The complexity of antibody-drug conjugates (ADCs) demands equally sophisticated analytical strategies. Researchers must navigate fragmented workflows, manual sample preparation, and experimental variability that can limit reproducibility and throughput. With growing pressure to accelerate the drug development process, automation is emerging as a key solution.
Shweta Shukradas
Associate Vice President
Automated Productivity Solutions
Agilent Technologies
In this Innovation Spotlight, Shweta Shukradas, the associate vice president of Automated Productivity Solutions at Agilent Technologies, shares how lab automation and integrated analytical platforms are addressing these limitations, enabling more reproducible characterization and faster iteration. These advances help researchers make more informed decisions earlier in drug discovery.
What are the biggest analytical and workflow challenges that researchers face when working with ADCs?
ADCs are large complex molecules that are inherently heterogeneous. They differ in a variety of critical quality attributes including drug‑to‑antibody ratio (DAR), glycosylation, and conjugation sites. The DAR directly impacts efficacy, safety, and pharmacokinetics.
Analytically, that means no single technique is sufficient. Researchers typically combine multiple orthogonal methods including hydrophobic interaction chromatography, exclusion chromatography, and liquid chromatography-mass spectrometry (LC-MS) to fully characterize an ADC, which fragments workflows and slows iteration.
Sample preparation is another major bottleneck. Many steps are still manual, limiting throughput and introducing variability, especially as programs scale. Overall, the biggest bottlenecks are around workflow integration, reproducibility, and speed.
How is lab automation transforming the way researchers develop and characterize ADCs during early drug discovery?
Lab automation is transforming characterization of ADCs by minimizing variability in the most time‑consuming steps, especially sample preparation. Automated affinity capture, deglycosylation, digestion, and cleanup allow researchers to process more ADC samples in parallel with much higher consistency than manual workflows.
Automation also simplifies data generation and interpretation by standardizing workflows and providing automated software for DAR calculation and characterization, reducing dependence on individual expertise.
Overall, automation is shifting ADC discovery from low‑throughput, expert‑driven workflows to scalable, repeatable processes that support faster and more informed decision‑making early in the pipeline.
How are Agilent’s technologies and instruments helping to enhance throughput and reproducibility in ADC drug discovery?
Agilent’s technologies are helping improve throughput and reproducibility in ADC discovery and development by standardizing and automating the most variable parts of the workflow.
The automated sample preparation platform AssayMAP Bravo enables parallel processing of key steps such as affinity capture, deglycosylation, digestion, and cleanup, which significantly reduces hands‑on time and minimizes user‑to‑user variability. That consistency is essential when evaluating DAR and conjugation profiles across many candidates.
On the analytical side, integrated LC, LC-MS, and software workflows support reproducible DAR characterization, intact and peptide‑level analysis, and standardized data interpretation. By combining automation with pre‑optimized methods and integrated data analysis, researchers can move from manual, low‑throughput workflows to scalable, repeatable processes.
What has been the response from scientists using Agilent automation for drug discovery?
Agilent automation is used across the ADC discovery and development process to enable researchers to scale throughput, remove variability from complex workflows and generate more reproducible data with less hands-on effort.
Automation is especially well received in ADC discovery, where sample preparation and characterization steps are traditionally manual and highly variable. By standardizing processes such as affinity capture, digestion, and cleanup, scientists can run more samples in parallel and compare results with greater confidence.
Overall, the feedback reflects a shift from labor-intensive, expert-dependent workflows toward more repeatable and scalable approaches that support faster decision making.
As ADC technologies continue to evolve, how do you see automation platforms adapting to support the next generation of therapeutics?
In general, automation platforms will need to become more flexible, integrated, and intelligence driven. We’re already seeing ADCs move toward greater molecular complexity, new linker and payload chemistries, and higher expectations for early-stage characterization.
In response, automation platforms are adapting by supporting modular, end-to-end workflows that can handle different ADC formats without requiring scientists to redesign processes from scratch. Automation is evolving from simply increasing throughput to enabling faster learning cycles, helping researchers explore more designs, generate higher quality data earlier, and make better decisions as new ADC modalities emerge.
Is there anything else you’d like our audience to know?
We are using our Jet Stream ion source to develop and automate accelerated biomolecular reactions to rapidly characterize biomolecular conjugates such as ADCs. We think this will be a transformative approach to the development and screening of these molecules.
Agilent’s autonomous lab focus is about innovation, accelerating discovery, helping researchers explore more ideas, generate higher quality data earlier, and making faster, more confident decisions as therapeutic complexity continues to increase.
