Liposomal Formulation of Hydroxychloroquine Can Inhibit Autophagy In Vivo
Bibliographic record
Abstract
Background/Objectives: Preclinical studies have shown that the anti-malarial drug hydroxychloroquine (HCQ) improves the anti-cancer effects of various therapeutic agents by impairing autophagy. These findings are difficult to translate in vivo as reaching an effective HCQ concentration at the tumor site for extended times is challenging. Previously, we found that free HCQ in combination with gefitinib (Iressa®, ZD1839) significantly reduced tumor volume in immunocompromised mice bearing gefitinib-resistant JIMT-1 breast cancer xenografts. Here, we sought to evaluate whether a liposomal formulation of HCQ could effectively modulate autophagy in vivo and augment treatment outcomes in the same tumor model. Methods: We developed two liposomal formulations of HCQ: a pH-loaded formulation and a formulation based on copper complexation. The pharmacokinetics of each formulation was evaluated in CD1 mice following intravenous administration. An efficacy study was performed in immunocompromised mice bearing established JIMT-1tumors. Autophagy markers in tumor tissue harvested after four weeks of treatment were assessed by Western blot. Results: The liposomal formulations engendered ~850-fold increases in total drug exposure over time relative to the free drug. Both liposomal and free HCQ in combination with gefitinib provided comparable therapeutic benefits (p > 0.05). An analysis of JIMT-1 tumor tissue indicated that the liposomal HCQ and gefitinib combination augmented the inhibition of autophagy in vivo compared to the free HCQ and gefitinib combination as demonstrated by increased LC3-II and p62/SQSTM1 (p62) protein levels. Conclusions: The results suggest that liposomal HCQ has a greater potential to modulate autophagy in vivo compared to free HCQ; however, this did not translate to better therapeutic effects when used in combination with gefitinib to treat a gefitinib-resistant tumor model.
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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".