01/ 04
Connect evidence
Connect starting material and reagent provenance with the affected batches.
01
← All modalities
CAR-T · TIL · NK
Cell therapies
Connect starting material, activation, expansion, modification and product assays while preserving the lineage of each program and batch. Scientific context helps teams understand why similar process conditions can produce different cellular outcomes.
CAR-T · TIL · NKCell therapies
OUTCOMES TRACKEDCell yieldviabilityphenotype
MODALITY-SPECIFIC PROCESS INTELLIGENCE
Preserve the context of a living, variable process.
01Starting material
02Activation
03Expansion
04Release evidence
FOCUS AREA · Starting material
Starting-material context
Follow donor or source information and material provenance into each process stage.
01
EVIDENCE · MODELS · DECISIONS
Keep every conclusion connected.
Evidence in Bio Hub
- Donor and material provenance
- Activation, expansion and process-stage records
- Viability, phenotype, composition and potency
Models in Bio Twin
- Expansion and variability analysis
- Process-stage comparisons
- Yield and operating-condition scenarios
Scientific decisions
- Investigate donor and batch variability
- Compare expansion strategies
- Trace process evidence to dose and potency
Process outcomes to evaluate: Cell yield · viability · phenotype
EXAMPLE INVESTIGATION
What changed between batches with similar expansion conditions?
02/ 04
Compare context
Align activation, expansion and modification stages, measurements and exceptions.
02
03/ 04
Evaluate explanations
Compare viability, phenotype, composition and potency evidence alongside cell yield.
03
04/ 04
Review next steps
Distinguish starting-material effects from process-stage effects and prepare targeted follow-up questions.
04
Bio Agents can coordinate the scientific work across evidence and models, while Bio OS preserves governance and human review. The configured workflow determines which tasks and models are available.
Bring us this process challenge ↗EVALUATION & SCOPE
Make the demonstration specific.
Define the cell product, intended dose and assays in scope. Demonstrating expansion analysis does not establish autonomous release decisions or prediction of clinical outcomes.
What evidence should we bring?
Donor and material provenance. Activation, expansion and process-stage records. Viability, phenotype, composition and potency. Include the process question, relevant run context and the analytical methods used to assess the outcome.
How do models support the workflow?
Expansion and variability analysis. Process-stage comparisons. Yield and operating-condition scenarios. Scope and evaluation criteria are defined for your process, data and intended decision.
What should we validate before using the results?
Define the cell product, intended dose and assays in scope. Demonstrating expansion analysis does not establish autonomous release decisions or prediction of clinical outcomes.
EXPLORE OTHER MODALITIES
ENTERPRISE USE CASE
Separating starting-material variability from cell-process performance
An autologous cell-therapy development use case connecting material attributes, stage performance, viable yield and product-specific potency.
Explore the complete use case ↗