An automated, high-throughput approach to generating cocrystals with solvent-drop grinding (SDG)

Importance of the Topic

The discovery of new drug candidates increasingly requires exploration of solid forms beyond traditional salts and polymorphs. Cocrystallization offers a versatile route for non-ionic APIs, enhancing solubility, bioavailability and stability.

Objectives and Study Overview

This study aimed to automate solvent-drop grinding (SDG) for high-throughput cocrystal screening using Unchained Labs’ Big Kahuna platform. The manual SDG of glutaric acid with 1,2-di(pyridin-4-yl)ethane was replicated and variables such as solvent volume, material loading and grinding ball parameters were systematically evaluated.

Methodology and Instrumentation Used

The automated workflow employed the Big Kahuna system for solid and liquid dispensing, vortex mixing (900 rpm, 1 hour) and weighing within a 96-well crystallizer assembly featuring a universal substrate and disposable liner inserts. Experimental designs were managed in Library Studio and LEA software, while X-ray powder diffraction data were collected on a Bruker XRD and analyzed via Spectra Studio.

Key Results and Discussion

Manual SDG required four grinding cycles to achieve complete conversion as monitored by XRD. Automated screening of 16 conditions identified optimal parameters: 10 mg total loading, 20 µL methanol and two medium-sized steel balls. Larger ball count and size improved conversion, and vortexing at 900 rpm for one hour was sufficient to form the target cocrystal. LEA integration enabled seamless data tracking from design to analysis.

Benefits and Practical Applications

Automated SDG reduces manual labor, material and solvent consumption, and accelerates cocrystal screening. This workflow expands experimental throughput, supports robust polymorph exploration and minimizes late-stage development risks.

Future Trends and Potential Applications

Scaling to full 96-well screening will facilitate rapid mapping of API-coformer-solvent spaces. Integration with complementary analytics (e.g., Raman, microscopy), and data-driven design using AI tools promise further efficiency gains in pharmaceutical preformulation and QA/QC workflows.

Conclusion

The Big Kahuna-based automated SDG method provides an efficient, high-throughput platform for cocrystal discovery. Optimal conditions were identified, demonstrating scalability and the potential to streamline pharmaceutical solid form development from screening to characterization.

References

1. Luu N et al. High-throughput 96-well solvent mediated sonic blending synthesis and on-plate solid/solution stability characterization of pharmaceutical cocrystals. Int J Pharm. 2013;441:356–364.