A major NIH-supported effort, MOSAIC, seeks to build advanced computer models that predict how a woman’s natural hormonal changes—from puberty through menopause and across cycles—affect drug pharmacokinetics and pharmacodynamics.

Michigan State University leads a multi-institution collaboration that has secured a 4.6 million NIH award (potentially up to 12.8 million over three years) to transform how medications for women are developed and prescribed.

The project aims to improve therapy design and prescribing, reduce adverse effects, and inform NIH guidelines, with all data and models openly available to researchers and healthcare professionals.

The initiative emphasizes translating data into actionable, mechanistic models that integrate hormone biology with metabolic therapies, addressing conditions like obesity, type 2 diabetes, cholesterol and thyroid disorders, and reproductive syndromes.

A broader goal is to ensure inclusive healthcare by accounting for sex-specific biological factors in drug research and clinical practice.

A strong SABV (sex as a biological variable) and life-course perspective are central to streamlining drug testing and accelerating bench-to-bedside translation.

The effort prioritizes translating SABV principles into innovative modeling approaches to speed therapeutics from research to clinical use.

Key voices stress using human data over traditional animal models to advance sex-specific insights and practical decision-making in care.

The work highlights gender gaps in metabolic health, noting hormones influence energy metabolism and drug responses, leading to different outcomes for women.

Initial test cases focus on bacterial vaginosis and MASLD, with the platform designed to generalize to broader therapeutic questions.

MOSAIC will apply to these conditions to understand how hormonal differences shape disease and treatment responses.

A central aim is to develop a personalized microbiome digital twin, blending individual microbial data with AI-driven mechanistic equations to simulate biology and interventions.