Evidence map›Paper›PMID 42505601›Full record

ReviewAntibiotics (Basel, Switzerland)2026

Smart Nano-Antibiotics: AI-Guided Stimuli-Responsive Nanoplatforms for Precision Antimicrobial Therapy.

Nargish Parvin, Keunhwan Park, Jae Hak Jung, Tapas Kumar Mandal

Abstract readReview
In one paragraph

Review in Antibiotics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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.

Nargish ParvinDepartment of Mechanical Engineering, Gachon University, Seongnam 13120, Republic of Korea.ORCID 0000-0002-1209-1507
Keunhwan ParkDepartment of Mechanical Engineering, Gachon University, Seongnam 13120, Republic of Korea.ORCID 0000-0003-1013-5382
Jae Hak JungSchool of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Tapas Kumar MandalSchool of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.

Funding

Korea Technology and Information Promotion Agency for SMEs RS-2025-21073018
6 · The paper itself

Abstract

The rapid rise of antimicrobial resistance (AMR) has created an urgent need for innovative therapeutic strategies beyond conventional antibiotics. Smart nano-antibiotics have emerged as advanced antimicrobial systems capable of improving drug delivery, enhancing pathogen targeting, overcoming biofilm-associated resistance, and reducing systemic toxicity. This review discusses recent progress in stimuli-responsive nanoplatforms, including pH-responsive, enzyme-responsive, temperature-sensitive, and redox-activated systems for precision antimicrobial therapy. The role of artificial intelligence in nanomaterial design, toxicity prediction, drug release optimization, and personalized treatment development is also critically examined. Furthermore, the review highlights targeted antimicrobial delivery, multifunctional nano-drug combination systems, biosensor integration, and autonomous AI-driven therapeutic platforms for combating multidrug-resistant infections. Current challenges related to toxicity, regulatory limitations, scalability, and AI data reliability are discussed alongside emerging clinical and industrial developments. Smart nano-antibiotics represent a promising next-generation approach for improving precision antimicrobial therapy and addressing the growing global burden of antimicrobial resistance.

Indexed as

antimicrobial resistanceartificial intelligencesmart nano-antibioticsstimuli-responsive nanocarrierstargeted drug delivery

Identifiers

PMID42505601
PMCPMC13404382

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.