Methods

Samples and reagents: A 18-mer 2ʹ-O-methoxyethyl phosphorothioated oligonucleotide with the same sequence as nusinersen, a drug developed for the treatment of spinal muscular atrophy, was obtained from IDT (desalted). In addition, 5’(n-1, 2, and 3) and 3’(n-1, 2, and 3) shortmers were ordered (desalted) from IDT to mimic metabolites of the FLP. The ion-pairing reagents 1,1,3,3,3-hexafluoroisopropanol (HFIP, ≥ 99.8%) and diisopropylethylamine (DIEA, ≥ 99.5%,), and ethylenediaminetetraacetic acid (EDTA), were purchased from Sigma Aldrich.

Sample preparation: 1 mL plasma was extracted using Clarity OTX solid phase extraction (SPE) cartridges (Phenomenex) following the manufacturer’s protocol for extracting oligo therapeutics from biological samples. After drying with nitrogen gas at 40°C, the plasma extract was reconstituted in 1 mL mobile phase A containing 100 µM EDTA.

Samples of 10 µg/mL FLP in the extracted plasma containing 100 µM EDTA were spiked with the six related shortmers at 0.1, 0.3, 1, 3 and 10% (w/w relative to FLP) in order to mimic a sample from an in-vitro metabolism study. The FLP was used as a control sample.

Chromatography: A Shimadzu LC-20 series HPLC system was used with water as mobile phase A and 90:10 methanol/water (v/v) as mobile phase B, both with 15 mM N,N‑diisopropylethylamine and 35 mM hexafluoroisopropanol. A gradient from 20-40% B in 5 min with a 1.5 min wash step at 90% B was used at a flow rate of 0.25 mL/min. The column was a Waters ACQUITY PREMIER Oligonucleotide C18 (2.1 × 50 mm, 1.7 µm, 130 Å) at 70ºC, and the injection volume was 10 µL.

Mass spectrometry: A ZenoTOF 7600 system was used in negative polarity using an MRM^HR method (method details available on request). To determine the precursor masses, the data from a TOF MS scan of the 10% spiked sample was processed using the Molecule Profiler software to extract the m/z values for the most abundant charge states for the FLP, the spiked-in metabolites, and the desulfurization products (back-exchange of 1 S to O) of each of these. Collision induced dissociation (CID) was used, and collision energies (CE) were selected that ensured the generation of fragment-rich MS/MS spectra. The parameters for the final MRM^HR method are summarized in Table 1. Data were acquired both with the Zeno trap on and off, to determine the effect of the Zeno trap functionality on MS/MS data quality The Zeno trap is located before the TOF pulser and accumulates ions during each TOF pulse, resulting in up to 90% duty cycle.

Data processing: Data were processed using the Molecule Profiler software version 1.2 from SCIEX. Considering the structure of the oligonucleotide, the number of bonds to break in the parent structure, and a comprehensive list of 83 possible standard transformations, the software identifies the different charge states of potential metabolites. This assignment is based on the accurate mass match—a 20 ppm tolerance was used for the work presented in this application note—and the match between the measured and theoretical isotope patterns. Additional transformations can be added, while also custom nucleotide residues can be used by defining the 5’ and 3’ linkers, sugar, base and phosphate groups (Figure 2). The list of found potential metabolites can be manually curated, and the different charge states of each potential metabolite are grouped together for relative quantification based on the TOF MS data.

For confirmation of the structure of each potential metabolite, MS/MS spectra can be automatically annotated with a, a-B, w, b, x, c, y and d fragments (see Figure 2). Sequence coverage is automatically calculated by the software.

Chromatography

Metabolites of oligonucleotides are difficult to baseline separate from the main product with reversed phase LC while providing medium to high throughput, especially if they differ only by 1 or 2 nucleotides. Modified backbones such as phosphorothioated species are essential for improved pharmacokinetic properties and binding to the target, but complicate separation even further because they are mixtures of diastereomers, resulting in peak broadening. Figure 3 shows the separation achieved for the different metabolites; only the 5’(n-3) showed (partial) separation from the FLP.

Relative quantification

Nusinersen is reported to mainly be metabolized via exonuclease‑mediated hydrolysis at the 5’ and 3’ ends.¹ Therefore, extracted plasma was spiked with 10 µg/mL FLP and the six 5’(n-1,2 and 3) and 3’(n-1,2 and 3) deletion metabolites at 0.1, 0.3, 1, 3 and 10% (w/w) to mimic a metabolism sample. In addition, the defined spike-in amounts allowed for an estimation of the sensitivity of the presented workflow. All samples and a control spiked with only 10 µg/mL FLP were measured and subsequently analyzed using the Molecule Profiler software. Processing parameters (Figure 4) were selected to allow for the identification of the FLP and metabolites. Based on the TOF MS data, the FLP and potential metabolites are being matched to the different charge states (see 3’(n-3) metabolite matching in Figure 5). All identified charge states can be easily grouped for an automatic calculation of the % area for the main component and each potential metabolite.

