Bradford Gutting , Joseph Gillard , Gabriel Intano
{"title":"Physiologically-based toxicokinetic model of botulinum neurotoxin biodistribution in mice and rats","authors":"Bradford Gutting , Joseph Gillard , Gabriel Intano","doi":"10.1016/j.comtox.2023.100278","DOIUrl":null,"url":null,"abstract":"<div><p>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 IgG<sub>4</sub> 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 <em>a priori</em> 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.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111323000191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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