Rutgers Researchers Partner on Project to Track How Drugs Interact With Female Hormones

Backed by a new $4.6 million award from the National Institutes of Health, Rutgers researchers are joining Michigan State University to launch a novel project to transform how medications are developed and prescribed for women. This is the first installment of an award worth up to $12.8 million over three years. Researchers from Emory, Tulane, University of Colorado, University of Michigan and the University of Utah are also collaborating with the MSU team to accomplish this goal.

Clinical trials often fail to account for the unique ways female hormones change throughout life — such as during menstrual cycles, pregnancy, birth control use, menopause and hormone replacement therapy. This oversight can lead to treatments that are less effective or cause more severe side effects in women than in men.

To bridge this gap, 13 researchers are collaborating to build advanced computer models that will predict how a woman’s natural hormonal changes affect how medicines move through and act within the body. Their findings are expected to result in stronger research studies, advances in precision medicine and better real-world outcomes for women.

“Developing computational models that provide insights into women’s health — including the consequences of disease, efficacy and side effects of medicines — and integrate age and reproductive cycle stage is a moon shot,” said Teresa K. Woodruff, the lead investigator on this project, president emerita of MSU and an MSU Research Foundation Distinguished Professor in the Department of Obstetrics, Gynecology and Reproductive Biology at the MSU College of Human Medicine and in the Department of Biomedical Engineering at MSU’s College of Engineering.

“We’ll supplement the project’s computer models with lab-grown biological models, known as new approach methodologies, or NAMs, that mimic hormone levels in multiple organs during key moments in women, like ovulation, menstruation, pregnancy, menopause or the onset of a hormone-altering disease. We’ll expose each of these wet-lab NAM models to common metabolic drugs and measure responses to inform the development of computer models that can help improve women’s medication use,” said Shuo Xiao, associate professor, Department of Pharmacology and Toxicology at the Ernest Mario School of Pharmacy at Rutgers. Jiyang Zhang, research assistant professor at the school, is also on the team.

This award is part of a national program, the Computational Modeling of Hormone Homeostasis Initiative, which is a joint effort by the NIH Office of Research on Women’s Health and the Division of Program Coordination, Planning and Strategic Initiatives to leverage advancements in computer modeling to deepen the understanding of sex-specific hormonal biology. A total of $21 million is being awarded to expand national research and high-impact science.

Research teams across the country will be developing the next generation of human-based data-driven tools that advance understanding of hormone-related health and improve care for people and communities.

Metabolic conditions such as obesity, type 2 diabetes, cholesterol imbalances and thyroid disorders are widespread in women. They also frequently co-occur with reproductive conditions like polyendocrine metabolic ovarian syndrome, or PMOS.

Because energy metabolism and female reproductive hormones directly influence each other, widely prescribed drugs — including insulin and GLP-1 weight-loss and diabetes medications — can produce different results in women depending on their individual hormone levels.

The team will use advanced computational and modeling frameworks to accomplish five main goals:

1.Artificial intelligence will digitize and organize over 40 years of hormone research data, creating a free, publicly accessible database.

2.A standard computer model will map out a normal 28-day menstrual cycle alongside how key organs (like the liver, muscle and fat tissues) are regulated by hormones to process nutrients.

3.Real-world patient data will expand the standard digital model to account for various groups — such as women going through menopause or taking birth control — and specific health conditions like diabetes and obesity.

4.Lab-grown organ testing will be achieved by using human-relevant three-dimensional tissue models and mini lab-grown organoids (such as liver, muscle and ovarian tissue) to inform, test and verify the computer predictions.

5.Personalized treatment tools for specific medications (including metformin, insulin and GLP-1 drugs) predicted by the computer models will help doctors better prescribe ideal doses and avoid dangerous side effects.

The final open-access computer platform will give healthcare providers practical tools to anticipate drug efficacies, prevent harmful side effects and tailor prescriptions for female patients.

Additionally, the findings will directly inform NIH guidelines, national safety standards and clinical protocols for testing new treatments.

All computer models and data produced through this NIH initiative will be freely available to researchers and healthcare professionals worldwide upon completion.