Multidisciplinary Solutions

Meta Q or

Nanotechnology, Biotechnology, Information Technology, and Cognitive Processing converging on new solutions to treat CNS disorders and assist AI in meeting its promise.

Nanotechnology

Precision at the molecular level

MetaQor's nanotechnology platform reworks biological systems at atomic precision — creating tools so small they work within the natural machinery of the cell.

LASING ACTION

Patented Bio-Nanolaser

Taking inspiration from a rare phenomenon in nature — the way spherical raindrops can act as miniature optical resonators and emit laser-like light — MetaQor engineered biological nanostructures that replicate this lasing action at the subcellular scale. The result is a precision optical tool that operates entirely within the body's own molecular framework, requiring no external materials foreign to living tissue.

Each beam tip color represents a distinct biological target the nanolaser can sense and interact with — cyan, gold, and magenta corresponding to different molecular signatures. A single bio-nanolaser platform; multiple simultaneous sensing channels.

Platform

Clathrin Nanoparticle Carrier

MetaQor's foundational carrier system exploits clathrin protein — the cell's own transporter mechanism — to ferry large-molecule therapeutics and diagnostics across the blood brain barrier with no synthetic particle required.

Innovation

Naturally Biocompatible

Because the nanostructures are derived from proteins the body already produces and recycles, immunogenicity and toxicity concerns are fundamentally sidestepped — a safety profile no synthetic nanocarrier can match.

IP Status

USPTO Patented Portfolio

Multiple issued patents protect the bio-nanolaser architecture and the clathrin-based delivery methodology, establishing a defensible foundation for all downstream therapeutic and diagnostic applications.

Biotechnology

Crossing the blood-brain barrier

Once dismissed as hardly possible, large-molecule biologics, including growth factors, antibodies, and other large molecule agents now cross the blood-brain barrier naturally — without barrier disruption.   In the instance of Alzheimer's disease, MetaQor is entering the FDA IND phase with preclinical evidence that it can both detect and reverse the disease by delivering brain derived neurotrophic factor (BDNF) to the brain.

Step 1

BDNF–Clathrin biologic conjugate

Step 2

Simplified administration for outpatient settings

Step 3

Clathrin transcytosis across blood-brain barrier

Step 4

Payload delivered to AD-affected brain regions

Outcome

MRI monitoring of treatment + cognitive restoration

Diagnostic

Seeing Alzheimer's Earlier, Using Conventional MRI

The diagnostic biologic carries an MRI-contrast ligand that accumulates in AD-affected brain regions. Standard MRI systems — already widely available — can then reveal early signatures of impaired neurogenesis, synaptic loss, and cognitive decline, potentially years before symptoms appear. No PET isotopes, no invasive procedures.

Therapeutic

Restoring Function, Not Just Slowing Decline

Preclinical studies demonstrated that therapeutic delivery of BDNF via the multi-patented clathrin carrier restored cognitive function in animal models rather than merely slowing decline. MetaQor's aim is disease reversal — a threshold no currently approved therapy has crossed.

Regulatory

On Track for First-in-Human Trials

Preclinical studies completed, MetaQor's IND application for treating Alzheimer’s is advancing on a clear, credentialed path. The program benefits from an established scientific foundation across both the diagnostic ligand and therapeutic BDNF programs, with regulatory strategy aligned for an efficient transition from IND approval to first-in-human trials.

Information Technology

Clinical AI for CNS medicine

MetaQor's SDPS (Subject Dimension Progression Score) is its multifunction AI platform turning biomarker data into actionable clinical intelligence. The platform is purpose-built to work alongside the CNS biologics — its clinical deployment in lock step from IND to allowance of the biologics.

SDPS Platform · Subject ID MQ-2024-0441 · Session 3 of 6
SDPS Score
72.4
↑ +4.1 vs baseline — improving
Imaging Biomarker Signal
0.38 s⁻¹
Within therapeutic window
Biomarker Gates Active
16 / XX
Multi-domain panel · fluid + imaging
Trial Simulation Engine
83%
Probability of endpoint success
Neural Density Index71%
Cognitive Composite Score58%
MRI Carrier Signal88%
SDPS Platform · Patent Pending · © MetaQor Inc.
Scoring

Integrated Biomarker Scoring

The SDPS platform integrates fluid biomarkers, MRI morphometrics, and cognitive composites into a single continuous score that tracks disease progression and treatment response in real time — giving clinicians a unified view of CNS health across every patient visit, with personalized progression tracking.

