A Hypothesis-Driven Smart Closed-Loop Framework for Hormone Replacement Therapy: The Cellular Response Threshold (CRT) Hypothesis and Clinical Implications

Abstract:Background: Conventional hormone replacement therapy (HRT) relies on static dosingstrategies that fail to account for intra-individual variability in hormonal requirements. Despitethe shift toward ultra-low-dose regimens following large-scale studies such as the Women'sHealth Initiative, current titration methods remain reactive and insufficiently personalized.Hypothesis: We propose the “Cellular Response Threshold” (CRT) hypothesis, whichconceptualizes estrogen receptor (ER-α and ER-β) signaling as a dynamic, non-linearprocess influenced by metabolic rate, circadian rhythms, and systemic inflammatory status.We hypothesize that maintaining hormone exposure within an individualized, time-varyingCRT window—rather than fixed dosing—may optimize therapeutic outcomes whileminimizing cumulative risk.Framework: We introduce a closed-loop,Artificial Intelligence (AI)-assisted HRT systemintegrating continuous biometric sensing with adaptive dose titration. The model incorporatescontextual signal discrimination, enabling differentiation between hormone-related symptomsand confounding physiological states (e.g., exercise, infection). Autonomous adjustmentsare constrained within physician-defined evidence-based safety corridors.Conclusion: This framework represents a conceptual shift from reactive to proactive HRTmanagement. By combining real-time physiological data, transvaginal drug delivery, andadaptive algorithms, the CRT-based model may offer a pathway toward precisionmenopause care. Prospective validation is required.