Interview with Karen Silence

As the complexity of immune-mediated diseases continues to challenge modern medicine, the next generation of therapies will depend not only on scientific innovation, but on how effectively expertise is combined, decisions are made, and data is leveraged.

In a recent discussion with Oxford Global’s Cerlin Roberts, Karen Silence, Vice President of Preclinical Product Development at argenx, shared insights into a model that is redefining antibody engineering—one built on deep collaboration, calculated risk-taking, and an evolving relationship with artificial intelligence.

Collaboration as a Core Strategy

At the heart of argenx’s approach is a simple but powerful idea: no single organisation can master biology alone.

Through its Innovation Immunology Program (IIP), argenx partners closely with academic researchers, integrating them directly into its development process. These collaborations go far beyond traditional outsourcing models.

“They’re not just collaborators—they’re part of the team,” Silence explained.

Academic partners contribute deep biological expertise, running experimental models and helping shape scientific decisions, while argenx brings its strengths in antibody engineering and clinical development. The result is a shared journey—from early discovery through to clinical trials—where knowledge flows in both directions.

The Art of Letting Go

While scientific rigour is essential, Silence highlights a less obvious challenge in drug development: knowing when to stop optimising.

For antibody engineers, the temptation to refine endlessly is strong. But in a competitive landscape, speed matters.

“Perfectionism is the enemy of good enough,” she noted.

Striking the balance between excellence and progress requires experience—and discipline. Equally important is the need for backup strategies. By developing alternative candidates early, teams can maintain momentum even when setbacks occur, a critical advantage in high-stakes development environments.

The Complexity of Autoimmune Disease

Unlike oncology, where targeting and eliminating tumour cells can be relatively direct, autoimmune diseases present a more intricate challenge.

Multiple biological pathways are often involved, making it difficult to fully cure a condition. Instead, therapies frequently aim to reduce disease burden rather than eliminate it entirely.

This complexity is driving a shift toward bispecific and multispecific therapies, designed to act on multiple targets simultaneously. The goal is not just additive effects, but true synergy—combining mechanisms in ways that enhance overall therapeutic impact.

Embracing Risk Through Novel Biology

argenx’s strategy reflects a willingness to embrace uncertainty. Rather than pursuing well-established targets, the company prioritises novel biology—even when it carries greater risk.

To mitigate this, targets are selected with multiple potential applications. If a therapy fails in one indication, it may still succeed in another, preserving value and momentum.

It is a calculated approach: high risk, but with built-in resilience.

Next-Generation Antibody Engineering

Among the most exciting developments in the field are advances in CAR-T therapies and T cell engagers, particularly as they expand beyond oncology into autoimmune disease.

These technologies offer the potential for deeper and more precise immune modulation—reaching tissues and targets that were previously difficult to access. While challenges remain, particularly for T cell engagers, the trajectory is clear.

“If we can achieve deeper tissue depletion, it could be a game-changer,” Silence said.

AI, Data, and the Road Ahead

Artificial intelligence and machine learning are increasingly shaping drug discovery, but their current applications remain focused on specific challenges such as developability and immunogenicity.

Silence sees a broader opportunity.

Rather than isolated tools, the future lies in integrated platforms that combine biological insight, target selection, and predictive modelling into a cohesive system. Such platforms could enable more informed decision-making across the entire development pipeline.

Yet significant hurdles remain—particularly around data.

High-quality datasets are essential for effective AI, but collecting and curating this information requires time and effort in already fast-paced environments. Convincing scientists to prioritise data entry and standardisation is an ongoing challenge, even as the long-term benefits become increasingly clear.

Immunogenicity, in particular, remains a critical unknown. Despite advances in prediction, the lack of comprehensive datasets means uncertainty persists when therapies move into human trials.

Building the Infrastructure for Innovation

The vision of a fully integrated AI-driven platform is widely shared across the industry—but achieving it will require substantial investment and cultural change.

As Silence points out, the technology is advancing rapidly, backed by significant financial resources. The next step is ensuring that organisations can effectively harness it—aligning systems, people, and processes to unlock its full potential.

Looking Ahead

The future of antibody engineering will be defined not just by scientific breakthroughs, but by how well companies navigate complexity—balancing innovation with execution, speed with precision, and independence with collaboration.

argenx’s approach offers a glimpse of what that future might look like: deeply collaborative, strategically bold, and increasingly data-driven.

In a field where the stakes are measured in patient outcomes, getting that balance right has never mattered more.