Evidence map›Paper›PMID 41582522›Full record

ArticleACS applied materials & interfaces2026

Structure-Selective Polydopamine Coating on Drug Nanoparticles.

Danna Niezni, Dana Meron Azagury, Maytal Avrashami, Orr Bar-Natan, Yosi Shamay

Abstract read
In one paragraph

Article in ACS applied materials & interfaces, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Danna NiezniDepartment of Biomedical Engineering Technion - Israel Institute of Technology, Haifa, 32000, Israel.
Dana Meron AzaguryDepartment of Biomedical Engineering Technion - Israel Institute of Technology, Haifa, 32000, Israel.
Maytal AvrashamiDepartment of Biomedical Engineering Technion - Israel Institute of Technology, Haifa, 32000, Israel.
Orr Bar-NatanDepartment of Biomedical Engineering Technion - Israel Institute of Technology, Haifa, 32000, Israel.ORCID 0009-0001-1713-7842
Yosi ShamayDepartment of Biomedical Engineering Technion - Israel Institute of Technology, Haifa, 32000, Israel.ORCID 0000-0002-8378-4853

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polydopamine (PDA) is widely regarded as a universal coating material with substrate-independent adhesion. Here we report the first demonstration of selective PDA coating on small molecule drugs, revealing unexpected structure-dependent behavior that challenges this paradigm. Systematic screening of 30 chemotherapeutic agents in IR783-stabilized nanoparticles (>90% drug loading) showed dramatic coating variations governed by molecular structure rather than conventional hydrophobic or π-π interactions. Using Dragon molecular descriptors and principal component analysis, we developed a predictive decision tree model based on nitrogen content and bonding topology that correctly classified 80% of validation compounds. Coating selectivity correlates primarily with nitrogen percentage and N-C-N motifs, fundamentally expanding the understanding of PDA surface chemistry beyond nonselective adhesion mechanisms. Representative drugs (trametinib, dasatinib) demonstrated that PDA coating significantly improves colloidal stability and reduces aggregation without compromising drug loading or release kinetics.

Indexed as

Antineoplastic AgentsDrug CarriersIndolesNanoparticlesPolymersAnimalsDasatinibHCT116 CellsHumansMiceAntineoplastic AgentsDasatinibDrug CarriersIndolespolydopaminePolymersdecision treedrug deliverynanomedicinenanoparticlesnanoprecipitationpolydopamine

Identifiers

PMID41582522
PMCPMC12884477

What OpenQuestion holds

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Registered trials

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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.