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Showing 1811 to 1814 of 5186 entries
Published in March 2021
Enhancement of the Mechanical and Drug-Releasing Properties of Poloxamer 407 Hydrogels with Casein.
Abstract: PURPOSE: Topical therapy of local disease (e.g. skin) is advantageous over oral therapy since there is less systemic drug distribution (so fewer side-effects), no first-pass effect, etc. However, patient compliance with topical therapy can be poor as it may require many applications a day and can last months. Here we propose a topical controlled release formulation with thermoresponsive gelation at body temperature and improved adhesiveness, making it easier to remain in contact with the body. METHODS: The formulation contains two excipients, poloxamer 407 (P407) and casein. Casein can modify the properties of the hydrogel through molecular entanglement. In addition, tissue reaction and drug release profile were evaluated. RESULTS: Changes in casein concentration affected adhesive strength, viscosity, mechanical properties and drug release, presumably by hydrophobic interactions between casein and P407. Two different concentrations of P407 were tested with two different concentrations of casein. Formulations containing 5% and 10% casein released 80% of model drug in 48 h, while formulations without casein released the same fraction in around 24 h hours. Formulations with 10% casein had almost twice the adhesive strength of those without casein. CONCLUSIONS: Addition of casein modified the mechanical properties and drug release rate of the hydrogel. There was no inflammation or injury after brief exposure in vivo.
Published in March 2021
A potentially effective drug for patients with recurrent glioma: sermorelin.
Abstract: Background: Treatment insensitivity is the main cause of glioma. This study was designed to screen out effective drugs for recurrent gliomas based on the transcriptomics data. Methods: A total of 1,018 glioma patients with transcriptome sequencing data and clinical data were included in this study. There were 325 patients in the discovery cohort, including 229 primary patients and 92 recurrent patients. There were 693 patients in the validation cohort, including 422 primary patients and 271 relapsed patients. Drug Resistant Scores (DRS) of 4,865 drugs of each patient were used for screening. The analysis and drawing in this study were mainly based on R language. Results: After high-throughput drug screening, we found that recurrent glioma patients were most sensitive to sermorelin. Further analysis revealed that sermorelin was suitable for recurrent patients with high grade, IDH-wildtype and 1p/19q non-codeletion status. GO and KEGG analyses found that sermorelin may inhibit tumor cell proliferation by cell cycle blocking. Moreover, sermorelin was also related to the immune system process and negatively regulated immune checkpoints and M0 macrophages. Lastly, the Kaplan-Meier method showed the patient's benefit from sermorelin was independent of postoperative adjuvant treatment. Conclusions: Recurrent glioma patients are sensitive to sermorelin and it makes effect through glioma cells proliferation inhibiting and immune response enhancing.
Published on March 31, 2021
Drugmonizome and Drugmonizome-ML: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning.
Abstract: Understanding the underlying molecular and structural similarities between seemingly heterogeneous sets of drugs can aid in identifying drug repurposing opportunities and assist in the discovery of novel properties of preclinical small molecules. A wealth of information about drug and small molecule structure, targets, indications and side effects; induced gene expression signatures; and other attributes are publicly available through web-based tools, databases and repositories. By processing, abstracting and aggregating information from these resources into drug set libraries, knowledge about novel properties of drugs and small molecules can be systematically imputed with machine learning. In addition, drug set libraries can be used as the underlying database for drug set enrichment analysis. Here, we present Drugmonizome, a database with a search engine for querying annotated sets of drugs and small molecules for performing drug set enrichment analysis. Utilizing the data within Drugmonizome, we also developed Drugmonizome-ML. Drugmonizome-ML enables users to construct customized machine learning pipelines using the drug set libraries from Drugmonizome. To demonstrate the utility of Drugmonizome, drug sets from 12 independent SARS-CoV-2 in vitro screens were subjected to consensus enrichment analysis. Despite the low overlap among these 12 independent in vitro screens, we identified common biological processes critical for blocking viral replication. To demonstrate Drugmonizome-ML, we constructed a machine learning pipeline to predict whether approved and preclinical drugs may induce peripheral neuropathy as a potential side effect. Overall, the Drugmonizome and Drugmonizome-ML resources provide rich and diverse knowledge about drugs and small molecules for direct systems pharmacology applications. Database URL: https://maayanlab.cloud/drugmonizome/.
