As drug developers look beyond traditional antibodies and small molecules, next-gen conjugates like antibody-oligonucleotide conjugates (AOCs) and radionuclide-drug conjugates (RDCs) emerge as promising modalities.
These therapies combine the targeting power of antibodies with either genetic or radioactive payloads, unlocking new ways to treat disease. But while they may sound similar, AOCs and RDCs rely on very different components, design strategies and development paths.
Their complexity also raises new questions for manufacturers and regulators — making their development strategy just as important as the science itself.
In this Xtalks Spotlight interview, we spoke with Dr Stephen Verespy, Senior Director of Scientific Operations, and Dr Jeffrey Mocny, Vice President of Regulatory Strategy at Abzena, about how their teams approach AOC and RDC development. Their insights reflected how science, strategy and structure needed to align to move these novel modalities forward.
What Are AOCs and RDCs — and How Are They Different?
The interview opened with a clear comparison between AOCs and RDCs. While both involve antibodies as targeting vehicles, their payloads and design principles diverge significantly.
“AOCs or antibody oligonucleotide conjugates are essentially antibody conjugates that have oligonucleotides such as siRNA or antisense oligonucleotides conjugated directly to the antibody,” said Dr. Verespy. “These are commonly done at a lower ratio, so you’re targeting a one-to-one or a two-to-one ratio of oligonucleotides to antibody.”
In simpler terms, this means scientists are attaching only a small number of oligonucleotide molecules to each antibody — usually one or two — to ensure unadulterated receptor target binding and avoid accelerated clearance from the body.
RDCs, on the other hand, involve chelated radioactive elements, meaning radioactive atoms are chelated to molecules such as DOTA which are conjugated to antibodies, to target cancer cells for therapeutic or imaging purposes. Their loading ratios can vary more widely — sometimes reaching up to eight per antibody — and they are often finalized shortly before use due to the short half-life of radioactive materials.
Because radioactive isotopes may decay quickly and lose stability over time, chelation often takes place at the point of care — just before administration — to preserve their therapeutic effectiveness, explained Dr. Mocny.
Both modalities fall under the broader category of antibody-drug conjugates (ADCs) from a regulatory standpoint. However, as Dr. Mocny noted, regulators assess and control each component — antibody, linker and payload — individually during characterization and manufacturing review.
“So while they’re different in their nature, as Stephen was talking about from their chemistry and how you make them, the way the regulators think about them also are slightly different,” noted Dr. Mocny.
Conjugation Strategies: One Size Doesn’t Fit All
The discussion then turned to the unique limitations around conjugation — particularly for AOCs. Dr. Verespy explained that, unlike RDCs or traditional ADCs, AOCs often require more controlled and selective conjugation methods.
“So commonly with AOCs, you don’t see the same type of conjugations that would occur for RDCs or other ADCs,” he said. “Because of that lower loading ratio, you’re employing strategies such as engineered cysteines on the antibody to conjugate a lower ratio.”
These lower ratios are not just a design constraint — they’re a functional necessity. Attaching too many oligonucleotide payloads can interfere with the antibody’s ability to bind its target and lead to faster clearance from the body. The size and charge of oligonucleotides make them particularly sensitive to how — and how much — they’re loaded onto the antibody.
In contrast, RDCs and ADCs can often tolerate higher loading ratios using stochastic approaches, such as stochastic lysine-based or inter-chain disulfide reduction strategies.
“You’re using something like a stochastic inter-chain disulfide reduction where you’re getting up to eight conjugated or eight labels per antibody,” Dr. Verespy added. “You can’t necessarily achieve that when you’re utilizing oligonucleotides.”
He pointed to Daiichi-Sankyo | AstraZeneca’s Enhertu (trastuzumab deruxtecan) as an example of an ADC with a higher drug-to-antibody ratio — about eight drug molecules per antibody (referred to as DAR 8) — achieved through stochastic conjugation. Some RDCs, he said, also reach similar levels, though these can vary based on preclinical and toxicology findings.
