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

PBTK 建模和模拟框架，用于确定高效且有效的儿科风险评估的关键数据要求

• 批准号: RGPIN-2017-05056
• 负责人: Edginton, Andrea
• 金额: $ 2.04万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682678
• 关键词: PBTK; modeling; simulation; framework; identify

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. *****

环境污染物暴露是一个日益令人关切的问题，评估与这种暴露相关的人类风险仍然是一门不精确的科学。风险在很大程度上是基于对动物的暴露和影响与人类的接触和影响的推断。外推通常采取一种简单的形式，即从动物研究中得出的阈值剂量除以调整因子(AFs)，以考虑毒物动力学(TK)和毒物动力学(TK)中的物种间和人类种群变异性。一种很有前途的方法是基于生理毒代动力学(PBTK)模型来精炼缺省AFS。这些数学模型有助于理解接触化合物或摄入剂量如何转化为全身(血浆)和靶(器官)暴露，并基于生物体生理、化合物物理化学和生化过程之间的相互作用。*由于不成熟，儿童是接触污染物的潜在易感人群，他们可能会，也可能不会使用默认的AFS得到充分的覆盖。然而，儿童预测PBTK模型有相当大的数据负担，这可能导致在没有进一步研究的情况下无法使用该方法进行房颤精细化。目前还没有努力确定哪些化合物特定的数据要求是准确的儿科剂量估算的关键。该计划的目标是开发PBTK建模和模拟框架，以确定有效和有效的儿科风险评估的关键数据需求。该框架旨在最大限度地减少数据负担，同时最大限度地提高对结果的信心。*该计划将专注于开发或进一步推广适合生命阶段的机械性吸入和口服吸收模型。此外，将开发使用各种假设化合物的儿科PBTK模型，并借助局部和全球敏感性分析方法，提供一种检查那些关键数据输入的手段，这些数据输入是产生准确的儿科暴露的依据。此外，将在风险评估期间用于模型开发的真实数据将位于文献中或通过实验得出。完整和精简的数据集将用于模型开发，这将提供对前面强调的特定情景至关重要的数据片段的确认，以及当不太关键的数据片段不可用时对预测准确性的影响的评估。*该框架将允许行业将新发现的污染物的实验目标定为对实现可接受的儿科暴露预测至关重要的那些数据要求。可接受的预测将减少设定过高或过低限制的可能性，前者将使儿科健康处于危险之中，后者过度保护，并给风险管理者带来挑战。*****