Physiologically-based toxicokinetic model of botulinum neurotoxin biodistribution in mice and rats

被引:0
作者
Gutting, Bradford [1 ,4 ]
Gillard, Joseph [2 ]
Intano, Gabriel [3 ,4 ]
机构
[1] Naval Surface Warfare Ctr Dahlgren Div, Dahlgren, VA USA
[2] Def Sci & Technol Lab, Porton Down, England
[3] Def Ctr Publ Hlth Aberdeen, Aberdeen, MD USA
[4] Dahlgren Div, Naval Surface Warfare Ctr, Concepts & Experimentat Branch B64, Bldg 1480,4045 Higley Rd, Suite 345, Dahlgren, VA 22448 USA
关键词
Botulinum; Model; Physiologically-based; Compartmentalized; Rodents; MEDIATED DRUG DISPOSITION; MONOCLONAL-ANTIBODIES; PHARMACOKINETIC MODEL; BLOOD-VOLUME; TOXIN; LOCALIZATION; PLASMA;
D O I
10.1016/j.comtox.2023.100278
中图分类号
R99 [毒物学(毒理学)];
学科分类号
100405 ;
摘要
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.
引用
收藏
页数:16
相关论文
共 50 条
[21]   Physiologically-Based Pharmacokinetic model for Ciprofloxacin in children with complicated Urinary Tract Infection [J].
Balbas-Martinez, Violeta ;
Michelet, Robin ;
Edginton, Andrea N. ;
Meesters, Kevin ;
Troconiz, Inaki F. ;
Vermeulen, An .
EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES, 2019, 128 :171-179
[22]   Extension and validation of a physiologically based toxicokinetic model for risk assessment of aluminium exposure in humans [J].
Hartung, Niklas ;
Wangorsch, Gaby ;
Huisinga, Wilhelm ;
Weisser, Karin .
ARCHIVES OF TOXICOLOGY, 2025, 99 (06) :2379-2395
[23]   A translational physiologically-based pharmacokinetic model for MMAE-based antibody-drug conjugates [J].
Chang, Hsuan-Ping ;
Shah, Dhaval K. .
JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS, 2025, 52 (03)
[24]   Development of a Physiologically-Based Pharmacokinetic Model for Whole-Body Disposition of MMAE Containing Antibody-Drug Conjugate in Mice [J].
Chang, Hsuan-Ping ;
Li, Zhe ;
Shah, Dhaval K. .
PHARMACEUTICAL RESEARCH, 2022, 39 (01) :1-24
[25]   Development of a Pediatric Physiologically-Based Pharmacokinetic Model of Clindamycin Using Opportunistic Pharmacokinetic Data [J].
Hornik, Christoph P. ;
Wu, Huali ;
Edginton, Andrea N. ;
Watt, Kevin ;
Cohen-Wolkowiez, Michael ;
Gonzalez, Daniel .
CLINICAL PHARMACOKINETICS, 2017, 56 (11) :1343-1353
[26]   A physiologically-based nanocarrier biopharmaceutics model to reverse-engineer the in vivo drug release [J].
Nagpal, Shakti ;
Braner, Svenja ;
Modh, Harshvardhan ;
Tan, Ada Xi Xin ;
Mast, Marc-Phillip ;
Chichakly, Karim ;
Albrecht, Volker ;
Wacker, Matthias G. .
EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS, 2020, 153 :257-272
[27]   A population physiologically-based pharmacokinetic model to characterize antibody disposition in pediatrics and evaluation of the model using infliximab [J].
Chang, Hsuan Ping ;
Shakhnovich, Valentina ;
Frymoyer, Adam ;
Funk, Ryan Sol ;
Becker, Mara L. ;
Park, K. T. ;
Shah, Dhaval K. .
BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 2022, 88 (01) :290-302
[28]   Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats [J].
Melillo, Nicola ;
Scotcher, Daniel ;
Kenna, J. Gerry ;
Green, Claudia ;
Hines, Catherine D. G. ;
Laitinen, Iina ;
Hockings, Paul D. ;
Ogungbenro, Kayode ;
Gunwhy, Ebony R. ;
Sourbron, Steven ;
Waterton, John C. ;
Schuetz, Gunnar ;
Galetin, Aleksandra .
PHARMACEUTICS, 2023, 15 (03)
[29]   Minimal physiologically-based pharmacokinetic (mPBPK) model for a monoclonal antibody against interleukin-6 in mice with collagen-induced arthritis [J].
Chen, Xi ;
Jiang, Xiling ;
Jusko, William J. ;
Zhou, Honghui ;
Wang, Weirong .
JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS, 2016, 43 (03) :291-304
[30]   Scale-up of a physiologically-based pharmacokinetic model to predict the disposition of monoclonal antibodies in monkeys [J].
Glassman, Patrick M. ;
Chen, Yang ;
Balthasar, Joseph P. .
JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS, 2015, 42 (05) :527-540