Unknown Constituent Identification in Topical Preparation using a Q-TOF Mass Spectrometer

Significance of the Topic

Topical drug formulations deliver active compounds to the skin but may also contain low-level unknown constituents that affect safety and efficacy. High-resolution mass spectrometry combined with advanced data analysis enables comprehensive profiling of these unknowns, supporting quality control, regulatory compliance, and patient safety.

Objectives and Overview of the Study

This study aims to develop and demonstrate a streamlined qualitative analysis workflow for detecting and identifying low-level unknown constituents in a commercial topical preparation. The approach utilizes a Shimadzu LCMS-9030 Q-TOF mass spectrometer coupled with the Shimadzu Insight Assign software to predict formulas and assign MS/MS fragments to candidate structures.

Methodology

An off-the-shelf topical product was analyzed under optimized LC and MS parameters:

• Liquid Chromatography: Shimadzu ODS-III column (150×2.1 mm), mobile phase A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile), 0.4 mL/min, 40°C.
• Mass Spectrometry: Shimadzu LCMS-9030 Q-TOF, positive ion mode, scan range m/z 100–1000, with UV detection at 237 nm.

Used Instrumentation

• Shimadzu LC-30AD ultra-high-performance liquid chromatograph.
• Shimadzu LCMS-9030 quadrupole time-of-flight mass spectrometer.
• Shimadzu Insight Assign formula prediction and fragment assignment software.

Main Results and Discussion

• Chromatograms revealed a prominent unknown peak at retention time 15.0 min (m/z 485.1907).
• Formula prediction generated 16 candidates based on accurate mass and isotopic patterns; the top formula was C25H31O5F2Cl.
• Database searching yielded two plausible structures: Halobetasol Propionate and 11-Propionate 21-chlorodiflorasone.
• Fragment assignment showed an 82.7% match for Halobetasol Propionate, leading to its tentative identification.
• Comparative MS/MS highlighted key differences in functional groups between the unknown and the main ingredient, diflorasone diacetate.

Benefits and Practical Applications of the Method

This workflow provides a rapid, reliable approach for qualitative profiling of unknown low-level constituents in complex topical matrices. It enhances laboratory capabilities for product quality control, formulary consistency checks, and regulatory investigations.

Future Trends and Potential Applications

• Integration of machine learning for automated structure elucidation.
• Expansion of spectral libraries with diverse topical product databases.
• Coupling Q-TOF with ion mobility spectrometry for enhanced separation and identification.
• Real-time monitoring of manufacturing processes for on-line quality assurance.

Conclusion

The combination of Shimadzu’s LCMS-9030 Q-TOF and Insight Assign software offers a straightforward and effective platform for the identification of unknown constituents in topical preparations, supporting improved quality control and patient safety.

Reference

• PubChem database
• Metlin database