RNA therapies are having a renaissance. With the triumph of COVID-19 mRNA vaccines and their record-speed development, big pharma companies are flocking to build up RNA therapy pipelines. Investments in this space doubled between 2015 and 2020. With the attention comes big expectations: the big hope is that we can address hitherto undruggable targets or develop and produce drugs faster thanks to sequence-based design.
But RNA therapies don’t appear by magic—they still require empirical testing in biological assays. To speed up RNA drug development, leaders in RNA research are scaling up these assays with new, higher throughput methods and platforms. At the same time, the influx of investment also means that companies or groups doing RNA therapy R&D are expanding rapidly. Scaling of both experimental throughput and organizations requires analysis and data management software and an IT infrastructure that can scale as well. In this blog post, we’ll talk about how to best address the data analysis and workflow challenges brought about by increased assay throughput and organizational growth in the RNA field.
What Are RNA Drugs?
Upscaling RNA Drug Discovery
Having reached a new stage of maturity and growth, the field is now scaling up key assays in RNA therapy R&D. The goal is to screen larger libraries, in search of drugs that are not only active but have good biodistribution profiles—e.g., drugs that reach both the right tissue and intracellular compartments. This is accomplished through large-scale screens for better performing sequences, chemistries, and formulations.
Towards this goal of scaling up, companies are miniaturizing and developing new methods to make relevant assays more amenable to higher throughput, as well as deploying laboratory automation hardware. For example, qPCR assays have been greatly scaled up, thanks to one-step, extraction-free reactions and multiplexed measurement of several genes in parallel. Furthermore, companies have accelerated these screens by building highly automated platforms that allow throughputs of up to 200,000 compounds per day.
Learn About RNA-targeted Drug Discovery at Evotec
On the mRNA side, companies like AstraZeneca are scaling up both the production and screening of LNPs for mRNA formulation. They’ve moved from producing LNPs with lower throughput microfluidic-based mixing to higher throughput liquid handling systems. In concert with this, they’ve automated LNP assessment in assays measuring size, encapsulation efficiency, acidity, and cellular function.
In addition to this upscaling of assays, the RNA field is witnessing an increase in organizational growth and complexity. As a company or research division grows, multiple assays once performed by a single scientist might now be performed by multiple people or across different teams. Someone—a lab head or data analyst—will also need to bring together and cross-compare different assay results from these different sources, to select the best hits or formulations. With growing capacity, organizatiions seek to broaden their assay portfolios and introduce new approaches.
The Right Software for RNA Therapeutics Discovery: Purpose-Built, Scalable, and Enterprise-Ready
Every RNA drug modality faces a special set of concerns, and in a fast-moving field, the methods used to address them can quickly change. At the same time, regardless of the RNA drug modality or assay, upscaling experiments also means increased data volumes. Cracks in your data workflows will start to show: whereas manual data crunching or workflows spread across multiple software tools might work for a small dataset, it becomes untenably inefficient and error prone as you scale. Add to this the issue of increased organizational complexity, where the number of people creating and processing data and the number of handoffs between team members has multiplied. Problems like lack of standardization, difficulties with data transfer, and loss of data provenance start to compound. This is where the right choice of a central software platform can really help. Here are a few key things to think about when choosing a data analysis and management system for your RNA therapy research:
It should be purpose-built for RNA research.
General-purpose analysis frameworks or software (like Excel or KNIME) are not built to address the specific needs of RNA research, creating the need to set up and maintain bespoke data processing pipelines.
In contrast, purpose-built, out-of-the box solutions supporting the analysis of assays particularly relevant for RNA therapy R&D mitigate these extra burdens. Several examples of this come from Genedata: for example, Genedata Expressionist can automate MS-based analysis of oligonucleotides, shortening the time required from hours to minutes.
Learn How Roche Automated MS-Based Oligonucleotide Process Development
In addition, Genedata Screener can accelerate analysis of assays widely used in RNA research and development. This includes analysis of cellular splicing assays, affinity mass spectrometry, and qPCR screens, including during RNA-targeting small molecule discovery. Screener has also been used to assess LNP uptake, for example in this high-content imaging-based CRISPR screen by AstraZeneca, and to analyze viral neutralization assays (ELISA) during mRNA vaccine discovery.
Furthermore, as an RNA scientist, you will need to triangulate the results of different assays. Sometimes the results of different assays will be at odds with each other: say, a compound with potent on-target activity but also strong off-target effects, or an LNP formulation with great packaging efficiency but poor cellular uptake. Ideally, the same software that is used to analyze all your RNA therapy assays can also be used for cross-assay comparisons, including visualization and automated flagging of best hits or formulations.
How Axxam Uses Screener Across Their RNA Discovery Platform
Finally, because RNA therapy research is quickly evolving, it pays to work with a software provider who brings both information technology and biology expertise to the table. Expert providers can work with you to develop solutions appropriate to your science and that reflect the latest approaches being used in the field.
It should be scalable.
Given the upscaling of assays used for RNA therapy discovery, a suitable software solution must automate data processing and quality control with high performance, even for the most sophisticated assays. For example, with Genedata Screener, a viral neutralization assay that once took a few hours takes only minutes to analyze. In another example, the Screener workflow for gene expression analysis automates normalization to housekeeping genes, Cq and fold change calculation, curve fitting, and flagging of cytotoxic molecules, enabling efficient processing of large-scale qPCR screens.
Read About Automating Gene Expression Analysis with Screener
In addition to scalability in terms of automation and performance for a given assay, fast-growing companies and research divisions require organizational scalability: they need software solutions that enable global deployment and centralized management of quality control standards and annotation schema. It is also helpful if software has a modular architecture, so that groups can adopt the functionalities they need as they need them, allowing the solution to expand with the organization.
It should integrate with your research informatics infrastructure.
Any software solution used for R&D needs be suited for an enterprise environment. It needs to integrate with your existing digital ecosystem, including data warehouses and molecule registration systems. Alternatively, structured knowledge management systems like Genedata Biologics or Genedata Bioprocess have dedicated functionality supporting RNA therapeutics R&D. They can register RNA sequences (including chemical modifications), capture every step of screening and production, track the history of each RNA molecule, and help develop the processes needed to produce a lead RNA molecule at the desired scale and quality. All the analytical solutions mentioned above can integrate with Genedata Biologics part of the Genedata Biopharma Platform.
Developing RNA Therapeutics with the Genedata Biopharma Platform
In addition, a strong support team can go a long way in helping ensure a smooth software roll-out. For instance, Genedata has experience deploying their software at organizations both big and small, with both on premise and cloud environments, and assigns each of its customers a dedicated team that provides configuration, integration, training, and customization support.
To conclude, the field of RNA therapy has reached a new stage of maturity, accompanied by fast-paced growth. Whether you are a biotech or large pharma, at the beginning of this journey or already in its midst, you will want the right software to support you as you move into the next stage for this exciting class of therapies.
Ming Wang, PhD, is Business Development Manager for Biotherapeutics, Genedata.