1. Physiologically Relevant 3D Models of Human Nasal Cavity
The transparent nasal cavity model (Koken Co., LTD., Tokyo, Japan) is an anatomically relevant model of the human nasal cavity that facilitates the studies on the IN aerosols’ performance and qualitative evaluation of the regional drug deposition in the nasal cavity. Due to the optical accessibility of this model, IN drug deposition can be assessed via imaging techniques and image analysis 
. Another anatomical model of the human nasal airway is the Alberta Idealised Nasal Inlet (Copely, UK) with separable sections, including the vestibule, conchae, olfactory region, and nasopharynx. Contrary to the transparent nasal cavity model, the detachable sections of the Alberta Idealised Nasal Inlet enable quantitative evaluations of the regional IN drugs’ deposition 
Although testing IN drugs using these models sheds insights into the deposition pattern, none of the current models integrates with meaningful biological interfaces. Hence, their throughputs can hardly be used to infer meaningful therapeutic actions of IN drugs in vitro, especially concerning drug interactions with the cells. In addition, these models can hardly represent the nasal geometry of the wider population (e.g., age, gender, race) given that this can vary significantly between humans and is further complicated by diseases, such as nasal polyps.
2. Microfluidic OoC Models of the Nasal Mucosa
The microfluidic OoC technology is a potential solution to overcome the shortcomings of conventional in vitro tissue models by mimicking the physiological, biological, chemical, and biomechanical features of the tissues in vivo. There have been attempts to use this technology to emulate the dynamic microenvironment of human nasal mucosa in vitro 
, where the donor–acceptor structure has been used for the ALI culture of nasal epithelial cells.
The physiologically resembled gland-like structure of the nasal mucosa morphology was replicated in the epithelial compartment of a microfluidic chip, where epithelial and endothelial cells were co-cultured at the opposite sides of an extracellular matrix (ECM) channel. The model enabled the evaluation of cell–cell and cell–ECM interactions. As a result of epithelium–endothelium co-culture, the gland-inducing factors secreted in the endothelial cell compartment promoted the generation of gland-like structures in the nasal epithelial compartment of the chip, where mucin protein (MUC5b) and gland development marker (Sox9) were indicated 
Further, OoC models of human nasal mucosa have been used to study the potential effects of fluid flow on drug permeation across the epithelial barrier model. This was achieved by mimicking the drug particle flow in the epithelial compartment of the chip, as well as the systemic flow in the acceptor compartment 
. These chips resembled ex vivo human nasal epithelium as they include the modelling of TEER, barrier function, and mucus secretion. In addition, integrating electrochemical sensors in the structure of the chips enabled the in situ real-time quantification of the drug permeation to the systemic flow.
The irritant effect of inhaled gaseous toxins on the nasal epithelium’s ciliary beating frequency (CBF) was demonstrated by a microfluidic chip fabricated by a modified Transwell®
insert, cultured with differentiated human nasal epithelial stem/progenitor cells, integrated into a PDMS-bonded cover glass. The CBF’s dose-dependent effect of gaseous formaldehyde was monitored in the chip using a microscope equipped with a high-speed camera 
. While only a few OoC studies have focused on the toxicology of inhaled toxins in the nasal airway, there have been more studies on the application of OoC technology on the toxic effect of inhaled drugs and toxins on the lower airway or the acute and chronic toxicity of inhaled drugs on the liver was demonstrated either by lung 
or lung–liver 
replicas, respectively. Such studies can pave the way for future research on the fabrication of nose–lung models, emulating intranasally administered drugs’ pulmonary side effects.
A multicompartment airway-on-chip platform was fabricated with the interconnected nasal passage, mid-bronchial airway region, and acinar region of the human respiratory system. The airflow rate at each compartment was established based on a preliminary computational fluid dynamics (CFD) analysis to mimic the physiological airflow rate in the system. Its application for modelling the viral infection transmitted through the respiratory system was demonstrated by using the SARS-CoV-2 virus 
. Future work is required to present the PK–PD relevance of this model to assess its suitability for toxicology studies.
