Broadly Applicable Bispecific Linker Approach to Noncovalently Target Therapeutic Nanoparticles to Tumor Cells Expressing Carcinoembryonic Antigen
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Design strategies that lead to a more focused in vivo delivery of functionalized nanoparticles (NPs) and their cargo can potentially maximize their therapeutic efficiency while reducing systemic effects, broadening their clinical applications. Here, we report the development of a noncovalent labeling approach where immunoglobulin G (IgG)-decorated NPs can be directed to a cancer cell using a simple, linear bispecific protein adaptor, termed MFE23-ZZ. MFE23-ZZ was created by fusing a single-chain fragment variable domain, termed MFE23, recognizing carcinoembryonic antigen (CEA) expressed on tumor cells, to a small protein ZZ module, which binds to the Fc fragment of IgG. As a proof of concept, monoclonal antibodies (mAbs) were generated against a NP coat protein, namely, gas vesicle protein A (GvpA) of Halobacterium salinarum gas vesicles (GVs). The surface of each GV was therapeutically derivatized with the photoreactive agent chlorin e 6 (C e 6GVs) and anti-GvpA mAbs were subsequently bound to GvpA on the surface of each C e 6GV. The bispecific ligand MFE23-ZZ was then bound to mAb-decorated C e 6GVs via their Fc domain, resulting in a noncovalent tripartite complex, namely, MFE23.ZZ-2B10–C e 6GV. This complex enhanced the intracellular uptake of C e 6GVs into human CEA-expressing murine MC38 colon carcinoma cells (MC38.CEA) relative to the CEA-negative parental cell line MC38 in vitro, making them more sensitive to light-induced cell killing. These results suggest that the surface of NP can be rapidly and noncovalently functionalized to target tumor-associated antigen-expressing tumor cells using simple bispecific linkers and any IgG-labeled cargo. This noncovalent approach is readily applicable to other types of functionalized NPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".