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Global skincare formulations often ignore genetic differences. This study introduces a mutation-aware framework and new metrics to create genetically aligned skincare, improving product relevance for diverse populations worldwide.

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Area of Science:

  • Genomic science
  • Dermatology
  • Cosmeceutical science

Background:

  • The skincare industry commonly uses generalized formulations, neglecting population-specific genetic variations.
  • Advances in dermatogenomics highlight the need for more tailored skincare approaches.

Purpose of the Study:

  • To introduce a mutation-aware framework for developing genetically aligned skincare.
  • To quantify regional genetic vulnerability and assess product-genomic compatibility.

Main Methods:

  • Developed two novel metrics: Mutation Burden Index (MBI) and Population Compatibility Burden (PCB).
  • Mapped ingredient functionality of over 200 cosmeceuticals against regional MBI profiles.
  • Utilized machine learning and SHAP analysis to identify key genetic drivers of product-region mismatch.

Main Results:

  • Identified a significant compatibility gap, with regions like Africa and South Asia receiving less aligned products (scores as low as 0.35) compared to Europe (>0.70).
  • Simulated formulations based on MBI scores improved compatibility in underserved regions to over 0.80.
  • Machine learning classifier achieved high performance (F1=0.837), pinpointing barrier and pigmentation pathways as critical factors.

Conclusions:

  • The developed framework provides a practical foundation for genomically aligned skincare, promoting population-scale equity.
  • This AI-enabled approach offers transparent, biologically grounded formulation logic, contrasting with current black-box personalization methods.
  • The study demonstrates the potential for substantial gains in biological relevance for skincare products across diverse global populations.