Series 2 Paper VIII Indication-Aware Cannabinoid Frameworks.

Cannabinoid-based interventions are increasingly applied across pain, psychiatric, inflammatory, metabolic, neurologic, and palliative contexts. Yet clinical deployment frequently proceeds without structured stratification by receptor bias, temporal exposure, or baseline biological vulnerability. Because the endocannabinoid system is a distributed regulatory network integrating neural, endocrine, metabolic, and immune domains, unstructured modulation risks recalibrating regulatory baselines rather than restoring adaptive oscillation. This paper advances an indication-aware cannabinoid framework organized across three interacting axes: receptor bias, temporal exposure, and biological terrain. CB1-leaning signalling primarily influences neuroendocrine and metabolic circuits governing stress responsivity, reward processing, and energy storage. CB2-leaning signalling modulates immune tone and inflammatory resolution. Acute exposure may approximate feedback modulation, whereas chronic exposure increases probability of receptor desensitization, altered intracellular signalling bias, and baseline recalibration through mechanisms including β arrestin recruitment and receptor trafficking. Biological terrain determines tolerance. Systems characterized by metabolic rigidity, endocrine fragility, immune compromise, multimorbidity, or reduced adaptive reserve are more susceptible to directional drift under sustained signalling pressure. Risk emerges not from receptor activation alone, but from alignment between bias, duration, and vulnerability. This model reframes cannabinoid care from symptom-driven empiricism toward physiology-informed governance. It distinguishes palliation from restoration, emphasizes preservation of oscillatory variability, and proposes a structured decision algorithm to anticipate regulatory narrowing. The future of cannabinoid-based care lies not in categorical endorsement or prohibition, but in stratified deployment grounded in molecular pharmacology, systems biology, and terrain-aware risk modelling.