ReviewJACS Au2022
Power in Numbers: Harnessing Combinatorial and Integrated Screens to Advance Nanomedicine.
Review in JACS Au, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 22 citations in OpenAlex.
- Subcellular nanoparticle trafficking investigated with label-free, live cell imaging.Nanoscale horizons · 2026Article
- High-Throughput Strategies for Streamlining Lipid Nanoparticle Development Pipeline.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Pooled Nanoparticle Screening Using a Chemical Barcoding Approach.Angewandte Chemie (International ed. in English) · 2025Article
- Multivariate Analysis of Cellular Uptake Characteristics for a (Co)polymer Particle Library.ACS biomaterials science & engineering · 2024Article
- Surface Presentation of Hyaluronic Acid Modulates Nanoparticle-Cell Association.Bioconjugate chemistry · 2022Article
- Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery.Science (New York, N.Y.) · 2022Article
- Prediction of Pulmonary Function Parameters Based on a Combination Algorithm.Bioengineering (Basel, Switzerland) · 2022Article
- Nanomaterials in diagnostics, imaging and delivery: Applications from COVID-19 to cancer.MRS communications · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Nanocarriers have significant potential to advance personalized medicine through targeted drug delivery. However, to date, efforts to improve nanoparticle accumulation at target disease sites have largely failed to translate clinically, stemming from an incomplete understanding of nano-bio interactions. While progress has been made to evaluate the effects of specific physical and chemical nanoparticle properties on trafficking and uptake, there is much to be gained from controlling these properties singularly and in combination to determine their interactions with different cell types. We and others have recently begun leveraging library-based nanoparticle screens to study structure-function relationships of lipid- and polymer-based drug delivery systems to guide nanoparticle design. These combinatorial screening efforts are showing promise in leading to the successful identification of critical characteristics that yield improved and specific accumulation at target sites. However, there is a crucial need to equally consider the influence of biological complexity on nanoparticle delivery, particularly in the context of clinical translation. For example, tissue and cellular heterogeneity presents an additional dimension to nanoparticle trafficking, uptake, and accumulation; applying imaging and screening tools as well as bioinformatics may further expand our understanding of how nanoparticles engage with cells and tissues. Given recent advances in the fields of omics and machine learning, there is substantial promise to revolutionize nanocarrier development through the use of integrated screens, harnessing the combinatorial parameter space afforded both by nanoparticle libraries and clinically annotated biological data sets in combination with high throughput
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.