Description This Zenodo record publishes a complete Method2Model (M2M) Stage-1 deliverables package for a dual-platform mRNA therapy concept in X-linked adrenoleukodystrophy (ALD), combining lipid nanoparticles (LNP) and extracellular vesicles (EV) in a peripheral-first → CNS strategy. Stage-1 is the de-risking layer that sits between an initial therapeutic idea and costly execution (wet-lab programs, CMC scale-up, or software implementation). Many advanced programs fail not because the science is irrational, but because critical assumptions remain implicit—until late review, replication friction, or real-world constraints force expensive rewrites. This package makes assumptions explicit, locks scope, and defines an implementation-ready architecture so teams can build and test without “architecture drift.” What’s included (aligned PDFs + README) Stage-1 Output Package — ALD_mRNA_Therapy_Stage_1_Output.pdfA compact, review-oriented dossier: scope lock, assumptions map/log, input specification sheet (ISS), Scenario Pack v1, and a verification-plan outline—designed for rapid stakeholder alignment. Architecture Specification — ALD_mRNA_Therapy_Architecture.pdfThe implementation-ready blueprint of the mechanistic model, structured as blocks A–G (delivery/transport, targeting, PD/disease, safety, CMC variability, uncertainty/scenarios, and decision logic). It defines indices, states vs observables, interfaces, and the governing relationships needed for later implementation. Study Execution Guide v1 — The_Study_Execution_Guide.pdfAn operational guide mapping experimental workstreams and measurements back to the architecture—so each assay contributes to calibration, uncertainty reduction, and defensible decisions. Intended use A reference example of what a client can expect from a Method2Model Stage-1 engagement. A template that can be adapted to other indications where delivery, CNS access, immunogenicity, and CMC variability create translational risk. License / Use Restrictions This record is shared for research and educational use only. Commercial use is not permitted.If you want to use, adapt, or integrate this material in a commercial setting (including internal commercial R&D workflows, paid services, or product development), please contact the author to request written permission. Citation Please cite this record using the DOI: 10.5281/zenodo.18069680