Diagnostic nanoparticle targeting of the EGF-receptor in complex biological conditions using single-domain antibodies
Bibliographic record
Abstract
For effective localization of functionalized nanoparticles at diseased tissues such as solid tumours or metastases through biorecognition, appropriate targeting vectors directed against selected tumour biomarkers are a key prerequisite. The diversity of such vector molecules ranges from proteins, including antibodies and fragments thereof, through aptamers and glycans to short peptides and small molecules. Here, we analyse the specific nanoparticle targeting capabilities of two previously suggested peptides (D4 and GE11) and a small camelid single-domain antibody (sdAb), representing potential recognition agents for the epidermal growth factor receptor (EGFR). We investigate specificity by way of receptor RNA silencing techniques and look at increasing complexity in vitro by introducing increasing concentrations of human or bovine serum. Peptides D4 and GE11 proved problematic to employ and conjugation resulted in non-receptor specific uptake into cells. Our results show that sdAb-functionalized particles can effectively target the EGFR, even in more complex bovine and human serum conditions where targeting specificity is largely conserved for increasing serum concentration. In human serum however, an inhibition of overall nanoparticle uptake is observed with increasing protein concentration. For highly affine targeting ligands such as sdAbs, targeting a receptor such as EGFR with low serum competitor abundance, receptor recognition function can still be partially realised in complex conditions. Here, we stress the value of evaluating the targeting efficiency of nanoparticle constructs in realistic biological milieu, prior to more extensive in vivo studies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".