xCellSense® pairs ex vivo drug sensitivity with machine-learning-based outcome modeling and genomic readouts to support myeloma regimen selection and patient segmentation.
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Multiple myeloma involves complex regimen sequencing and selection, deep biological heterogeneity, and increasingly MRD-driven endpoints. The questions are which regimen for which patient and who responds.
1
Ex vivo drug sensitivity
The response of patient-derived myeloma cells to your asset and comparators
2
ML outcome modeling
Machine-learning models that inform regimen selection and predict response
3
Genomic and biomarker support
Mutation profiling and responder segmentation, with MRD-oriented biomarker workstreams
4
Patient segmentation
Distinct responder subgroups for trial enrichment and companion-diagnostic strategy (research-framed)
Our npj Precision Oncology (2023) study presented a machine-learning sequential model to guide first-line VMP vs. RD selection for 706 newly diagnosed myeloma patients. The platform's myeloma work has also been recognized as an Innovative Medical Device in Korea and has been extended with an automated MRD analysis algorithm.
Regimen-selection insight, responder enrichment, and an MRD/biomarker strategy grounded in large-cohort modeling.
Key Deliverables
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A scientist — not a sales desk — will scope a tailored MM study and return a design and timeline.
