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Image‐guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies

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Abstract While plasma concentration kinetics has traditionally been the predictor of drug pharmacological effects, it can occasionally fail to represent kinetics at the site of action, particularly for solid tumors. This is especially true in the case of delivery of therapeutic macromolecules (drug‐loaded nanomaterials or monoclonal antibodies), which can experience challenges to effective delivery due to particle size‐dependent diffusion barriers at the target site. As a result, disparity between therapeutic plasma kinetics and kinetics at the site of action may exist, highlighting the importance of target site concentration kinetics in determining the pharmacodynamic effects of macromolecular therapeutic agents. Assessment of concentration kinetics at the target site has been facilitated by non‐invasive in vivo imaging modalities. This allows for visualization and quantification of the whole‐body disposition behavior of therapeutics that is essential for a comprehensive understanding of their pharmacokinetics and pharmacodynamics. Quantitative non‐invasive imaging can also help guide the development and parameterization of mathematical models for descriptive and predictive purposes. Here, we present a review of the application of state‐of‐the‐art imaging modalities for quantitative pharmacological evaluation of therapeutic nanoparticles and monoclonal antibodies, with a focus on their integration with mathematical models, and identify challenges and opportunities. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology
Non‐invasive optical imaging methods to evaluate in vivo biodistribution. (a) Principles of fluorescence and bioluminescence imaging. (b) Representative in vivo and ex vivo fluorescence imaging (IVIS Spectrum). (c) Geometrical demonstration of Abbe's diffraction limit for optical methods. (d) Biodistribution of fluorescent porous silica microdisks estimated via fluorescent signal (IVIS spectrum) vs quantitative optical imaging (homogenized organs). Left y‐axis refers to %ID/g as measured with quantitative optical imaging, right y‐axis to fluorescence intensity as measured via IVIS. Results are shown after background subtraction. (Reproduced with permission from Nizzero et al., . Copyright 2019 Elsevier)
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Schematic representation of nuclear magnetic resonance, the working principle of magnetic resonance imaging
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Transport of macromolecules from site of injection to target site. Delivery of nanoparticles and monoclonal antibodies to the site of action is characterized by perfusion, hydraulic conductivity, permeability, and diffusion. (Reprinted with permission from Dogra, Butner, et al. (). Copyright 2019 Springer)
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Spatiotemporal resolution, penetration depth, and sensitivity of the key imaging modalities in (a) clinical and (b) preclinical settings. Temporal resolution is represented by solid (few seconds to minutes), dashed (few minutes), and dotted (few minutes to hour) boundaries of the cuboids whose color denotes the imaging modality‐type, and the x‐, y‐, and z‐axis represent spatial resolution, penetration depth, and sensitivity, respectively. Key: MRI, magnetic resonance imaging; PET, positron emission tomography; SPECT, single photon emission computed tomography; biolum., bioluminescence; fluor., fluorescence
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Intravital microscopic images of NP circulation in (a) healthy and (b) tumorous vessels. (c) Quantification of NP circulation and accumulation kinetics. (d) Computational modeling of therapeutic efficacy of NP‐delivered doxorubicin in a virtual tumor under different initial conditions. (Reproduced with permission from van de Ven et al. (). Copyright 2012 AIP Publishing)
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Representative single photon emission computed tomography/CT (SPECT/CT) images showing the whole‐body spatiotemporal evolution of trimethylsilane (TMS)‐, quaternary amine (QA)‐, and polyethylene imine (PEI)‐coated mesoporous silica NPs of variable sizes and surface charges (a) TMS25: 25 nm, −5 mV, (b) TMS50: 50 nm, −7 mV, (c) TMS150: 150 nm, −4 mV, (d) QA50: 50 nm, +38 mV, and (e) PEI50: 50 nm, +37 mV, in vivo. (f, g) Model fits to the longitudinal systemic concentration kinetics data obtained from quantified SPECT/CT images of the heart region‐of‐interest. (Reproduced with permission from Dogra et al. (). Copyright 2018 Springer Nature)
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Noninvasive imaging‐guided mathematical modeling to evaluate the pharmacokinetic (PK) of ultrasmall porous silica nanoparticles (UPSNs). (a) Representative longitudinal positron emission tomography (PET)/computed tomography (CT) images of 4T1 (upper panel) and MDA‐MB 231 (lower panel) tumor‐bearing mice injected with 64Cu‐labeled ultrasmall porous silica nanoparticles (UPSNs). (b) Two‐compartment PK modeling of plasma concentration kinetics data of UPSNs obtained in healthy mice. (c) Schematic of reduced physiologically based pharmacokinetic (PBPK) model to investigate the disposition kinetic of UPSNs in tumor‐bearing mice. (d, e) PBPK‐model fits obtained through nonlinear regression of the data. (Reproduced with permission from Goel et al., . Copyright 2019 Wiley)
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Nanotechnology Approaches to Biology > Nanoscale Systems in Biology
Diagnostic Tools > In Vivo Nanodiagnostics and Imaging
Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease

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