Category Archives: Resource Library

A commonly used strategy in the peptide therapeutics field to introduce chemical modifications such as cyclisation, changing the stereochemistry of an amino acid, substitution of natural amino acids to chemically modified ones and others to improve their efficacy and ADME profile. The study of the metabolic degradation products for synthetically modified therapeutic peptides using LC-MS is a challenging issue. Different chemoinformatics approaches are used for automated metabolite identification. These tools propose metabolite structures based on the combination of metabolite prediction and analysis of MS data. This makes building the virtual set of all potential metabolites time and computational intensive. Fragmentation analysis requires even more computation time. Finally, a third challenge related to the depiction of the parent and the metabolites.

Methods

New algorithms that address the challenges in highly modified therapeutic peptide structure elucidation have been developed. The peak detection algorithm was improved to use the Most Abundant Isotope for parent and potential metabolites. A new algorithm is producing all virtual metabolites applying the library of chemical reactions to each monomer while maintaining the connection to the atoms. A third improvement is the fragmentation algorithm that has two layers of analysis, one at the monomer level and the other one at the bond level. Finally, we will also show the results of the implemented algorithm for the analysis results visualization.

Preliminary data

Using MassMetaSite we analyzed a collection of experimental data for a set of peptides where the data was collected on a Q-Exactive Thermo instrument. The metabolite identification study was performed using a peptide set that included eight compounds: somatostatin and its seven synthetic analogues. All test compounds were incubated in serum. These peptides are all cyclic peptides and seven of them had unnatural amino acids. The structural assignments were performed for 17 degradation products with high mass accuracy (ppm<3). Most of the metabolites resulting from one or two metabolic modifications were produced by amide hydrolysis and were detected by the new algorithm. Each of these compounds showed a suitable fragmentation pattern that could be compared to the parent by the fragmentation algorithm to assign the shifted and non-shifted fragments. All the metabolites were checked manually including a review of the assigned fragments. Metabolites were considered as reliable because the fragmentation was adequate, isotope pattern was as expected, the small differences between the m/z of observed and theoretical, and the mass score was high. As previously reported in the literature, the first two most abundant metabolites with assigned structure correspond to cleavage of the linear part of the somatostatin. Finally, we analyzed experimental data for the insulin and semaglutide data where the data was collected on a Waters QToF. Insulin is a cyclic peptide that contains 3 disulfide bridges and semaglutide is a linear peptide that contains linkages and fatty acid. The visualization algorithm developed allows to show results for these complex structures in a such a manner that it is easy to interpret due to the constraint structure alignment between the substrate and the metabolite (keeping same orientation), a possibility to combine monomer and atom/bond notation. Therefore, the metabolic changes in the structure can be easily seen by the User.

Collisional-induced dissociation (CID) has been the main workhorse for small molecule structure elucidation in drug metabolite identification studies.  Software tools, such as Massmetasite, have been developed to assist in the automatic interpretation of CID MS/MS spectra.  The challenge with CID is that for many drug metabolites, non-informative fragmentation occurs, resulting in a lack of useful structural assignments for these metabolites.  Commercialization of electron activated dissociation (EAD) on a quadrupole time of flight mass spectrometer provides a potentially powerful new tool for small molecule structure elucidation in drug discovery.  Automated interpretation of EAD MS/MS spectra using existing algorithms needs to be tested, modified, and implemented. A comparison of CID and EAD fragmentation using automated interpretation will be presented in this work.

Methods

Small molecule drugs were incubated in rat hepatocytes at 1 µM. Time points:  0, 30 and 120 min were pulled from the incubation and quenched with 1 volume of CH₃CN. Samples were vortexed, centrifuged, and the supernatant transferred to an HPLC vial for analysis.

LC separation was performed on a Phenomenex Luna Omega Polar C18, 150 mm column using 0.5µL or 5 µL injection volumes. Gradient separation 0.1% formic acid in water and acetonitrile was performed over 4.75 minutes from 5%B to 95%B with a total of 6.5 minutes.

The samples were analyzed using ZenoTOF 7600 CID-IDA(DDA) and EIEIO IDA(DDA). TOFMS was scanned between m/z 100-1000, CID/EAD MS/MS from 60-1000.  Data was processed in MassMetaSite with CID and EAD fragmentation.

Preliminary data

EAD is a free electron fragmentation mode available recently introduced to accurate mass LC-MS/MS. It utilizes high energy electrons which allows for the dissociation of singly charged precursors, in this work an electron kinetic energy of 10 eV was utilized.  The MS/MS spectra show significant increases in the number of fragment ions observed when going from Zeno CID to Zeno EAD spectra.  The larger number of fragment ions makes it even more important for automated assignment using software tools such as Massmetasite.  Many of the new fragments are the result of radical bond cleavage driven by electron-impact excitation of ions from organics (EIEIO) mechanism.  Typical software algorithms for MS/MS focus on even electron species, typical of CID fragmentation.  With EAD and the production of odd electron fragments, modification of the algorithm is required.  The results to date show much richer fragmentation spectra using EAD versus CID and that the modified algorithm can assign the new odd electron fragments.