(M1530-08-52) Enhancing Amorphous Solid Dispersion Polymer Screening: A High-Throughput Approach with Fully Automated Sampling

Purpose: Delivery of Biopharmaceutics Classification System (BCS) II and IV active pharmaceutical ingredients (API) as amorphous solid dispersions (ASD) is a commercially proven strategy to enable supersaturation of API and improve oral bioavailability.^1,2 Efficient and high-throughput ASD screening methodologies are essential for optimizing polymer selection and streamlining early formulation development. In the preclinical space, the Dimethyl Sulfoxide (DMSO) Drop Test is a commonly adopted screening method for identifying polymers.³ In this assay, a small amount (~2-4 mg) of solubilized API in DMSO is introduced into a vial containing a polymer solution in fasted state simulated small intestinal fluid (FaSSIF). Upon its introduction to FaSSIF, the API may precipitate immediately in a presumed amorphous state and is subsequently stabilized by the polymer. The API soluble fraction is then monitored at various timepoints to evaluate the ability of the polymer to sustain API supersaturation. This workflow requires extensive analyst intervention for incubation preparation and sample collection, averaging 30 minutes per timepoint for each API-polymer combination. In this work, we report the development of a miniaturized, automated high-throughput (HT) format of the DMSO Drop Test that addresses these limitations by reducing both labor and material requirements, thereby accelerating early-stage ASD formulation development.

Methods: A 96-well plate format was configured for HT assay screening, replacing vials with individual wells. This change reduced the required API to approximately 0.1 mg per polymer per timepoint, up to an 8-fold reduction in material consumption. In the HT workflow, 2 µL of the API stock solution in DMSO was introduced into each well containing FaSSIF and varying types and concentrations of polymers. Each row represented a different polymer composition, while each column corresponded to a specific timepoint (e.g., 0.5, 1, 3, 6, 24 hours), effectively eliminating the need for manual timepoint sampling across conditions. After incubation, the samples were filtered using a 96-well filter plate, and the filtrate was subsequently diluted for high-performance liquid chromatography (HPLC) analysis. The feasibility of automating this workflow was demonstrated using a Hamilton STARPlus liquid handler equipped with an integrated heater/shaker. The manual and automated HT formats were directly compared by evaluating five commonly used ASD polymers: hydroxypropyl methylcellulose acetate succinate high and low grades (HPMCAS-H, HPMCAS-L), hydroxypropyl methylcellulose (HPMC E15), Eudragit L100-55, and polyvinylpyrrolidone-vinyl acetate copolymer (PVP VA 64). Each polymer was screened for its ability to maintain supersaturation of a representative poorly soluble API at a 1:10 API:polymer ratio.

Results: The DMSO Drop Test HT approach was successfully employed to screen and rank five polymers (HPMCAS-H, HPMCAS-L, HPMC E15, Eudragit L100-55, and PVP VA 64) with a representative poorly soluble API. The automation of the process with a Hamilton STAR liquid handler demonstrated good reproducibility and accuracy, shown by the strong correlation between API concentration profiles. Polymer ranking results were consistent for both manual and automated assays, as illustrated in Figure 1, indicating the robustness of the developed platform. Moreover, automation significantly enhanced screening efficiency leading to a 10-fold reduction in the required analyst labor. The integration of automated liquid handling and incubation not only improved throughput but also minimized variability introduced by manual sampling.

Conclusion: In conclusion, the developed automated ASD polymer screening platform significantly enhances formulation development by enabling rapid, early-stage screening with minimal material requirements. Its scalability and efficiency make it potentially valuable in preclinical drug development, where expeditious data-driven decision-making is critical.

References: 1. Wu, K., Kwon, S. H., Zhou, X., Fuller, C., Wang, X., Vadgama, J., & Wu, Y. (2024). Overcoming Challenges in Small-Molecule Drug Bioavailability: A Review of Key Factors and Approaches. International Journal of Molecular Sciences, 25(23), 13121.
2. Tambe, S., Jain, D., Meruva, S. K., Rongala, G., Juluri, A., Nihalani, G., Mamidi, H. K., Nukala, P. K., & Bolla, P. K. (2022). Recent Advances in Amorphous Solid Dispersions: Preformulation, Formulation Strategies, Technological Advancements and Characterization. Pharmaceutics, 14(10), 2203.
3. He Y, Ho C. Amorphous Solid Dispersions: Utilization and Challenges in Drug Discovery and Development. J Pharm Sci. 2015 Oct;104(10):3237-58.

Figure 1. Comparison of automated and manual high-throughput ASD polymer screening methods at 1:10 API:polymer ratio showing consistent API concentration profiles and polymer ranking across both approaches. Same axis scale used across graphs.