Reduction of Intracellular pH as a Strategy to Enhance the pH-Dependent Cytotoxic Effects of Melphalan for Human Breast Cancer Cells
Bibliographic record
Abstract
The microenvironment within solid tumors is slightly acidic, and manipulation of this extracellular acidity to cause intracellular acidification might be used to increase selective antitumor effects of some anticancer drugs. Potential mechanisms include inhibition of repair of DNA damage and inhibition of repopulation of tumor cells between successive courses of chemotherapy. Here, we evaluate the influence of extracellular pH (pHe) and of two agents that lead to intracellular acidification (cariporide and S3705) on toxicity of melphalan for two human breast cancer cell lines (MDA-MB231 and MCF7). Both the total number and number of colony-forming cells were evaluated during and after three sequential weekly drug treatments. Our results indicate the following: (a) Slow or absent repopulation after the first course of treatment that is influenced minimally by pHe. (b) Rapid repopulation after the second course of treatment that may be inhibited at low pHe. (c) Effects of low pHe following treatment with melphalan to increase cell kill. (d) Small effects of incubation in cariporide and S3705 at low pHe to increase the net cell kill after treatment with melphalan. Although these results add to evidence that manipulation of intracellular pH within the acidic environment of solid tumors can influence the effects of chemotherapy, they are too small and inconsistent to warrant clinical evaluation.
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 | 0.000 |
| 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".