Therapeutic potential of new 4-hydroxy-tamoxifen-loaded pH-gradient liposomes in a multiple myeloma experimental model

New drug candidates for liposomal delivery identified by computer modeling of liposomes' remote loading and leakage

Remote drug loading into nano-liposomes is in most cases the best method for achieving high concentrations of active pharmaceutical ingredients (API) per nano-liposome that enable therapeutically viable API-loaded nano-liposomes, referred to as nano-drugs. This approach also enables controlled drug release. Recently, we constructed computational models to identify APIs that can achieve the desired high concentrations in nano-liposomes by remote loading. While those previous models included a broad spectrum of experimental conditions and dealt only with loading, here we reduced the scope to the molecular characteristics alone. We model and predict API suitability for nano-liposomal delivery by fixing the main experimental conditions: liposome lipid composition and size to be similar to those of Doxil® liposomes. On that basis, we add a prediction of drug leakage from the nano-liposomes during storage. The latter is critical for having pharmaceutically viable nano-drugs. The "load and leak" models were used to screen two large molecular databases in search of candidate APIs for delivery by nano-liposomes. The distribution of positive instances in both loading and leakage models was similar in the two databases screened. The screening process identified 667 molecules that were positives by both loading and leakage models (i.e., both high-loading and stable). Among them, 318 molecules received a high score in both properties and of these, 67 are FDA-approved drugs. This group of molecules, having diverse pharmacological activities, may be the basis for future liposomal drug development.

Liposomal trichostatin A: therapeutic potential in hormone-dependent and -independent breast cancer xenograft models

BACKGROUND

Trichostatin A (TSA) is one of the most potent histone deacetylase inhibitors (HDACi) in vitro but it lacks biological activity in vivo when injected intravenously owing to its fast metabolism.

MATERIALS AND METHODS

TSA was incorporated into Stealth® liposomes (TSA-lipo) at a high loading and its anticancer activity was evaluated in several types of breast cancer cells and xenografts.

RESULTS

In estrogen receptor α (ERα)-positive MCF-7 and T47-D cells, TSA induced a long-term degradation of cyclin A and a proteasome-dependent loss of ERα and cyclin D1, allowed derepression of p21WAF1/CIP1, HDAC1 and RhoB GTPase, concomitantly with blockade in G2/M of the cell cycle and apoptosis induction. In MDA-MB-231 (MDA) and SKBr-3 cells, TSA increased ERα mRNA and p21WAF1/CIP1 protein expression, but decreased cyclin A with a G2/M blockade and cleavage of polyADP-ribose polymerase (PARP). No significant restoration of any ER protein was noticed in any cells. TSA-lipo markedly inhibited tumor growth in MCF-7 and MDA cells xenografts following intravenous injection. Their anticancer effects were characterized by inhibition of Ki-67 labeling, the inhibition of tumor vasculature and an increase of p21WAF1/CIP1 in both tumors. In MCF-7 cell tumors, enhanced RhoB accumulation in the cytoplasm of epithelial cells was noticed, inversely to ERα that was strongly decreased.

CONCLUSION

Such anticancer activity of TSA-lipo is exp-lained by the protection provided by HDACi encapsulation and by the strong tumor accumulation of the nanocarriers as revealed by fluorescence confocal microscopy experi-ments. Together with its lack of toxicity, the enhanced stability of TSA-lipo in vivo justifies its development for therapeutic use in the treatment estradiol-dependent and -independent breast cancers.

Formulation of Anti-miR-21 and 4-Hydroxytamoxifen Co-loaded Biodegradable Polymer Nanoparticles and Their Antiproliferative Effect on Breast Cancer Cells

