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Record W4409634947 · doi:10.1158/1538-7445.am2025-2523

Abstract 2523: Porphyrin-lipid nanoparticle-based multifunctional cancer: from cell to patient

2025· article· en· W4409634947 on OpenAlexaff
Robert Weersink, Theo Husby, Ivan Košík, Michael J. Daly, Jason L. Townson, Ben Motx, Alessandra Ruaro, Juan Chen, Christine Démoré, Stéphanie Lheureux, Amit M. Oza, Jonathon C. Irish, Gang Zheng, Brian C. Wilson

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPorphyrinCancerCancer researchNanoparticleNanotechnologyChemistryMedicineBiochemistryInternal medicineMaterials science

Abstract

fetched live from OpenAlex

Abstract Background: Porphysomes (PS) are first-in-class organic porphyrin-lipid nanoparticles with unprecedented intrinsic multifunctionality that enables a range of complementary imaging, image-guided interventions and therapies. Examples are presented in a range of preclinical tumor models, including head & neck, lung and gynecological cancers, demonstrating progress towards first-in-human trials. Methods & Results: High-density packing of the porphyrin-lipid monomers in the nanoparticle bilayer enables efficient light absorption and conversion to heat for enhanced photoacoustic imaging (PAI) and photothermal therapy (PTT). Using wide-band acoustic detection at low frequencies multispectral PAI can quantify PS concentration in tumors that span sub-mucosal disease to several cm depth. A novel method has also been developed for direct temperature mapping to monitor PS-mediated PTT delivery. Upon structural dissociation following cell uptake, the fluorescence (FL) and photodynamic therapy (PDT) activity of the porphyrin monomers are restored. Corresponding diffuse optical spectroscopy (DOS) has measured longitudinal PS pharmacokinetics in vivo. This will allow identification of residual tumor during surgical resection and also serve as a component of PDT dosimetry. FL imaging of tumor-targeted PS has been demonstrated in veterinary clinical trials of oral cancer, showing efficacy in tumor localization and surgical guidance. AI-assisted spatial-frequency-domain FL imaging in particular shows promise in measuring tumor depth prior to resection and tumor extent during resection. Attaching gadolinium to the PS lipid chains enables combined intra-operative surgical planning and guidance, using Gd-contrast MRI for pre-resection planning and higher-resolution FGS during resection. We are evaluating MR-FGS in pre-clinical models, comparing PS localization using MRI and DOS to pathology and surgical resection outcomes. Finally, PS chelated with 64Cu for PET imaging of tumors has been demonstrated in several large animal models and serves as the key modality to assess PS pharmacokinetics in first-in-human studies in advanced ovarian cancer, leading to first therapeutic trials. 64CuPS imaging will also be used for planning and dosimetry of 67CuPS radioisotope therapy of disseminated disease. Conclusions: Porphysomes provide unique multifunctional imaging and theranostic capabilities for cancer detection, localization and personalized therapy, both stand-alone and combined with standard modalities. Citation Format: Robert A. Weersink, Theo Husby, Ivan Kosik, Michael Daly, Jason Townson, Ben Motx, Alessandra Ruaro, Juan Chen, Christine Demore, Stephanie Lheureux, Amit Oza, Jonathon Irish, Gang Zheng, Brian C. Wilson. Porphyrin-lipid nanoparticle-based multifunctional cancer: from cell to patient [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2523.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.321
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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