Tellurophene‐Tagging of Teniposide Facilitates Monitoring by Mass Cytometry
Bibliographic record
Abstract
Target engagement and the biodistribution of exogenously administered small molecules is rarely homogenous. Methods to determine the biodistribution at the cellular level are limited by the ability to detect the small molecule and simultaneously identify the cell types or tissue structures with which it is associated. The highly multiplexed nature of mass cytometry could facilitate these studies provided a heavy isotope label was available in the molecule of interest. Here we show it is possible to append a tellurophene to a known chemotherapeutic, teniposide, to follow this molecule in vivo. A semi-synthetic approach offers an efficient route to the teniposide analogue which is found to have similar characteristics when compared with the parent teniposide in vitro. Using mass cytometry we find the teniposide analogue has significant nonspecific binding to cells. In vivo the tellurium bearing teniposide produces the expected DNA damage in a PANC-1 xenograft model. The distribution of Te in the tissue is near the limits of detection and further work will be required to characterize the localization of this analogue with respect to cell type distributions.
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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) | 0.002 | 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".