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The translation of nanotechnology form the bench to the market imposed several challenges. General issues to consider during the development of nanomedicine products including physicochemical characterization, biocompatibility, and nanotoxicology evaluation, pharmacokinetics and pharmacodynamics assessment, process control, and scale-reproducibility (Figure ) are discussed in the sections that follow.

The characterization of a nanomedicine is necessary to understand its behavior in the human body, and to provide guidance for the process control and safety assessment. This characterization is not consensual in the number of parameters required for a correct and complete characterization. Internationally standardized methodologies and the use of reference nanomaterials are the key to harmonize all the different opinions about this topic (Lin et al., 2014; Zhao and Chen, 2016).

Ideally, the characterization of a nanomaterial should be carried out at different stages throughout its life cycle, from the design to the evaluation of its in vitro and in vivo performance. The interaction with the biological system or even the sample preparation or extraction procedures may modify some properties and interfere with some measurements. In addition, the determination of the in vivo and in vitro physicochemical properties is important for the understanding of the potential risk of nanomaterials (Lin et al., 2014; Zhao and Chen, 2016).

The Organization for Economic Co-operation and Development started a Working Party on Manufactured Nanomaterials with the International Organization for Standardization to provide scientific advice for the safety use of nanomaterials that include the respective physicochemical characterization and the metrology. However, there is not an effective list of minimum parameters. The following characteristics should be a starting point to the characterization: particle size, shape and size distribution, aggregation and agglomeration state, crystal structure, specific surface area, porosity, chemical composition, surface chemistry, charge, photocatalytic activity, zeta potential, water solubility, dissolution rate/kinetics, and dustiness (McCall et al., 2013; Lin et al., 2014).

Concerning the chemical composition, nanomaterials can be classified as organic, inorganic, crystalline or amorphous particles and can be organized as single particles, aggregates, agglomerate powders or dispersed in a matrix which give rise to suspensions, emulsions, nanolayers, or films (Luther, 2004).

Regarding dimension, if a nanomaterial has three dimensions below 100 nm, it can be for example a particle, a quantum dot or hollow sphere. If it has two dimensions below 100 nm it can be a tube, fiber or wire and if it has one dimension below 100 nm it can be a film, a coating or a multilayer (Luther, 2004).

Different techniques are available for the analysis of these parameters. They can be grouped in different categories, involving counting, ensemble, separation and integral methods, among others (Linsinger et al., 2012; Contado, 2015).

Counting methods make possible the individualization of the different particles that compose a nanomaterial, the measurement of their different sizes and visualization of their morphology. The particles visualization is preferentially performed using microscopy methods, which include several variations of these techniques. Transmission Electron Microscopy (TEM), High-Resolution TEM, Scanning Electron Microscopy (SEM), cryo-SEM, Atomic Force Microscopy and Particle Tracking Analysis are just some of the examples. The main disadvantage of these methods is the operation under high-vacuum, although recently with the development of cryo-SEM sample dehydration has been prevented under high-vacuum conditions (Linsinger et al., 2012; Contado, 2015; Hodoroaba and Mielke, 2015).

These methods involve two steps of sample treatment: the separation of the particles into a monodisperse fraction, followed by the detection of each fraction. Field-Flow Fractionation (FFF), Analytical Centrifugation (AC) and Differential Electrical Mobility Analysis are some of the techniques that can be applied. The FFF techniques include different methods which separate the particles according to the force field applied. AC separates the particles through centrifugal sedimentation (Linsinger et al., 2012; Contado, 2015; Hodoroaba and Mielke, 2015).

Ensemble methods allow the report of intensity-weighted particle sizes. The variation of the measured signal over time give the size distribution of the particles extracted from a combined signal. Dynamic Light Scattering (DLS), Small-angle X-ray Scattering (SAXS) and X-ray Diffraction (XRD) are some of the examples. DLS and QELS are based on the Brownian motion of the sample. XRD is a good technique to obtain information about the chemical composition, crystal structure and physical properties (Linsinger et al., 2012; Contado, 2015; Hodoroaba and Mielke, 2015).

The integral methods only measure an integral property of the particle and they are mostly used to determine the specific surface area. Brunauer Emmet Teller is the principal method used and is based on the adsorption of an inert gas on the surface of the nanomaterial (Linsinger et al., 2012; Contado, 2015; Hodoroaba and Mielke, 2015).

Other relevant technique is the electrophoretic light scattering (ELS) used to determine zeta potential, which is a parameter related to the overall charge a particle acquires in a particular medium. ELS measures the electrophoretic mobility of particles in dispersion, based on the principle of electrophoresis (Linsinger et al., 2012).

The Table shows some of principal methods for the characterization of the nanomaterials including the operational principle, physicochemical parameters analyzed and respective limitations.

