- Favour Folashade Badmus¹, Elizabeth Chidinma Michael², Ayomide Zainab Adeshina³, Aisha Ibimina Jaja⁴
- DOI: 10.5281/zenodo.22211810
- GAS Journal of Clinical Medicine and Medical Research (GASJCMMR)
Interindividual
variability in drug response arises from the complex interplay between
inherited pharmacogenomic traits and environmental exposures. While genetic
polymorphisms in cytochrome P450 (CYP450) enzymes are well-established
determinants of metabolic capacity, the modulatory effects of environmental
pollutants on these pharmacogenomic profiles remain insufficiently quantified
in murine models. This systematic review and quantitative synthesis examined
the impact of heavy metals, polycyclic aromatic hydrocarbons (PAHs), dioxins,
pesticides, and airborne particulate matter on CYP450-mediated drug metabolism
in mice, with emphasis on exposure-specific mechanisms and pharmacogenomic
interactions. A comprehensive search of PubMed, Scopus, Web of Science, and
Google Scholar (2000–2024) identified 85 relevant studies, of which 42 met
inclusion criteria and provided quantitative data. Dioxins produced the most
potent CYP1A1 induction (mean fold change: 5.8 ± 2.3; n = 8 studies),
followed by PAHs (4.2 ± 1.8; n = 12), airborne PM2.5 (3.5 ± 1.2; n
= 4), and heavy metals (2.1 ± 0.9; n = 6), whereas heavy metals also
induced CYP2E1 (2.9 ± 1.1; n = 7) and pesticides induced CYP3A11 (2.0 ±
0.7; n = 6). Three primary mechanistic pathways were identified: nuclear
receptor-mediated transcriptional induction (AhR, CAR, PXR), epigenetic
modification (DNA methylation, histone alteration), and oxidative stress with
direct protein damage. Genetic background significantly modified pollutant
responses, with Cyp1a1-null and humanized CYP transgenic mice demonstrating
strain-specific metabolic outcomes. These findings demonstrate that
environmental pollutants profoundly reshape pharmacogenomic landscapes of drug
metabolism in mice through compound-specific, dose-dependent, and genetically
modified pathways, supporting the integration of exposomic data into
pharmacogenomic frameworks to predict drug response variability in contaminated
environments.
Keywords: pharmacogenomics; drug metabolism; environmental pollutants; cytochrome P450; mice; xenobiotics; heavy metals; polycyclic aromatic hydrocarbons; exposome.
