Evidence map›Paper›PMID 31941022›Full record

ReviewAntibiotics (Basel, Switzerland)2020

Development and Challenges of Antimicrobial Peptides for Therapeutic Applications.

Charles H Chen, Timothy K Lu

Open access · goldAbstract readReview
In one paragraph

Review in Antibiotics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 269 papers.

0numbers the graph read from it
0cells of the map it votes in
269citing papers in PubMed
47.9field-weighted citation impact, top 1% of its field
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

269 citing papers in PubMed, 583 citations in OpenAlex.

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  6. Adaptive Response ofAntibiotics (Basel, Switzerland) · 2026
    Article
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209 more citing papers are in PubMed but not listed here.

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

2 authors at 2 institutions in 1 country.

Charles H ChenSynthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
Timothy K LuSynthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
Broad Institute · USHarvard–MIT Division of Health Sciences and Technology · US

Funding

Innovative technologies to transform antibiotic discovery. Project 4 Infection site-specific amplification of antimicrobial conjugatesU19AI142780 · NIAID · BROAD INSTITUTE, INC. · PI HUNG, DEBORAH T · 2019 to 2023
$33.9M
NIH HHS U19AI142780
6 · The paper itself

Abstract

More than 3000 antimicrobial peptides (AMPs) have been discovered, seven of which have been approved by the U.S. Food and Drug Administration (FDA). Now commercialized, these seven peptides have mostly been utilized for topical medications, though some have been injected into the body to treat severe bacterial infections. To understand the translational potential for AMPs, we analyzed FDA-approved drugs in the FDA drug database. We examined their physicochemical properties, secondary structures, and mechanisms of action, and compared them with the peptides in the AMP database. All FDA-approved AMPs were discovered in Gram-positive soil bacteria, and 98% of known AMPs also come from natural sources (skin secretions of frogs and toxins from different species). However, AMPs can have undesirable properties as drugs, including instability and toxicity. Thus, the design and construction of effective AMPs require an understanding of the mechanisms of known peptides and their effects on the human body. This review provides an overview to guide the development of AMPs that can potentially be used as antimicrobial drugs.

Indexed as

antibiotic resistanceantibioticsantimicrobial peptidesFDA-approved peptidespeptide therapeuticsrational protein design

Identifiers

PMID31941022
PMCPMC7168295
OpenAlexW2999569426

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.