An integrated method for metabolite detection and identification using a linear ion trap/Orbitrap mass spectrometer...

Area of Science:

• Analytical Chemistry
• Pharmacology
• Biochemistry

Background:

• Understanding drug metabolism is crucial for drug development and safety.
• Indinavir is an antiretroviral protease inhibitor with complex metabolic pathways.
• Characterizing drug metabolites aids in predicting drug efficacy and potential toxicity.

Purpose of the Study:

• To evaluate a novel analytical strategy for detecting and characterizing in vitro indinavir metabolites.
• To assess the effectiveness of multiple post-acquisition data mining techniques in metabolite identification.
• To identify new indinavir metabolites and elucidate their structures.

Main Methods:

• Utilized a hybrid linear ion trap/Orbitrap mass spectrometer for accurate-mass, full-scan MS and MS/MS data acquisition.
• Employed a generic data-dependent acquisition method.
• Applied post-acquisition data mining techniques: extracted-ion chromatography (EIC), mass-defect filter (MDF), product-ion filter (PIF), and neutral-loss filter (NLF).

Main Results:

• The EIC process effectively detected common metabolites with predicted molecular weights.
• The MDF process identified uncommon metabolites by analyzing mass defect similarities.
• PIF and NLF processes selectively identified metabolites based on fragmentation patterns, leading to the detection of 15 metabolites, including two novel ones.

Conclusions:

• The integrated analytical strategy enables high-throughput acquisition of accurate-mass LC/MS data.
• Complementary data mining techniques effectively detect both common and uncommon metabolites.
• The study provides comprehensive structural characterization of indinavir metabolites, including novel compounds.