Over the past decade, new drug modalities like peptides, oligonucleotides, poteolysis targeting chimeras (PROTACs), antibody–drug conjugates (ADCs), peptide–drug conjugates (PDCs), small molecule–drug conjugates (SMDCs), and antibody–peptide conjugates (APCs) AOCs have been introduced to improve drug efficacy, safety, and DMPK properties or to target previously untreatable diseases. Many biotech and pharmaceutical companies have adopted these modalities, but their metabolic pathways often differ markedly from those of small-molecule drugs. Common LC-HRMS data processing tools, such as extracted ion chromatogram (EIC) and mass defect filtering (MDF), traditionally used to detect phase I and II metabolites, are often unsuitable for profiling metabolites from these novel drug modalities. Additionally, software developed for peptide and oligonucleotide metabolite profiling frequently fails to detect oxidative metabolites and conjugates.

To address these challenges, XenoFinder has developed several novel LC-HRMS workflows for profiling metabolites formed through protein degradation, peptide and oligonucleotide hydrolysis, and/or phase I and II metabolism. These workflows, including a universal LC-HRMS platform for identifying metabolites of peptide-based therapeutics, leverage the background subtraction filter (BSF) and advanced peptide/oligonucleotide metabolite identification software.

o Case Study (2): Identification of a new payload release pathway of DS-0821a using the untargeted LC-HRMS method

o Case Study (3): Detection and identification of in vitro metabolites of inclisiran using a novel and universal LC-HRMS method

o Case Study (4): A universal LC-HRMS method for metabolite Detection and identification of peptide-based drugs