Researchers suggested that differences in melanin-based skin pigmentation may significantly influence drug pharmacokinetics and pharmacodynamics, potentially affecting therapeutic efficacy and adverse drug reactions among various human ancestries, according to a new study

In the study, published in Human Genomics, the researchers proposed a four-pillar workflow to predict melanin-based differential drug responses in preclinical research:

• Biochemical analysis
• Inclusive in silico analysis and prediction
• Inclusive analysis of cellular kinetics using New Approach Methods (NAMs)
• Clinical trials.

Among the key findings were:

• Darker skin tones can contain up to 10 times more melanosomes compared with lighter skin tones, potentially affecting drug distribution and efficacy.
• Eumelanin, one of the two primary chemical forms of melanin pigments, plays a significant role in drug interactions because of its polyionic nature.
• A table of 27 compounds across 13 drug classes with known binding affinity for eumelanin includes antibiotics, antipsychotics, and antimalarials.
• Clozapine, an antipsychotic medication, showed lower plasma concentrations in individuals of sub-Saharan African ancestry compared with those of European ancestry at the same dose.
• Animal studies demonstrated that nicotine accumulated at 20 times higher levels in pigmented hair compared with nonpigmented hair.

The researchers emphasized the importance of using advanced in vitro three-dimensional skin cell models that accurately represented variation in human skin pigmentation. They noted that reconstructed human epidermis (RHE) models, such as SkinEthic and MelanoDerm, are available with varying pigmentation levels.

The study highlighted recent advancements enabling an improved examination of melanin-drug interactions:

• Development of microphysiological systems (MPS) for recreating multicellular skin structures in vitro
• Reduced DNA and RNA sequencing costs, enabling large-scale genome-wide association studies (GWAS) of skin pigmentation genetics
• Progress in computational and AI-supported modeling for biochemical toxicology and drug target development.

The researchers stressed the need for comprehensive metadata for cell models used in preclinical research, including:

• Genetic ancestry of cell donors
• Quantitative measures of skin pigmentation
• Melanosome count and quality
• Distribution of melanin in model layers.

They argued that this detailed information may be crucial for developing more accurate predictive models using artificial intelligence and machine learning techniques.

The broader impacts of their findings could extend beyond the pharmaceutical industry to sectors such as cosmetics, cleaning products, and agriculture. For example, the herbicide paraquat and metals used in crop disease control were noted for their interactions with melanin, potentially accumulating in pigmented skin and posing toxicity risks.

The researchers called for a more inclusive approach to drug development that considers the diversity of human skin pigmentation. Implementing their proposed workflow could lead to more equitable pharmacologic interventions and improved health outcomes across diverse populations.

The authors declared no competing interests.