Physiological Sciences Department, TNO Nutrition and Food Research, Utrechtseweg 48, P.O. Box 360, 3700 AJ Zeist, The Netherlands.
Environ Toxicol Pharmacol. 2004 Dec;18(3):185-92. doi: 10.1016/j.etap.2004.07.005.
This paper describes the "quest" of our institute trying to assess the toxicology of chemical mixtures. In this overview, we will discuss some critical developments in hazard identification and risk assessment of chemical mixtures during these past 15 years. We will stand still at empirical and mechanistic modeling. "Empirical" means that only information on doses or concentrations and effects is available in addition to an often empirically selected quantitative dose-response relationship. Empirical models have played a dominant role in the last decade to identify health and safety characteristics of chemical mixtures. Many of these models are based on the work of pioneers in mixture toxicology who defined three basic types of action for combinations of chemicals: simple similar action, simple dissimilar action and interaction. Nowadays, empirical models are mainly based on response-surface analysis and make use of advanced statistical designs. However, possible interactions between components in a mixture can also be given in terms of mechanistic models. In terms of "mechanistic" (or biological) understanding, interactions between compounds may occur in the kinetic phase (processes of uptake, distribution, metabolism and excretion) or in the dynamic phase (effects of chemicals on the receptor, cellular target or organ). A biological phenomenon such as competitive agonism as described for mixtures of drugs (biotransformation enzymes) or sensory irritants (nerve receptors) can accurately predict the effect of any of these mixtures. Thus, far mechanistic and empirical analyses of interactions are usually unrelated. It is one of the future challenges for mixtures research to combine information from both approaches. Also, our current biology-based models have their limitations, since they cannot integrate every relevant biological mechanism. In this respect, mechanistic modeling of mixtures may benefit from the developments coming from the arena of molecular biology (toxicogenomics) which offers an in-depth analysis of several involved enzymatic pathways in parallel through the use of a systems biology approach. This was illustrated with mixtures of food additives known to affect the liver. Key to further maturation of mixture toxicology is collaboration of experimental toxicologists, biomathematicians, biologists, pharmacologists, model developers, molecular biologists and bioinformaticians to ensure parallel and coordinated research in this challenging area of toxicology. For this reason, the next sequel will be even more challenging and exciting to that first 15 years of empirical testing.
这篇论文描述了我们研究所评估化学混合物毒理学的“探索”之旅。在这篇综述中,我们将讨论过去 15 年来在化学混合物的危害识别和风险评估方面的一些关键进展。我们将停留在经验和机制模型上。“经验”是指除了经常通过经验选择的定量剂量-反应关系之外,仅提供有关剂量或浓度和效应的信息。在过去的十年中,经验模型在识别化学混合物的健康和安全特征方面发挥了主导作用。其中许多模型基于混合物毒理学先驱的工作,他们定义了化学物质组合的三种基本作用类型:简单相似作用、简单不同作用和相互作用。如今,经验模型主要基于响应面分析,并利用先进的统计设计。然而,混合物中成分之间的可能相互作用也可以用机制模型来表示。从“机制”(或生物学)的角度理解,化合物之间的相互作用可能发生在动力学阶段(吸收、分布、代谢和排泄过程)或动态阶段(化学物质对受体、细胞靶标或器官的作用)。如药物混合物(生物转化酶)或感觉刺激性混合物(神经受体)中的竞争激动剂等生物学现象可以准确预测任何这些混合物的效果。因此,目前对相互作用的机制和经验分析通常是不相关的。将这两种方法的信息结合起来是混合物研究的未来挑战之一。此外,我们当前基于生物学的模型也存在局限性,因为它们不能整合所有相关的生物学机制。在这方面,混合物的机制建模可能受益于分子生物学(毒理学基因组学)领域的发展,该领域通过使用系统生物学方法并行深入分析几个涉及的酶途径。这通过使用已知影响肝脏的食品添加剂混合物来说明。进一步成熟的混合物毒理学的关键是实验毒理学家、生物数学家、生物学家、药理学家、模型开发人员、分子生物学家和生物信息学家的合作,以确保在这个具有挑战性的毒理学领域进行平行和协调的研究。出于这个原因,下一个续集将比最初的 15 年经验测试更具挑战性和令人兴奋。