Rapid and comprehensive identification of the metabolic soft spots of peptide drugs with highly cross-linked disulfide bonds by integrating established proteomic workflow with a product ion filtering strategy.

Disulfide bonds are conserved structural determinants in peptides, reinforcing conformational integrity and stability. Peptides constrained by disulfide bonds resist enzymatic cleavage and exhibit suppressed b/y-ion formation in bottom-up mass spectrometry. Furthermore, disulfide bond reshuffling is often overlooked by existing software, leading to incomplete metabolite identification and inaccurate mapping of metabolic soft spots. Current tools reliably identify peptides containing native or nonnative disulfide bonds, but fail to resolve peptides with highly cross-linked disulfide architectures. To address this gap, an integrated analytical strategy combining established proteomic workflow with product ion filtering was developed based on previous work. Linaclotide, insulin glargine, and insulin degludec were selected as representative peptide drugs. First, the integrated strategy's feasibility was verified using pepsin-digested linaclotide. Subsequently, the strategy was employed to map metabolic soft spots of therapeutic peptides across multiple human biological matrices. From linaclotide digests, 3 peptic fragments were confidently identified, whereas 1 in-source fragmentation artifact was excluded based on diagnostic neutral losses and retention time alignment. In human liver S9 incubation, 6 metabolites were identified with high-confidence spectral matches, and 7 metabolites were characterized in human intestinal S9 incubation, enabling unambiguous localization of linaclotide's metabolic soft spots. In addition, analysis of insulin glargine in human serum under in vitro incubation conditions identified 20 metabolites and 3 in-source fragmentation artifacts, with metabolic soft spots unambiguously assigned. Notably, 95 metabolites of insulin degludec, along with their metabolic soft spots, were mapped using the strategy, including 4 peptides containing glutamic-C16 fatty diacid that were identified by product ion filtering. SIGNIFICANCE STATEMENT: To our knowledge, this study represents the first comprehensive identification of the metabolic soft spots of linaclotide and insulin degludec across physiologically relevant enzymatic systems and biological matrices. Reduction and alkylation eliminate analytical complexity from cross-linked disulfide architectures, enabling comprehensive peptide metabolite characterization. This study provides a transferable, integrated analytical strategy for the comprehensive identification of metabolic soft spots and metabolites in other peptides with highly cross-linked disulfide bonds.