Dataset Information

Development of a Novel Class of Self-Assembling dsRNA Cancer Therapeutics: A Proof-of-Concept Investigation.

ABSTRACT: Cancer has proven to be an extremely difficult challenge to treat. Several fundamental issues currently underlie cancer treatment, including differentiating self from nonself, functional coupling of the recognition and therapeutic components of various therapies, and the propensity of cancerous cells to develop resistance to common treatment modalities via evolutionary pressure. Given these limitations, there is an increasing need to develop an all-encompassing therapeutic that can uniquely target malignant cells, decouple recognition from treatment, and overcome evolutionarily driven cancer resistance. We describe herein a new class of programmable self-assembling double-stranded RNA (dsRNA)-based cancer therapeutics that uniquely targets aberrant genetic sequences and in a functionally decoupled manner, undergoes oncogenic RNA-activated displacement (ORAD), initiating a therapeutic cascade that induces apoptosis and immune activation. As a proof of concept, we show that RNA strands targeting the EWS/Fli1 fusion gene in Ewing sarcoma cells that are end blocked with phosphorothioate bonds and additionally sealed with a 2'-deoxyuridine (2'-U)-modified DNA protector can be used to induce specific and potent killing of cells containing the target oncogenic sequence but not wild type.

SUBMITTER: Asthana V

PROVIDER: S-EPMC7452102 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of a Novel Class of Self-Assembling dsRNA Cancer Therapeutics: A Proof-of-Concept Investigation.

Asthana Vishwaratn V Stern Brett S BS Tang Yuqi Y Bugga Pallavi P Li Ang A Ferguson Adam A Asthana Anantratn A Bao Gang G Drezek Rebekah A RA

Molecular therapy oncolytics 20200731

Cancer has proven to be an extremely difficult challenge to treat. Several fundamental issues currently underlie cancer treatment, including differentiating self from nonself, functional coupling of the recognition and therapeutic components of various therapies, and the propensity of cancerous cells to develop resistance to common treatment modalities via evolutionary pressure. Given these limitations, there is an increasing need to develop an all-encompassing therapeutic that can uniquely targ ...[more]

PMID: 32913891

Similar Datasets

Project description:UNLABELLED:Intraarterial microdosing (IAM) is a novel drug development approach combining intraarterial drug delivery and microdosing. We aimed to demonstrate that IAM leads to target exposure similar to that of systemic full-dose administration but with minimal systemic exposure. IAM could enable the safe, inexpensive, and early study of novel drugs at the first-in-human stage and the study of established drugs in vulnerable populations. METHODS:Insulin was administered intraarterially (ipsilateral femoral artery) or systemically to 8 CD IGS rats just before blood sampling or 60-min (18)F-FDG uptake PET imaging of ipsilateral and contralateral leg muscles (lateral gastrocnemius) and systemic muscles (spinotrapezius). The (18)F-FDG uptake slope analysis was used to compare the interventions. Plasma levels of insulin and glucose were compared using area under the curve calculated by the linear trapezoidal method. A physiologically based computational pharmacokinetics/pharmacodynamics model was constructed to simulate the relationship between the administered dose and response over time. RESULTS:(18)F-FDG slope analysis found no difference between IAM and systemic full-dose slopes (0.0066 and 0.0061, respectively; 95% confidence interval [CI], -0.024 to 0.029; P = 0.7895), but IAM slope was statistically significantly greater than systemic microdose (0.0018; 95% CI, -0.045 to -0.007; P = 0.0147) and sham intervention (-0.0015; 95% CI, 0.023-0.058; P = 0.0052). The pharmacokinetics/pharmacodynamics data were used to identify model parameters that describe membrane insulin binding and glucose-insulin dynamics. CONCLUSION:Target exposure after IAM was similar to systemic full dose administration but with minimal systemic effects. The computational pharmacokinetics/pharmacodynamics model can be generalized to predict whole-body response. Findings should be validated in larger, controlled studies in animals and humans using a range of targets and classes of drugs.

