(W1130-01-06) DoE-Enabled Development of Osimertinib-Encapsulated PLGA Nanoparticles for the Treatment of Non-Small Cell Lung Cancer

Purpose: Non-small cell lung cancer (NSCLC) is one of the primary causes of cancer-related deaths worldwide. Tyrosine kinase inhibitors (TKIs) have been recognized as the standard treatment for patients with NSCLCs. Nonetheless, nearly all patients eventually experience disease progression due to resistance acquired from TKI therapy. The resistance mechanisms following TKI exposure frequently involve the EGFR or its downstream pathways. Osimertinib, a third-generation TKI, has been prescribed for patients with resistance-causing EGFR mutations. Osimertinib (OSI) is generally well-tolerated, though it frequently causes adverse effects such as diarrhea, skin rashes, and ocular issues, potentially leading to treatment discontinuation. Improving safety while preserving efficacy is essential, and approaches such as nanocarrier-based local delivery have proven promising. In recent years, poly(lactic-co-glycolic acid) (PLGA) has been widely explored due to its biocompatibility and biodegradability. By understanding and manipulating the molecular weight of PLGA, nanoparticles' drug loading, particle size, and distribution can be modulated to achieve the desired characteristics. Formulation variables, such as the concentration of PVA and sonication time, also impact the physicochemical properties of nanoparticles. In this project, we employed the Design of Experiments (DoE) to understand the impact of different formulation variables on drug loading, particle size, and distribution.

Methods: A Box-Behnken study design was employed to optimize the various variables involved in producing PLGA nanoparticles and to understand how these variables interacted with each other. The Quality Target Product Profile (QTPP) was defined for three parameters: % drug loading (highest), size ( < 200 nm), and PDI ( < 0.3). The PLGA nanoparticles were prepared using the emulsion-solvent evaporation technique. Briefly, 6 mg of OSI and 60 mg of PLGA polymers (lactide/glycolide ratio: 50/50), with molecular weights ranging from 10 kDa to 100 kDa, were dissolved in dichloromethane (DCM; 3 ml) and were emulsified by probe sonication while varying sonication times between one to four minutes and PVA concentrations (aqueous phase) ranging from 1% to 5%. The Box-Behnken study design resulted in 14 formulations (n=3), which were analyzed for drug loading percentage, particle size, and distribution. Three-dimensional contour plots were constructed to study the effects of each variable.

Results: The top two formulations with the highest drug loading percentages in the Box-Behnken design data set were observed to be Run No. 4 and Run No. 1 (Fig. 1). Run No. 4 achieved the highest drug loading at 2.7±0.7%. This formulation used a PVA concentration of 1%, a PLGA molecular weight range of 10-15 kDa, and a sonication time of 2 minutes, resulting in a particle size of 189.3±11.6 nm and a polydispersity index (PDI) of 0.11±0.03. Following closely was Run No. 1, where the drug loading reached 2.6±0.5%. This formulation also utilized a PVA concentration of 1%, a PLGA molecular weight range of 35-45 kDa, and a sonication time of 1 minute, yielding a particle size of 198.8±7.0 nm and a PDI of 0.10±0.03 (Fig. 1). Both formulations exhibited a significant increase in drug loading efficiency, suggesting effective optimization of the variables involved in the nanoparticle production process. Given their different PLGA molecular weights, these two formulations will be subjected to further studies to assess potential differences in drug release characteristics and therapeutic efficacy. 3D surface response plots were constructed to understand the interactions of variables with each other concerning the production of PLGA particles (Fig. 2A). The surface model Fig. 2B suggests a trend where an increase in PVA concentration and a decrease in PLGA molecular weight could lead to higher drug loading. Conversely, lower PVA concentrations and higher molecular weights might result in lower drug loading. The 3D surface plot Fig. 2C illustrating the relationship between PVA concentration and PLGA molecular weight in influencing the particle size (in nanometers) suggests that increases in both PVA concentration and PLGA molecular weight tend to produce larger nanoparticles. Furthermore, increasing both PVA concentration and PLGA molecular weight tends to decrease the PDI, suggesting a more uniform particle size distribution as these parameters increase (Fig. 2D).

Conclusion: This project utilized a Design of Experiments (DoE) approach to systematically investigate the impact of various formulation variables on the characteristics of nanoparticles. The selected formulations will be subjected to intensive studies such as drug release kinetics, in vitro and in vivo efficacy. These findings underscore the critical role of careful formulation design in optimizing drug delivery systems for treating NSCLC with TKIs like Osimertinib, potentially improving patient outcomes by mitigating side effects and enhancing therapeutic effectiveness.

References: 1) This project utilized a Design of Experiments (DoE) approach to systematically investigate the impact of various formulation variables on the characteristics of nanoparticles. The selected formulations will be subjected to intensive studies such as drug release kinetics, in vitro and in vivo efficacy. These findings underscore the critical role of careful formulation design in optimizing drug delivery systems for treating NSCLC with TKIs like Osimertinib, potentially improving patient outcomes by mitigating side effects and enhancing therapeutic effectiveness.
2)Okuma, Yusuke, et al. "First-Line Osimertinib for Previously Untreated Patients With NSCLC and Uncommon EGFR Mutations: The UNICORN Phase 2 Nonrandomized Clinical Trial." JAMA oncology 10.1 (2024): 43-51.

Acknowledgements: The authors declare no conflict of interest


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