Some of the principal methods for the characterization of the nanomaterials, operation principle, physicochemical parameters analyzed, and respective limitations (Luther, 2004; Linsinger et al., 2012; Lin et al., 2014; Contado, 2015; Hodoroaba and Mielke, 2015).

Another challenge in the pharmaceutical development is the control of the manufacturing process by the identification of the critical parameters and technologies required to analyse them (Gaspar, 2010; Gaspar et al., 2014; Sainz et al., 2015).

New approaches have arisen from the pharmaceutical innovation and the concern about the quality and safety of new medicines by regulatory agencies (Gaspar, 2010; Gaspar et al., 2014; Sainz et al., 2015).

Quality-by-Design (QbD), supported by Process Analytical Technologies (PAT) is one of the pharmaceutical development approaches that were recognized for the systematic evaluation and control of nanomedicines (FDA, 2004; Gaspar, 2010; Gaspar et al., 2014; Sainz et al., 2015; European Medicines Agency, 2017).

Note that some of the physicochemical characteristics of nanomaterials can change during the manufacturing process, which compromises the quality and safety of the final nanomedicine. The basis of QbD relies on the identification of the Quality Attributes (QA), which refers to the chemical, physical or biological properties or another relevant characteristic of the nanomaterial. Some of them may be modified by the manufacturing and should be within a specific range for quality control purposes. In this situation, these characteristics are considered Critical Quality Attributes (CQA). The variability of the CQA can be caused by the critical material attributes and process parameters (Verma et al., 2009; Riley and Li, 2011; Bastogne, 2017; European Medicines Agency, 2017).

The quality should not be tested in nanomedicine, but built on it instead, by the understanding of the therapeutic purpose, pharmacological, pharmacokinetic, toxicological, chemical and physical properties of the medicine, process formulation, packaging, and the design of the manufacturing process. This new approach allows better focus on the relevant relationships between the characteristics, parameters of the formulation and process in order to develop effective processes to ensure the quality of the nanomedicines (FDA, 2014).

According to the FDA definition PAT is a system for designing, analzsing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes, with the goal of ensuring final product quality (FDA, 2014). The PAT tools analyse the critical quality and performance attributes. The main point of the PAT is to assure and enhance the understanding of the manufacturing concept (Verma et al., 2009; Riley and Li, 2011; FDA, 2014; Bastogne, 2017; European Medicines Agency, 2017).

Biocompatibility is another essential property in the design of drug delivery systems. One very general and brief definition of a biocompatible surface is that it cannot trigger an undesired' response from the organism. Biocompatibility is alternatively defined as the ability of a material to perform with an appropriate response in a specific application (Williams, 2003; Keck and Mller, 2013).

Pre-clinical assessment of nanomaterials involve a thorough biocompatibility testing program, which typically comprises in vivo studies complemented by selected in vitro assays to prove safety. If the biocompatibility of nanomaterials cannot be warranted, potentially advantageous properties of nanosystems may raise toxicological concerns.

Regulatory agencies, pharmaceutical industry, government, and academia are making efforts to accomplish specific and appropriate guidelines for risk assessment of nanomaterials (Hussain et al., 2015).

In spite of efforts to harmonize the procedures for safety evaluation, nanoscale materials are still mostly treated as conventional chemicals, thus lacking clear specific guidelines for establishing regulations and appropriate standard protocols. However, several initiatives, including scientific opinions, guidelines and specific European regulations and OECD guidelines such as those for cosmetics, food contact materials, medical devices, FDA regulations, as well as European Commission scientific projects (NanoTEST project, http://www.nanotest-fp7.eu) specifically address nanomaterials safety (Juillerat-Jeanneret et al., 2015).

In this context, it is important to identify the properties, to understand the mechanisms by which nanomaterials interact with living systems and thus to understand exposure, hazards and their possible risks.

Note that the pharmacokinetics and distribution of nanoparticles in the body depends on their surface physicochemical characteristics, shape and size. For example, nanoparticles with 10 nm in size were preferentially found in blood, liver, spleen, kidney, testis, thymus, heart, lung, and brain, while larger particles are detected only in spleen, liver, and blood (De Jong et al., 2008; Adabi et al., 2017).

In turn, the surface of nanoparticles also impacts upon their distribution in these organs, since their combination with serum proteins available in systemic circulation, influencing their cellular uptake. It should be recalled that a biocompatible material generates no immune response. One of the cause for an immune response can rely on the adsorption pattern of body proteins. An assessment of the in vivo protein profile is therefore crucial to address these interactions and to establish biocompatibility (Keck et al., 2013).

