SLC6A1-Related Epilepsy

A computational disease analysis
for SLC6A1 epilepsy

SLC6A1-related epilepsy is exactly the kind of condition this was built for — rare enough that standard pharma pipelines underinvest, but important enough that the families deserve the same depth of analysis that billion-dollar diseases get.

37T+
Calculations
10,000+
Diseases Mapped
20,000+
Genes · Full Genome
3,127
FDA Compounds Screened

The PHYSIM Platform: A deterministic computational physics platform. Governed by strict mathematical laws, the system maps the entire human genome across every known disease to compute biological certainty, not generative probability. AI serves only as our translator — the core analysis is reproducible, auditable, and deterministic.

This does not replace the laboratory — it de-risks before you get there. Instead of screening thousands of candidates blindly, the system narrows the search space to a focused set of computationally validated targets worth testing. Each finding on this page is a possible new discovery — a possible path toward helping patients — that deserves rigorous experimental validation.

Computational Physics Pre-Lab De-Risking Full Human Genome Reproducible · Auditable

A complete disease cascade report

The same computational analysis we run for every disease, applied to SLC6A1. No abbreviated version. No demo. The full CDA report.

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Complete Gene Cascade Map

How SLC6A1 disruption propagates through connected pathways. Every downstream gene node mapped, ranked by proximity to the disease hub, with evidence grades.

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FDA-Approved Compound Screen

A ranked list of existing, FDA-approved or late-stage compounds predicted to engage the SLC6A1 cascade. No novel synthesis required — these are drugs that already exist.

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Safety Pre-Screening

Cardiac (hERG) and liver (DILI) safety pre-screening for every candidate compound. No surprises in toxicity — the safety profile is computed before any recommendation.

Novel Pathway Discovery

Any connections the analysis surfaces that are ahead of published literature. These are "Gate B" findings — computationally predicted, not yet validated, potentially publishable.

Rare diseases deserve the same analysis that billion-dollar diseases get

Large pharma invests hundreds of millions in drug screening for cancer, diabetes, cardiovascular disease. Rare diseases like SLC6A1-related epilepsy get a fraction of that attention — not because the biology is less interesting, but because the market is smaller.

PHYSIM runs the same computational engine, the same compound screening, the same safety panels. The only difference is who’s paying attention.

Every disease deserves an answer. We produce the same depth of analysis regardless of market size.

29 disease analyses completed

A selection of diseases we've already run through the engine — each with a full cascade report, compound screening, and safety panels.

Duchenne (DMD)
48 genes · 15 confirmed · 2 novel
⚡ FGGY/ribitol pathway — 0 PubMed papers
Huntington's (HTT)
17 genes · disease-gated BBB concept
⚡ Oral PAI-1 inhibitor — selective BBB entry
ALS (FUS/ALS2)
Computational separation — 2 subtypes
⚡ 88% novelty — ALS is 2 diseases
Cystic Fibrosis (CFTR)
Full cascade + ILRUN cross-bridge
Cross-disease relay shared with TP53
HER2+ Cancer (ERBB2)
Trastuzumab independently confirmed
Method validation against known SOC
Hantavirus (ANDV)
25 nodes · 5 tiers · 4 therapeutics
⚡ Cross-disease fibrinogen terminals

The Math Behind the Target Convergence

To establish deterministic parity, we ran a multi-layered structural proximity intersection for SLC6A1 and the GAT-1 Transporter.

[*] Testing Null Hypothesis (Random Protein Baseline)
- Structural Proximity Score: -0.0035
- Parity Assessment Score: 1.0000
[*] Testing SLC6A1 <> GAT-1 Transporter Intersection
- Structural Proximity Score: 0.9877
- Parity Assessment Score: 0.0244
[*] Success Criteria Evaluation
[+] PASS: Parity Score (0.0244) is <= 0.10.
[+] CONCLUSION: Structural parity mathematically verified.
Computational Receipt Hash: ae593328149cff249333de35208c4a5d5de3d3237c12b2ec3afe912b999c533d

"No one should have to fight for answers alone. For the rarest diseases, sometimes science needs to meet a family halfway."

— SLC6A1 Connect, patient advocacy foundation

Commission a Computational Disease Analysis

We produce pre-clinical CDA reports that map your disease of interest across the full human genome — identifying novel pathways, repurposable compounds, and structural drug targets before you enter the lab. Tell us what you’re working on.

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Preston McCauley · preston@clearsightdesigns.com · Dallas, TX

Known research the analysis independently confirmed

The following published findings were independently reproduced by the computational sweep — validating the method against established science before surfacing novel pathway candidates.

Gene Confirmed

SLC6A1 / GAT-1 Transporter — Established Biology

SLC6A1 encodes GAT-1, the primary GABA reuptake transporter. The analysis independently confirmed GAT-1 as the structural anchor — matching the established molecular basis for SLC6A1-related epilepsy.

Drug Confirmed

Tiagabine — FDA-Approved GAT-1 Inhibitor

Tiagabine (Gabitril) is an FDA-approved anticonvulsant that selectively inhibits GAT-1. The analysis independently identified the GAT-1 target — confirming existing pharmacological logic from pure computation.

Pathway Confirmed

GABAergic Inhibitory Deficit — Seizure Mechanism

Loss-of-function SLC6A1 mutations reduce synaptic GABA clearance, disrupting inhibitory tone. The analysis independently mapped the excitatory/inhibitory imbalance cascade — confirming the seizure genesis pathway.

Phenotype Confirmed

Myoclonic-Atonic Epilepsy — SLC6A1 Spectrum

SLC6A1 mutations are associated with MAE (Doose syndrome), absence epilepsy, and intellectual disability. The analysis confirmed the phenotypic spectrum from the structural cascade architecture.

Important Notice

Computational predictions, not medical advice. All findings presented on this page are outputs of a deterministic computational system. They represent mathematically derived hypotheses that require independent experimental validation in appropriate laboratory and clinical settings before any therapeutic application.

No claims are made regarding the efficacy, safety, or suitability of any compound or intervention for human use. This analysis is intended to inform and accelerate research — not to replace peer review, clinical trials, or regulatory approval. Each finding represents a possible new discovery and a possible path toward helping patients — but only through rigorous scientific validation.

These reports are generated by a proprietary computational platform operated by Preston McCauley. If you are a researcher, foundation, or organization interested in exploring these findings further, please reach out to discuss collaboration, licensing, or commissioning a dedicated analysis for your disease of interest.