By Christian Olsen Associate VP, Industry Principal, Biologics at Dotmatics
Biologics are changing the way we treat disease, affecting where companies invest, and, most importantly, impacting patients’ lives. Last year, nearly half of the 37 new drugs approved by the U.S. Center for Drug Evaluation and Research (CDER) were biologics, while its partner agency the U.S. Center for Biologics Evaluation and Research (CBER) approved a dozen additional products. These approvals span many modalities and cover a range of conditions, including many rare and hard-to-treat diseases. In 2022, we saw:
- 5 protein drugs (for indications including diabetes, kidney and liver disease, lipid-buildup disease, and skin conditions)
- 11 antibodies (for indications including multiple sclerosis, lymphoma, diabetes, cancer, psoriasis, eye disease, and blood disorders)
- 1 antibody-drug conjugate (for ovarian cancer)
- 1 small interfering RNA therapeutic (for polyneuropathy)
- 12 blood products, gene therapies, and vaccines (for conditions such as cancer, blood disease, neurologic dysfunction, and infection)
The huge strides made in biologics R&D laboratories around the world are nothing short of incredible, but it’s certainly not a miracle. It’s thanks to years of work by those who uncovered disease processes, created software to make sense of sequence, structure, and experimental data, and embraced new ways of working across traditional boundaries.
The willingness of biology labs to evolve has been essential to biologics’ incredible rise. For many labs, advancing biologics discovery has meant adopting new technology, including sequencing platforms, specialized bioinformatics, proteomics, and molecular biology tools to scrutinize data and characterize modifications, and cloud computing to analyze huge volumes of data.
The next game-changer? Getting those software tools to work seamlessly, so the data can be layered and flow smoothly through the entire discovery process.
The next great change: uniting tools and data
Biologics R&D can be a tangle to navigate: the complexity and scale of the biological systems, the analysis workflows, the data computing and storage requirements, all the way down to the manufacturing and regulatory requirements. These produce an incredible amount of complex data that researchers need to sift through to make the right decisions that lead to discoveries.
Rich data visualization is a critical part of the R&D process. And, certainly, visualization of a wide array of data types can help researchers better understand and validate their work. But biologics teams don’t want to see their data as disconnected. They need to access and interrogate how the data relate to each other at any given moment throughout the discovery process. Context is king and they want to use it — quickly and collaboratively — to propel their research forward. What makes a difference for biologics R&D teams is to close the gaps in software tools and data to accelerate innovation and make better research decisions, all while stretching research dollars.
Unified data flow example scenarios
Imagine a new researcher joins an antibody discovery team and needs insight into previous efforts. In the past, this might have been impossible due to poor record keeping and brain drain. But if the team’s work has been detailed and preserved in a centralized FAIR data repository, the new member can access all historical R&D data. They can see what work has been done, when, how, and by whom. For example, they can quickly search for and explore registered antibodies, see all associated data like associated clones or expression vectors, and view production-related data (such as expression and purification information) and assay results.
Next, they may want to narrow down which antibody candidates to pursue by pulling in assay data to “lay over” their sequence data and annotations (like amino acid liabilities) and then performing alignments. With these data and tools at their fingertips, the new team member can quickly onboard, avoid redundant efforts, build off existing knowledge, and focus on the best antibody candidates. This kind of access to the data also lets the new researcher easily go back and add new data to a prior analysis, which is common when new insights or trends have emerged since the original analysis. Adding new data may be necessary to re-evaluate the original conclusions or to discover new patterns.
Another situation to consider is a biologics program director who needs to decide which disease target to pursue next. This typically involves an incredible amount of legwork to manually collect, process, and collate all the data needed to qualify protein targets, which is often done in an external program that traps the results forever. If that director could instead view relevant data in simple-to-interpret reports and dashboards, they could more quickly uncover the most promising paths. Imagine, for example, the time that could be saved and knowledge gained if it were easy to collate data across a wide array of assay experiments, perform bulk analyses like multiple curve calculations, view the results, and then quickly filter through their data to produce figures that communicate discoveries clearly.
Discovery through tightly integrated software
Biologics R&D teams benefit immensely when all their research tools are seamlessly connected. While it’s useful to select test results and visualize the associated data at once, it’s a game changer to move one step further and allow researchers to quickly branch their research questions, register and track the process changes, easily order or perform new tests or analyses — all from a central hub. Think of it like “Google Maps” for science, helping researchers orient themselves, see where they have been, where they want to go, and what’s the best way to get there.
On top of integrating R&D instruments and software, the data these instruments produce must be linked to get the most value out of the data. Creating a centralized data repository can be a huge undertaking. There are numerous considerations, including standardizing file formats, managing copious amounts of data, establishing permission controls for internal users and external partners, and facilitating backend data exchange. All data within a central depository must be findable, accessible, interoperable, and reusable, or “FAIR.”
A centralized research platform can deliver quick access to both the software tools and data that teams need to accelerate biologics discovery. With everyone from lab techs to antibody scientists to program directors united on the same platform, teams can flow data between their specialty research tools, build off each other’s knowledge, streamline workflows and lab requests, and make data-driven decisions at every step.