QuEST Lifesciences

From Fundamental Physics to Drug Discovery

This research demonstrates a novel application of QuEST's fixed-point string theory framework to computational drug design. By leveraging the mathematical structures underlying the theory, a computational platform was developed that can rapidly generate therapeutic candidates — completing two full design cycles to produce five drug candidates targeting neglected African diseases.

Targeting Neglected Diseases

• Onchocerciasis (River Blindness): A parasitic disease transmitted by blackflies, causing severe itching, skin lesions, and irreversible blindness.
• Schistosomiasis: A parasitic infection caused by blood flukes, leading to chronic organ damage affecting the liver, intestines, and urogenital system.
• Leishmaniasis: A group of diseases caused by protozoan parasites, ranging from skin ulcers to fatal visceral infection if left untreated.

Results: High-Quality Drug Candidates

• Lipinski's Rule of Five: Full adherence, indicating good oral bioavailability.
• ADMET Profiles: Optimal absorption, distribution, metabolism, excretion, and toxicity properties.
• QED Scores: 0.804 to 0.865, well above typical drug-likeness thresholds.
• Safety: Zero cytochrome P450 interactions, reducing adverse drug interaction risk.

A New Metric for Understanding Cancer

This study introduces "informational isolation" — a transcriptome-derived metric that captures internal loop development, loop stability, and diminished external coupling within tumors. Rather than relying solely on mutation counts, this metric provides a fundamentally different lens for understanding tumor behavior rooted in QuEST's information-theoretic framework.

Multi-Cancer Analysis

Using bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA), the study examined four major cancer types:

• Breast cancer — the most common cancer worldwide
• Lung cancer — the leading cause of cancer death globally
• Colorectal cancer — the third most diagnosed cancer
• Kidney cancer — with rising incidence rates worldwide

Three Operational Regimes

The analysis revealed three distinct regimes — graded, saturated, and inverted — that characterize how informational isolation relates to tumor mutation burden. A critical finding emerged: aggressive tumors with low genetic mutation counts frequently appear in the saturated or inverted regimes, where mutation burden alone shows weak correlation with clinical outcomes.

The work proposes that malignancy functions as a phase transition, where informational isolation provides superior explanatory power for clinical observations compared to mutational load independently.

The First Biological Realization of QuEST Isolation

This groundbreaking work applies Quantum Entanglement Spacetime Theory directly to biological systems. The core idea: a finite domain becomes causally isolated when its internal entanglement flux exceeds the information-carrying capacity of its boundary. The study develops a QEP-compliant Stabilized Spacetime Domain (SSD) criterion and translates it into measurable biological quantities based on gene expression patterns.

Methodology

The analysis examined two breast cancer single-cell RNA sequencing datasets from the Gene Expression Omnibus (GSE122743):

• T47D cell line — a luminal A breast cancer model widely used in hormone receptor studies
• MCF7 cell line — a well-characterized estrogen receptor-positive breast cancer line
• 47,000+ individual cells analyzed with gene-expression measures including internal loop strength, boundary curvature entropy, and estrogen receptor signal strength

Key Findings

The analysis reveals large stabilized sub-populations and condition-dependent population shifts that suggest movement across the SSD boundary. This represents the first empirical realization of the QuEST isolation condition in a biological system — demonstrating that the same mathematical principles governing spacetime structure at the quantum level can describe information-processing boundaries in living cells.