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Daiichi Sankyo is tapping BostonGene’s AI-based translational platform to analyze multimodal data from hundreds of thousands of multiomic and histopathologic patient profiles, aiming to distinguish ADC responders from non-responders and sharpen patient selection. The partners say the work will move beyond exploratory biomarkers to inform development decisions across Daiichi’s ADC portfolio, though specific assets weren’t named.
Why It Matters To Oncology
ADC efficacy and toxicity can be highly context-dependent; multiomic + histopathologic profiling is positioned here as a way to define molecular subgroups most likely to benefit and avoid “ambiguous efficacy” from broad enrollment.
BostonGene says its “digital twin” approach integrates NGS, flow cytometry, imaging and cell-free DNA to predict drug response and tolerability, and to map resistance pathways and tumor microenvironment features that can shape trial design.
For clinicians, the promise is more actionable stratification: identifying a biomarker-defined subgroup, then narrowing enrollment to improve signal clarity and speed time-to-right-patient access.
The Financials
Financial terms were not disclosed.
Daiichi did not specify which ADC assets are included under the agreement.
BostonGene framed the collaboration as “de-risking” development by lowering the “cost of uncertainty” across stages, from early differentiation to later-stage positioning.
What They're Saying
“Success in modern drug development is no longer defined by data volume, but by the speed and accuracy with which we translate biological complexity into clinical outcomes,” said Nathan Fowler, MD, chief medical officer at BostonGene.
A BostonGene spokesperson said a practical output could be identifying a molecularly defined subgroup more likely to benefit, enabling Daiichi to narrow enrollment and increase the probability of trial success.
What's Next
Watch for whether Daiichi and BostonGene disclose specific ADC programs, target indications, and the biomarkers used to drive inclusion/exclusion or enrichment strategies.
Key proof points will be prospective validation: pre-specified biomarker hypotheses, improved response-rate separation between predicted responders vs non-responders, and clearer benefit-risk in defined subgroups.
Expect emphasis on resistance and tumor microenvironment readouts to inform combination strategies and line-of-therapy positioning.
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