Discovery of a Flavonoid FM04 as a Potent Inhibitor to Reverse P-Glycoprotein-Mediated Drug Resistance in Xenografts and Improve Oral Bioavailability of Paclitaxel
Bibliographic record
Abstract
Biotransformation of flavonoid dimer FD18 resulted in an active metabolite FM04. It was more druggable because of its improved physicochemical properties. FM04 (EC50 = 83 nM) was 1.8-fold more potent than FD18 in reversing P-glycoprotein (P-gp)-mediated paclitaxel (PTX) resistance in vitro. Similar to FD18, FM04 chemosensitized LCC6MDR cells towards multiple anticancer drugs by inhibiting the transport activity of P-gp and restoring intracellular drug levels. It stimulated the P-gp ATPase by 3.3-fold at 100 μM. Different from FD18, FM04 itself was not a transport substrate of P-gp and presumably, it cannot work as a competitive inhibitor. In the human melanoma MDA435/LCC6MDR xenograft, the co-administration of FM04 (28 mg/kg, I.P.) with PTX (12 mg/kg, I.V.) directly modulated P-gp-mediated PTX resistance and caused a 56% (*, p < 0.05) reduction in tumor volume without toxicity or animal death. When FM04 was administered orally at 45 mg/kg as a dual inhibitor of P-gp/CYP2C8 or 3A4 enzymes in the intestine, it increased the intestinal absorption of PTX from 0.2% to 14% in mice and caused about 57- to 66-fold improvement of AUC as compared to a single oral dose of PTX. Oral co-administration of FM04 (45 mg/kg) with PTX (40, 60 or 70 mg/kg) suppressed the human melanoma MDA435/LCC6 tumor growth with at least a 73% (***, p < 0.001) reduction in tumor volume without serious toxicity. Therefore, FM04 can be developed into a novel combination chemotherapy to treat cancer by directly targeting the P-gp overexpressed tumors or potentiating the oral bioavailability of P-gp substrate drugs.
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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".