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Overcoming Analytical Bottlenecks in Oligonucleotide Drug Development with Automation

August 25, 2025
Arielle Mann

Oligonucleotide therapeutics have revolutionized modern medicine by offering targeted treatments for various diseases, including rare conditions once deemed untreatable. These therapies use synthetic RNA or RNA–DNA hybrids designed to bind specific RNA targets and modify gene or protein expression.1 Beyond their therapeutic potential, oligonucleotides are widely applied in molecular diagnostics, genetic analysis, microarrays, gene therapy, forensic science, and biomedical research.2 However, despite significant advancements in oligonucleotide synthesis, downstream processing, especially the purification and isolation of these compounds, remains a critical bottleneck in the production of therapeutic oligonucleotides. This challenge is not unique to any one organization but is a widespread issue across the biopharma industry.3

Roche, a global pharmaceutical leader in diagnostics and therapeutics, encountered similar difficulties in managing the large volume of samples generated during oligonucleotide synthesis. Their analytical teams faced high throughput demands, complicated by time-consuming impurity quantification processes and complex impurity profiles. These bottlenecks hindered the depth of analysis and increased the workload on experts. In response, Roche partnered with Genedata to develop a customized mass spectrometry (MS) data processing solution that significantly improved throughput and impurity characterization, while also reducing the workload on mass spectrometry experts.

Challenges in the Biopharma Industry’s Analytical Processes

Biopharmaceuticals account for over 30% of the drug pipeline, with thousands of products in development.4 However, drug development is complex, time-consuming, and costly, often taking 10–15 years and over $2.5 billion to bring a new drug to market.5 This process faces significant challenges, including the following:

High Volume of Complex Data

Drug development workflows are hindered by issues in data procurement, validation, analysis, and sharing, particularly with unstructured data and clinical research. High error rates in clinical databases due to manual input and misinterpretation often go unnoticed by standard detection methods, highlighting the need for better data quality strategies.6,7

Time-Consuming Manual Processes

Manual data processing, including analyzing mass chromatograms to identify and quantify fragments, is slow and labor-intensive.8 Essential data management practices, such as entry, validation, and database locking, are crucial but time-consuming.9,10 The process is further complicated by inconsistent data extractions and the challenge of scaling manual chart reviews without automation.9 

Inability to Scale Analytical Systems Efficiently

Analytical systems struggle to scale owing to siloed data, inconsistent standards, and manual processes, especially in clinical research. The volume and heterogeneity of data often require costly expert intervention, leading to delays and inefficiencies.6,11 Reliance on error-prone workflows, poor system interoperability, and limited predictive accuracy further complicate timely decision-making.12,13

 

The Biopharma Industry’s Need for Automation in Oligonucleotide Therapeutics

The growing demand for longer, chemically modified, and highly pure oligonucleotides has surpassed the capacity of traditional manual methods. As the molecular complexity of these compounds increases, accurately quantifying them through conventional approaches becomes more challenging. This highlights the urgent need for scalable, automated solutions that ensure both scalability and reproducibility.14 

To address bottlenecks in oligonucleotide analysis, Roche sought a flexible solution to support its expanding therapeutic pipeline. Traditional manual data processing for high-performance liquid chromatography and mass spectrometry was limited in throughput and efficiency. Roche implemented a customized mass spectrometry workflow that streamlined analysis, enhanced characterization depth, and reduced the burden on experts, thereby accelerating the optimization of their oligonucleotide drug development process.

Streamlining Oligonucleotide Synthesis Analysis Through Automated Mass Spectrometry

Genedata offers advanced software solutions that transform complex experimental data into actionable insights for large-scale life sciences R&D. Our open, adaptable platforms automate and standardize mass spectrometry workflows across biopharma organizations, enabling high-quality results while saving significant time and costs. Customizable, transparent, and fully flexible, Genedata unifies workflows across instruments and teams, delivering fast, reproducible, and reliable data without the need for multiple software packages.

In collaboration with Genedata, Roche developed an automated LC-UV-MS data processing solution designed to support high-throughput analysis for optimized oligonucleotide synthesis processes. This solution dramatically reduced the analysis time for complex samples, decreasing it from 5–6 hours to just 30 minutes.

The automation provided by Genedata also enabled Roche to perform more comprehensive impurity characterization early in the development process, reducing downstream risks and enhancing the quality of reporting. Roche’s improved ability to detect impurities provided deeper insights into their oligonucleotide products, ensuring both therapeutic safety and efficacy. 

By automating routine tasks, analysts were freed to focus on higher-value, strategic work, improving resource allocation and boosting productivity, job satisfaction, and retention. The Genedata platform significantly enhanced Roche’s oligonucleotide drug development by automating mass spectrometry data analysis. This reduced time spent on routine tasks, enabling faster, more accurate results, and increasing throughput with high-quality analysis in less time, ultimately improving overall R&D efficiency.

Roche’s experience demonstrates how automation can address challenges in oligonucleotide drug development. The solution provided flexibility to meet the growing demands of their therapeutic pipeline while ensuring efficiency, compliance, and data integrity.

For biopharma companies facing similar data management challenges, automation offers a competitive edge by improving speed, reducing errors, and optimizing resource use. Roche’s success serves as a model for overcoming analytical bottlenecks and driving innovation in next-generation therapeutics.

Learn how Roche accelerated the oligonucleotide drug development workflow by automating LC-UV-MS workflows with Genedata Expressionist.

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References

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