RNA-based therapies are calling for an R&D data management revolution
We are entering a new era of medicine. From the approval of the first antisense oligonucleotide (ASO), aptamer, and SiRNA interference the pipeline of RNA-based therapies and vaccines has expanded dramatically.1-3 By the end of Q4 of 2021, there were 15 RNA therapies approved globally and over 750 RNA-based drug candidates in the clinical pipeline, with 73% of them in preclinical development.4-5 mRNA-based vaccines became the most common modality in development, with RNA interference (RNAi) and ASO as additional modalities of exploration. In addition to the success of the now-famous mRNA-based COVID-19 vaccines, the influx of RNA clinical candidates is owed to advances in -omics technology and innovations for RNA delivery (e.g., nanoparticles)—two barriers that kept RNA therapeutics from thriving as viable drug candidates for decades. But even as the science behind RNA-based technology evolves, outdated and suboptimal R&D data management software is preventing companies from unlocking the true potential of RNA-based drug candidates today.
The limitations of existing R&D data management technologies for RNA-based therapies
It comes down to an issue with the wide variety and volume of data
Scientists should be able to identify a specific target sequence of interest, order the synthesis of a custom ASO or siRNA, introduce it into their model organism, and observe and report the effects. However, in reality, RNA R&D is not this simple, due to the complexity and variety of data involved.
For instance, researchers need to be able to design and compare the performance of multiple oligos to improve the chance of identifying one that is particularly efficacious. Screening reveals which chemical modifications offer resistance to nucleases, which oligo length is most stable and specific, and if a stretch of purines in the target sequence stabilizes the heteroduplex. This all generates an abundance of different types of data that needs to be stored, tracked, and analyzed.
Many life science companies have attempted to leverage the value of data using stand-alone legacy solutions, such as on-premises laboratory information management systems (LIMS). However, legacy solutions were built to manage small molecule R&D data. They have scalability, integration, flexibility, and capacity constraints that cannot accommodate the data diversity and workloads involved in R&D for RNA-based therapeutics.
When used in an RNA lab, these shortcomings make it difficult to sufficiently do even the most fundamental tasks of data management: store and organize the data that is generated by models, simulations, and experiments throughout the R&D workflow.
Legacy software cannot accommodate the diversity of use cases in an RNA workflow
Compared with small molecule drug development, where 10 medicinal chemists might be working together in a room to discuss project updates and make decisions, RNA drug development often involves teams of biologists, chemists, bioinformaticists, and immunologists. Each team performs different experiments and uses field-specific instruments, generating vast amounts of diverse data that all need to be collected, stored, analyzed, and transferred to and between each group.
Reflecting on the distinguishing experiments performed by each team, it is no surprise that the specific data management software needs of each discipline will also vary. Legacy software does not contain the necessary fields to store, organize, or classify the entire suite of data associated with RNA drug research (e.g., sequences, chemical modifications, gene expression, protein expression, etc.). This creates the long-standing data management challenge of data silos, which can explain why researchers spend 30%–40% of their time searching for, aggregating, and cleansing data. To address this problem, life science companies have invested billions of dollars on disconnected, on-premise systems and web-based applications. However, the data silos continue to be a problem, putting companies at a disadvantage for getting to market as fast as possible.
RNA drug researchers are unable to efficiently and deeply mine data with on-premise data management tools
The ability to deeply mine the abundance of sequence variations, gene and protein expression data, bioprocessing parameters, and all data generated throughout the full R&D life cycle is critical for finding and mobilizing both scientific and business insights. Comparing historical data to optimize RNA sequences with multi-site chemical modifications, analyzing results of various in vitro and cell-based assays to assess pharmacological properties, and managing the amount of primers, buffer, media, and other reagents to ensure the inventory remains stocked are tasks that the data management tools should enable RNA-based companies to do.
The choice of R&D data management software heavily influences this ability. There are existing entity registry applications, LIMS, electronic lab notebooks (ELNs), and workflow and inventory management applications. However, they are only going to make a transformative impact on efficiency and insight generation if they are fully connected—to each other and to data-generating instruments. Connectivity can enable the software tools to draw relationships between samples, experiments, inventory, and results.
Unfortunately, on-premise (and legacy) software solutions often have integration and configuration limitations, keeping companies from being able to connect their entire digital ecosystem. The inefficiency of thorough data mining is also a product of legacy systems being too narrow when it comes to the diversity of data it can manage. The disconnect between data results in researchers having to switch back and forth between five or more applications for a single task, which is not only a frustrating way to work, but adds the risk of missing key trends because an important piece of information was overlooked.
Disparate applications slow information transfer between teams
Given the multidisciplinary nature of RNA R&D, and the nearly endless therapeutic possibilities, it is unsurprising that collaborations form to leverage the strengths of multiple organizations. For instance, in 2021, Lilly and MiNA Therapeutics formed a collaboration to develop novel RNA drug candidates. MiNA is utilizing its small activating RNA (saRNA) platform to research multiple targets selected by Lilly, and Lilly is responsible for preclinical and clinical development of candidates. This type of partnership is becoming the norm. In fact, many pharmaceutical giants, such as Pfizer, Amgen and Bayer AG, are turning to partnerships rather than acquisitions, giving them the opportunity to explore cutting-edge fields without massive capital investment. Within each organization, internal collaborations between scientific teams are also necessary to push RNA therapeutics forward. And operations and business units need to remain well informed to optimize workflows and make go-to-market decisions.