Ultimately, each modality demands its own conjugation strategy — tailored to the physiochemical and pharmacological behavior of the payload and informed by data at every development stage.
The Role of Integrated CDMO-CRO Partners
For developers navigating these shifts, working with a partner that bridges early science and regulatory strategy could be an asset. Both speakers underscored the benefits of working with an integrated CDMO-CRO — especially for complex therapies like AOCs and RDCs.
For Dr. Mocny, one key benefit was direct access to seasoned scientific expertise that could accelerate program timelines.
“Sponsors are able to bring to bear that experience to help accelerate the program… and also complement that with a regulatory strategy that helps move clients quickly through the development cycle,” said Dr. Mocny.
From technical analytics to regulatory submission prep, an integrated partner offered continuity that reduced risk and delay. Dr. Verespy explained how this plays out at Abzena:
“Oftentimes we’ll be performing early developability studies on the same molecules that are going into preclinical studies… then further onto the GMP batches for clinical trials.”
This end-to-end familiarity meant insights from early-stage work directly inform scale-up and GMP manufacturing, saving both time and cost.
Dr. Mocny also noted that broad internal capabilities meant better access to specialized tools, assays and expert teams — whether working with siRNA, radiolabels or traditional small molecules.
Anticipating Regulatory Shifts: AI and Structured Data
Dr. Mocny and Dr. Verespy also spoke to recent changes in FDA staffing, and how the growing role of AI could affect regulatory review.
Dr. Mocny noted that the FDA had already begun shifting to AI-assisted application review, with the goal of reducing bias and streamlining decision-making. But the bigger shift, he explained, might be in how data was submitted.
“The introduction to AI tools is going to be very helpful. It should make review quicker, easier and remove some of the bias that human review can impart,” he said.
Dr. Mocny also pointed to an emerging trend toward replacing traditional narrative-based submissions with structured data formats — particularly those replacing legacy modules like Module 3.2.S and Module 3.2.P. “
“There’s a desire to move away from the current filing structure altogether… toward a structured data approach,” he added.
While antibody-oligonucleotide conjugates (AOCs) are still gaining regulatory familiarity, Dr. Mocny noted they are “new, but not brand new.” Developers can apply knowledge from ADCs and nucleotide-based therapeutics, but merging these domains into a single molecule adds complexity that demands specialized expertise.
He also explained that regulators are moving toward structured data formats that enable clearer comparisons across similar therapies. This approach could help agencies better understand a therapy’s place in the treatment landscape by linking safety, efficacy, and behavior data across modalities.
As regulatory expectations evolve, Dr. Mocny said, it’s essential for CDMOs and CROs to help sponsors present these complex datasets in a way that supports meaningful review.
Looking Ahead: Converging Science and Strategy
Dr. Verespy and Dr. Mocny concluded the conversation with reflections on how regulatory and development strategies must evolve together. While AOCs are still relatively new, both speakers stressed that they draw from well-established oligonucleotide and ADC frameworks.
“So while it’s not a brand new therapy, it is a newer modality… but I think that puts a lot of onus on the sponsor and CDMOs and CROs like ourselves… to drive that argument and that description across in a meaningful way,” said Dr. Mocny.
Dr. Verespy added that AI tools might not change what data is submitted, but how it’s structured — favoring tabular formats over visual graphs to allow machine-based review to interpret findings more accurately.
Dr. Mocny emphasized the need for strong “knowledge graphs” linking data points across manufacturing and testing workflows. This, he said, would reduce the risk of “AI hallucination” and improve regulatory confidence.
“Understanding how this data point relates to all the other data points that are collected is really important… how these discrete data points impact one another and how they’re linked together.”
As next-gen conjugates continue to gain ground, both Dr. Mocny and Dr. Verespy reaffirmed that close coordination between scientific and regulatory teams will be essential to navigating what comes next.
