Computational, In Vitro, and In Vivo Models for Nose-to-Brain Drug Delivery Studies
Abstract
:1. Introduction
2. Computational Analyses
2.1. Computational Fluid Dynamics (CFD) Models for Drug Deposition
2.2. Carrier/Mucosa Interaction Models
2.3. Mucus Layer Penetration Models
3. In Vitro Methods
3.1. Primary Cell Cultures
3.2. Immortalized Cell Lines
3.2.1. RPMI 2650 Cell Line (Human Nasal Septum Quasi Diploid Tumour Cells)
3.2.2. Calu-3 Cell Line (Human Lung Cancer Cells)
3.2.3. Caco-2 Cell Line (Human Epithelial Colorectal Adenocarcinoma Cells)
3.2.4. Other Cell Lines
4. Ex Vivo Methods
Diffusion Chamber Devices for Ex Vivo Permeation Studies
5. In Vivo Methods
In Vivo Imaging Modalities
6. Challenges and Future Perspectives
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model | Advantages | Disadvantages |
---|---|---|
In silico (computational) | can predict drug/carrier deposition, absorption high throughput at a reasonable cost usually are based on existing human data, so their predictions are directly applicable to humans | needs to apply several models to predict the impact of one component at a time |
In vitro | no ethical considerations low-cost relative to in vivo control of experiment conditions feasibility of transport mechanism study | lack of actual anatomy or physiology of the nasal mucosa large intra- and interlaboratory variability lack interindividual differences |
Ex vivo | maintain the integrity of the nasal mucosa availability of human nasal mucosal segments | uncontrolled experimental conditions tissue viability static system lacking blood supply |
In vivo | intact physiological processes and disease featuresgold standard in preclinical phases | ethical considerations species differences between humans and experimental animals time-consuming and expensive |
Type of Formulation (Drug) | Targeted Disease | In Vitro Model | In Vivo Model | Ref. |
---|---|---|---|---|
PLGA NPs (eletriptan) | Migraine | Caco-2 cell line | Wistar Albino rats | [37] |
Nanosuspension (efavirenz) | Neuro-AIDS | Goat nasal mucosa | Wistar rats | [38] |
PLGA NPs (levodopa) | PD | PC-12 neural-like cells | CD57/BL6 mice | [39] |
Albumin NPs (R-flurbiprofen) | AD | CHO-APP695 (Chinese hamster ovary cells transfected with mouse Aβ precursor protein 695)-AD cell model | C57BL/6 mice | [40] |
Solid lipid NPs (buspirone) | Anxiety disorder | Sheep nasal mucosa | Wistar Albino rats | [41] |
Chitosan NPs (pramipexole) | PD | Goat nasal mucosa | Sprague–Dawley rats | [42] |
Nanostructured lipid carriers, nano emulsion (escitalopram, paroxetine) | Depression | RPMI 2650 | CD-1 mice | [43] |
PLGA NPs (lamotrigine) | Neuropathic pain, Epilepsy | Neuro-2a cell line, RAW murine macrophage cell lines | Sprague–Dawley rats | [44] |
Chitosan lipid NPs (risperidone) | Schizophrenia | Porcine nasal mucosa | Albino rat | [45] |
PLGA/chitosan NPs (alpha-cyano-4-hydroxycinnamic acid, cetuximab) | Glioblastoma | Porcine nasal mucosa | Wistar rats | [46] |
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Boyuklieva, R.; Zagorchev, P.; Pilicheva, B. Computational, In Vitro, and In Vivo Models for Nose-to-Brain Drug Delivery Studies. Biomedicines 2023, 11, 2198. https://doi.org/10.3390/biomedicines11082198
Boyuklieva R, Zagorchev P, Pilicheva B. Computational, In Vitro, and In Vivo Models for Nose-to-Brain Drug Delivery Studies. Biomedicines. 2023; 11(8):2198. https://doi.org/10.3390/biomedicines11082198
Chicago/Turabian StyleBoyuklieva, Radka, Plamen Zagorchev, and Bissera Pilicheva. 2023. "Computational, In Vitro, and In Vivo Models for Nose-to-Brain Drug Delivery Studies" Biomedicines 11, no. 8: 2198. https://doi.org/10.3390/biomedicines11082198
APA StyleBoyuklieva, R., Zagorchev, P., & Pilicheva, B. (2023). Computational, In Vitro, and In Vivo Models for Nose-to-Brain Drug Delivery Studies. Biomedicines, 11(8), 2198. https://doi.org/10.3390/biomedicines11082198