Nucleai and Gilead Expand AI-Powered Tissue Analytics Collaboration to Support ADC Development

Nucleai, a company specializing in AI-powered Tissue Intelligence, announced an ongoing translational research collaboration with Gilead Sciences supporting Gilead's global antibody-drug conjugate (ADC) clinical development programs.¹ As part of the collaboration, Nucleai has analyzed a large dataset of hematoxylin and eosin and immunohistochemistry whole-slide images across several clinical studies spanning multiple oncology indications, generating novel biological insights and candidate spatial biomarkers intended for future scientific presentations and publications.¹

How does Nucleai's Tissue Intelligence platform work?

Nucleai's AI-native platform integrates computational pathology, spatial biology, clinical outcomes, and multimodal data to transform routine pathology images into quantitative biological insights.¹ Unlike traditional image analysis approaches, the platform is designed to enable standardized biomarker assessment from preclinical research through late-stage clinical development, while working to reveal mechanisms of response, resistance, and disease progression.¹ The approach reflects a broader shift in ADC-focused biomarker development, where multiple companies have recently turned to AI-driven pathology platforms specifically to address the complexity of ADC response prediction.

Why does biomarker-driven development matter specifically for ADCs?

ADCs combine a targeted antibody with a cytotoxic payload, and their clinical performance depends heavily on selecting patient populations with the right underlying tumor biology and antigen expression, a relationship that has proven more complex than simple binary target-expression testing can capture.² As more ADCs enter overlapping indications and competition intensifies, target expression alone has proven insufficient to fully explain ADC response, prompting a shift toward integrating protein expression with tissue architecture, tumor heterogeneity, and microenvironmental context to better characterize the biological drivers of therapeutic efficacy.¹ Continuous, quantitative scoring approaches are increasingly viewed as more informative than traditional categorical cutoffs for resolving subtle gradients in target expression, potentially improving how patients are matched to therapy and how trial eligibility criteria are defined.²

What did company leadership say?

Avi Veidman, chief executive officer of Nucleai, said in a press release, "Precision oncology is entering a new phase, where understanding tissue architecture is becoming just as important as understanding molecular alterations. Tissue Intelligence is becoming a foundational capability for precision medicine, helping identify the patients most likely to benefit while enabling pharmaceutical companies to translate tissue biology into reproducible biomarkers that improve the speed and success of oncology drug development."¹ Ken Bloom, MD, FCAP, head of pathology at Nucleai, added, "Scale and reproducibility are becoming essential requirements for biomarker development. Our platform enables standardized spatial analyses across thousands of clinical samples while directly linking tissue biology to patient outcomes, generating evidence that can support translational research, biomarker qualification, and future companion diagnostic strategies."¹

What happens next?

Nucleai said it continues to expand collaborations with pharmaceutical companies beyond Gilead to advance biomarker discovery, translational medicine, companion diagnostic development, and tissue analytics across the oncology development lifecycle.¹ The company did not disclose a timeline for when specific biomarker findings from the Gilead collaboration will be published or presented.¹

References

1. Nucleai advances AI-powered Tissue Intelligence through large-scale oncology ADC collaboration. News release. Business Wire. August 11, 2026. Accessed August 12, 2026.
2. Daiichi Sankyo taps Imagene AI for biomarker discovery. BioPharm International. August 2026. Accessed August 12, 2026.