Kavox Labs operates a hypothesis-generation platform with a proprietary validation loop. We prioritize disease-associated genes for Alzheimer's and Parkinson's through multi-scale graph architectures, falsifying candidate hypotheses in human 3D organoid structures before wet-lab capital commitment.
Over nine out of ten clinical trials in Alzheimer's and Parkinson's fail. Most programs fail not because of formulation or clinical trial operations, but because the underlying biological hypothesis was flawed from inception.
Conventional therapeutic development spends 4 to 5 years and hundreds of millions of dollars progressing molecules against hypotheses validated only in non-human animal models. Rodent neurobiology consistently fails to capture the complex, age-dependent pathology of human neurodegenerative disorders.
Furthermore, standard computational approaches often rely on uncalibrated black-box affinity scores or superficial literature scraping, producing correlations driven by publication popularity rather than causal biological mechanisms.
Three technical disciplines have reached an inflection point simultaneously: multi-scale biological knowledge graphs capable of representing millions of relational edges, path-based graph neural networks that provide interpretable mechanistic routes, and high-content 3D human organoid systems.
SINA bridges these disciplines into a systematic triage platform, evaluating computational candidate prioritization against physiological human tissue before substantial wet-lab capital is committed.
| Dimension | Traditional Pre-Clinical Model | SINA Platform Framework |
|---|---|---|
| Hypothesis Generation | Manual literature review biased toward historical hubs; uncalibrated correlation models. | Multi-scale graph AI with strict zero-shot evaluation and degree-bias null model controls. |
| Prioritization Criteria | Isolated binding metrics without explicit direction-of-effect or evolutionary context. | Multi-dimensional scoring integrating human genetics, evolutionary constraint, and declared direction. |
| Biological Screening | Transgenic rodent models with poor translational concordance to human CNS pathology. | Patient-derived human iPSC cortical and midbrain 3D organoids evaluated in staged phenotypic cascades. |
| Outcome Utilization | Negative and inconclusive data discarded, perpetuating publication bias and model drift. | All outcomes (positive, negative, toxic, inconclusive) recorded in an append-only evidence ledger. |
SINA executes candidate prioritization through three gated phases designed to eliminate data contamination, establish transparent biological routes, and verify phenotypic activity.
Harmonization of heterogeneous biological knowledge—genomic constraints, disease ontologies, pathway memberships, and compound properties—into a unified multi-scale relational graph.
Application of state-of-the-art graph neural networks to trace explicit biological paths connecting candidate genes to disease phenotypes, replacing black-box scoring with interpretable routes.
Testing prioritized candidate hypotheses in physical human CNS 3D organoids, recording autophagy-flux modulation, viability, and multiomic readouts directly into the evidence ledger.
Candidate prioritization balances multiple independent biological evidence layers, empirical-CDF normalized to avoid artificial hub bias.
Rather than collapsing all evidence into an uncalibrated scalar, SINA evaluates candidate disease-associated genes across distinct, transparent dimensions:
Evaluation of loss-of-function intolerance, genome-wide association study significance, and clinical variant associations.
Cross-species ortholog conservation, evolutionary constraint scoring, and pathway-level conservation across model organisms.
Pathway enrichment for cellular clearance, proteostasis maintenance, and lysosomal turnover mechanisms relevant to neurodegeneration.
High-confidence pocket accessibility, chemical probe availability, and declared direction-of-effect (inhibition, activation, or degradation).
Overcoming computational bias by recording all biological outcomes—positive, negative, toxic, and inconclusive—into a tamper-evident audit trail.
Standard AI drug models degrade over time because public databases almost exclusively report positive findings. Negative and inconclusive experiments are routinely discarded, depriving machine learning systems of the negative labels required for accurate calibration.
The M7 Evidence Ledger operates as an append-only, WORM (Write-Once-Read-Many) protected registry. Every experimental result generated in the organoid validation cascade is cryptographically hash-chained, establishing an auditable, compounding data moat.
Evaluating prioritized hypotheses in physiological 3D human cellular architecture to establish translational confidence.
Utilization of patient-derived induced pluripotent stem cell (iPSC) cortical and midbrain organoids, preserving human genetic backgrounds and complex cell-type interactions.
High-content imaging and functional assays measuring autophagy-flux, cellular viability, neuroprotection, and direction-matched target engagement.
Differential gene expression (RNA-seq) and mass spectrometry proteomics confirming pathway-level modulation and target specificity.
We welcome inquiries from biotechnology venture investors, SBIR grant program reviewers, and pharmaceutical research partners.
Kavox Labs, Inc.
Delaware C-Corporation
Menlo Park, California