Simulation

Clinical Trial Simulation Engine

The SDPS platform's built-in simulation module models trial outcomes across user-defined cohort parameters — giving MetaQor a statistical power map before a single patient is enrolled and substantially reducing the time and cost of reaching pivotal endpoints.

Imaging

AI-Powered MRI Analysis

A deep-learning imaging pipeline processes conventional MRI acquisitions to automatically extract the contrast signature produced by the MetaQor biologic ligand — linking the imaging readout directly to the SDPS score for fully integrated subject monitoring.

Cognitive Processing

Built from structure, not scale

Today's leading AI systems are enormous, power-hungry black boxes that can confidently contradict themselves. MetaQor's approach to cognitive system design takes the opposite path: compact, transparent, and self-checking — every answer traceable, every inference verifiable. As development advances toward deployment, this MetaQor architecture is positioned to redefine the standard for trustworthy AI in high-stakes clinical and scientific environments.

Conventional AI

Billions of parameters; megawatt-scale power draw
Opaque “black box” reasoning — no audit trail
Confident hallucinations with no self-check
Requires cloud-scale hardware to run
Cannot verify own internal consistency
Output correctness unknowable without human review
Enormous energy requirements impact development and deployment

MetaQor Cognitive System

Compact architecture; fraction of the energy cost
Every inference flows through named, auditable channels
Built-in consistency rules catch contradictions before output
Runs standalone on edge hardware
Mathematically self-consistent by design
Can sit atop any commercial AI system to audit and correct it
Can provide an energy reducing benefit to any commercial AI system
Architecture

Structure-First Design

Rather than brute-forcing intelligence through scale, MetaQor's cognitive system is being built on a precise mathematical framework grounded in the same principles that govern information flow in biological cognitive processes — producing a system that is compact, consistent, and fully interpretable.

Transparency

Self-Checking Inference

Every reasoning step flows through named, auditable channels with built-in consistency verification. The system catches its own contradictions before they reach output — a capability that does not exist in today's large-scale AI — making every answer explainable and traceable to its logical origin and providing a critically needed auditing layer deployable over any commercial AI system.

Compatibility

Quantum-Era Ready

The system's mathematical foundation aligns naturally with the requirements of quantum information processing, positioning it as a cognitive layer that logically bridges conventional computers with quantum systems — without architectural redesign.

Platform Convergence

SDPS and the cognitive processing system — connected, not merged

MetaQor platform convergence architecture Structural diagram showing SDPS as a self-contained core platform with an external AI integration layer through which the MetaQor cognitive processing system and other AI applications connect without modifying SDPS internals. SDPS PLATFORM Scoring engine Multimodal CNS biomarker scoring Clinical trial simulation External AI integration layer Real-time session data Structured analytical outputs Cognitive processing Self-consistent · auditable Other AI apps LLM summarization · imaging AI predictive analytics Future modules extensible by design Connected, Not Merged — SDPS Core Is Architecturally Unchanged

The SDPS platform stands on its own. The cognitive processing system connects to it from the outside — and that distinction is the point.

SDPS is a self-contained CNS diagnostic platform with its own scoring engine, biomarker gate architecture, imaging pipeline, and clinical trial simulation module. It requires no cognitive processing integration to function — and for IP and business development purposes, it will remain that way. What SDPS already has, however, is a live external AI integration layer: a set of application interfaces through which outside AI systems connect to SDPS, receive its real-time session data, and return structured analytical outputs that augment what clinicians and researchers see on screen.

MetaQor plans to use that same integration pathway to connect the cognitive processing system to SDPS as a backend module — without touching the core architecture of SDPS and operating alongside any existing AI applications connected to SDPS. Through this connection pathway, the cognitive system will apply its mathematically self-consistent reasoning to the SDPS data stream: cross-checking biomarker gate patterns for internal contradictions, flagging scoring anomalies that rule-based systems cannot detect, and providing an auditable inference layer over every clinical output the platform generates.

This integration strategy is itself the demonstration. SDPS working alongside and leveraging the cognitive processing system shows how any existing application — clinical, commercial, or research — can benefit from cognitive processing integration without architectural redesign. The cognitive system connects at the interface layer, reasons over the data it receives, and returns outputs that are traceable, self-consistent, and explainable in a way no current large-scale AI can match. MetaQor's intent is to make this cooperative integration feature available across multiple application domains — with SDPS as the first working proof of concept.