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Record W1989493029 · doi:10.1158/1538-7445.am10-1387

Abstract 1387: Development of novel antiangiogenic biologics: multifunctional VEGF traps

2010· article· en· W1989493029 on OpenAlexaff
Iacovos P. Michael, Hoon‐Ki Sung, András Nagy

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsAntibodyFibronectinCancer researchAngiogenesisRecombinant DNACancerExtracellular matrixDoxycyclineChemistryMolecular biologyMedicineBiologyImmunologyBiochemistryInternal medicineGene

Abstract

fetched live from OpenAlex

Abstract Current anti-VEGF biologics have been successfully used as therapeutic agents for cancer and age-related macular degeneration (AMD). Since these strategies target VEGF systemically, side effects, long-term toxicities, such as proteinuria and thromboembolic events, and need for frequent eye injections in AMD treatment, prevail. Therefore, the aim of this study was to generate novel anti-VEGF biologics that inhibit VEGF activity specifically at the desired target site. Two classes of antibody-based recombinant proteins were engineered that simultaneously bind VEGF and either: 1) the extracellular matrix (ECM) or 2) target-site specific antigens. The first subgroup of proteins, “shV-H traps”, is comprised of a Fc domain shortened form of VEGF-trap linked to a sequence of hydrophobic amino acids, with various affinities for heparin sulfate proteoglycans of the ECM, designed to have a short systemic half-life. The second subgroup of molecules, “V-lassos”, is composed of a C-terminus positioned form of VEGF-trap linked to single-chain variable domain antibodies specific for either HER-2 or fibronectin extra domain B (EDB), expressed on tumour cell surfaces or in the vascular bed of solid tumours, respectively. Recombinant proteins were expressed in transgenic cancer cell lines in a doxycycline inducible manner and were shown to inhibit VEGF activity and also retain the native function of their constituent domains. Specifically, the shV-H traps adhered to the ECM and the HER-2 V-lasso inhibited the proliferation of HER-2 positive cancer cell lines. Xenograft studies were performed using the aforementioned transgenic cancer cell lines in order to determine the efficacy of shV-H traps and Vlasso. shV-H traps were able to inhibit or delay tumour growth of A-673, Pc-3 and HT-29 xenografts. In contrast to soluble VEGF-trap, sh-VH traps were retained at the tumour site and were undetectable in the circulation. Moreover, sh-VH traps did not cause any of the side-effects observed with soluble VEGF-trap, such as delay of wound healing and regression of trachea blood vessels. Both Vlassos were able to inhibit or delay the tumour growth of SKOV-3 and A-673 xenografts. Furthermore, the transgenic approach for inducible expression indicated that HER-2 Vlasso is more effective compared to anti-HER-2 Ab and VEGF-trap used alone or in combination. Preliminary results suggest that high recombinant protein levels can be generated via large-scale production in 293F cells, indicating that the new multifunctional VEGF-traps can be produced in large amounts for clinical studies. These novel classes of anti-angiogenic molecules could potentially be advantageous in a clinical setting. Furthermore, they can be useful in order to further study and understand the mechanisms involved in VEGF inhibition. Finally, similar dual function proteins can be designed for inhibition of other molecules with disease relevance. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1387.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.087
GPT teacher head0.399
Teacher spread0.312 · 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
Published2010
Admission routes1
Has abstractyes

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