Evidence map›Paper›PMID 39630483›Full record

ArticleACS applied materials & interfaces2024

Optimizing Ligand Valency to Maximize Tendon Accumulation of Peptide-Targeted Nanoparticles.

Emmanuela Adjei-Sowah, Vigneshkumar Rangasami, Alayna E Loiselle, Danielle S W Benoit

Abstract read
In one paragraph

Article in ACS applied materials & interfaces, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
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  3. Review
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  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Emmanuela Adjei-SowahDepartment of Biomedical Engineering, University of Rochester, Rochester, New York 14623, United States.
Vigneshkumar RangasamiDepartment of Bioengineering, Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, Oregon 97403, United States.
Alayna E LoiselleDepartment of Biomedical Engineering, University of Rochester, Rochester, New York 14623, United States.ORCID 0000-0002-7548-6653
Danielle S W BenoitDepartment of Biomedical Engineering, University of Rochester, Rochester, New York 14623, United States.ORCID 0000-0001-7137-8164

Funding

Engineered salivary gland tissue chips (Administrative Supplement)UH3DE027695 · NIDCR · UNIVERSITY OF ROCHESTER · PI BENOIT, DANIELLE S., DELOUISE, LISA A · 2019 to 2021
$3.0M
Modulating Cell-fate to Promote Regenerative Tendon HealingR01AR077527 · NIAMS · UNIVERSITY OF ROCHESTER · PI LOISELLE, ALAYNA · 2021 to 2025
$2.0M
Orchestrating Tendon Regeneration through Nanoparticle Drug DeliveryR01AR085951 · NIAMS · UNIVERSITY OF OREGON · PI BENOIT, DANIELLE S., LOISELLE, ALAYNA · 2025 to 2025
$2.0M
s100a4 Signaling in Fibrotic Diabetic Tendon HealingR01AR073169 · NIAMS · UNIVERSITY OF ROCHESTER · PI LOISELLE, ALAYNA · 2018 to 2022
$1.7M
Engineered salivary gland tissue chipsUG3DE027695 · NIDCR · UNIVERSITY OF ROCHESTER · PI BENOIT, DANIELLE S., DELOUISE, LISA A · 2017 to 2018
$1.5M
Bone-targeted polymer therapeutics for nonunion fracture healingR21AG072692 · NIA · UNIVERSITY OF ROCHESTER · PI BENOIT, DANIELLE S. · 2022 to 2023
$406k
Tendon TRAP: Targeted Therapeutic Delivery to Enhance Tendon HealingR21AR081063 · NIAMS · UNIVERSITY OF ROCHESTER · PI BENOIT, DANIELLE S., LOISELLE, ALAYNA · 2022 to 2023
$373k
Molecular Multispectral ImagingS10RR026542 · NCRR · UNIVERSITY OF ROCHESTER · PI AWAD, HANI A · 2010 to 2010
$370k
Matrix-Assisted Laser Desorption Ionization Time-of-Flight (MALDI-TOF/TOF) Mass SpectrometerS10OD030302 · OD · UNIVERSITY OF ROCHESTER · PI NILSSON, BRADLEY L. · 2021 to 2021
$304k
NCRR NIH HHS S10 RR026542NIAMS NIH HHS R01 AR073169NIAMS NIH HHS R01 AR077527NIAMS NIH HHS R01 AR085951NIAMS NIH HHS R21 AR081063NIA NIH HHS R21 AG072692NIDCR NIH HHS UG3 DE027695NIDCR NIH HHS UH3 DE027695NIH HHS S10 OD030302
6 · The paper itself

Abstract

In many tissues, including musculoskeletal tissues such as tendon, systemic delivery typically results in poor targeting of free drugs. Hence, we previously developed a targeted drug delivery nanoparticle (NP) system for tendon healing, leveraging a tartrate resistant acid phosphatase (TRAP) binding peptide (TBP) ligand. The greatest tendon targeting was observed with NPs functionalized with 30 000 TBP ligands per NP at day 7 during the proliferative healing phase, relative to the inflammatory (day 3) and early remodeling (day 14) phases of healing. Nevertheless, TRAP activity varies throughout healing and, therefore, may offer an opportunity for optimizing temporal therapeutic targeting through multivalent interactions. Hence, in this study, we hypothesized that the ligand density (9000-55,000 TBPs per NP) can optimize tendon accumulation on the basis of variable TRAP levels. The multivalent nanoparticles were loaded with three different fluorophores. In vitro, the ligand density and fluorophore had no effect on the physicochemical properties of the NPs, including size, charge, polydispersity index, or dye loading efficiency; however, the TRAP binding affinity correlated positively with the ligand density. In vivo, the ligand density correlated positively with NP homing and retention in the tendon, establishing opportunities to leverage ligand density for tendon targeting across the tendon healing cascade, during aging, and in other tendon pathologies, including tendinopathies.

Indexed as

NanoparticlesPeptidesTendonsAnimalsFluorescent DyesHumansLigandsMaleMiceRatsTartrate-Resistant Acid PhosphataseFluorescent DyesLigandsPeptidesTartrate-Resistant Acid Phosphatasedrug deliveryligand densitymultivalencynanoparticlestendon healing

Identifiers

PMID39630483
PMCPMC13532700

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.