The National Institutes of Health (NIH) is awarding $21 million to launch a research program aimed at spurring the development of computer-based methods that model and simulate the self-regulating processes of human hormones, known as hormone homeostasis. Hormone activity varies widely, resulting in differences in how men and women react to drug therapy, including varying outcomes to dosing, treatment response, and drug toxicity. The program, known as the Computational Modeling of Hormone Homeostasis Initiative, will advance novel human-based, data-driven models tailored to a man or a woman's complex physiology through advanced computational techniques.
"Current mathematical and animal models do not sufficiently capture variations between men and women. Addressing this gap is critical to the understanding of sex-specific biology and the development of more individualized approaches to therapeutics," said Nicole Kleinstreuer, Ph.D., NIH Deputy Director for Program Coordination, Planning, and Strategic Initiatives. "By leveraging advanced computer-based models that use human biological data to simulate hormone activity across the lifespan, we can help researchers improve the evaluation of new treatments and support more informed clinical decisions."
Sex hormones interact with many tissues and body systems beyond reproductive tissues, including the lungs, bones, and immune system. Changes in hormone homeostasis, such as fluctuations that occur during puberty, the menstrual cycle, pregnancy, and menopause, can trigger systemic responses and long-term changes to the body. Research accounting for these life-course fluctuations and changes to multiple organ systems is urgently needed to harness full therapeutic and clinical benefit.
The initiative is supporting awards focused on advancing computational techniques that model and simulate human hormone homeostasis. These projects will focus on developing sex-specific models to test drugs and therapeutics for effectiveness and toxicity. Awarded techniques include interactive machine learning, multi-scale models, AI-enhanced multi-scale models, physics-informed models of mechanical stress response and tissue remodeling, and virtual replicas of biological systems, known as digital twins.
"Accounting for sex as a biological variable (SABV) and expanding our understanding of the relationship between hormones and therapeutics using a lifespan perspective holds promise for streamlining drug and biologics testing and propelling bench-to-bedside translation," said Janine Clayton, M.D., FARVO, NIH Associate Director for Research on Women's Health. "This forward-looking program exemplifies the power of applying SABV principles to innovative methods to accelerate progress and amplify impact."
