Machine Learning-Based Screening of Cosmetic Ingredients Identifies Vat Blue 6 as a Thyroid Hormone Receptor β Disruptor

Thyroid disorders are among the most prevalent endocrine conditions worldwide, exhibiting a rising incidence and disproportionately affecting women. In this study, we hypothesized that cosmetics may contain previously unidentified thyroid-disrupting chemicals. To evaluate this possibility, we compiled a comprehensive data set of cosmetic ingredients and developed a random forest regression-based machine learning model to predict their potential to disrupt thyroid hormone receptor β (TRβ), a critical regulator of thyroid function. From the top 40 compounds ranked by the model, 12 frequently used cosmetic ingredients were selected for experimental validation. Of these, six demonstrated measurable binding affinity toward TRβ. Notably, Vat Blue 6 (VB6), a colorant utilized in cosmetic formulations, exhibited structural characteristics potentially mimicking thyroid hormones and displayed potent TRβ binding with an affinity (<i>K</i>_d) as low as 0.7 μM. Subsequent <i>in vitro</i> assays and <i>in vivo</i> experiments in mice confirmed VB6's thyroid-disrupting effects, evidenced by dose-dependent reductions in serum thyroid hormone concentrations and morphological alterations of thyroid tissue. This study highlights the efficacy of machine learning approaches in rapidly screening large chemical inventories to identify potential thyroid disruptors and underscores the critical need for further toxicological assessment of cosmetic ingredients, particularly considering their frequent and prolonged exposure among female populations.