3. Challenges with Studying in Drugs Toxicity by OoC Models
To enable clinically relevant toxicology studies using OoC models of the human nasal mucosa, a significant enhancement of these models are required to predict the side effects of IN drugs. The potential challenges of developing advanced models closely relevant to the native nasal tissue are elaborated as follows.
3.1. Mimicking the Cellular Architecture and Tissue–Tissue Crosstalk
Mimicking the heterogeneous nasal epithelium is required to model the native nasal mucosa-on-a-chip. The cellular composition of the nasal epithelium varies from a stratified squamous epithelium to a pseudostratified columnar ciliated epithelium, depending on the location. A stratified squamous epithelium covers the vestibule, the inferior meatus (the area beneath the inferior concha), and the pharynx. However, a larger portion of the nasal mucosa, including the conchae and the nasal septum, is lined by a pseudostratified columnar ciliated epithelium, which includes ciliated and non-ciliated columnar cells, basal cells, goblet cells (mucus segregating cells), and brush cells. The apex of the nasal cavity, i.e., olfactory region, is covered by the pseudostratified columnar olfactory epithelium constructed of bipolar olfactory neurons, sustentacular cells, and basal cells. In addition, tubuloalveolar Bowman’s glands exist in the lamina propria of the olfactory epithelium 
In addition, the interactions between tissues (e.g., epithelium/lamina propria/capillaries or olfactory epithelium/olfactory bulb) and inter-organ crosstalk (e.g., nose–brain, nose–lungs, nose–kidney, and nose–liver) have yet to be simulated by the current nasal OoC models. Specifically, there is a need to develop such multi-OoC models that include nasal mucosa analogues such that these can be used to study the potential effects of IN drugs on the neighbouring tissues or the side effects observed in other organs in addition to the local effects and the interaction of the nasal mucosa with the drug treatments. Given that both the kidney and liver tissues are involved in detoxification processes, hepatotoxicity and nephrotoxicity are two major reasons for drug withdrawal from the market; the integration of liver and kidney analogues with the nasal mucosa by OoCs should hence be considered in future studies. To undertake this work meaningfully, it requires the implementation of accurate design parameters, e.g., surface area or volume of each compartment, and fluid mechanics, e.g., flow rate, so that the relevant in vivo pharmacokinetics–pharmacodynamics (PK–PD) 
, toxicokinetics–toxicodynamics (TK–TD), and the absorption–distribution–metabolism–excretion (ADME) of IN drugs can be mimicked. Data from clinical studies might be used to determine these parameters to design multi-OoCs 
. One of the first body-on-a-chip devices was prepared by Shuler et al. The device consists of colon cancer cells, myeloblasts, and hepatoma cells, and the device was used to examine the cytotoxic effect of tegafur when metabolized by liver cells into 5-fluorouracil 
. Another example worth noting is a system that involves cardiac, muscular, neuronal and liver modules, which was created to study the toxicity of acetaminophen, doxorubicin, valproic acid, atorvastatin calcium, and N
. A platform of up to ten interconnected human organ replicas that involves liver/immune, lung, gut/immune, endometrium, brain, heart, pancreas, kidney, skin, and skeletal muscle was also presented in recent years, and its application to mimic the distribution kinetics (PK) of diclofenac in vitro was demonstrated 
. Another potential advantage of using OoCs for drug safety studies is that tissue pathology and the relevant PK–PD of diseased tissues can be simulated. Improved knowledge in this area is critical to help predict drug response and toxicity in diseases.
