(M1230-11-71) Design and Development of Nanoformulation of GMC1, a Novel Agent for the Treatment of Castration Resistant Prostate Cancer

Purpose: Prostate Cancer (PCa) is the most commonly diagnosed malignancy among men in the USA and the second leading cause of cancer mortality in men. Although Androgen Deprivation Therapy (ADT) is the primary standard of care for advanced Pca, ADT does not eliminate androgens and therefore PCa progresses to metastatic castration resistant prostate cancer (mCRPC), late-stage metastatic cancer with very few treatment options. Therefore, new therapeutic targets for PCa were being investigated and FKBP52 was identified which is a specific positive regulator of AR, glucocorticoid receptor (GR), and progesterone receptor (PR) signaling. Therefore, direct targeting of FKBP52 would target the activity of AR, GR, and PR simultaneously. GMC1 was identified as a potent and direct inhibitor of FKBP52 which has the potential to provide targeted therapy by simultaneously hit multiple pathways known to have, or suspected of having, a role in PCa. Nanoemulsions (NE) are thermodynamically stable isotropic system in which two immiscible liquids are mixed to form a single phase by means of an emulsifying agent, i.e., surfactant and co-surfactant. The size range typically varies from 20-200 nm. The small-sized droplets with greater surface area provide greater absorption, improve bioavailability of drugs and can thus nanoemulsions can serve as a potential alternative to liposomes and other nanoparticulate delivery system. In this study we developed a nanoemulsion formulation for GMC1 using design of experiment. Nanoemulsions (NE) are thermodynamically stable isotropic system in which two immiscible liquids are mixed to form a single phase by means of an emulsifying agent, i.e., surfactant and co-surfactant. The size range typically varies from 20-200 nm. The small-sized droplets with greater surface area provide greater absorption, improve bioavailability of drugs and can thus nanoemulsions can serve as a potential alternative to liposomes and other nanoparticulate delivery system. In this study we developed a nanoemulsion formulation for GMC1 using design of experiment.

Methods: O/W nanoemulsions were prepared using the low energy spontaneous emulsification method to encapsulate GMC1. Box Behnken design (response surface methodology) was employed to evaluate the effect of the concentrations of individual components of the nanomulsions on the physicochemical properties of the nanoemulsions. The formulations consisted of oil (Caprylic/Capric Triglyceride), solubilizer (Caprylocaproyl Polyoxyl-8 glycerides ) and surfactant (Polyoxyl 15 Hydrostearate). The physico-chemical properties of GMC1 Nanoemulsion such as particle size, zeta potential and encapsulation efficiency were tested as three dependent response variables. The particle size, and zeta potential were measured using Malvern Zetasize Nano ZS. GMC1 concentration was measured by a validated UPLC method. Design-Expert software Version 13.0 (Stat-Ease, Inc., Minneapolis, MN) was used to generate the CCD matrix and the response surfaces plots and also provide statistical analyses.

Results: A three-factor three-level CCD was used to determine the optimal levels of concentrations of oil, surfactant and solubilizer in order to achieve desired particle size, zeta potential and encapsulation efficiency using only 17 experimental runs. The various NE formulations resulting from the experimental design had particle size, zeta potential and encapsulation efficiency ranging from 50.9 to 849.9 nm, -10.2 to +4.0 mV and 37.0 to 101.8 %, respectively. The particle size and encapsulation efficiency responses best fitted a quadratic model whereas the zeta potential responses fitted a 2FI model. The best-fit models were selected taking into consideration low standard deviation, high multiple correlation coefficient (R2) and low predicted residual sum of squares, and insignificant lack of fit.The data is shown in Table 1 The best-fit models for each response variable were statistically evaluated using ANOVA (significance at p< 0.05). Concentration of the surfactant has significant effect on the all the response variables, whereas oil has the most effect on particle size and zeta potential, and the solubilizer has significant effect on particle size and encapsulation efficiency. The 3D surface model for each of the response variables is shown in Figure 1. The model predicted that a NE formulation consisting of 12% oil, 28% solubilizer and 25% surfactant will yield the desired particle size within 120nm and maximum encapsulation efficiency.

Conclusion: This study showed that the response surface methodology using Box Behnken design could efficiently be applied for optimizing the nanoemulsion composition for GMC1.

Acknowledgements:National Institute on Minority Health and Health Disparities of the National Institutes of Health (NIH) under award number U54 MD007605

Perturbation and 3D surface plots showing the effect of concentration of the nanoemulsion components on the response variable

Model Selection criteria and model equation