Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 6 Articles
Accumulating clinical evidence show that not all rheumatoid arthritis (RA) patients\nbenefit to the same extent from a Tripterygium wilfordii Hook F (TwHF)-based\ntherapy-Tripterysium glycosides tablets (TG tablets), which emphasizes the need of\npredictive biomarkers and tools for drug response. Herein, we integrated TG tablets�\nresponse-related miRNA and mRNA expression profiles obtained from the clinical\ncohort-based microarray, miRNA target prediction, miRNA-target gene coexpression, as\nwell as gene-gene interactions, to identify four candidate circulating miRNA biomarkers\nthat were predictive of response to TG tablets. Moreover, we applied the support\nvector machines (SVM) algorithm to construct the prediction model for the treatment\noutcome of TG tablets based on the levels of the candidate miRNA biomarkers, and also\nconfirmed its good performance via both 5-fold cross-validation and the independent\nclinical cohort validations. Collectively, this circulating miRNA-based biomarker model\nmay assist in screening the responsive RA patients to TG tablets and thus potentially\nbenefit individualized therapy of RA in a daily clinical setting....
Inherited retinal dystrophies (IRDs) are a leading cause of visual impairment in the developing world. These conditions present an\nirreversible dysfunction or loss of neural retinal cells, which significantly impacts quality of life. Due to the anatomical accessibility\nand immunoprivileged status of the eye, ophthalmological research has been at the forefront of innovative and advanced gene- and\ncell-based therapies, both of which represent great potential as therapeutic treatments for IRD patients. However, due to a genetic\nand clinical heterogeneity, certain IRDs are not candidates for these approaches. New advances in the field of genome editing using\nClustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein (Cas) have provided an\naccurate and efficient way to edit the human genome and represent an appealing alternative for treating IRDs. We provide a\nbrief update on current gene augmentation therapies for retinal dystrophies. Furthermore, we discuss recent advances in the\nfield of genome editing and stem cell technologies, which together enable precise and personalized therapies for patients. Lastly,\nwe highlight current technological limitations and barriers that need to be overcome before this technology can become a viable\ntreatment option for patients....
5-aza-2,2-difluorodeoxycytidine (NUC013) has been shown to be significantly safer\nand more effective than decitabine in xenograft models of human leukemia and colon cancer.\nHowever, it suffers from a similar short half-life as other DNA methyltransferase inhibitors\nwith a 5-azacytosine base, which is problematic for nucleosides that primarily target tumor cells\nin S phase. Because of the relative instability of 5-azanucleosides, a prodrug approach was\ndeveloped to improve the pharmacology of NUC013. NUC013 was conjugated with trimethylsilanol\n(TMS) at the 3 and 5 position of the sugar, rendering the molecule hydrophobic and producing\n3,5-di-trimethylsilyl-2,2-difluoro-5-azadeoxycytidine (NUC041). NUC041 was designed to be\nformulated in a hydrophobic vehicle, protecting it from deamination and hydrolysis. In contact\nwith blood, the TMS moieties are readily hydrolyzed to release NUC013. The half-life of NUC013\nadministered intravenously in mice is 20.1 min, while that of NUC013 derived from intramuscular\nNUC041 formulated in a pegylated-phospholipid depot is 3.4 h. In a NCI-H460 xenograft of non-small\ncell lung cancer, NUC013 was shown to significantly inhibit tumor growth and improve survival.\nTreatment with NUC041 also led to significant tumor growth inhibition. However, NUC041-treated\nmice had significantly more tumors ulcerate than either NUC013 treated mice or saline control\nmice, and such ulceration occurred at significantly lower tumor volumes. In these nude mice,\ntumor regression was likely mediated by the derepression of the tumor suppressor gene p53 and\nresultant activation of natural killer (NK) cells....
The eye is at the forefront of the application of gene therapy techniques to medicine. In the United States, a gene therapy treatment\nfor Leber�s congenital amaurosis, a rare inherited retinal disease, recently became the first gene therapy to be approved by the FDA\nfor the treatment of disease caused by mutations in a specific gene. Phase III clinical trials of gene therapy for other single-gene\ndefect diseases of the retina and optic nerve are also currently underway. However, for optic nerve diseases not caused by\nsingle-gene defects, gene therapy strategies are likely to focus on slowing or preventing neuronal death through the expression\nof neuroprotective agents. In addition to these strategies, there has also been recent interest in the potential use of precise\ngenome editing techniques to treat ocular disease. This review focuses on recent developments in gene therapy techniques for\nthe treatment of glaucoma and Leber�s hereditary optic neuropathy (LHON). We discuss recent successes in clinical trials for\nthe treatment of LHON using gene supplementation therapy, promising neuroprotective strategies that have been employed in\nanimal models of glaucoma and the potential use of genome editing techniques in treating optic nerve disease....
Aim of the Review. The aim of this review is to discuss recent advances in clinical aspects of stem cell therapy in chronic\nnonischemic heart failure (DCMP) with emphasis on patient selection, stem cell types, and delivery methods. Recent Findings.\nSeveral stem cell types have been considered for the treatment of DCMP patients. Bone marrow-derived cells and CD34+ cells\nhave been demonstrated to improve myocardial performance, functional capacity, and neurohumoral activation. Furthermore,\nallogeneic mesenchymal stem cells were also shown to be effective in improving heart function in this patient population; this\nmay represent an important step towards the development of a standardized stem cell product for widespread clinical use in\npatients with DCMP. Summary. The trials of stem cell therapy in DCMP patients have shown some promising results, thus\nmaking DCMP apparently more inviting target for stem cell therapy than chronic ischemic heart failure, where studies to date\nfailed to demonstrate a consistent effect of stem cells on myocardial performance. Future stem cell strategies should aim for\nmore personalized therapeutic approach by establishing the optimal stem cell type or their combination, dose, and delivery\nmethod for an individual patient adjusted for patient�s age and stage of the disease....
The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for\nselecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if\ncomputational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts havemet with\nlimited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to\nidentify the potential biological effect of such treatment.While these efforts have met with some level of success, there exists much\nopportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response,\nthere is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies\nhave yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular\ndatasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for\ndisease factors.Here, we propose an algorithmto specifically predict synergistic effects of drug combinations on various diseases, by\nintegrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles.We have\ndemonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease\nprediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling\ndata such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to\nbe readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the\nalgorithm can provide the basis for further refinement towards addressing a large clinical need....
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