In addition to developing OoCs, microfluidics technology has also been used to cultivate cells on two- and three-dimensional chip devices, described as cell culture on a chip. These chips have been used to develop microfluidic models of tumours and to study anti-cancer drug toxicity. For example, Chen et al. modelled potential metabolism pathways and the cytotoxicity of doxorubicin and paclitaxel in vitro using a microwell-based microfluidic chip 
. The efficacy of doxorubicin was also tested by Fang et al. using a microfluidic device, where highly proliferative HepG2 cells were cultured in a 3D sidewall-attached droplet array 
In addition to the abovementioned challenges related to the fabrication and operation of multiple organs in one platform, the liver or kidney analogues to be included in such platforms require the organ-specific complex cellular composition and physiological functions, which are currently being studied in the liver and kidney-on-a-chip research studies. For instance, Jang et al. 
included liver sinusoidal endothelial cells (LSECs), stellate cells, and Kupffer cells in the vascular channel of a liver-on-a-chip in addition to the hepatocytes in the parenchymal channel of a liver-on-a-chip. In addition to better mimicking the physiological liver function, such as albumin secretion, this model enabled the simulation of various drug-induced liver toxicity phenotypes, e.g., depletion of Kupffer cells, steatosis (retention of fat by hepatocytes and hypertrophy of stellate cells), cholestasis (hepatocellular accumulation of bile salts), and fibrosis caused by different drugs with varying toxicity mechanisms that target different cell types. Similar models are likely helpful in uncovering the unknown mechanisms of toxicity. Moreover, it will help with simulating the functions and toxicity of other tissues and organs while mimicking multiple organs, tissues, or cell types in one platform.
3.2. Mimicking Complex Geometry of Nasal Cavity
The current 2D in vitro nasal mucosa models, where the cells are cultured on a flat membrane, fail to represent the nasal airway’s intricate geometry and the 3D in vivo environment (Figure 1
). Due to the use of such simple devices, complex airflow dynamics, such as the velocity and pressure profiles at different regions of the nasal cavity, and their consequential effects on the nasal spray and aerosol flow characteristics and sectional deposition patterns are ignored 
Figure 1. Illustration of the Transwell inserts as the current traditional nasal drug test platform with a flat membrane and even drug particle distribution along the cell layer (left) and the heterogenous deposition of nasal drug particles in the complex geometry of the human nasal cavity in vivo (right). Created with Biorender.com.
3.3. Evaluation of IN Drugs’ Side Effects by OoCs
The analytical approaches to evaluate the drug-induced toxicity in OoC models may vary depending on the toxicity mechanisms or signalling pathway of the drug, where the biological characteristics and functions of cells and gene or protein levels are comprehensively assessed. Herein, some of the most common measurements reported in the literature to determine the drug-induced hepatotoxicity and nephrotoxicity by OoCs are summarised, which can be helpful for future toxicology studies in multi-OoC platforms involving nasal mucosa analogues.
The hepatotoxicity of drugs simulated by the OoC models involving liver analogue is evaluated by determining the cells’ survival (viability) 
and tissue morphology 
during inhibition in the expression or downregulation of the activity of metabolizing enzymes 
, i.e., CYPs enzyme family. This superfamily of enzymes, mainly found in liver cells 
, is involved in hepatic metabolism, catalysing a variety of biotransformations, metabolic reactions, and the bioactivation of drugs and pro-drugs. The liver dysfunction associated with liver injury is also evaluated by monitoring the decrease in albumin secretion 
, which is the essential function of hepatocytes that maintains the intravascular oncotic pressure. The increase in the release of liver injury biomarkers, such as miR-122 and keratin 18 
, and reactive oxygen species (ROS) by hepatocytes, as well as the depletion of cellular glutathione (GSH) by both hepatocytes and non-parenchymal cells 
are also observed in the liver-on-chip devices mimicking hepatotoxicity.