Published in March 2021
Frequency of Prescription Claims for Drugs that May Interact with Janus Kinase Inhibitors Among Patients with Rheumatoid Arthritis in the US.
Abstract: INTRODUCTION: This study describes the frequency of prescription claims for drugs that may interact with Janus kinase (JAK) inhibitors among adult patients with rheumatoid arthritis (RA) in a large US claims database. METHODS: This observational, retrospective, cross-sectional study of the IBM((R)) MarketScan((R)) Research Commercial and the Medicare Supplemental Database included adults (>/= 18 years) with >/= 2 outpatient claims 30 or more days apart or >/= 1 inpatient visit claim with an RA diagnosis between January 1, 2013 and March 31, 2017 (the index period). During the study period, from January 1, 2013 to March 31, 2018, strong organic anion transporter (OAT3) inhibitors, strong cytochrome P450 (CYP) 3A4 inhibitors, and moderate or strong CYP3A4 inhibitors in combination with strong CYP2C19 inhibitors, were identified as drugs with potential for drug-drug interactions (DDIs) with JAK inhibitors approved for RA treatment in the US. Descriptive statistics were conducted. RESULTS: A total of 152,853 patients met eligibility criteria. Approximately 76% were women and the median age was 57 years. Of these patients, < 0.1% had a claim for a strong OAT3 inhibitor, and 1% had claims for the combination of a strong CYP3A4 and strong CYP2C19 inhibitor; 3% of patients had a claim for a strong CYP3A4 inhibitor and almost 10% had claims for both a moderate CYP3A4 and a strong CYP2C19 inhibitor. CONCLUSIONS: Up to 10% of RA patients have been prescribed a drug with a potential JAK interaction. Rheumatologists should consider potential DDIs when managing patients with RA.
Published in March 2021
Network-based systems pharmacology reveals heterogeneity in LCK and BCL2 signaling and therapeutic sensitivity of T-cell acute lymphoblastic leukemia.
Abstract: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy, and novel therapeutics are much needed. Profiling patient leukemia' drug sensitivities ex vivo, we discovered that 44.4% of childhood and 16.7% of adult T-ALL cases exquisitely respond to dasatinib. Applying network-based systems pharmacology analyses to examine signal circuitry, we identified preTCR-LCK activation as the driver of dasatinib sensitivity, and T-ALL-specific LCK dependency was confirmed in genome-wide CRISPR-Cas9 screens. Dasatinib-sensitive T-ALLs exhibited high BCL-XL and low BCL2 activity and venetoclax resistance. Discordant sensitivity of T-ALL to dasatinib and venetoclax is strongly correlated with T-cell differentiation, particularly with the dynamic shift in LCK vs. BCL2 activation. Finally, single-cell analysis identified leukemia heterogeneity in LCK and BCL2 signaling and T-cell maturation stage, consistent with dasatinib response. In conclusion, our results indicate that developmental arrest in T-ALL drives differential activation of preTCR-LCK and BCL2 signaling in this leukemia, providing unique opportunities for targeted therapy.
Published in March 2021
Discovering pathways in benign prostate hyperplasia: A functional genomics pilot study.
