Control Laws in Aging and Longevity: A Control Theory of Aging for Gerotherapeutic Drug Discovery

Existing aging theories describe what changes with age but do not prescribe how to intervene.

We propose a control-theoretic framework that is not merely descriptive but prescriptive: it specifies which intervention, at which dose and sequence, under which safety constraints, will restore a measured biological state to a functional region.

Aging is defined as progressive loss of safe controllability; biological age is the minimum safe control cost of functional restoration.

Drugs are modeled as vector fields on biological state space whose non-commutativity, quantified by Lie brackets, predicts that intervention order determines outcome.

The core differentiation from prior theories is operational: the framework outputs ranked targets, optimal sequences, safety-constrained protocols, and falsifiable predictions directly usable in drug discovery, rather than mechanistic ontologies or correlative biomarkers.

We present a five-dimensional ODE model with analytic Lie-bracket derivation, a modality-aware control layer, three translational case studies, an implementation architecture with power analysis, and empirical scoring of aging interventions across five biological epochs.

Twenty falsifiable predictions are enumerated.

The central claim is that control-value reduction predicts translational success better than Hallmark annotation or biomarker reversal alone.

If validated, this provides the missing interventional layer connecting aging biology to rational gerotherapeutic discovery.