When Statistical SuccessDoes Not RestoreDecision-Ready Evidence

Most ALD mRNA programs don’t fail on biology—they fail on decision ambiguity: unclear success definitions, CNS “signal” without thresholds, and CMC variability that quietly changes what a “dose” means. In this case study, Method2Model turns a dual-platform ABCD1 mRNA concept (LNP + EV, Peripheral-First → CNS) into a stage-locked decision engine: a full A–G architecture, a formal formula pack, and an executable Stage-3 core (mouse branch) with verification evidence. The result is decision-grade Go/No-Go/Pivot governance—so teams can move faster with fewer reruns, fewer false conclusions, and fewer late-stage surprises.

Equilibrium Window of Health is a stage-locked, event-aware early-drift monitoring core designed for real-world longitudinal data. It infers a patient-specific equilibrium baseline, tracks a five-axis latent state with uncertainty, and separates chronic drift from recorded acute events via transparent attribution tags. Built with a formal I/O contract and QC artifacts, it produces reproducible, audit-friendly outputs across cheap/mixed/full measurement regimes.

This case study documents an end-to-end, stage-locked in-vitro screening system for sOMF exposure in 5HN and CD8 models. The work combines a complete Stage-1 execution and governance architecture (protocol, QC, audit artifacts) with a Stage-2 mathematical formalization that removes ambiguity from normalization, drift handling, evaluability constraints, and tiered selection outcomes. The result is an implementation-ready decision framework that is deterministic, traceable, and robust to common early-screening failure modes.

Used when: early drift detection, longitudinal biomarkers, prevention decision thresholds.. A lightweight, PB-inspired dynamic health model demo for small longitudinal biomarker series. It builds a personal baseline, detects early drift in log-space, and offers an optional 4D “prevention core” to visualize simple intervention effects—focusing on clarity, transparency, and reproducibility rather than clinical deployment.