Increased Identification of Extractable and Leachable Compounds by the Use of Chemical Ionization and Custom Databases

Significance of the Topic

Understanding and controlling extractables and leachables (E&L) is critical for ensuring safety and quality of pharmaceutical products in contact with packaging. Analytical techniques that enhance detection and identification of semi-volatile E&L compounds support risk assessment and regulatory compliance.

Objectives and Overview of the Study

This study aims to demonstrate how chemical ionization (CI) mode paired with electron ionization (EI) on a high-resolution GC/Q-TOF system, along with custom databases, improves identification of extractable and leachable compounds from an ophthalmic drug product (ODP). The workflow includes stress testing, sample extraction, data acquisition in dual ionization modes, and advanced data processing.

Methodology and Instrumentation

Sample preparation

• An empty ODP bottle was extracted with n-hexane under sonication for extractables.
• Leachables were obtained by heating the ODP formulation at 60°C for 24 hours (stressed) and storing controls at recommended conditions (non-stressed).

Instrumentation

• Agilent 7200 GC/Q-TOF system with Agilent 7890A GC.
• Dual ion source capable of EI (70 eV, 35 µA) and positive CI (methane reagent gas).
• DB-5ms GC column, 30 m × 250 µm × 0.25 µm.
• Data acquisition rate: 5 spectra/sec, mass range 55–700 amu.

Data analysis

• MassHunter Qualitative Analysis and Unknowns Analysis for peak deconvolution.
• Mass Profiler Professional for normalization, blank subtraction, fold-change analysis, and Venn diagrams.
• Custom Personal Compound Database (PCD) built from literature E&L lists.
• CI MS/MS using targeted fragmentation in MassHunter for structural confirmation.

Main Results and Discussion

• CI mode revealed molecular ion peaks absent in EI spectra for key compounds (e.g., decylcyclopentane).
• Custom database searches increased the number of identified E&L compounds, with average mass accuracy below 3 ppm.
• Venn analysis showed overlap of 15 compounds between stressed and non-stressed leachables and extractables.
• Structural confirmation by CI MS/MS validated tentative identifications such as ethyl 4-ethoxybenzoate.
• Semi-quantitative estimates using triphenyl phosphate internal standard highlighted compounds exceeding the analytical evaluation threshold.

Benefits and Practical Applications

• Combined EI and CI acquisition improves confidence in compound identification by providing both fragmentation and accurate molecular mass.
• Custom PCDs facilitate rapid screening against known E&L compounds.
• Enhanced detection supports safety assessment of pharmaceutical packaging materials.
• Workflow is adaptable to other product contact materials and regulatory guidelines.

Future Trends and Potential Applications

• Expansion of custom and public databases with emerging E&L profiles.
• Integration of machine learning for automated non-targeted data interpretation.
• Application of in silico toxicity prediction linked to analytical results.
• Continued improvement in high-resolution instrumentation for trace-level detection.

Conclusion

The dual-mode GC/Q-TOF approach combining EI and CI with custom databases substantially increases the identification of extractables and leachables in ophthalmic product studies. The methodology offers enhanced structural information, efficient data processing, and adaptable workflows for improved safety assessment.

References

• Recommendations for Extractables and Leachables Testing Part 1: Introduction, Regulatory Issues, and Risk Assessment, BioProcess International, Dec 2007.
• Jenke D, et al. Utilization of Internal Standard Response Factors to Estimate the Concentration of Organic Compounds Leached from Pharmaceutical Packaging Systems and Application of Such Estimated Concentrations to Safety Assessment, Journal of Chromatographic Science, 50 (2012) 206–212.
• Lateef SS, et al. Differential Analysis in Screening Assays for an Extractables and Leachables Study using an Agilent 7200 GC/Q-TOF System combined with Data Mining Software, Agilent Application Note, 5991-6688EN, March 2016.