of cytotoxic drugs administered against numerous cancer cell lines in a dose-response manner. The analysed dataset was generated by the Netherlands Translational Research Center B.V. (Oncolines). This approach allows for an accurate and consistent analysis of cytotoxic agents' effects on cancer cell lines and reveals the presence or absence of the bystander effect through the interaction constant. By calculating the susceptibility function, we see the value of IC50 coinciding with the peak of this measure of the system's sensitivity to external perturbations.
Published in February 2020
Bleomycin Induces Drug Efflux in Lungs. A Pitfall for Pharmacological Studies of Pulmonary Fibrosis.
Abstract: ATP-binding cassette (ABC) transporters are evolutionarily conserved membrane proteins that pump a variety of endogenous substrates across cell membranes. Certain subfamilies are known to interact with pharmaceutical compounds, potentially influencing drug delivery and treatment efficacy. However, the role of drug resistance-associated ABC transporters has not been examined in idiopathic pulmonary fibrosis (IPF) or its animal model: the bleomycin (BLM)-induced murine model. Here, we investigate the expression of two ABC transporters, P-gp (permeability glycoprotein) and BCRP (breast cancer resistance protein), in human IPF lung tissue and two different BLM-induced mouse models of pulmonary fibrosis. We obtained human IPF specimens from patients during lung transplantation and administered BLM to male C57BL/6J mice either by oropharyngeal aspiration (1 U/kg) or subcutaneous osmotic infusion (100 U/kg over 7 d). We report that P-gp and BCRP expression in lungs of patients with IPF was comparable to controls. However, murine lungs expressed increased levels of P-gp and BCRP after oropharyngeal and subcutaneous BLM administration. We localized this upregulation to multiple pulmonary cell types, including alveolar fibroblasts, endothelial cells, and type 2 epithelial cells. Functionally, this effect reduced murine lung exposure to nintedanib, a U.S. Food and Drug Administration-approved IPF therapy known to be a P-gp substrate. The study reveals a discrepancy between IPF pathophysiology and the common animal model of lung fibrosis. BLM-induced drug efflux in the murine lungs may present an uncontrolled confounding variable in the preclinical study of IPF drug candidates, and these findings will facilitate disease model validation and enhance new drug discoveries that will ultimately improve patient outcomes.
Published in February 2020
Current molecular aspects in the development and treatment of diabetes.
Abstract: Diabetes mellitus (DM) leads to microvascular, macrovascular, and neurological complications. Less is understood about the mechanisms of this disease that give rise to weak bones. The many molecular mechanisms proposed to explain the damage caused by chronic hyperglycemia are organ and tissue dependent. Since all the different treatments for DM involve therapeutic activity combined with side effects and each patient represents a unique condition, there is no generalized therapy. The alterations stemming from hyperglycemia affect metabolism, osmotic pressure, oxidative stress, and inflammation. In part, hemodynamic modifications are linked to the osmotic potential of the excess of carbohydrates implicated in the disease. The change in osmotic balance increases as the disease progresses because hyperglycemia becomes chronic. The aim of the current contribution is to provide an updated overview of the molecular mechanisms that participate in the development and treatment of diabetes.
Published in February 2020
Emerging Challenges to the Safe and Effective Use of Methadone for Cancer-Related Pain in Paediatric and Adult Patient Populations.
Abstract: Methadone continues to be an important medication for the treatment of paediatric and adult cancer-related pain. Appropriate patient selection to ensure safe and effective treatment by a team of clinicians who appreciate and are familiar with methadone and its unique pharmacology is crucial. Unlike morphine and other more common opioids, methadone is purported to have involvement with delta-opioid receptor and higher affinity as an N-methyl-D-aspartate-receptor antagonist. Clinically this gives it the advantage of being effective for both nociceptive and neuropathic pain, but also may be useful in the setting of tolerance to other opioids. Methadone also comes in multiple available formulations that can be administrated through a variety of routes beyond the oral route. Challenges with methadone in treating cancer-related pain include drug interactions specifically as it relates to new targeted cancer therapies. Recent guidelines recommend electrocardiogram monitoring with methadone and there is potential for additive cardiac toxicity in the oncology setting. Appropriate dosing of methadone for pain management given age, organ dysfunction, and patients who are on methadone maintenance therapy are also key factors. This article aims to provide clinicians with evidence and clinical practice guidelines for safe and appropriate use of methadone including indication, initiation, and monitoring given its complexity for management of pain in the dynamic oncology setting.
Published in February 2020
Classification of clear cell renal cell carcinoma based on PKM alternative splicing.
Abstract: Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.
Published in February 2020
CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.
Abstract: BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of approximately 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.
Published in February 2020
Applying whole-genome sequencing in relation to phenotype and outcomes in siblings with cystic fibrosis.
Abstract: Variations in disease onset and/or severity have often been observed in siblings with cystic fibrosis (CF), despite the same CFTR genotype and environment. We postulated that genomic variation (modifier and/or pharmacogenomic variants) might explain these clinical discordances. From a cohort of patients included in the Wisconsin randomized clinical trial (RCT) of newborn screening (NBS) for CF, we identified two brothers who showed discordant lung disease courses as children, with one milder and the other more severe than average, and a third, eldest brother, who also has severe lung disease. Leukocytes were harvested as the source of DNA, and whole-genome sequencing (WGS) was performed. Variants were identified and analyzed using in-house-developed informatics tools. Lung disease onset and severity were quantitatively different between brothers during childhood. The youngest, less severely affected brother is homozygous for HFE p.H63D. He also has a very rare PLG p.D238N variant that may influence host-pathogen interaction during chronic lung infection. Other variants of interest were found differentially between the siblings. Pharmacogenomics findings were consistent with the middle, most severely affected brother having poor outcomes to common CF treatments. We conclude that genomic variation between siblings with CF is expected. Variable lung disease severity may be associated with differences acting as genetic modifiers and/or pharmacogenomic factors, but large cohort studies are needed to assess this hypothesis.
Published in February 2020
Discovery of sulfone-resistant dihydropteroate synthase (DHPS) as a target enzyme for kaempferol, a natural flavanoid.
Abstract: Kaempferol is a ubiquitous flavonoid, found in various plants having a wide range of known pharmacological activities, including antioxidant, antiinflammatory, anticancer, antiallergic, antidiabetic, neuroprotective, cardioprotective and antimicrobial activities. Nonetheless various evidence suggest that kaempferol is also able to interact with many unknown therapeutic targets modulating signalling pathways, thus providing an opportunity to explore the potential target space of kaempferol. In this study, we have employed various ligand-based approaches to identify the potential targets of kaempferol, followed by validations using modelling and docking studies. Molecular dynamics, free energy calculations, volume and residue contact map analyses were made to delineate the cause of drug-resistance among mutants. We have discovered dihydropteroate synthase (DHPS) as a novel potential therapeutic target for kaempferol. Further studies employing molecular dynamics simulations and binding free energies indicate that kaempferol has potential to inhibit even the sulfone-resistant DHPS mutants, which makes it a very attractive antibiotic agent. The identification of natural-product based kaempferol opens up the door for the design of antibiotics in a quick and high throughput fashion for identifying antibiotic leads.
Showing 2601 to 2604 of 5186 entries