What Q2 2026 reveals about the direction of antibody therapeutics

^{From first-in-class approvals to billion-dollar deals, Q2 2026 signals a new era for antibody innovation}

The second quarter of 2026 involved six antibody drugs that received first approvals: four in China and two in the United States.

The modalities ranged from antibody-drug conjugates (ADCs), bispecific antibodies, monoclonal antibodies, infectious disease antibodies and rare disease programs:

The approvals show how far antibody formats have diversified. For example, silevimig is a fully human bispecific antibody approved in China for rabies post-exposure passive immunisation, highlighting bispecific development beyond oncology. Meanwhile, iza-bren is especially notable as the first bispecific ADC to receive regulatory approval globally, combining dual-target binding with targeted payload delivery to potentially improve tumor selectivity, internalisation and therapeutic potency compared with conventional single-target ADCs.

Deals: assets, platforms and infrastructure

Business development was also active this quarter, with the largest antibody-related transactions combining clinical assets, discovery platforms and research infrastructure.

The BMS-Hengrui collaboration stands out for scale, covering 13 programs and up to about $15.2 billion in potential value. Pfizer’s Innovent agreement, worth up to $10.5 billion, also underlines continued interest in China-originated antibody innovation, particularly ADCs and multispecific formats.

Not every large deal was centred on a therapeutic asset, as Merck KGaA’s proposed $11.3 billion acquisition of Bio-Techne points to the importance of enabling technologies: reagents, proteins, antibodies, analytics and workflow tools that support discovery and development.

AI is changing the bottleneck

AI-enabled antibody discovery is also a central theme. Models can support sequence generation, de novo design, structure prediction, virtual screening, affinity optimisation and developability assessment.

But computational output is not the same as a drug candidate. Binding affinity, specificity, expression, stability, aggregation risk and developability still require wet-lab validation. As AI systems generate larger antibody panels, the pressure shifts toward high-throughput expression, rapid affinity testing and structured experimental datasets.

In practice, discovery is becoming more iterative: design, build, test, learn then repeat. The quality of experimental data may matter as much as the model used to generate candidates.

Conditional activation: aiming for therapeutic index

Conditionally activatable antibodies are another area to watch. These formats are designed to remain silent or attenuated in circulation, then activate preferentially in disease-associated microenvironments.

The rationale is that many targets are not fully disease-specific. They may be enriched in tumors or inflamed tissue but also present in healthy tissue. For ADCs, T cell engagers and immune-activating antibodies, this raises the risk of on-target, off-tumor toxicity.

Activation strategies can include protease-sensitive masking, pH-dependent binding, ATP-responsive designs or other disease-linked triggers. The broader question is no longer only whether an antibody binds, but where and under what biological conditions it binds.

What to watch next

Several regulatory decisions are expected soon:

For readers following antibody approvals, deal activity, AI-enabled discovery, emerging antibody formats and upcoming regulatory milestones, Biointron has released Antibody Industry Trends for Q2 2026, a report covering first approvals, major antibody drug deals, AI-driven discovery trends, conditionally activatable antibodies and antibody drugs to watch in the coming quarter.