The drug-induced liver dysfunction is also evaluated by the elevation in the secretion of liver enzymes, including the alpha-glutathione-s-transferase (α-GST) and the transaminase family, i.e., alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), and gamma-glutamyl transpeptidase (GGT) 
, the essential liver enzymes that catalyse the synthesis of amino acids. The liver inflammation could also be mimicked by the liver-on-chip, where the expressed inflammation cytokines, i.e., IL-6 and monocyte chemotactic protein-1 (MCP-1) by the primary hepatocytes, were quantified 
In addition to the abovementioned assessments and depending on the drug’s toxic mechanism, other analytical approaches may also be adopted. For instance, the inhibition of bile salts export pump (BSEP) in hepatocytes caused by bosetan was assessed by determining the intracellular accumulation of BSEP substrate, i.e., cholyl-lysyl-fluorescein (CLF) via fluorescent microscopic imaging and determining the decrease in BSEP protein and BSEP mRNA. In addition, the accumulated lipid in the hepatocytes observed in the steatosis phenotype of liver injury and the associated α-smooth muscle actin (α-SMA) expression within stellate cells was also monitored microscopically 
The drug-induced kidney injury has been assessed by OoCs in terms of apoptosis detection assays, e.g., live/dead cells staining, as well as the release of lactate dehydrogenase (LDH) 
. The latter is performed as the increase in the urinary LDH efflux is associated with the acute kidney injury (AKI) 
. The expression of genotoxicity markers, e.g., IL-6, CDK1, CCNA2, ATF3, MYC, and SRPX2 
, is another method used to evaluate AKI on-chip. Furthermore, the damage to the filtration function of the glomerular endothelium as a barrier against large molecules is also evaluated on-chip, usually by measuring the permeation of fluorescein tracer IgG (MW = 150 kDa) and albumin (MW = 70 kDa) after drug treatment 
. The disturbance of calcium homeostasis is another key factor known in the development of AKI, where the overload of the intracellular Ca2+
results in the tubular epithelial cells injury 
. The increase in the intracellular Ca2+
release is modelled and evaluated by OoCs involving kidney replica exposed to ifosfamide, with known nephrotoxic effect when metabolised, by using Fluo-4 AM calcium indicator and obtaining fluorescent microscopic images of the cells that could be analysed for measuring the fluorescence intensity 
. Importantly, the quantification of the injury-associated biomarkers such as kidney injury molecule-1 (KIM-1), osteoactivin, vascular endothelial growth factor (VEGF), and heme oxygenase 1 gene (HMOX1) by the OoC models is performed to evaluate nephrotoxicity by kidney tissue analogues on-chip. The induced oxidative stress in the cells in response to the drugs’ toxic effect is also evaluated by measuring the production of ROS by using fluorescent indicators of cellular and mitochondrial ROS, e.g., CellROXTM
and MitoSOX reagents 
. Additionally, the urinary miRNA biomarkers observed in AKI patients has been quantified in the effluent of kidney analogue on-chips, which includes miRNA-21, -200c, -132, -155, -16, -24, and -30e 
Fabrication and Operation of OoCs
Despite the improved throughput of drug tests by OoCs as discussed earlier, some challenges will remain with fabricating and operating these devices, including the expensive, time-consuming fabrication processes that sometimes fail to translate the clinical data. The unavailability of human organ-specific cells may also hinder the construction of the OoC platforms. While the marketed human-specific organ cells may not be stable for long-term use in culture media, potential ethical concerns may also be associated with using patient-derived primary cells. Human induced pluripotent stem cells (iPSCs) can be produced by using an individual’s genetic information to reprogram fibroblasts into stem cells. The iPSCs can be used as a promising alternative to evaluate disease mechanisms and the responses of organs to the therapies 
Another challenge in the fabrication of OoC models is related to the co-culture of different tissues in multi-OoC platforms. Maintaining different cell types in these platforms requires the perfusion of a universal culture medium 
. To overcome this problem, a serum-free medium could be used. For example, Maschmeyer et al. used a serum-free medium in a study to investigate drug-induced toxicity in a four-interconnected OoC system that consists of the intestine, skin, liver, and kidney 
. However, secreted biological factors may accumulate in the medium when it circulates for an extended period of time.