The Impact of Scaling Factor Variability on Risk-Relevant Pharmacokinetic Outcomes in Children: A Case Study using Bromodichloromethane (BDCM).

Biotransformation rates extrapolated from in vitro data are used increasingly in human physiologically based pharmacokinetic (PBPK) models. This practice requires use of scaling factors, including microsomal content (mg of microsomal protein/g liver, MPPGL), enzyme specific content, and liver mass as a fraction of body weight (FVL). Previous analyses indicated that scaling factor variability impacts pharmacokinetic (PK) outcomes used in adult population dose-response studies. This analysis was extended to pediatric populations because large inter-individual differences in enzyme ontogeny likely would further contribute to scaling factor variability. An adult bromodichloromethane (BDCM) model (Kenyon, E. M., Eklund, C., Leavens, T. L., and Pegram, R. A. (2016a). Development and application of a human PBPK model for bromodichloromethane (BDCM) to investigate impacts of multi-route exposure. J. Appl. Toxicol. 36, 1095-1111) was re-parameterized for neonates, infants, and toddlers. Monte Carlo analysis was used to assess the impact of pediatric scaling factor variation on model-derived PK outcomes compared with adult findings. BDCM dose metrics were estimated following a single 0.05-liter drink of water or a 20-min bath, under typical (5 mu g/l) and plausible higher (20 mu g/l) BDCM concentrations. MPPGL, CYP2E1, and FVL values reflected the distribution of reported pediatric population values. The impact of scaling factor variability on PK outcome variation was different for each exposure scenario, but similar for each BDCM water concentration. The higher CYP2E1 expression variability during early childhood was reflected in greater variability in predicted PK outcomes in younger age groups, particularly for the oral exposure route. Sensitivity analysis confirmed the most influential parameter for this variability was CYP2E1, particularly in neonates. These findings demonstrate the importance of age-dependent scaling factor variation used for in vitro to in vivo extrapolation of biotransformation rates.