The Tyrosine Kinase Inhibitor Imatinib Mesylate Enhances the Efficacy of Photodynamic Therapy by Inhibiting ABCG2
Bibliographic record
Abstract
PURPOSE: The ATP-binding cassette protein ABCG2 (breast cancer resistance protein) effluxes some of the photosensitizers used in photodynamic therapy (PDT) and, thus, may confer resistance to this treatment modality. Tyrosine kinase inhibitors (TKI) can block the function of ABCG2. Therefore, we tested the effects of the TKI imatinib mesylate (Gleevec) on photosensitizer accumulation and in vitro and in vivo PDT efficacy. EXPERIMENTAL DESIGN: Energy-dependent photosensitizer efflux and imatinib mesylate's effects on intracellular accumulation of clinically used second- and first-generation photosensitizers were studied by flow cytometry in murine and human cells with and without ABCG2 expression. Effects of ABCG2 inhibition on PDT were examined in vitro using cell viability assays and in vivo measuring photosensitizer accumulation and time to regrowth in a RIF-1 tumor model. RESULTS: Energy-dependent efflux of 2-(1-hexyloxethyl)-2-devinyl pyropheophorbide-a (HPPH, Photochlor), endogenous protoporphyrin IX (PpIX) synthesized from 5-aminolevulenic acid, and the benzoporphyrin derivative monoacid ring A (BPD-MA, Verteporfin) was shown in ABCG2+ cell lines, but the first-generation multimeric photosensitizer porfimer sodium (Photofrin) and a novel derivative of HPPH conjugated to galactose were minimally transported. Imatinib mesylate increased accumulation of HPPH, PpIX, and BPD-MA from 1.3- to 6-fold in ABCG2+ cells, but not in ABCG2- cells, and enhanced PDT efficacy both in vitro and in vivo. CONCLUSIONS: Second-generation clinical photosensitizers are transported out of cells by ABCG2, and this effect can be abrogated by coadministration of imatinib mesylate. By increasing intracellular photosensitizer levels in ABCG2+ tumors, imatinib mesylate or other ABCG2 transport inhibitors may enhance efficacy and selectivity of clinical PDT.
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".