Navigating Data Challenges in RNA Therapeutics: From Complexity to Clarity
August 3, 2026
Ming Wang
RNA therapies are scaling rapidly across the biopharmaceutical landscape. Following the success of COVID-19 mRNA vaccines, large biopharma companies have expanded RNA therapy pipelines, with investments doubling between 2015 and 2020.1 With this momentum come heightened expectations: RNA-based approaches may help address hard-to-drug targets and, in some cases, accelerate development through sequence-based design.
RNA therapies still require empirical testing in biological assays. To increase throughput, RNA discovery teams are scaling key screening assays across modalities using higher-throughput methods and automation. At the same time, investment has accelerated organizational growth across RNA R&D. Scaling both experimentation and teams requires robust analysis and data management software and an IT infrastructure that can scale with demand.
In this post, we describe the data analysis and workflow challenges that emerge as RNA discovery scales, driven by increased assay throughput and growing organizational complexity.
Scaling RNA Drug Discovery
Having reached a new stage of maturity and growth, RNA drug discovery is now scaling key assays across RNA therapeutics R&D. The goal is to screen larger libraries to identify candidates that are not only biologically active but also demonstrate favorable biodistribution, such as reaching the right tissue and intracellular compartments. This is achieved through large-scale screens of higher-performing sequences, chemistries, and formulations.
High-Throughput Screening and Laboratory Automation
To enable this scale, companies are miniaturizing and developing methods that make them more amenable to higher throughput, while also deploying laboratory automation. For example, qPCR assays have been significantly scaled through one-step, extraction-free reactions and multiplexed measurement of multiple genes in parallel.2 In addition, highly automated platforms now enable throughput of up to hundreds of thousands of compounds per day.
Scaling mRNA Formulation and LNP Screening
On the mRNA side, companies such as AstraZeneca3 are scaling up both the production and screening of liquid nanoparticles (LNPs) for mRNA formulation. Many have moved from lower-throughput, microfluidic-based mixing toward higher-throughput liquid-handling systems, while also automating LNP assessment across assays measuring size, encapsulation efficiency, acidity, and cellular function.
Growing Organizational Complexity in RNA Research
Beyond assay upscaling, the RNA field is also experiencing increased organizational complexity. Assays once performed by a single scientist may now be distributed across multiple individuals or teams. Lab heads or data analysts must then aggregate and compare results from different assay types to identify the most promising hits or formulations. As capacity grows, organizations often broaden their assay portfolios and introduce new experimental approaches, increasing the need for consistent data handling across teams.
The Right Software for RNA Therapeutics Discovery: Purpose-Built, Scalable, and Enterprise-Ready
Every RNA drug modality presents a distinct set of challenges, and in a fast-moving field, methods used to address them can change quickly. Regardless of modality or assay type, however, scaling experiments inevitably leads to increased data volumes.
As RNA discovery scales, analytical and informatics workflows must scale with it. Manual data processing or workflows spread across multiple software tools may work for small datasets but become inefficient and error-prone at larger scale. Increased organizational complexity compounds this challenge, as more people generate and process data and more handoffs occur between teams. Problems such as lack of standardization, difficult data transfer, and loss of data provenance can accumulate.
In practice, addressing these challenges increasingly requires an integrated platform approach that spans data capture, analysis, and governance across RNA discovery and development workflows. In this context, the choice of a central software platform becomes increasingly important.
Purpose-Built RNA Analysis Software for Efficient RNA Therapy Research
As RNA therapeutics R&D scales, analysis software must reflect the specific complexity of RNA modalities and assays. General-purpose analysis tools, such as spreadsheets or generic workflow platforms, are not optimized for RNA research and often require teams to build and maintain custom data-processing pipelines that are difficult to scale and standardize.
Purpose-built, out-of-the box solutions can reduce this burden by providing native support for RNA-relevant assay types, including qPCR, cellular splicing assays, affinity-based measurements, and formulation characterization. Automated, assay-specific analytics reduce reliance on manual data handling or tailored scripts, while scalable processing supports high‑throughput RNA screening environments.
Examples include Genedata Expressionist®, which automates mass spectrometry-based analysis of oligonucleotides and can substantially reduce analysis time, and Genedata Screener®, which supports data analysis across assay types commonly used in RNA discovery programs. Part of the Genedata Biopharma Platform, these tools have been applied in contexts such as gene expression and qPCR-based screening, high-content imaging-based CRISPR screens, and viral neutralization assays during mRNA vaccine discovery.
