Pre-Clinical Drug Validation
Test drug safety and functional impact on normal B-cell differentiation before animal studies
The Challenge
Expensive Animal Studies for Every Target
Testing each drug candidate in mouse models requires significant investment and extended timelines. Many fail due to on-target toxicity to normal plasma cells that could be detected earlier.
Tumor Selectivity Unknown
Your myeloma drug may kill tumor cells, but does it also kill normal plasma cells and cause immunodeficiency? Finding out in Phase I is too late—patients suffer, trials halt.
Slow Target Validation Cycle
Traditional validation: In vitro tumor cell lines → animal models → Phase I. Each failed target wastes extended timelines and substantial investment before discovering safety issues.
Limited Mechanistic Insight
Tumor cell line testing doesn't reveal HOW the drug affects normal differentiation. Is it blocking the MBC→plasmablast transition? Or plasma cell maturation? You need mechanistic clarity.
"We invested heavily in a myeloma drug that worked beautifully in tumor cells but caused severe hypogammaglobulinemia in patients because it blocked normal plasma cell formation. We should have tested differentiation effects before Phase I."
The Helomnix Solution
Helomnix offers a unique in-vitro model of normal B-cell to plasma cell differentiation (Memory B-cell → Pre-plasmablast → Plasmablast → Plasma cell) completed in just 10 days. This model is used by researchers worldwide and has been validated in multiple published studies.
Test your drug candidates on normal differentiation BEFORE expensive animal studies. Dose-response curves reveal whether your drug selectively kills myeloma cells or also disrupts normal plasma cell formation. ScRNA-seq profiling at each differentiation stage identifies the exact mechanistic blockade.
This pre-clinical validation de-risks your pipeline by eliminating targets with on-target toxicity early, reducing costs per failed target and supporting faster clinic entry.
Unique Differentiator
Our proprietary in-vitro differentiation model is the ONLY commercially available normal plasma cell differentiation platform with published validation. Used in peer-reviewed research on Waldenström macroglobulinemia, autoimmunity, and myeloma biology.
How It Works
Drug Testing Setup
We culture primary human memory B-cells through our 10-day differentiation protocol while exposing them to your drug candidate at multiple doses (0.1x-10x IC50).
Differentiation Monitoring
Flow cytometry tracks cell viability and differentiation markers (CD19, CD27, CD38, CD138) at days 0, 3, 7, 10 to identify which differentiation stage is affected by your drug.
ScRNA-Seq Profiling
Single-cell RNA-seq at key timepoints (days 3, 7, 10) identifies gene expression changes, pathway disruptions, and cell state transitions caused by drug exposure.
Mechanistic Report
You receive dose-response curves, viability data, differentiation stage impact assessment, and scRNA-seq analysis identifying the molecular mechanism of any toxicity.
Real-World Application
A pharma partner was developing a compound targeting a transcription factor essential for plasma cell differentiation in multiple myeloma, raising concerns about on-target toxicity to normal antibody production.
Before
Standard approach: Test compound in myeloma cell lines (effective), then proceed to costly mouse xenograft studies. Risk: Unknown effect on normal plasma cells until Phase I.
After
Tested compound on our B→PC differentiation model first. Found dose-dependent blockade of plasmablast→plasma cell transition at therapeutic doses. ScRNA-seq revealed antibody production was substantially reduced.
Outcome
The perturbation data informed dosing strategy considerations. De-risked safety profile supported program advancement.
Value to Your Organization
Cost Efficiency
Eliminate failed targets before expensive animal studies. Avoid wasting substantial resources per target on mouse models that fail due to detectable on-target toxicity.
Time Savings
In-vitro validation takes weeks vs. extended timelines for animal studies. Fast-track promising candidates and eliminate targets with detectable toxicity early.
De-Risked Pipeline
Identify on-target toxicity to normal plasma cells before Phase I, preventing clinical trial halts and patient safety issues.
Our Methodology
Data Inputs
- Drug compound (IC50 data from tumor cell lines)
- Desired dose range for testing (typically 0.1x-10x IC50)
- Target information (gene/pathway)
- Mechanism of action (if known)
AI/ML Techniques
- Primary human memory B-cell isolation from healthy donors
- 10-day in-vitro differentiation protocol (MBC→Pre-PB→PB→PC)
- Flow cytometry for viability and differentiation markers
- Single-cell RNA-seq (10X Genomics) at days 3, 7, 10
- Dose-response curve analysis and EC50 calculation
- Pathway enrichment analysis to identify mechanism
Deliverables
- Viability dose-response curves for each differentiation stage
- Flow cytometry differentiation marker analysis
- ScRNA-seq dataset with differential expression analysis
- Mechanistic report: Which differentiation stage is blocked and how
- Safety assessment: Therapeutic window vs. normal cell toxicity
- Structured evidence summary to support program decisions
Discuss a Translational Application
We welcome discussions about how this approach can support your translational research.