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Objective biomarkers for CNS drug development

Sleep-derived biomarkers that give research and clinical teams earlier, objective signal in CNS drug development.

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The Challenge

CNS trials lack objective signal

Most CNS programs still rely on subjective rating scales and infrequent clinic visits to measure treatment effect. These tools are noisy, slow to change, and poorly tied to underlying biology.

The result: programs that might work get killed too early, and programs that won't work run too long.

The missing piece isn't better drugs. It's better measurement.

90%
of CNS drug trials fail, the highest rate of any therapeutic area.
Cummings et al., Alzheimer's Research & Therapy, 2014
585K+ hours of PSG data
130+ conditions represented
200K+ recordings scored
What Changes

Biomarkers that inform drug development decisions

Identify dose-response signal
Pharmacodynamic biomarkers can detect drug effects that subjective scales may miss. Objective, continuous signal to inform dose selection.
Characterize patient populations
Stratify subjects using sleep-derived phenotypes. Compare real-world cohorts with normative data for more precise trial design.
Support label expansion
Quantitative sleep endpoints provide supporting evidence for label claims. Demonstrated sleep improvement supports both clinical relevance and commercial differentiation.
Track disease progression earlier
Measure cognitive trajectories where traditional outcomes may take years to manifest. Earlier signal can support more timely intervention decisions.
Where We Fit

Across the drug development lifecycle

From translational research to clinical practice, Insai provides objective biomarkers at every stage.

01.
Translational research
Analyze existing EEG data to uncover disease mechanisms, inform PK/PD analysis, and characterize patient populations before trials begin.
02.
Clinical trial optimization
Demonstrate target engagement, guide dose selection, and deploy quantitative endpoints to improve probability of trial and regulatory success.
03.
Label support and post-market evidence
Provide quantitative evidence of treatment efficacy to support label expansion into new therapeutic applications.
How It Works

From sleep data to research endpoints

Sleep shifts early in disease, responds to treatment, and can be measured at home. Insai turns that signal into structured, validated endpoints for your research and development program.

01
Capture
Collect multi-night EEG at home with clinical-grade wearables, or analyze existing PSG and EEG from your archive. Real-world data across days and weeks, not single-visit snapshots.
02
Analyze
Foundation models trained on 585K+ hours of neurophysiology data extract sleep architecture, spectral biomarkers, and event markers from raw EEG.
03
Report
Structured biomarker outputs delivered as data tables, visual reports, or via API. Built for research teams, regulatory submissions, and endpoint packages.
Earlier biological signal from real-world sleep data. Better-informed go/no-go decisions. Stronger regulatory submissions.
Why Insai

Built as a platform, not a point solution

Most digital biomarker approaches are narrow: one algorithm, one device, one indication. Insai is built to generalize.

Foundation models, not hand-crafted algorithms
Our models learn from large-scale brain data. They improve with more recordings and transfer across conditions without retraining.
Biomarkers, not raw metrics
Outputs are structured for research interpretation, validation, and regulatory submission.
Cross-device by design
Works across PSG, clinical EEG, headbands, and ear-EEG. No vendor lock-in.
Longitudinal, real-world signal
Multi-night measurement captures true biological variability, not single-visit snapshots.

Better measurement starts here.

Let's talk about your program

Whether you're exploring biomarker options for an upcoming trial or want to analyze existing data, we'd like to hear about it.

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