Abstract: Benign prostate hyperplasia (BPH) is one of the well-known urological neoplasms common in males with an increasing number of associated deaths in aging males. It causes uncomfortable urinary symptoms, including urine flow blockage, and may cause bladder, urinary tract or kidney problems. The histopathological and clinical knowledge regarding BPH is limited. In the present study, an in silico approach was applied that uses genome-scale microarray expression data to discover a wide range of protein-protein interactions in addition to focusing on specific genes responsible for BPH to develop prognostic biomarkers. Various genes that were differentially expressed in BPH were identified. Gene and functional annotation clusters were determined and an interaction analysis with disease phenotypes of BPH was performed, as well as an RNA tissue specificity analysis. Furthermore, a molecular docking study of certain short-listed gene biomarkers, namely anterior gradient 2 (AGR2; PDB ID: 2LNT), steroid 5alpha-reductase 2 (PDB ID: 6OQX), zinc finger protein 3 (PDB ID: 5T00) and collagen type XII alpha1 chain (PDB ID: 1U5M), was performed in order to identify alternative Chinese herbal agents for the treatment of BPH. Data from the present study revealed that AGR2 receptor (PDB ID: 2LNT) and berberine (Huang Bo) form the most stable complex and therefore may be assessed in further pharmacological studies for the treatment of BPH.
Published in March 2021
Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics.
Abstract: Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation.
Published on March 31, 2021
The Main Alkaloids in Uncaria rhynchophylla and Their Anti-Alzheimer's Disease Mechanism Determined by a Network Pharmacology Approach.
Abstract: Alzheimer's disease (AD) is a growing concern in modern society, and effective drugs for its treatment are lacking. Uncaria rhynchophylla (UR) and its main alkaloids have been studied to treat neurodegenerative diseases such as AD. This study aimed to uncover the key components and mechanism of the anti-AD effect of UR alkaloids through a network pharmacology approach. The analysis identified 10 alkaloids from UR based on HPLC that corresponded to 90 anti-AD targets. A potential alkaloid target-AD target network indicated that corynoxine, corynantheine, isorhynchophylline, dihydrocorynatheine, and isocorynoxeine are likely to become key components for AD treatment. KEGG pathway enrichment analysis revealed the Alzheimers disease (hsa05010) was the pathway most significantly enriched in alkaloids against AD. Further analysis revealed that 28 out of 90 targets were significantly correlated with Abeta and tau pathology. These targets were validated using a Gene Expression Omnibus (GEO) dataset. Molecular docking studies were carried out to verify the binding of corynoxine and corynantheine to core targets related to Abeta and tau pathology. In addition, the cholinergic synapse (hsa04725) and dopaminergic synapse (hsa04728) pathways were significantly enriched. Our findings indicate that UR alkaloids directly exert an AD treatment effect by acting on multiple pathological processes in AD.
Published on March 30, 2021
Network bioinformatics analysis provides insight into drug repurposing for COVID-19.
Abstract: The COVID-19 disease caused by the SARS-CoV-2 virus is a health crisis worldwide. While developing novel drugs and vaccines is long, repurposing existing drugs against COVID-19 can yield treatments with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter clinical trials. In this study, we present a novel network-based drug repurposing platform to identify candidates for the treatment of COVID-19. At the time of the initial outbreak, knowledge about SARS-CoV-2 was lacking, but based on its similarity with other viruses, we sought to identify repurposing candidates to be tested rapidly at the clinical or preclinical levels. We first analyzed the genome sequence of SARS-CoV-2 and confirmed SARS as the closest virus by genome similarity, followed by MERS and other human coronaviruses. Using text mining and database searches, we obtained 34 COVID-19-related genes to seed the construction of a molecular network where our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules, and 78 drugs to repurpose. Based on clinical knowledge, we re-prioritized 30 potentially repurposable drugs against COVID-19 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide). Our work shows how in silico repurposing analyses can yield testable candidates to accelerate the response to novel disease outbreaks.
Published on March 30, 2021
Large-scale labeling and assessment of sex bias in publicly available expression data.
Abstract: BACKGROUND: Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. RESULTS: Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%). CONCLUSIONS: Our results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.
Showing 1811 to 1814 of 5186 entries