Target Validation
Confirm candidate targets through state-specific essentiality, patient-level dependency mapping, and orthogonal evidence integration
The Challenge
Discovered Targets Lack Orthogonal Confirmation
Composite scores from knowledge base annotations are not the same as functional validation. Without orthogonal confirmation, high-scoring candidates may still fail experimentally.
Cell Line Essentiality Does Not Equal Patient Relevance
Essentiality data from cell lines does not capture patient heterogeneity. Validation requires mapping essentiality onto patient molecular profiles.
Validation Is Disease-State-Blind
Standard validation treats a disease as a single entity. State-specific validation determines which patient populations a target-directed therapy would serve.
Synthetic Lethality Requires Contextual Analysis
Confirming synthetic lethal relationships requires correlating essentiality with the specific genomic and transcriptomic context of each disease state.
"Our top-ranked target looked essential in aggregate analysis, but when we stratified by disease state, essentiality was confined to a single subgroup representing a fraction of patients. We would have designed a study for the wrong population."
The Helomnix Solution
Helomnix validates candidate targets at the disease-state level, not the disease level. State-specific essentiality mapping determines whether a target is functionally relevant in each molecularly defined functional disease state, preventing investment in targets that appear essential in aggregate but lack functional evidence in most states.
Digital Twin Map patient projection quantifies the addressable patient population for each validated target. By mapping essentiality profiles onto real patient molecular data, we characterize which functional disease states show functional dependency on the target and estimate the addressable patient population defined by molecular context.
Orthogonal evidence streams — essentiality, survival correlation, drug response association, and synthetic lethality context — converge to produce a validated target profile. Synthetic lethality confirmation correlates essentiality with the specific genomic and transcriptomic context of each disease state, identifying combination therapy opportunities.
Unique Differentiator
We validate at the disease-state level, not the disease level. State-specific essentiality combined with Digital Twin Map patient projection distinguishes states where a target is functionally relevant from states where it is not.
How It Works
State-Specific Essentiality Mapping
Map essentiality data onto molecularly defined disease states. Determine whether each candidate target is functionally essential in specific functional disease states rather than in aggregate.
Digital Twin Map Projection
Project essentiality-validated targets onto the Digital Twin Map to quantify the addressable patient population for each target in each disease state.
Survival & Response Correlation
Correlate target expression and essentiality with clinical outcomes — survival, treatment response, and relapse — to confirm clinical relevance of validated targets.
Synthetic Lethality Confirmation
Correlate essentiality with genomic alterations and transcriptomic context within each disease state. Confirm synthetic lethal relationships and identify combination therapy opportunities.
Real-World Application
Following target discovery, a set of prioritized candidates required orthogonal validation before experimental investment. Helomnix applied state-specific essentiality mapping and Digital Twin Map projection to confirm which targets warranted further development.
Before
Standard approach: Aggregate essentiality analysis suggested TARGET_A was broadly essential. The team planned a Phase I trial in an unselected patient population based on disease-level data.
After
Helomnix validation: State-specific mapping revealed TARGET_A essentiality was confined to two of five disease states. Digital Twin Map projection showed these states represented a defined molecular context comprising a subset of the patient population. Three other states showed no functional dependency despite high expression.
Outcome
Validation narrowed the candidate list by revealing variable essentiality across states. Prevented investment in targets appearing promising in aggregate but lacking functional evidence in most states. Supported a biomarker-informed study design focused on functional disease states with confirmed target dependency.
Value to Your Organization
Experimental De-Risking
Orthogonal computational validation before experimental investment reduces the risk of pursuing targets that fail in functional assays or clinical trials.
Patient Population Quantification
Digital Twin Map projection quantifies which functional disease states show target dependency, informing study design considerations and addressable patient population estimation.
Therapeutic Strategy Confirmation
Convergent evidence from essentiality, survival, drug response, and synthetic lethality streams provides convergent computational evidence supporting target prioritization before committing resources.
Our Methodology
Data Inputs
- Candidate target list from discovery pipeline
- Patient multi-omics data (RNA-seq, proteomics, genomics)
- Disease state annotations or Digital Twin Map classifications
- Clinical outcomes (survival, response, relapse)
- Cell line essentiality data
- Genomic alteration profiles (mutations, CNVs)
AI/ML Techniques
- State-specific essentiality mapping across molecularly defined subgroups
- Digital Twin Map patient projection for addressable population quantification
- Survival and treatment response correlation analysis
- Synthetic lethality confirmation (essentiality x genomic context)
- Orthogonal evidence convergence scoring
- Biomarker signature derivation for population characterization
Deliverables
- Validated target list with state-specific essentiality profiles
- Digital Twin Map projection: addressable patient population per target
- Clinical correlation evidence (survival, response associations)
- Synthetic lethality confirmation and combination opportunities
- Biomarker signatures supporting population characterization for enriched study designs
- Orthogonal evidence summary per target
- Structured evidence summaries: convergent evidence strength, addressable population scope, and deprioritization rationale
Discuss a Translational Application
We welcome discussions about how this approach can support your translational research.