Targeting Cancer Cells by Novel Engineered Modular Transporters
Bibliographic record
Abstract
A major problem in the treatment of cancer is the specific targeting of drugs to these abnormal cells. Ideally, such a drug should act over short distances to minimize damage to healthy cells and target subcellular compartments that have the highest sensitivity to the drug. We describe the novel approach of using modular recombinant transporters to target photosensitizers to the nucleus, where their action is most pronounced, of cancer cells overexpressing ErbB1 receptors. We have produced a new generation of the transporters consisting of (a) epidermal growth factor as the internalizable ligand module to ErbB1 receptors, (b) the optimized nuclear localization sequence of SV40 large T-antigen, (c) a translocation domain of diphtheria toxin as an endosomolytic module, and (d) the Escherichia coli hemoglobin-like protein HMP as a carrier module. The modules retained their functions within the transporter chimera: they showed high-affinity interactions with ErbB1 receptors and alpha/beta-importin dimers and formed holes in lipid bilayers at endosomal pH. A photosensitizer conjugated with the transporter produced singlet oxygen and (*)OH radicals similar to the free photosensitizer. Photosensitizers-transporter conjugates have >3,000 times greater efficacy than free photosensitizers for target cells and were not photocytotoxic at these concentrations for cells expressing a few ErbB1 receptors per cell, in contrast to free photosensitizers. The different modules of the transporters, which are highly expressed and easily purified to retain full activity of each of the modules, are interchangeable, meaning that they can be tailored for particular applications.
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 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) | 0.001 | 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".