ArticleACS nano2021
Kinetic Modeling to Accelerate the Development of Nucleic Acid Formulations.
Article in ACS nano, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Mathematical model of RNA-directed DNA methylation predicts tuning of negative feedback required for stable maintenance.Open biology · 2024Article
- A kinetic modeling platform for predicting the efficacy of siRNA formulationsSTAR protocols · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
A critical hurdle in the clinical translation of nucleic acid drugs is the inefficiency in testing formulations for therapeutic potential. Specifically, the ability to quantitatively predict gene expression is lacking when transitioning between cell culture and animal studies. We address this challenge by developing a mathematical framework that can reliably predict short-interfering RNA (siRNA)-mediated gene silencing with as few as one experimental data point as an input, evaluate the efficacies of existing formulations in an expeditious manner, and ultimately guide the design of nanocarriers with optimized performances. The model herein consisted of only essential rate-limiting steps and parameters with easily characterizable values of the RNA interference process, enabling the easy identification of which parameters play dominant roles in determining the potencies of siRNA formulations. Predictions from our framework were in close agreement with
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.