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The Platform

From multimodal cohort data to subgroup failure predictions

Valinor Discovery's platform ingests clinical and genomic data, builds probabilistic virtual patient cohorts, and simulates trial population outcomes across candidate compounds. All of this before a single participant is enrolled.

Data Ingestion

Designed for the data formats clinical development teams already produce

No bespoke data pipelines. No manual reformatting. The platform connects directly to the structured data outputs from your existing clinical data systems.

CDISC SDTM and ADaM

Structured trial datasets in standard CDISC format are ingested directly. Subject-level domains (DM, EX, LB, VS) and derived analysis datasets map into the virtual patient parameter space without transformation.

HL7 FHIR patient records

De-identified, aggregate-level FHIR bundles covering Patient, Condition, Observation, and MedicationRequest resources. We work from cohort-level conditional statistics, not individual patient records.

Genomic panel outputs

VCF files and targeted sequencing outputs from WES and gene panel assays. Variant allele frequencies and copy number variation summaries integrate into the biomarker axis conditioning for each virtual patient.

Lab time-series and phenotypic annotations

CBC, metabolic panel, and proteomic marker longitudinal data alongside ICD-10 coded phenotypic annotation files. Temporal conditioning captures the comorbidity and disease progression patterns that drive subgroup heterogeneity.

Simulation Engine

A Bayesian network at the center of every virtual patient

Each virtual patient is a statistical draw from a probabilistic model trained on real cohort data. The network encodes conditional dependencies between biomarker axes, comorbidities, and treatment histories. Structural relationships from the data, not assumptions.

01

Cohort statistics extraction

Aggregate-level statistics are extracted from the ingested datasets: means, variances, and joint frequency tables across biomarker and clinical axes. No individual patient record leaves your environment.

02

Bayesian network construction

A directed acyclic graph is fit to the extracted conditional distributions, incorporating literature-curated epidemiological priors for disease prevalence and comorbidity co-occurrence.

03

Virtual cohort sampling

1,000 to 10,000 virtual patients are sampled from the fitted network. The resulting synthetic cohort has the correct epidemiological shape for your development context without containing any real patient data.

04

Subgroup signal detection

Trial exposure conditions are simulated across the virtual cohort. The platform identifies which biomarker-defined subgroups show efficacy signal collapse at realistic enrichment thresholds. This happens before protocol lock, when the design can still change.

Platform Output

What the platform returns to your development team

Simulation results are delivered as structured reports, not raw model outputs. Each report is designed to map directly to a protocol design or target prioritization decision.

A

Synthetic cohort composition report

Cohort profile summary for 1,000 to 10,000 virtual patients: biomarker distributions, comorbidity prevalence, treatment history composition. Includes confidence intervals reflecting uncertainty in input data coverage.

B

Subgroup failure probability distributions

For each candidate compound and exposure condition, a probability distribution over subgroup-level efficacy outcomes. Identifies which biomarker-defined strata are likely to dilute overall trial response.

C

Enrichment criteria recommendations

Ranked enrichment strategies with predicted improvement in trial success probability, expressed as specific inclusion/exclusion criteria language for protocol review.

D

Target-population alignment score

A single summary metric per candidate compound expressing the concordance between the predicted responder subgroup and the intended trial population. Designed for direct integration into target prioritization decisions.

Integration

Fits into your existing translational workflow

Valinor Discovery is not a standalone analytics environment. The platform is designed to insert at specific decision points in Phase I-II development without requiring a separate data team workflow.

CDISC SDTM
HL7 FHIR
VCF genomics
Lab panels
ICD-10
ADaM datasets
Protocol design: enrichment criteria language for inclusion/exclusion review
Target prioritization: candidate ranking by subgroup failure risk score
Site selection: modeled site-eligibility estimates for rare disease programs
Regulatory filing: simulation methodology documentation for agency submissions

Technical Specifications

Platform specifications for development teams evaluating data readiness

Cohort size
1,000 to 10,000 virtual patients per simulation run
Configurable based on statistical power requirements and available cohort reference data density.
Minimum input dataset
200 real patient records or equivalent aggregate statistics
Lower coverage increases simulation uncertainty bounds; the platform reports confidence intervals explicitly.
Biomarker axes
Up to 40 simultaneous conditioning dimensions
Genomic, proteomic, metabolic, and clinical phenotype axes included in a single joint model.
Data privacy model
Aggregate-only input processing
No individual patient-level records leave your environment. All computation runs against cohort-level conditional statistics.
Output formats
PDF report, structured JSON, and CSV subgroup probability tables
Designed to integrate into study team review workflows and electronic trial master files.
Turnaround time
Typical 5 to 10 business days from data intake to final report
Depends on input data completeness and the number of candidate compounds included in the simulation scope.

Ready to evaluate data readiness for your program?

We work with a small number of translational and clinical development teams at a time. Tell us about your compound, your data, and the development question you are trying to answer before Phase II enrolment.

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