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Record W2170409676 · doi:10.3109/10611860008997904

Bispecific MAb Aided Liposomal Drug Delivery

2000· article· en· W2170409676 on OpenAlex
Yanguang Cao, M.R. Suresh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of drug targeting · 2000
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of Alberta
FundersMedical Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsBiotinylationLiposomeMonoclonal antibodyDrug deliveryTargeted drug deliveryChemistryMolecular biologyDrugPharmacologyBiologyBiochemistryAntibodyImmunology

Abstract

fetched live from OpenAlex

We have developed a new method for specifically delivering liposomal model drugs to tumor cells. Bispecific monoclonal antibodies (bsMAb) (174H.64 x anti-biotin) which can bind tumor-specific antigen and biotin were developed and characterized. Biotinylated stealth liposome loaded with model drug 99mTc-DTPA can bind to the biotin-binding arm of bsMAb. This targeted liposomal delivery strategy was tested in mouse KLN-205 squamous carcinoma model. bsMAbs were administered 24h in advance into tumor allograft bearing mice, which allow them to bind to tumor cells through the anti-tumor binding arm. After clearance of circulating bsMAb, biotinylated stealth liposomes were introduced to specifically bind to the tumor sites where bsMAb localized earlier. The results show that pretargeted bsMAb can enhance liposomal drug targeting by four times, 3.61% dose/g vs. 0.89% dose/g. This bsMAb/liposome strategy show the broad possibilities of selective delivery of cytotoxic drugs or genes to the specific targets.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.014
GPT teacher head0.274
Teacher spread0.260 · 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