Determination of Purity, Assay, and Impurity Profile for a Ligand-Conjugated Antisense Oligonucleotide
Applications | 2024 | Agilent TechnologiesInstrumentation
Software, LC/MS, LC/SQ, HPLC
IndustriesPharma & Biopharma
ManufacturerAgilent Technologies
Summary
Importance of the Topic
Understanding purity, assay, and impurity profiling of ligand-conjugated antisense oligonucleotides is critical for quality control and regulatory compliance in therapeutic development. Advanced LC/MS methods offer sensitivity and specificity beyond traditional HPLC, enabling accurate quantitation of both high-level and trace impurities. Streamlining data analysis with specialized tools enhances throughput and data integrity, accelerating drug candidate progression.Objectives and Study Overview
This application note presents the implementation of the Agilent Oligo Analysis Accelerator (OAA) integrated with OpenLab CDS to characterize a phosphorothioate ligand-conjugated antisense oligonucleotide (LICA). The goals were to automate the determination of sample purity, assay value, and impurity profiling while leveraging existing data integrity frameworks and reducing manual processing time to under one hour per sample.Methodology and Instrumentation
The analytical workflow was based on a validated ion pair reversed-phase LC/MS method adapted from Rentel et al (2022). Key steps included:- Four-level calibration using UV and MS signals for quantitation.
- Dual MS acquisition conditions (standard and harsh) to resolve buffer adducts.
- Extraction of multiple extracted ion chromatograms (EICs) for known and unknown impurities.
- Automated calculation of system suitability, ion classification, and integration boundaries using retention time offsets between UV and MS signals.
Used Instrumentation
- Agilent 1290 Infinity II LC with Hybrid C18 column (3.5 µm, 2.1×150 mm)
- Agilent InfinityLab LC/MSD XT single quadrupole mass spectrometer
- Agilent OpenLab CDS software version 2.8
- Agilent Oligo Analysis Accelerator version 1.0
Main Results and Discussion
Application of OAA automated critical steps including system suitability evaluation, EEI/LEI integration for the main UV peak, ion classification against theoretical m/z lists, and area integration of EICs. The LICA sample exhibited a main product purity of 89.8% by area. Low-level impurities including adducts and truncated sequences were quantified at sub-1% levels. Automated overlays of standard and harsh spectra facilitated accurate ion classifications and minimized manual inspection.Benefits and Practical Applications
- Significant reduction in manual data processing time, improving throughput to less than one hour per sample.
- Minimized transcription errors through automated integration, classification, and calculation steps.
- Enhanced traceability and compliance via audit trails recording all manual adjustments.
- Scalable workflow within existing OpenLab CDS reporting and electronic signature framework.
Future Trends and Applications
- Integration of machine learning algorithms for predictive impurity pattern recognition.
- Expansion to multi-dimensional separations and high-resolution MS platforms for complex conjugates.
- Cloud-based data processing and collaborative review workflows to further reduce turnaround times.
- Adaptation to other oligonucleotide modalities such as siRNA and mRNA therapeutics.
Conclusion
The Agilent Oligo Analysis Accelerator for OpenLab CDS streamlines the LC/MS-based characterization of ligand-conjugated antisense oligonucleotides, delivering rapid and reliable purity, assay, and impurity profiles. By automating labor-intensive steps and leveraging existing compliance infrastructure, this approach enhances analytical productivity and data integrity in oligonucleotide QC testing.References
1 Rentel C Gaus H Bradley K Luu N Kolkey K Mai B Madsen M Pearce M Bock B Capaldi D Assay purity and impurity profile of phosphorothioate oligonucleotide therapeutics by ion pair HPLC MS Nucleic Acid Ther 2022 32 3 206-220Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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