Automated qNMR data processing and analysis in the behind-the-scenes of fragment-based drug discovery

Importance of the topic

The development of new drug candidates is a resource-intensive endeavor that relies heavily on early-stage fragment screening and accurate solubility measurements. Aqueous solubility directly influences compound delivery and bioavailability, making it a critical parameter in fragment-based drug discovery workflows.

Objectives and overview of the study

This study aimed to implement a high-throughput, automated qNMR screening process to evaluate fragment solubility in an aqueous medium. Researchers at The Institute of Cancer Research established a 2,860-compound fragment library and sought to exclude poorly soluble fragments (<500 µM) early, thereby optimizing downstream drug development efforts.

Methodology and used instrumentation

• Two 500 MHz NMR spectrometers for spectral acquisition of fragment samples.
• Mnova Gears (Mgears) automation module to execute post-acquisition qNMR analysis.
• Predefined Standard Operating Procedures encoding calculation parameters and formulas.
• Centralized batch processing on a dedicated computer with results stored in the ICR internal database.

Main results and discussion

• Automation now handles approximately 80% of high-throughput qNMR analysis, reducing per-spectrum processing time by nearly sixfold.
• Consistent HTML reports are generated automatically, enabling rapid identification and exclusion of fragments below the 500 µM threshold.
• Homogeneous output formatting and enhanced data management practices minimize manual reporting overhead.
• A built-in result viewer facilitates inspection, correction, and reprocessing of individual spectra without deep NMR expertise.

Benefits and practical applications

• Increased laboratory productivity and robustness of solubility screening operations.
• Experts can redirect efforts toward innovative tasks, while non-specialists execute SOP-driven analyses via a simple interface.
• Rapid decision-making in fragment prioritization streamlines the drug discovery pipeline.
• Broad adoption across medicinal chemistry teams expanded usage from one project in 2017 to thirteen in recent years.

Future trends and potential applications

• Integration of machine learning models to predict solubility and guide experimental design.
• Extension of automation frameworks to additional NMR-based assays such as binding affinity or kinetic measurements.
• Cloud-based data processing and collaborative platforms for multi-site research networks.
• Community-driven SOP standardization to facilitate reproducibility across laboratories.

Conclusion

The adoption of Mnova Gears for automated qNMR solubility screening has significantly accelerated fragment-based drug discovery workflows, delivering reliable, high-quality data with minimal manual intervention. This approach not only enhances throughput and consistency but also democratizes access to NMR-based assays across multidisciplinary research teams.

References

• Pollock K, Liu M, Zaleska M et al. Fragment-based screening identifies molecules targeting the substrate-binding ankyrin repeat domains of tankyrase. Sci Rep 9, 19130 (2019).
• Bellenie BR, Cheung KMJ, Varela A et al. Achieving In Vivo Target Depletion through the Discovery and Optimization of Benzimidazolone BCL6 Degraders. J Med Chem 63(8), 4047-4068 (2020).