Breast cancer is the second leading cause of cancer-related death in women. The majority of breast tumors are estrogen receptor-positive (ER+) and hormone-dependent. Neoadjuvant anti-estrogen therapy has been widely employed to reduce tumor mass prior to surgery. Tamoxifen is a broadly used anti-estrogen for early and advanced ER+ breast cancers in women and the most common hormone treatment for male breast cancer. 4-Hydroxytamoxifen (4-OHT) is an active metabolite of tamoxifen that functions as an estrogen receptor antagonist and displays higher affinity for estrogen receptors than that of tamoxifen and its other metabolites. MicroRNA-21 (miR-21) is a small noncoding RNA of 23 nucleotides that regulates several apoptotic and tumor suppressor genes and contributes to chemoresistance in numerous cancers, including breast cancer. The present study investigated the therapeutic potential of 4-OHT and anti-miR-21 coadministration in an attempt to combat tamoxifen resistance, a common problem often encountered in anti-estrogen therapy. A biodegradable poly(d,l-lactide-co-glycolide)-block-poly(ethylene glycol) (PLGA-b-PEG-COOH) copolymer was utilized as a carrier to codeliver 4-OHT and anti-miR-21 to ER+ breast cancer cells. 4-OHT and anti-miR-21 co-loaded PLGA-b-PEG nanoparticles (NPs) were developed using emulsion-diffusion evaporation (EDE) and water-in-oil-in-water (w/o/w) double emulsion methods. The EDE method was found to be best method for 4-OHT loading, and the w/o/w method proved to be more effective for coloading NPs with anti-miR-21 and 4-OHT. The optimal NPs, which were prepared using the double emulsion method, were evaluated for their antiproliferative and apoptotic effects against MCF7, ZR-75-1, and BT-474 human breast cancer cells as well as against 4T1 mouse mammary carcinoma cells. We demonstrated that PLGA-b-PEG NP encapsulation significantly extended 4-OHT’s stability and biological activity compared to that of free 4-OHT. MTT assays indicated that treatment of MCF7 cells with 4-OHT–anti-miR-21 co-loaded NPs resulted in dose-dependent antiproliferative effects at 24 h, which was significantly higher than what was achieved with free 4-OHT at 48 and 72 h post-treatment. Cell proliferation analysis showed that 4-OHT and anti-miR-21 co-loaded NPs significantly inhibited MCF-7 cell growth compared to that of free 4-OHT (1.9-fold) and untreated cells (5.4-fold) at 1 μM concentration. The growth rate of MCF7 cells treated with control NPs or NPs loaded with anti-miR-21 showed no significant difference from that of untreated cells. These findings demonstrate the utility of the PLGA-b-PEG polymer NPs as an effective nanocarrier for co-delivery of anti-miR-21 and 4-OHT as well as the potential of this drug combination for use in the treatment of ER+ breast cancer.

Deazaneplanocin a is a promising drug to kill multiple myeloma cells in their niche

Tumoral plasma cells has retained stemness features and in particular, a polycomb-silenced gene expression signature. Therefore, epigenetic therapy could be a mean to fight for multiple myeloma (MM), still an incurable pathology. Deazaneplanocin A (DZNep), a S-adenosyl-L-homocysteine hydrolase inhibitor, targets enhancer of zest homolog 2 (EZH2), a component of polycomb repressive complex 2 (PRC2) and is capable to induce the death of cancer cells. We show here that, in some MM cell lines, DZNep induced both caspase-dependent and -independent apoptosis. However, the induction of cell death was not mediated through its effect on EZH2 and the trimethylation on lysine 27 of histone H3 (H3K27me3). DZNep likely acted through non-epigenetic mechanisms in myeloma cells. In vivo, in xenograft models, and in vitro DZNep showed potent antimyeloma activity alone or in combination with bortezomib. These preclinical data let us to envisage new therapeutic strategies for myeloma.

Antiestrogen-binding site ligands induce autophagy in myeloma cells that proceeds through alteration of cholesterol metabolism

Multiple myeloma (MM) is a malignancy characterized by the accumulation of clonal plasma cells in the bone marrow. Despite extensive efforts to design drugs targeting tumoral cells and their microenvironment, MM remains an incurable disease for which new therapeutic strategies are needed. We demonstrated here that antiestrogens (AEs) belonging to selective estrogen receptor modulators family induce a caspase-dependent apoptosis and trigger a protective autophagy. Autophagy was recognized by monodansylcadaverin staining, detection of autophagosomes by electronic microscopy, and detection of the cleaved form of the microtubule-associated protein light chain 3. Moreover, autophagy was inhibited by drugs such as bafilomycin A1 and 3-methyladenosine. Autophagy was mediated by the binding of AEs to a class of receptors called the antiestrogen binding site (AEBS) different from the classical estrogen nuclear receptors. The binding of specific ligands to the AEBS was accompanied by alteration of cholesterol metabolism and in particular accumulation of sterols: zymostenol or desmosterol depending on the ligand. This was due to the inhibition of the cholesterol-5,6-epoxide hydrolase activity borne by the AEBS. We further showed that the phosphoinositide 3-kinase/AKT/mammalian target of rapamycin pathway mediated autophagy signaling. Moreover, AEBS ligands restored sensitivity to dexamethasone in resistant MM cells. Since we showed previously that AEs arrest MM tumor growth in xenografted mice, we propose that AEBS ligands may have a potent antimyeloma activity alone or in combination with drugs used in clinic.

Quantitative structure-property relationship modeling of remote liposome loading of drugs

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a data set including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and 5-fold external validation. The external prediction accuracy for binary models was as high as 91-96%; for continuous models the mean coefficient R(2) for regression between predicted versus observed values was 0.76-0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments.