Peptide-Nanoparticle Platforms for Antisense Therapeutics: A Coarse-Grained Modeling Approach to Brain Delivery

dc.contributor.author Uner, B.Y.
dc.contributor.author Demir, A.
dc.contributor.author Zhou, P.
dc.contributor.author Taşkiran, E.Z.
dc.contributor.author Wassenaar, T.
dc.date.accessioned 2026-02-25T15:10:21Z
dc.date.available 2026-02-25T15:10:21Z
dc.date.issued 2026
dc.description.abstract Traumatic brain injury (TBI) is a leading cause of long-term neurological deficits, often resulting in complex, unresolved molecular and cellular dysfunctions. Among these, gene–circuit disruptions—particularly those affecting neuroinflammation, oxidative stress, and mitochondrial dynamics—have emerged as critical mediators of post-traumatic neuropathology. In this study, we utilized artificial intelligence (AI)-driven proteomics and RNA sequence integration to map altered signaling pathways following TBI. Computational predictions identified specific gene–circuit nodes susceptible to therapeutic intervention, including redox-sensitive mitochondrial regulators and genes involved in the neuroimmune interface. Importantly, although our analyses are derived from rodent models, the conserved signaling pathways and regulatory circuits identified here provide a translational window with strong relevance to human TBI pathophysiology, thereby bridging preclinical findings with potential therapeutic application. Based on these insights, we designed a suite of responsive nanoparticle formulations optimized in silico for targeted delivery to dysregulated brain regions. These carriers incorporated ligands targeting disrupted circuits and incorporated redox-sensitive release mechanisms. Our platform demonstrates the feasibility of a closed-loop, data-guided strategy that integrates AI-based gene network profiling with rational nanocarrier design. This approach provides a scalable framework for precision neurotherapeutics, particularly for complex disorders such as TBI where conventional monotherapies have proven inadequate. © 2026 Elsevier Ltd. en_US
dc.identifier.doi 10.1016/j.compbiomed.2026.111479
dc.identifier.issn 0010-4825
dc.identifier.scopus 2-s2.0-105028474417
dc.identifier.uri https://doi.org/10.1016/j.compbiomed.2026.111479
dc.identifier.uri https://hdl.handle.net/20.500.14365/8723
dc.language.iso en en_US
dc.publisher Elsevier Ltd en_US
dc.relation.ispartof Computers in Biology and Medicine en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Antisense Oligonucleotide en_US
dc.subject In Silico Modeling en_US
dc.subject Nanoparticle Delivery en_US
dc.subject Nasal-to-Brain Targeting en_US
dc.subject Neuroinflammation en_US
dc.subject Peptide Conjugation en_US
dc.subject Traumatic Brain Injury en_US
dc.subject Trem2 Targeting en_US
dc.title Peptide-Nanoparticle Platforms for Antisense Therapeutics: A Coarse-Grained Modeling Approach to Brain Delivery en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.scopusid 57302092900
gdc.author.scopusid 57549355800
gdc.author.scopusid 35226856700
gdc.author.scopusid 36946728400
gdc.author.scopusid 60349920200
gdc.description.department İzmir Ekonomi Üniversitesi en_US
gdc.description.departmenttemp [Uner] Burcu Yesildag, Faculty of Engineering, Yeditepe University, Istanbul, Turkey; [Demir] Alper, Department of Computer Engineering, Izmir Ekonomi Üniversitesi, Izmir, Turkey, The University of Edinburgh, Edinburgh, Scotland, United Kingdom; [Zhou] Pingkun, Department of Biology, Beijing Institute of Radiation Medicine, Beijing, China; [Taşkiran] Ekim Zihni, Department of Medical Genetics, Hacettepe Üniversitesi, Ankara, Turkey; [Wassenaar] Tsjerk, Professorship of Multiscale Modeling of Fluid Materials, Technische Universität München, Munich, Bayern, Germany en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.volume 203 en_US
gdc.description.wosquality Q1
gdc.identifier.pmid 41581469
gdc.index.type Scopus
gdc.index.type PubMed
gdc.virtual.author Demir, Alper
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