Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 339

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Synergistic Transcutaneous Immunotherapy Enhances Antitumor Immune Responses through Delivery of Checkpoint Inhibitors

Open access
Ye, Yanqi, Wang, Jinqiang, Hu, Quanyin, Hochu, Gabrielle M., Xin, Hongliang, Wang, Chao, Gu, Zhen·2016·ACS Nano

Abstract

Despite the promising efficacy of immunoregulation in cancer therapy, the clinical benefit has been restricted by inefficient infiltration of lymphocytes in the evolution of immune evasion. Also, immune-related adverse events have often occurred due to the off-target binding of therapeutics to normal tissues after systematic treatment. In light of this, we have developed a synergistic immunotherapy strategy that locally targets the immunoinhibitory receptor programmed cell death protein 1 (PD1) and immunosuppressive enzyme indoleamine 2,3-dioxygenase (IDO) for the treatment of melanoma through a microneedle-based transcutaneous delivery approach. The embedded immunotherapeutic nanocapsule loaded with anti-PD1 antibody (aPD1) is assembled from hyaluronic acid modified with 1-methyl-dl-tryptophan (1-MT), an inhibitor of IDO. This formulation method based on the combination strategy of "drug A in carriers formed by incorporation of drug B" facilitates the loading capacity of therapeutics. Moreover, the resulting delivery device elicits the sustained release and enhances retention of checkpoint inhibitors in the tumor microenvironment. Using a B16F10 mouse melanoma model, we demonstrate that this synergistic treatment has achieved potent antitumor efficacy, which is accompanied by enhanced effective T cell immunity as well as reduced immunosuppression in the local site.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acsnano.6b04989

Cites

41 of 41 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 339

Metadata

Authors
Ye, Y., Wang, J., Hu, Q., Hochu, G. M., Xin, H., Wang, C., & Gu, Z.
Venue
ACS Nano
Published
2016-09-06
Volume
10
Issue
9
Pages
8956-8963
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2513541135
Cited by
339
References
41
FWCI
15.63
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.