Clinical Toxicology Research Group, University of Newcastle, Newcastle 2298, Australia.
School of Pharmacy, University of Otago, Dunedin 9016, New Zealand.
Toxins (Basel). 2020 Mar 16;12(3):188. doi: 10.3390/toxins12030188.
Snake venom is comprised of a combination of different proteins and peptides with a wide range of molecular weights and different disposition processes inherent to each compound. This causes venom to have a complex exposure profile. Our study investigates 1) how each molecular weight fraction (toxin) of venom contributes to the overall time course of the snake venom, and 2) the ability to determine toxin profiles based on the profile of the overall venom only. We undertook an simulation and modelling study. Sixteen variations of venom, comprising of two to nine toxins with different molecular weights were investigated. The pharmacokinetic parameters (i.e., clearance,, and volume of distribution,) of each toxin were generated based on a log-linear relationship with molecular weight. The concentration-time data of each toxin were simulated for 100 virtual patients using MATLAB and the total concentration-time data of each toxin were modelled using NONMEM. We found that the data of sixteen mixtures were best described by either two- or three-compartment models, despite the venom being made up of more than three different toxins. This suggests that it is generally not possible to determine individual toxin profiles based on measurements of total venom concentrations only.
蛇毒是由多种不同分子量的蛋白质和肽组成的混合物，每种化合物都有其固有的不同处置过程。这导致毒液具有复杂的暴露特征。我们的研究调查了 1）毒液的每个分子量分数（毒素）如何影响毒液的整体时程，以及 2）仅根据整体毒液的特征来确定毒素特征的能力。我们进行了模拟和建模研究。研究了由两种到九种不同分子量的毒素组成的 16 种毒液变体。每个毒素的药代动力学参数（即清除率、和分布容积）根据与分子量的对数线性关系生成。使用 MATLAB 对每个毒素的浓度-时间数据进行了 100 个虚拟患者的模拟，并使用 NONMEM 对每个毒素的总浓度-时间数据进行了建模。我们发现，尽管毒液由三种以上的不同毒素组成，但十六种混合物的数据最好用两室或三室模型来描述。这表明通常不可能仅根据对总毒液浓度的测量来确定单个毒素特征。
