PBTK modeling and simulation framework to identify critical data requirements for efficient and effective pediatric risk assessment

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Environmental contaminant exposure is an escalating concern and assessing the human risks associated with such exposure continues to be an inexact science. Risks are largely based upon the extrapolation of exposure and effect in animals to those in humans. Extrapolation customarily takes a simplistic form, wherein a threshold dose derived from animal studies is divided by adjustment factors (AFs) to account for interspecies and human population variability in toxicokinetics (TK) and toxicodynamics. One promising method to refine default AFs is based on physiologically based toxicokinetic (PBTK) models. These mathematical models facilitate the understanding of how exposure to a compound, or ingested dose, translates into systemic (plasma) and target (organ) exposure and are based on the interplay between organism physiology, compound physicochemistry and biochemical processes. ******Due to immaturity, children represent a potentially susceptible population to contaminant exposure and they may, or may not, be adequately covered using default AFs. There is however a considerable data burden for predictive pediatric PBTK modeling that may lead to an inability to use this method for AF refinement without further research. There have been no efforts to define what compound-specific data requirements are critical for accurate pediatric dosimetry estimation. The objective of this program is to develop a PBTK modeling and simulation framework to identify critical data requirements for efficient and effective pediatric risk assessment. The framework would aim to minimize data burden while maximizing confidence in outcomes.******The program will focus on developing or further extending mechanistic inhalation and oral absorption models to be life-stage appropriate. Further, pediatric PBTK models using a wide range of hypothetical compounds will be developed and, with the aid of local and global sensitivity analysis methods, offer a means to examine those critical data inputs that are relied upon to generate accurate pediatric exposures. Further, real data that would be used for model development during a risk assessment will be located in literature or experimentally derived. Complete and reduced datasets will be used for model development and this will provide for both a confirmation of those data pieces that are critical for a particular scenario as highlighted previously and an assessment of the impact on prediction accuracy when less critical data pieces are unavailable.******The framework will allow industry to target experimentation for newly identified contaminants to only those data requirements that will be important for achieving acceptable pediatric exposure prediction. Acceptable predictions will reduce the probability of setting limits that are either too high, which will put pediatric health at risk, or too low, which is overprotective and presents challenges to risk managers. *****