Hormone circuit explains why most HPA drugs fail for mood disorders and predicts the few that work

Tomer Milo, Shiraz Nir Halber, Moriya Raz, Dor Danan, Avi Mayo, Uri Alon

Research output: Contribution to journalArticlepeer-review

Abstract

Elevated cortisol in chronic stress and mood disorders causes morbidity including metabolic and cardiovascular diseases. There is therefore interest in developing drugs that lower cortisol by targeting its endocrine pathway, the hypothalamic–pituitary–adrenal (HPA) axis. However, several promising HPA-modulating drugs have failed to reduce long-term cortisol in mood disorders, despite effectiveness in other hypercortisolism conditions such as Cushing’s syndrome. The reasons for these failures remain unclear. Here, we use a mathematical model of the HPA axis to demonstrate that the pituitary and adrenal glands compensate for drug effects by adjusting their functional mass, a feedback mechanism absent in Cushing tumors. Our systematic in silico analysis identifies two interventions targeting corticotropin-releasing hormone (CRH) as effective for lowering long-term cortisol. Other targets either fail due to gland mass compensation or harm other aspects of the HPA axis. We propose CRH-neutralizing antibodies and CRH-synthesis inhibitors as potential targets for reducing long-term cortisol in mood disorders and chronic stress. More generally, this study indicates that understanding the slow compensatory mechanisms in endocrine axes can be crucial to prioritize drug targets.

Original languageEnglish
Article number18
Pages (from-to)254-273
Number of pages20
JournalMolecular Systems Biology
Volume21
Issue number3
DOIs
StatePublished - 3 Mar 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

Keywords

  • Dynamic Compensation
  • HPA Axis
  • Mood Disorders
  • Systems Endocrinology
  • Systems Pharmacology

ASJC Scopus subject areas

  • Information Systems
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Computational Theory and Mathematics
  • Applied Mathematics

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