Abstract 1334: Lipid-based nanoparticulate hydroxychloroquine (HCQ) formulations for use in combination with autophagy inducing drugs for treatment of breast cancer
Bibliographic record
Abstract
Abstract Many targeted and broad spectrum anticancer drugs used to treat breast cancer trigger survival responses exemplified by the induction of cytoprotective macroautophagy (autophagy). Previously, we and others have shown that the anti-malarial agent hydroxychloroquine (HCQ) can improve the effects of anticancer drugs by inhibiting autophagy when used in high concentrations (1-20 μM). These levels are difficult to attain in vivo, thus, we developed novel liposomal formulations of HCQ (L-HCQ) designed to maintain therapeutic concentrations in plasma and tumor sites over extended periods of time. Liposomes (1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC) and cholesterol (CHOL) (55:45 molar ratio)) were prepared by extrusion to exhibit a mean particle size of 100 ± 20 nm. Copper HCQ complexation or ammnonium sulphate methods were used for loading HCQ into liposomes achieving >99% encapsulation efficiency (HCQ to lipid ratio: 0.22 ± 0.02 (mol:mol)). In vitro stability studies indicated that more than 80% of the liposomal associated HCQ was retained in the formulation for at least 24 h at 37 °C. In vivo pharmacokinetic studies, demonstrated that free HCQ was eliminated from the plasma compartment within 30 minutes following i.v. injection while the L-HCQ formulations maintained significantly higher plasma HCQ levels (>100 μM) over 24 h regardless of the loading method. Tolerability studies in non-tumor bearing CD1 mice showed no signs of toxicity following single and multiple doses (3 x week, i.v., 75 mg/kg). Inhibition of autophagy in vivo was examined in liver, heart and pancreas tissues of C57B1/6 mice 6 h after dosing with L-HCQ or free HCQ in combination with the autophagy inducing drug rapamycin. The results show that L-HCQ inhibited rapamycin-induced autophagy more effectively than free HCQ, as evident by a significant increase in LC3-II levels in all the examined tissue. Finally, the efficacy of L-HCQ alone (3 x week, i.v., 60 mg/kg) or in combination with the autophagy promoting drug gefitinib, an EGFR tyrosine kinase inhibitor (5 x week, oral gavage, 100 mg/kg), was tested in the JIMT-1 breast cancer xenograft model (s.c.) established in Rag2M mice. After four weeks of treatment, there were no significant differences in tumor volume between untreated and L-HCQ or gefitinib alone treated animals (p>0.05). In contrast, the gefitinib and L-HCQ combination engendered a significant inhibition of tumor growth compared to untreated controls (p<0.05). Moreover, molecular analysis confirmed inhibition of gefitinib-induced autophagy in vivo by L-HCQ, as judged by increased LC3-II and p62 protein levels in tumor tissue. In summary, this study established that L-HCQ was able to inhibit autophagy and improved sensitivity in an in vivo model of breast cancer treated with gefitinib. Citation Format: Jagbir Singh, Wieslawa H. Dragowska, Malathi Anantha, Ashleen S. Prasad, Jenna S. Rawji, Norman S. Chow, Marcel B. Bally. Lipid-based nanoparticulate hydroxychloroquine (HCQ) formulations for use in combination with autophagy inducing drugs for treatment of breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1334.
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".