The % area is the sum of the TOF MS areas of all found charge states of each identified analyte as a percentage of the total area of all analytes found. In Table 2 the % areas of the spiked-in metabolites are listed for each of the spike-in levels. The purity found for the control sample is consistent with the information from the manufacturer for products that have not been purified with HPLC.

The main impurities found in the control sample were desulfurization (16.1%), and di-desulfurization (1.37%). Correlation between the spiked-in amounts, and the reported areas was good for the 5’(n-1), 5’(n-3), 3’(n-1) and 3’(n‑3) metabolites, with small amounts found in the control sample (Table 2). For the 5’(n-2) metabolite the correlation was found to be poor (Table 2). Upon further inspection of the data this could be attributed to an overlap of the -8 charge state isotopes of the metabolite with those of the -9 charge state of the FLP. For relative quantification of this compound based on MS data, a better chromatographic separation will be required. Alternatively the Analytics module in SCIEX OS software can be used to perform quantification based on reconstruction of the TOF MS data, or by quantification based on fragment masses using MRM^HR data. Note that the Molecule Profiler software also supports relative quantification based on UV data (not shown). No signal was found for any of the charge states of the 3’(n-2) metabolite at the 0.1 and 0.3% spike-in levels. Possible causes for this could be sequence dependent adsorption losses or ion suppression.

Correlation

The Molecule Profiler software also allows for comparing the peak areas of metabolites between different samples. The software can be used to compare samples taken at various times after administering a therapeutic, or compare samples taken from different test animals. In Figure 6, this function of the software was used to graph the peak areas for several of the metabolites at the different spike-in levels.

Structural confirmation

The potential metabolites suggested by the software are based on accurate mass and isotope pattern matching of the TOF MS data, which does not provide information for the localization of a modification, or the sequence of a potential metabolite. As the structure of a metabolite can be important for determining its toxicity, understanding and confirming the correct structures is critical. The Molecule Profiler software can help in confirming the structures of each of the potential metabolites by annotating the MS/MS spectra. Allowing for a, w, c, y and d terminal fragments, and allowing for the loss of 1 base or water molecule, the Molecule Profiler software was used to annotate the MS/MS spectra of the different charge states and calculate the consecutive sequence coverage. An m/z tolerance of 10 ppm and a minimum S/N ratio of 20 were used. Figure 7 shows the sequence coverage from the MS/MS spectra of the -9 charge state for the 3’(n‑3) metabolite at the various spike-in levels. Full sequence coverage was found down to the 1% level when the Zeno trap functionality was employed. Without the Zeno trap, the coverage was significantly lower, as expected. When allowing for 2 bonds to break, full sequence coverage was seen at the 0.3% and 0.1% levels as well with the Zeno trap on (data not shown). Figure 8 shows a summary of the sequence coverage found based on the MS/MS spectra for the FLP and all metabolites spiked-in at the 1% level. Except for the 5’(n-3) metabolite, sequence coverage was 100% for the metabolites. To enable easy review of information, the software combines assigned fragments from the MS/MS spectra from all charged states for a given metabolite. This can improve sequence coverage significantly, as some fragments might only be generated from specific charge states.

Figure 9 illustrates how much more information-rich MS/MS spectra with higher S/N can be acquired using the Zeno trap compared to traditional MS/MS analysis. The higher quality data enabled the assignment of many more fragments with great S/N by the software. The high-quality MS/MS information can be used to confirm the identification based on MS even further.

Figure 10 shows an example of an annotated MS/MS spectra in the Molecule Profiler software. All assigned fragments are displayed in a table format and the sequence coverage is visualized with colored marks to facilitate the overview of detected fragments. Manual curation of the detected fragments can be easily achieved by unchecking fragments in the “use” column, which automatically updates the sequence coverage.

Conclusions
 

• Excellent MS data quality and mass accuracy were leveraged for the identification of FLP and spiked-in metabolites down to a 0.1% level
• A fast and accurate relative quantification of oligonucleotides and their metabolites based on MS or UV data can be performed in Molecule Profiler enhancing product understanding and PK/PD and toxicology studies
• The annotation of MS/MS with the most commonly found fragment ions based on a proposed structures in Molecule Profiler software greatly reduces manual workload and speeds up the correct identification of oligonucleotide-based drugs and metabolites
• MS/MS data quality and fragment assignment can significantly be enhanced using the Zeno trap with the ZenoTOF 7600 system, which allows for the identification of even very low abundant metabolites

References

1. Committee for Medicinal Products for Human Use (CHMP) Assessment report EMA/289068/2017.