Built an open-source REGAL + valuation model and wanted to share it here properly (had been posting in daily threads / other subs until someone pointed me to the main sub).

Live: https://sls-model.vercel.app/
GitHub: https://github.com/sterno874/SLS-Model

What it is

This is not a price target or bull post. This is a scenario explorer that tries to compile primary sources (SEC/IR, NCT04382200, design paper, event PRs) and let you stress-test different structural assumptions under the locked 60 / 72 / 78 event anchors.

Tab 1 — REGAL: Mixture-cure survival, implied HR, forward + anchor-constrained inverse solve, Monte Carlo P(win). Presets include biology-first Best Available Guess, Moderate (~0.40), critique/ridge stress cases, etc.

Tab 2 — SLS-009: Phase 1b/II PTCL context + hypothetical Phase 3 power.

Tab 3 — Valuation: Risk-adjusted buyout / $/sh (P(GPS) is separate from Tab 1 clinical P(win)). Dilution stress: 181M basic / 222M FD / 240M ATM.

Explain + Biology tabs: ELI5 → PhD walkthroughs with citations; GPS / WT1 / CDK9 biology.

Important framing

Blinded pooled counts fix the timeline but not arm-level tails. The "Biology-first" readout HR ~0.26 and moderate ~0.40 can both fit anchors under different priors. The tool is meant to show that probability space, not pick one HR.

CW / Thetamancer / uhdisj41-style critiques are baked in where I could (Bayes strawman, binding IA, BAT cap, etc.), tell me where it's still wrong or misleading.

Ask

• Are any anchors, trial IDs, or citations wrong/stale?
• What bear cases or variables are missing?
• Bug reports, GitHub issues or PRs are welcomed!
• Any feedback to help improve the tool

Not financial advice. I do hold SLS. All readout outcomes uncertain until unblinded data.