基于生理学的肉毒毒素在小鼠和大鼠体内生物分布的毒代动力学模型

IF 3.1 Q2 TOXICOLOGY
Bradford Gutting , Joseph Gillard , Gabriel Intano
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引用次数: 0

摘要

肉毒杆菌神经毒素(BoNT)是一种高毒性蛋白质,是一级生物防御选择剂和毒素。BoNT也是一种广泛使用的治疗和化妆品。尽管具有毒理学和药理学意义,但人们对其在体内的生物分布知之甚少。本研究的目的是建立单次静脉给药后BoNT在啮齿动物体内生物分布的剂量依赖性、物种特异性生理毒性动力学(PBTK)模型。PBTK模型基于已发表的治疗性单克隆抗体(mAb)生物分布的基于生理的药代动力学(PBPK)模型,因为BoNT的大小和电荷几乎与典型的IgG4 mAb相同,并且大小/电荷是控制蛋白质生物分布的主要因素。生理区室包括循环、淋巴管和按毛细孔特征分组的组织。宿主物种特异性参数包括体重、血浆体积、淋巴体积/流量和组织间质液参数。BoNT参数包括从血液到组织的外渗、电荷、与内部片层或胆碱能神经元受体的结合。参数值从文献中获得或使用近似贝叶斯计算-序列蒙特卡罗算法估计,以使模型与已发表的小鼠BoNT低剂量时程血浆浓度数据拟合。拟合很好地捕获了低剂量小鼠数据,参数估计在生物学上似乎是合理的。采用全参数化模型模拟小鼠大剂量静脉注射数据。模型结果与已发表的数据比较良好。最后,重新参数化模型以反映大鼠生理。两种不同大小的大鼠注射不同剂量BoNT的模型毒性动力学与已发表的大鼠静脉注射数据很好地吻合(先验的跨物种外推)。这些结果表明,BoNT模型预测了啮齿动物和大鼠的剂量依赖性生物分布,而没有来自大鼠的任何BoNT特异性数据。据我们所知,这代表了一种基于生理学的大型蛋白质毒素模型。结果在一般和人类模拟的背景下进行讨论,以支持BoNT风险评估和治疗研究目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physiologically-based toxicokinetic model of botulinum neurotoxin biodistribution in mice and rats

Botulinum neurotoxin (BoNT) is a highly toxic protein and a Tier 1 Biodefense Select Agent and Toxin. BoNT is also a widely used therapeutic and cosmetic. Despite the toxicological and pharmacological interest, little is known about its biodistribution in the body. The objective herein was to develop a dose-dependent, species-specific physiologically-based toxicokinetic (PBTK) model of BoNT biodistribution in rodents following a single intravenous dose. The PBTK model was based on published physiologically-based pharmacokinetic (PBPK) models of therapeutic monoclonal antibody (mAb) biodistribution because the size and charge of BoNT is nearly identical to a typical IgG4 mAb and size/charge are main factors governing protein biodistribution. Physiological compartments included the circulation, lymphatics and tissues grouped by capillary pore characteristics. Host species-specific parameters included weight, plasma volume, lymph volume/flow, and tissue interstitial fluid parameters. BoNT parameters included extravasation from blood to tissues, charge, binding to internal lamella or cholinergic neuron receptors. Parameter values were obtained from the literature or estimated using an Approximate Bayesian Computation-Sequential Monte Carlo algorithm, to fit the model to published mouse BoNT low-dose, time-course plasma concentration data. Fits captured the low-dose mouse data well and parameter estimates appeared biologically plausible. The fully-parameterized model was then used to simulate mouse high-dose IV data. Model results compared well with published data. Finally, the model was re-parameterized to reflect rat physiology. Model toxicokinetics agreed well with published rat BoNT intravenous data for two different sized rats with different intravenous doses (an a priori cross-species extrapolation). These results suggested the BoNT model predicted dose-dependent biodistribution in rodents, and for rats, without any BoNT-specific data from rats. To our knowledge, this represented a first-in-kind physiologically-based model for a large protein toxin. Results are discussed in general and in the context of human simulations to support BoNT risk assessment and therapeutic research objectives.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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