Project description:The cellulosic ethanol industry has developed efficient strategies for converting sugars obtained from various cellulosic feedstocks to bioethanol. However, any further major improvements in ethanol productivity will require development of novel and innovative fermentation strategies that enhance incumbent technologies in a cost-effective manner. The present study investigates the feasibility of applying self-cycling fermentation (SCF) to cellulosic ethanol production to elevate productivity. SCF is a semi-continuous cycling process that employs the following strategy: once the onset of stationary phase is detected, half of the broth volume is automatically harvested and replaced with fresh medium to initiate the next cycle. SCF has been shown to increase product yield and/or productivity in many types of microbial cultivation. To test whether this cycling process could increase productivity during ethanol fermentations, we mimicked the process by manually cycling the fermentation for five cycles in shake flasks, and then compared the results to batch operation.Mimicking SCF for five cycles resulted in regular patterns with regards to glucose consumption, ethanol titer, pH, and biomass production. Compared to batch fermentation, our cycling strategy displayed improved ethanol volumetric productivity (the titer of ethanol produced in a given cycle per corresponding cycle time) and specific productivity (the amount of ethanol produced per cellular biomass) by 43.1 ± 11.6 and 42.7 ± 9.8%, respectively. Five successive cycles contributed to an improvement of overall productivity (the aggregate amount of ethanol produced at the end of a given cycle per total processing time) and the estimated annual ethanol productivity (the amount of ethanol produced per year) by 64.4 ± 3.3 and 33.1 ± 7.2%, respectively.This study provides proof of concept that applying SCF to ethanol production could significantly increase productivities, which will help strengthen the cellulosic ethanol industry.

Project description:To assess the feasibility of developing a Combined Clinical and Pharmacogenetic Predictive Test, comprised of multiple single nucleotide polymorphisms (SNPs) that is associated with poor bronchodilator response (BDR).We genotyped SNPs that tagged the whole genome of the parents and children in the Childhood Asthma Management Program (CAMP) and implemented an algorithm using a family-based association test that ranked SNPs by statistical power. The top eight SNPs that were associated with BDR comprised the Pharmacogenetic Predictive Test. The Clinical Predictive Test was comprised of baseline forced expiratory volume in 1 s (FEV1). We evaluated these predictive tests and a Combined Clinical and Pharmacogenetic Predictive Test in three distinct populations: the children of the CAMP trial and two additional clinical trial populations of asthma. Our outcome measure was poor BDR, defined as BDR of less than 20th percentile in each population. BDR was calculated as the percent difference between the prebronchodilator and postbronchodilator (two puffs of albuterol at 180 microg/puff) FEV1 value. To assess the predictive ability of the test, the corresponding area under the receiver operating characteristic curves (AUROCs) were calculated for each population.The AUROC values for the Clinical Predictive Test alone were not significantly different from 0.50, the AUROC of a random classifier. Our Combined Clinical and Pharmacogenetic Predictive Test comprised of genetic polymorphisms in addition to FEV1 predicted poor BDR with an AUROC of 0.65 in the CAMP children (n = 422) and 0.60 (n = 475) and 0.63 (n = 235) in the two independent populations. Both the Combined Clinical and Pharmacogenetic Predictive Test and the Pharmacogenetic Predictive Test were significantly more accurate than the Clinical Predictive Test (AUROC between 0.44 and 0.55) in each of the populations.Our finding that genetic polymorphisms with a clinical trait are associated with BDR suggests that there is promise in using multiple genetic polymorphisms simultaneously to predict which asthmatics are likely to respond poorly to bronchodilators.

Dataset Information

Development of a Novel Class of Self-Assembling dsRNA Cancer Therapeutics: A Proof-of-Concept Investigation.

Publications

Development of a Novel Class of Self-Assembling dsRNA Cancer Therapeutics: A Proof-of-Concept Investigation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets