SIGNARA  Predictive Oncology Platform

Detecting cancer risk before tumors form

PreDiXome Bio is developing disease-state sensing technologies designed to identify integrated biological changes associated with cancer before conventional detection becomes possible.

“We believe cancer creates measurable biological state transitions before conventional detection, and we are building the tools to interpret them.”

Preliminary translational research underway
Provisional intellectual property filing in progress
NIH-oriented validation strategy under development
Scientific collaborations in translational oncology
The Challenge

Cancer Begins Long Before
It Becomes Clinically Visible

Biological changes associated with cancer emerge before tumors become clinically detectable. Current diagnostic approaches primarily identify disease after measurable burden has developed. PreDiXome is investigating whether these earlier biological state transitions can be identified and interpreted.

Healthy Biology
Baseline cellular and systemic state
Early Biological Shift
Measurable functional state transition
PreDiXome focus
Tumor Formation
Disease burden becomes physical
Clinical Detection
Identified by current diagnostics
Today's standard
The Opportunity

Earlier Biological Insight Could Transform Outcomes

80%
Cases diagnosed after disease progression
Source: aggregated ovarian cancer epidemiology literature
90%
Five-year survival when detected early
Source: stage-stratified survival data
~30%
Five-year survival when detected late
Source: stage-stratified survival data
Our Scientific Thesis

Cancer creates measurable biological state transitions before conventional detection.

These transitions may reflect coordinated changes across cellular signaling, redox biology, metabolic function, and extracellular vesicle communication.

The Signara platform is being developed to identify and interpret these integrated disease-state signatures.

The Scientific Insight

Disease Emergence Is Reflected Across Multiple Biological Systems

Disease emergence is not defined by a single biomarker. It is reflected through coordinated biological changes across cellular, metabolic, and signaling networks.

01 — Membrane Interface

Membrane Interface

Structural and electrical changes associated with cellular state disruption.

02 — Redox Dynamics

Redox Dynamics

Electron transfer imbalance and oxidative stress signatures.

03 — Mitochondrial Dysfunction

Mitochondrial Dysfunction

Energetic instability and altered system behavior.

These processes converge into a measurable disease-state signature.

The Differentiation

Beyond Isolated Biomarkers

Cancer-associated biology emerges through interconnected system-level changes. PreDiXome is developing approaches designed to investigate integrated biological patterns rather than relying on single analytes alone.

Conventional Approaches

  • Detect existing disease
  • Single-analyte focus
  • Fragmented biomarker interpretation
  • Reactive detection
  • Requires measurable disease burden

Integrated Disease-State Analysis

  • Functional disease-state sensing
  • Integrated biological signal analysis
  • Predictive detection approach
  • Earlier biological transition identification
  • Systems-level disease-state modeling

PreDiXome is not measuring fragmented biomarkers.
It is investigating integrated functional disease states.

The Platform

The Signara Disease-State Sensing Architecture

The Signara platform is designed to integrate biological signal capture, advanced sensing technologies, and computational interpretation into a unified disease-state analysis framework.

01
Biological Signals
02
Signal Integration
03
Advanced Biosensing
04
Disease-State Interpretation
05
Risk Insight
Component 01

Exosomal Multi-Omics

Capturing integrated biological state information reflected in extracellular vesicles.

Component 02 — Core Differentiation

Advanced Bioelectrochemical Sensing

A proprietary sensing architecture designed to detect coordinated electrochemical and biological state changes associated with disease emergence.

▸  Proprietary architecture · technology moat
Component 03

Computational Disease-State Modeling

Transforming multidimensional biological signals into predictive disease-state intelligence.

Platform Vision

Building Toward Disease-State Intelligence Across Cancer Types

Ovarian cancer serves as the initial application of the Signara platform. The underlying architecture is designed to support expansion across multiple cancers through integrated disease-state analysis.

Platform Origin

Deep expertise in ovarian cancer biology and early disease emergence.

Platform Expansion

Apply and validate the platform across multiple high-impact cancer types.

Future State

Converging data and signal intelligence into a single, scalable MCED platform.

Integrated Disease-State Signals
Proprietary Biosensing Platform
Computational Intelligence
Scalable Across Cancers
Future State Multi-Cancer Disease-State Intelligence
Translational Validation

Building Clinical and Biological Evidence

Phase 1

Signal Discovery and Prototype Development

Phase 2

Expanded Biological Validation

Phase 3

Retrospective Biospecimen Validation Studies

Phase 4

Predictive Digital Twin Modeling

Phase 5

Clinical Expansion and Longitudinal Learning

Establishing foundational biological datasets required to characterize disease-state biosignatures associated with ovarian cancer.

Leadership

Scientific & Translational Leadership

Founder & Scientific Architect

Physician-Scientist · Founder

Physician-scientist focused on translating biological insight into clinically meaningful disease-state sensing technologies.

Clinical Oncology Translational Diagnostics Systems Biology Predictive Oncology
Scientific Advisor

Translational Oncology

Academic medical institution
Disease-state biology · early disease emergence
Scientific Advisor

Redox Biology

Research university
Oxidative stress signatures · electron transfer dynamics
Advisory Board

Disease-State Biology

Translational research center
Systems biology · biomarker validation strategy
Advisory Board

Computational Modeling

Translational research center
Predictive modeling · disease-state digital twins

Advisor profiles and institutional affiliations to be confirmed and finalized for public release.

Financing

Advancing Scientific Validation

Funding foundational validation studies, biosensing development, translational research activities, and platform maturation.

Raise Target
$1.8M
Runway
18 Months

Key Milestones

  • Proprietary Biosensing Validation
  • Disease-State Signal Characterization
  • Biospecimen Validation Studies
  • Intellectual Property Development
  • NIH Grant Preparation
Engage

Building the Future of Predictive Oncology

Collaboration Areas

  • Clinical Research Partners
  • Academic Collaborators
  • Biosensing Technology Partners
  • Computational Biology Partners
  • Translational Oncology Programs
The PreDiXome Thesis

We believe cancer creates measurable biological state transitions before conventional detection, and that understanding these transitions may enable a new generation of predictive oncology.

PreDiXome Bio is advancing a new approach to disease-state sensing — beginning with ovarian cancer and building toward a broader future of predictive oncology.