Automating Mass Spectrometry Analysis to Accelerate Oligonucleotide Drug Development

July 17, 2025
Ada Yee

Oligonucleotide therapeutics (OTs) are revolutionizing the treatment of various diseases by precisely targeting genes. They work by silencing genes, modulating gene splicing, and activating gene expression, offering an alternative to traditional small-molecule drugs. Their versatility supports personalized medicine, as they can target patient-specific mutations, difficult transcript variants, and “undruggable” genes.^1,2

As of March 2024, 20 oligonucleotide drugs have received approval from the Food and Drug Administration (FDA) and European Medicines Agency (EMA). With an average of two approvals per year since 2016, this trend highlights the increasing success and interest in oligonucleotide-based drugs.^3–5

Liquid chromatography-mass spectrometry (LC-MS) is a gold standard method for oligonucleotide bioanalysis, or the characterization and quantification of oligos and their metabolites in biological samples.⁶ In addition to challenges concerning sample preparation and method selection, MS analysis of OTs is further complicated by the large volume of data and the high molecular diversity of these compounds.^7,8

Key Challenges in Bioanalysis of Oligonucleotide Therapeutics

This was the case at Novartis, who uses ultra-performance liquid chromatography (UPLC) and high-resolution MS for oligonucleotide ADME (absorption, distribution, metabolism, and excretion) studies. Even beyond ADME, for OTs, MS can be used to evaluate key OT quality attributes, such as impurities, stability, metabolites, and sequence accuracy. However, MS analysis of OTs can bring specific challenges that can slow down metabolic studies, affect productivity, and impact overall cost-effectiveness.

Challenge 1: Mass Spectrometry Sample Preparation and Methods

Like many others in across the biopharma industry, Novartis uses LC-MS for its sensitive yet selective detection of molecules. Nevertheless, experimental challenges such as metal adduct formation and poor retention on reversed-phase columns can reduce MS performance. Other concerns, such as low or variable analyte recovery, dirty extracts, and reagent integrity, can also affect results. As such, choice of chromatographic extraction techniques and reagents as well as type of MS instrument (triple quadrupole MS/MS, HRMS, etc.) are key considerations.⁷

Challenge 2: Analyzing Complex Mass Spectrometry Data

With MS data, multiple charge states, unknown chemical species, chemical noise, overlapping isotope envelopes (OIEs) from unresolved species are all issues that can hinder accurate analysis. In addition to these general challenges with MS analysis, OTs present special challenges for biotransformation studies. Unlike traditional small molecules, which are metabolized by cytochrome P450 enzymes, oligonucleotides are mainly broken down by endo- and exonucleases, producing various truncated fragments and mononucleotides. Further taking into account chemical modifications, this increases the diversity of resulting analytes, making metabolite profiling more complex – especially for longer oligos.⁹

Manual data consolidation, generation of metabolite libraries, and expert-driven analyte identification is labor-intensive and error-prone. Moreover, metabolite species are multi-charged nature, which complicates data interpretation and necessitates data deconvolution. On top of this, as data volume and complexity increase, organizations must overcome challenges related to data integration, quality, accessibility, and regulatory compliance.⁸ Altogether, this highlights the need for automated approaches that can streamline MS-based bioanalysis of OTs.

How Genedata Expressionist Automates Mass Spectrometry Data Processing for Oligonucleotide Bioanalysis

For Novartis, the need for a solution to accelerate mass spectrometry data processing was clear. They partnered with Genedata to automate the MS analysis process, with a software platform that automatically generated sample-specific metabolite search libraries based on the parent drug structure. It also featured extensive data visualization capabilities and customizable reports to support regulatory submission and regulatory compliance. Purpose-built, automated yet configurable workflows helped to both boost efficiency and productivity as well as mitigate risk.

Adopting this solution had a transformative impact on Novartis’ workflows, by minimizing manual work and significantly reducing analysis time and human error. This helped to streamline the entire workflow, from raw data to regulatory-ready reports, and accelerate drug development timelines.

Read the full customer success story to discover how Novartis is advancing its oligonucleotide drug development with Genedata Expressionist.

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