Bibliographic record
Abstract
Current anti-VEGF biologics, such as bevacizumab and VEGF trap, have been successfully used as therapeutic agents for cancer and age-related macular degeneration (AMD). Since these strategies target VEGF systemically, their toxicity profile, including proteinuria and thromboembolic events, and need for frequent eye injections in AMD treatment, prevail. Therefore, the aim of this PhD thesis was to generate novel anti-VEGF biologics that inhibit VEGF activity specifically at the desired target site.\nTwo classes of biologics were engineered that simultaneously bind VEGF and either: 1) the extracellular matrix (ECM) or 2) target-site specific antigens. The first subgroup, “sticky-traps”, is composed of VEGF trap linked to a sequence of hydrophobic amino acids, with affinity for heparin sulfate proteoglycans of the ECM. The second subgroup, “lassos”, is composed of a C-terminus positioned form of VEGF trap linked to single-chain variable domain antibodies specific for either HER2 (HER2/V lasso) or fibronectin extra domain B (EDB; EDB/V lasso), expressed on breast cancer cell surfaces or in the vascular bed of solid tumours, respectively.\nii\nUsing a novel transgenic method, piggyBac transposons, biologics were expressed in transgenic cancer cell lines in a doxycycline inducible manner. They were shown to inhibit VEGF activity and also retain the native function of their constituent domains. Specifically, the sticky-traps adhered to the ECM and the HER2/V lasso inhibited the proliferation of HER2 positive cancer cell lines.\nSticky-traps as well as lassos were able to inhibit or delay tumour growth of A-673, Pc-3, SKOV-3 and HT-29 xenografts. In contrast to soluble VEGF trap, sticky-traps were retained at the tumour site and were undetectable in the circulation. Moreover, sticky-traps, in contrast to VEGF trap, did not delay wound healing and regression of trachea blood vessels. Furthermore, transgenic studies indicated that HER2/V lasso is more effective compared to anti-HER2 Ab and VEGF trap used alone or in combination.\nThese novel classes of antiangiogenic molecules could be advantageous in a clinical setting. Using the principles established in my PhD thesis work, similar dual function biologics can be designed for inhibition of other molecules with disease relevance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".