Enabling Cross‑Assay Comparison and Informed Decision‑Making
RNA scientists often need to triangulate results across multiple assay types, and outcomes can sometimes conflict — for example, a compound with strong on-target activity but pronounced off-target effects, or an LNP formulation with efficient encapsulation but limited cellular uptake. A consistent analysis environment can also support cross-assay comparison through visualization and automated flagging of promising candidates or formulations.
Because RNA therapy research continues to evolve evolving, working with a software provider that combines information technology expertise with deep life science knowledge can help ensure that analytical solutions keep pace with both scientific and operational change.
Scalable Data Processing and Automation Solutions for High-Throughput RNA Therapeutics
Given the scale of assays used in RNA therapy discovery, software solutions must automate data processing and quality control with high performance, even for complex assay formats. Automated workflows can significantly reduce analysis turnaround for assays that previously required hours of manual processing. Examples include high-throughput RT‑qPCR screening, LC‑UV‑MS analytics for therapeutic oligonucleotide process development, and gene expression analysis acrossassay formats.
As throughput increases, scalable gene expression workflows can help standardize routine analytical steps, such as normalization to housekeeping genes, Cq and fold-change calculation, curve fitting, and automated flagging of cytotoxic effects. Together, these capabilities support more efficient and consistent processing of large‑scale qPCR screens.
In addition to assay-level scalability, fast-growing organizations also require scalability at the operational level. As RNA discovery programs expand across teams and sites, software needs to support global deployment and centralized management of quality control standards and metadata. A modular architecture can further support this growth by allowing teams to adopt new functionality incrementally, enabling the system to evolve alongside both scientific and organizational needs.
Integration of RNA Therapy Data Management Software with Enterprise Research Informatics Systems
Software used for R&D needs to be suited for an enterprise environment and integrate with the existing digital ecosystem, including data warehouses and molecule registration systems. The structured knowledge management capabilities of the Genedata Biopharma Platform support RNA therapeutics R&D by registering RNA sequences, including chemical modifications. The platform captures key steps in screening and production, and tracks the history of each molecule as programs scale toward development.
In addition, experienced implementation teams can help ensure a smooth software rollout. For example, Genedata has deployed software across organizations of different sizes in both on-premises and cloud environments and assigns customers dedicated teams for configuration, integration, training, and customization support.
Conclusion
RNA therapy R&D has entered a phase defined by scale, complexity, and opportunity. From high-throughput screening to formulation and delivery, biopharma organizations increasingly need tools and processes that enable growth without slowing innovation. With robust analytical workflows and scalable data handling in place, RNA therapeutics are well positioned to build on their initial successes and continue expanding across modalities and applications.
Edited by Fiona Siebrits, Marketing Content Creator
FAQs
- What are RNA therapies?
- Why is RNA drug discovery becoming more complex?
- Why do RNA therapies require extensive experimental screening?
- How does high‑throughput screening impact RNA R&D workflows?
- What kinds of data challenges do RNA discovery teams face at scale?
RNA therapies are medicines that use RNA molecules — such as mRNA, antisense oligonucleotides (ASOs), or small interfering RNA (siRNA) — to influence gene expression. Unlike traditional drugs that target proteins directly, RNA therapies act at the level of genetic instructions, enabling novel therapeutic approaches.
As RNA programs scale, discovery teams screen larger libraries across multiple RNA modalities, assay types, and delivery strategies. This increases experimental throughput, data volume, and organizational complexity, making data integration and analysis more challenging.
Although RNA drugs are designed computationally, their biological behavior depends on factors such as sequence, chemistry, delivery, and cellular context. Empirical assays are therefore required to assess activity, specificity, toxicity, and formulation performance before candidates progress.
Higher‑throughput assays enable faster identification of promising RNA candidates but also generate significantly more data. Without scalable analysis workflows, manual data processing can become a bottleneck that slows decision‑making rather than accelerating it.
Teams often need to analyze results across multiple assays, technologies, and teams while maintaining data consistency, traceability, and quality. As organizations grow, fragmented tools and manual workflows can make it difficult to compare results or reproduce findings.
References
- Wang, F., Zuroske, T., Watts, J. K. (2020). RNA therapeutics on the rise. Nature Reviews Drug Discovery 19, 441–442
- Bittker, J. A. (2012). High-Throughput RT-PCR for small-molecule screening assays. Current Protocols in Chemical Biology, 4(1), 49–63
- Cui, L., Pereira, S., Sonzini, S., Sally Van Pelt, Romanelli, S. M., Liang, L., Ulkoski, D., Krishnamurthy, V. R., Brannigan, E., Brankin, C., & Desai, A. (2022) Development of a high-throughput platform for screening lipid nanoparticles for mRNA delivery. Nanoscale, 14(4), 1480–1491