Structural Modifications of Mitochondria-Targeted Chlorambucil Alter Cell Death Mechanism but Preserve MDR Evasion
Bibliographic record
Abstract
Multidrug resistance (MDR) remains one of the major obstacles in chemotherapy, potentially rendering a multitude of drugs ineffective. Previously, we have demonstrated that mitochondrial targeting of DNA damaging agents is a promising tool for evading a number of common resistance factors that are present in the nucleus or cytosol. In particular, mitochondria-targeted chlorambucil (mt-Cbl) has increased potency and activity against resistant cancer cells compared to the parent compound chlorambucil (Cbl). However, it was found that, due to its high reactivity, mt-Cbl induces a necrotic type of cell death via rapid nonspecific alkylation of mitochondrial proteins. Here, we demonstrate that by tuning the alkylating activity of mt-Cbl via chemical modification, the rate of generation of protein adducts can be reduced, resulting in a shift of the cell death mechanism from necrosis to a more controlled apoptotic pathway. Moreover, we demonstrate that all of the modified mt-Cbl compounds effectively evade MDR resulting from cytosolic GST-μ upregulation by rapidly accumulating in mitochondria, inducing cell death directly from within. In this study, we systematically elucidated the advantages and limitations of targeting alkylating agents with varying reactivity to mitochondria.
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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) | 0.001 | 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".