Naturally, these collaborations—whether they are between organizations or internal teams—require information transfer between groups. With legacy systems, information is segmented into separate fields, encouraging the formation of knowledge silos between chemists, biologists, immunologists, process developers, business development, and other groups. This makes efficient and complete data sharing near impossible without modern digital data management tools that offer full connectivity. What’s more, sample hand-offs often occur in parallel with data transfer. For instance, primers and plasmids are transferred with sequence data, ASO’s are transferred with related gene expression data, and stocks of cell lines are transferred with optimal culture conditions. The location of the sample, its record of use, and all the data associated with it must be tracked and documented. Data silos formed due to a lack of full connectivity between digital technologies and teams can become the source of inefficient reporting, incomplete data transfer, and a disconnect between sample and related information.
Traditional data management solutions are ill-equipped to standardize syntax
Without a clear standard to represent chemical modifications and sequence design, scientists and RNA vendors have come up with their own syntaxes. These teams spend a significant amount of time translating their bases into a shared language. Standardized nomenclature will make data sharing easier and accelerate oligo design. However, legacy systems lack built-in options for establishing a common language, and paper notebooks and Excel sheets—where you would need to manually enter the oligo name—can lead to errors and confusion among team members.
Modern R&D data management platforms for RNA-based therapeutics
The promise of the cloud
The cloud is a data enabler that can get drugs to market faster and cheaper. Deloitte research shows that shared infrastructure and resources for master protocols can reduce the research cycle time by 13%–18% and overall costs by 12%–15%. These benefits stem from the cloud’s ability to centralize data, eliminate data silos, and offer complete connectivity—throughout the entire digital ecosystem as well between teams spread across geographical regions. With the cloud, researchers and analysts know exactly where to go to get the data they need: the centralized data warehouse—a single source of truth.
With the cloud, researchers can instantly find the optimized formulation of an mRNA vaccine, easily find and compare all the gene expression assay results for RNAi candidates, and view the complete experimental history of an ASO and all of the related modifications.
The ability to easily find and keep track of data is only one of many advantages of cloud-based data management technologies. For instance, Moderna leveraged a modern, multi-cloud data stack during mRNA vaccine development to gain a more complete view of clinical trials, increase scientific efficiency and collaboration, and reduce the time scientists spent on manual data manipulation. Intellia Therapeutics, a biotechnology company using CRISPR gene editing to develop in vivo and ex vivo therapeutics, used Benchling’s cloud-based platform and found that “Benchling has provided us a single solution for multiple problems, from sequence design and alignment to a centralized database. Our speed has doubled, communication has improved exponentially, and it's decreased scientist frustration level beyond measure,” said Principal scientist, Brenda Minesinger.
Today, nearly every function you need for data management in RNA therapeutics R&D is offered or included as a cloud-based application. These include cloud-based notebooks, oligo and sequence design tools, registry applications, LIMS, analytical tools, inventory managers, workflow managers, and more. The optimal setup would be a completely unified digital and instrument ecosystem, making it possible to access, analyze, and monitor everything using a single connected platform.
The next digital transformation of life sciences R&D is here. Life science companies continue to transition away from paper notebooks, mini-datasets in Excel, and legacy LIMS solutions in order to adopt cloud-based solutions. The adoption of the cloud will increase as the therapeutic landscape continues to increase in the number of DNA-based, RNA-based, and protein-based drugs. Smart company leadership with a current or emerging RNA-based drug portfolio recognizes that the cloud is an excellent accelerator for R&D—and already have plans to adopt the solution. To make more breakthrough discoveries, and then progress those discoveries through the R&D cycle, the cloud is the only way forward.
Benchling is designed for the R&D data management needs of today and tomorrow
Design, develop, and characterize novel RNA therapeutics on a unified digital platform
Benchling is a modern, fully configurable, and easy-to-use solution that adapts to the rapidly evolving needs of RNA therapeutics R&D. It includes a full suite of connected applications: Notebook, Molecular Biology, Registry, Inventory, Workflows, and Insights.
For the first time, scientists can design chemically modified oligonucleotides, standardize on syntax, centralize experimental results, and collaborate with teammates more effectively on a single platform. The platform is flexible and purpose-built to drive efficient experimentation. With Benchling, researchers creating cutting-edge RNA therapeutics can bring these breakthrough therapies to market faster than ever before.
Benchling helps scientists:
Design chemically modified oligonucleotides with ease
Centralize experimental data for faster, contextually-relevant insights
Collaborate with teammates focused on biology and chemistry
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Zhang, M. M., Bahal, R., Rasmussen, T. P., Manautou, J. E., & Zhong, X. B. (2021). The growth of siRNA-based therapeutics: Updated clinical studies. Biochemical Pharmacology, 189, 114432. https://doi.org/10.1016/j.bcp.2021.114432
Barrett, D., Foss-Campbell, M., Wendland, A., Millington, S., Micklus, A., Nguyen-Jatkoe, L. (2021). Gene, Cell, & RNA Therapy Landscape Q3 2021 Quarterly Data Report. [PowerPoint slides]. American Society of Gene & Cell Therapy, Informa Pharma Intelligence. https://asgct.org/global/documents/asgct-pharma-intelligence-quarterly-report-q3-2021.aspx
Barrett, D., Wendland, A., Rose, D., Millington, S., Micklus, A., Nguyen-Jatkoe, L. (2021). Gene, Cell, & RNA Therapy Landscape Q4 2021 Quarterly Data Report. [PowerPoint slides]. American Society of Gene & Cell Therapy, Informa Pharma Intelligence. https://asgct.org/global/documents/asgct-pharma-intelligence-quarterly-report-q4-2021.aspx