Tailored workflows in discovery research: Challenges, benefits, and how to implement them today

The development of therapeutic antibody drugs has revolutionized medicine. As a result, there has been an explosion in the discovery of new medicines for treating a wide range of human diseases including cancers, autoimmune disorders, and infectious diseases¹. By the end of 2023, there were nearly 200 therapeutic antibodies that had been granted marketing approval or were under regulatory review in at least one country and this list continues to grow². Yet, despite the significant progress made in this area, the design and discovery of new candidates remain challenging. This article will discuss the challenges and benefits of implementing customized discovery workflows and introduce our Last Mile Service; a comprehensive service that aims to take your antibody discovery journey to the next level.

Challenges of customized discovery workflows

Advances in high-throughput computing and computational tools have made it easier to handle large volumes of data, while also simplifying the process of aligning data from different outputs, making it easier to customize your approach. These advances have increased the potential of finding antibodies with desired specificity or biophysical properties. Additionally, the development of computational tools has facilitated data analysis and sharing, enabling access to more diverse data, and facilitating the development of novel therapeutics³. Yet, despite these advances, there are still several major roadblocks when it comes to workflow customization solutions:

• One size fits all approach: most solution providers deliver tools that are tailored to the needs of the majority of their clients. However, this means that their solutions are not comprehensive enough to meet the unique needs of specific customers.
• Creating, maintaining, and updating internal tooling and scripts is time-consuming: adapting pipelines to new workflows and ensuring they are compatible with new bioinformatics packages can take a considerable amount of time. Moreover, bioinformaticians need to make the tooling available for other team members in a scalable way, especially if multiple tooling is being used, not to mention ensuring the compatibility with new bioinformatics (e.g. python) packages.
• High training effort: the continuous emergence of new tools requires teams to ensure they stay up to date with training efforts.
• Specific expertise: developing a complete solution in-house requires experience in discovery in-vitro and in-silico platforms, sequencing technologies, data science, and software engineering, and often results in reinventing the wheel.

As a result, research teams experience slow discovery pipelines, inefficient resource distribution, and slower adoption of new technologies.

What are the benefits of customized workflows?

Workflow customization plays a pivotal role in optimizing performance and enhancing the output of your discovery pipeline, enabling research teams to adapt workflows to changes that occur over time. By streamlining the development process, you can eliminate unnecessary steps and prioritize specific stages which can save both time and resources. The most obvious benefit of customized workflows is that they save considerable time by enabling research teams to run their analysis faster. Moreover, customized workflows enable research teams to standardize their workflow so that everyone is using the analysis consistently, promoting continuity.

Introducing Last Mile Service for IGX Platform

At ENPICOM, we understand the critical importance of bridging the gap between standardized solutions and the unique needs of our clients. Our new Last Mile Service aims to take your therapeutics journey to the next level by drawing on new technology and the expertise of our team to help you devise a customized discovery workflow and overcome your research hurdles. Our experienced team keeps pace with emerging technologies, enabling us to help you find leads against hard targets using a flexible platform that can adapt to any sequencing approach and screening technology.

This new service builds on the strength of our IGX Platform which enables researchers to improve their candidate selection through advanced analytics, visualizations, and comprehensive workflows. All custom workflows can easily be integrated into this platform which can then be used to create information-rich visualizations that can guide candidate selection and prioritization.

How can you benefit from the Last Mile Service?

Our team has substantial experience in therapeutics discovery and bioinformatics and can help you tackle even the most complex of challenges, reducing the considerable time and effort spent on implementing a unique drug discovery workflow.

This service provides several tailored deliverables including:

• Special pre- or post-processing steps required by your library prep
• Synchronizing input and output with instruments or databases
• Tailored reports including specific visualizations for managers or customers
• Automating SOP steps to minimize clicks and ensure correct workflows
• Running external algorithms and incorporating the results into your data

Summary

Our Last Mile Service aims to take your antibody discovery to the next level by providing a dedicated service tailored to your individual research needs. Just as the ‘last mile in logistics’ focuses on delivering goods to their final destination efficiently, our Last Mile Service aims to ensure that you reach your destination without experiencing any major or minor roadblocks or delays along the way, resulting in the successful implementation of your unique therapeutics discovery pipeline. Talk to our experts today to find out how we can facilitate the success of your next project.

References

1. Carter, P. J., & Rajpal, A. (2022). Designing antibodies as therapeutics. Cell, 185(15), 2789-2805, https://doi.org/10.1016/j.cell.2022.05.029
2. Crescioli, S., Kaplon, H., Chenoweth, A., Wang, L., Visweswaraiah, J., & Reichert, J. M. (2024, December). Antibodies to watch in 2024. In MAbs(Vol. 16, No. 1, p. 2297450). Taylor & Francis, https://doi.org/10.1080/19420862.2023.2297450
3. Wiktoria Wilman, Sonia Wróbel, Weronika Bielska, Piotr Deszynski, Paweł Dudzic, Igor Jaszczyszyn, Jędrzej Kaniewski, Jakub Młokosiewicz, Anahita Rouyan, Tadeusz Satława, Sandeep Kumar, Victor Greiff, Konrad Krawczyk, Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery, Briefings in Bioinformatics, Volume 23, Issue 4, July 2022, bbac267, https://doi.org/10.1093/bib/bbac267