Top Five R&D Challenges for Chemically-Modified Biologics Data

Chemically-modified biologics, including various types of conjugates, modified peptides, and novel nucleic acids, are becoming increasingly important in R&D programs. These novel entities can help reach new pharmacological space and overcome issues that conventional biologics often face, such as dosing, targeting, pharmacokinetics, and immunogenicity.

Two new treatments approved by the U.S. FDA in 2022 illustrate this progress:

• ​ELAHERE™ (irvetuximab - soravtansine-gynx): An antibody-drug conjugate providing a new targeted-treatment option for patients with treatment-resistant ovarian, fallopian tube, or primary peritoneal cancer.
• ​AMVUTTRA™ (vutrisiran): An RNA interference drug treating neurological damage caused by hereditary transthyretin-mediated amyloidosis. It can be delivered subcutaneously just four times a year due to novel chemical-conjugate delivery technology that improves metabolic stability, potency, and duration.

Despite their potential, chemically-modified biologics create unique data management challenges for researchers. The root cause is the nature of these entities: biologically-based molecules with artificial chemical modifications. This requires close collaboration between biology and chemistry teams, who have traditionally worked in isolation using different data types, workflows, and specialty R&D tools. The union of these disciplines is changing how teams innovate and increasing demands on the tools used to capture, analyze, and share data.

Here are the top five challenges multidisciplinary teams face when collaborating on chemically-modified biologics R&D data:

1. Cross Domain Data Support

The biggest challenge is finding an informatics platform that fully supports both chemical/small molecule structure and biological sequence data and metadata, and can link these entities. Without this, biology and chemistry teams often use separate systems that don’t integrate, slowing innovation and leading to competing sources of truth, unclear entity descriptions, and inevitable data silos.

2. Data Quality and Loss

High-quality data capture is essential for interpreting results, progressing research, and applying advanced analytics like machine learning. When teams manually move data between disconnected systems or synchronize data transfers, they risk introducing errors or losing data, which can slow cycle times, increase costs, and impact candidate selection.

3. Data Flexibility for User Needs

Chemistry and biology teams often want to view the same data in different ways. Easy toggling between structure, sequence, and assay views is essential. Informatics solutions must also support various R&D tools, processes, and data types used by cross-discipline researchers. For example, molecular biologists may want to explore sequence variations, protein scientists may need to record purification protocols and analyze mass spectrometry results, medicinal chemists may want to enumerate compound libraries and analyze chemical properties, and analytical scientists may want to determine drug-antibody ratios. Few solutions offer the breadth and configurability needed to let diverse researchers capture, view, and analyze data intuitively while feeding it into a unified source of truth.

4. Data Searchability and Traceability

Making data searchable and traceable is a significant challenge, especially with the many entities generated in chemically-modified biologics R&D. Teams deal with cross-discipline work, iterative research cycles, diverse tools, and large data sets. Manually collecting and relating all this data is impossible. R&D platforms must record experiment results and capture contextual data related to entity design, production, purification, experimentation steps, batch data, and entity relationships. Biologists and chemists need to search across all related data in ways intuitive to them, such as by name, structure, functional group, or sequence motifs. Any data point should link back to the exact test entity, allowing researchers to see what work was done, when, how, and by whom, and to use that data as a starting point for further innovation.

A solution that unifies data management and scientific analysis tools for chemically-modified biologics R&D is a gamechanger.

5. Data Exchange

Chemically-modified biologics R&D presents data-exchange challenges, especially when working across different scientific domains, locations, or partner organizations. Activities such as compound synthesis or assay screening may be contracted to CROs, or large pharma may sponsor discovery pipelines at smaller biotechs. Partnerships need efficient ways to receive requests and return results without relying on email attachments or manual uploads. Without web-based, permission-controlled access to a unified R&D platform, cross-site teams risk wasting time, data errors, loss, and compromised security.

Dotmatics Solutions for Chemically-Modified Biologics R&D

Dotmatics offers solutions to support research in chemically-modified biologics, including:

• ​Unifying diverse biology and chemistry data on an enterprise data platform
• ​Facilitating collaborative cross-discipline research
• ​Simplifying informatics R&D infrastructure and reducing total cost of ownership
• ​End-to-end R&D data platform with flexible scientific search
• ​Personalizable ELNs with flexible support for biology and chemistry workflows
• ​Complete biology and chemistry entity registration
• ​Specialty tools for molecular biology, sequence analysis, proteomics, and compound design
• ​Robust assay data management and inventory/lab-request management
• ​United statistical analysis, visualization, and reporting for all R&D data

References

1. 1.Halford, B. 37 new drugs achieved FDA approval in 2022. Chemical & Engineering News. January 21, 2023.
2. 2.ELAHERETM (irvetuximab - soravtansine-gynx) product website. ImmunoGen, Inc. (Accessed February 14, 2023).
3. 3.Amvuttra (vutrisiran) product website. Alnylam Pharmaceuticals. (Accessed February 14, 2023).