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Dose-Incidence Relationships Derived from Superposition of Distributions of Individual Susceptibility on Mechanism-Based Dose Responses for Biological Effects

机译:个体易感性分布与生物效应的基于机制的剂量反应的叠加所产生的剂量-发病关系

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摘要

Dose-response relationships for incidence are based on quantal response measures. A defined effect is either present or not present in an individual. The dose-incidence curve therefore reflects differences in individual susceptibility (the "tolerance distribution”). At low dose, only the more susceptible individuals manifest the effect, while higher doses are required for more resistant individuals to be recruited into the affected fraction of the group. Here, we analyze how such dose-incidence relationships are related to mechanism-based dose-response relationships for biological effects described on a continuous scale. As an example, we use the quantal effect "cell division” triggered by occupancy of growth factor receptors (R) by a hormone or mitogenic ligand (L). The biologically effective dose (BED) is receptor occupancy (RL). The dose-BED relationship is described by the hyperbolic Michaelis-Menten function, RL/Rtot = L / (L + KD). For the conversion of the dose-BED relationship to a dose-cell division relationship, the dose-BED curve has to be combined with a function that describes the distribution of susceptibilities among the cells to be triggered into mitosis. We assumed a symmetrical sigmoid curve for this function, approximated by a truncated normal distribution. Because of the supralinear dose-BED relationship due to the asymptotic saturation of the Michaelis-Menten function, the composite curve that describes cell division (incidence) as a function of dose becomes skewed to the right. Logarithmic transformation of the dose axis reverses this skewing and provides a nearly perfect fit to a normal distribution in the central 95% incidence range. This observation may explain why dose-incidence relationships can often be described by a cumulative normal curve using the logarithm of the administered dose. The dominant role of the tolerance distribution for dose-incidence relationships is also illustrated with the example of a linear dose-BED relationship, using adducts to protein or DNA as the BED. Superimposed by a sigmoid distribution of individual susceptibilities, a sigmoid dose-incidence curve results. Linearity is no longer observed. We conclude that differences in susceptibility should always be considered for toxicological risk assessment and extrapolation to low dose
机译:发病率的剂量反应关系基于定量反应量度。在个体中存在或不存在确定的作用。因此,剂量-发病曲线反映了个体敏感性的差异(“耐受性分布”),在低剂量下,只有较易感的个体才能表现出这种效应,而更高的剂量需要更多的耐药个体才能招募到受影响的个体中。在这里,我们分析了这种剂量-入射关系如何与基于机制的剂量-反应关系之间的相关性进行连续描述,例如,我们使用了由生长因子占据引起的量子效应“细胞分裂”受体(R)由激素或促有丝分裂的配体(L)组成。生物学有效剂量(BED)为受体占有率(RL)。剂量-BED关系由双曲线Michaelis-Menten函数描述,RL / Rtot = L /(L + KD)。为了将剂量-BED关系转换为剂量-细胞分裂关系,必须将剂量-BED曲线与一个函数结合起来,该函数描述了要触发有丝分裂的细胞之间的磁化率分布。我们为此函数假设了一个对称的S形曲线,近似于正态分布。由于Michaelis-Menten函数的渐近饱和,由于超线性剂量-BED关系,描述细胞分裂(发生率)与剂量的函数的复合曲线向右偏斜。剂量轴的对数变换可逆转此偏斜,并在中心95%入射范围内为正态分布提供几乎完美的拟合。该观察结果可以解释为什么经常通过使用给药剂量的对数的累积正态曲线来描述剂量-入射关系。以线性剂量-BED关系为例,以蛋白质或DNA的加合物作为BED,也说明了剂量分布关系的耐受性分布的主要作用。通过各个敏感度的S形分布叠加,得出S形剂量-入射曲线。不再观察到线性。我们得出结论,在进行毒理学风险评估和外推至低剂量时,应始终考虑药敏性的差异

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