4D Path, a company dedicated to personalising cancer care through a novel, physics-informed approach to predicting tumour response to therapy, announced a collaboration with Daiichi Sankyo to develop next-generation predictive biomarkers in an antibody drug conjugate (ADC) clinical development program.

ADCs are among the most promising therapeutic classes in oncology, yet there remains a significant unmet need for scalable biomarkers that predict which patients are most likely to benefit from increasingly complex regimens and combination therapies. This collaboration brings together 4D Path’s ability to compute biologically grounded, physics-informed treatment predictive biomarkers from routine pathology specimens with the ADC innovation leadership of Daiichi Sankyo.

Under the collaboration, 4D Path will apply its proprietary Q-Plasia OncoReader (QPOR) platform to standard Hematoxylin and Eosin (H&E)-stained tumour biopsy slides to compute interpretable, quantitative biomarkers associated with cell-cycle deregulation and tumour microenvironment dynamics. These biomarkers will be evaluated for their ability to identify patients most likely to benefit from the select ADC, helping enable more precise, non-invasive, and cost-effective patient selection, potentially improving response rates and accelerating clinical trials.

This approach is designed to be compatible with both retrospective analyses of archived clinical specimens and prospective evaluation in ongoing and future studies.

“While the introduction of ADCs has improved outcomes for patients, more advanced biomarkers that are predictive of response to these agents are needed. 4D Path’s novel approach to utilising biological and physical characteristics from routine H&E-stained biopsy slides to predict benefit from ADCs has the potential to improve outcomes, helping patients get the right therapy at the optimal time in their disease course,” said Lee Schwartzberg, medical oncologist and Scientific Advisory Board member, 4D Path.

The collaboration is expected to also generate functional mechanistic insights into tumour-specific patterns of response and resistance—helping illuminate how biological context may interact with ADC designs. By transforming routine pathology images into actionable, physics-informed collective tumour state variables, the agreement aims to enrich translational understanding while supporting more personalised and effective treatment strategies.