Finally, the clearance of nanoparticles is also size and surface dependent. Small nanoparticles, bellow 2030 nm, are rapidly cleared by renal excretion, while 200 nm or larger particles are more efficiently taken up by mononuclear phagocytic system (reticuloendothelial system) located in the liver, spleen, and bone marrow (Moghimi et al., 2001; Adabi et al., 2017).

Studies are required to address how nanomaterials penetrate cells and tissues, and the respective biodistribution, degradation, and excretion.

Due to all these issues, a new field in toxicology termed nanotoxicology has emerged, which aims at studying the nanomaterial effects deriving from their interaction with biological systems (Donaldson et al., 2004; Oberdrster, 2010; Fadeel, 2013).

The evaluation of possible toxic effects of the nanomaterials can be ascribed to the presence of well-known molecular responses in the cell. Nanomaterials are able to disrupt the balance of the redox systems and, consequently, lead to the production of reactive species of oxygen (ROS). ROS comprise hydroxyl radicals, superoxide anion and hydrogen peroxide. Under normal conditions, the cells produce these reactive species as a result of the metabolism. However, when exposed to nanomaterials the production of ROS increases. Cells have the capacity to defend itself through reduced glutathione, superoxide dismutase, glutathione peroxidase and catalase mechanisms. The superoxide dismutase converts superoxide anion into hydrogen peroxide and catalase, in contrast, converts it into water and molecular oxygen (Nel et al., 2006; Arora et al., 2012; Azhdarzadeh et al., 2015). Glutathione peroxidase uses glutathione to reduce some of the hydroperoxides. Under normal conditions, the glutathione is almost totally reduced. Nevertheless, an increase in ROS lead to the depletion of the glutathione and the capacity to neutralize the free radicals is decreased. The free radicals will induce oxidative stress and interact with the fatty acids in the membranes of the cell (Nel et al., 2006; Arora et al., 2012; Azhdarzadeh et al., 2015).

Consequently, the viability of the cell will be compromised by the disruption of cell membranes, inflammation responses caused by the upregulation of transcription factors like the nuclear factor kappa , activator protein, extracellular signal regulated kinases c-Jun, N-terminal kinases and others. All these biological responses can result on cell apoptosis or necrosis. Distinct physiological outcomes are possible due to the different pathways for cell injury after the interaction between nanomaterials and cells and tissues (Nel et al., 2006; Arora et al., 2012; Azhdarzadeh et al., 2015).

Over the last years, the number of scientific publications regarding toxicological effects of nanomaterials have increased exponentially. However, there is a big concern about the results of the experiments, because they were not performed following standard and harmonized protocols. The nanomaterial characterization can be considered weak once there are not standard nanomaterials to use as reference and the doses used in the experiences sometimes cannot be applied in the biological system. Therefore, the results are not comparable. For a correct comparison, it is necessary to perform a precise and thorough physicochemical characterization to define risk assessment guidelines. This is the first step for the comparison between data from biological and toxicological experiments (Warheit, 2008; Fadeel et al., 2015; Costa and Fadeel, 2016).

Although nanomaterials may have an identical composition, slight differences e.g., in the surface charge, size, or shape could impact on their respective activity and, consequently, on their cellular fate and accumulation in the human body, leading to different biological responses (Sayes and Warheit, 2009).

Sayes and Warheit (2009) proposed a three phases model for a comprehensive characterization of nanomaterials. Accordingly, the primary phase is achieved in the native state of the nanomaterial, specifically, in its dry state. The secondary characterization is performed with the nanomaterials in the wet phase, e.g., as solution or suspension. The tertiary characterization includes in vitro and in vivo interactions with biological systems. The tertiary characterization is the most difficult from the technical point of view, especially in vivo, because of all the ethical questions concerning the use of animals in experiments (Sayes and Warheit, 2009).

Traditional toxicology uses of animals to conduct tests. These types of experiments using nanomaterials can be considered impracticable and unethical. In addition, it is time-consuming, expensive and sometimes the end points achieved are not enough to correctly correlate with what happens in the biological systems of animals and the translation to the human body (Collins et al., 2017).

In vitro studies are the first assays used for the evaluation of cytotoxicity. This approach usually uses cell lines, primary cells from the tissues, and/or a mixture of different cells in a culture to assess the toxicity of the nanomaterials. Different in vitro cytotoxicity assays to the analysis of the cell viability, stress, and inflammatory responses are available. There are several cellular processes to determine the cell viability, which consequently results in different assays with distinct endpoints. The evaluation of mitochondrial activity, the lactate dehydrogenase release from the cytosol by tretazolium salts and the detection of the biological marker Caspase-3 are some of the examples that imposes experimental variability in this analysis. The stress response is another example which can be analyzed by probes in the evaluation of the inflammatory response via enzyme linked immunosorbent assay are used (Kroll et al., 2009).

As a first approach, in vitro assays can predict the interaction of the nanomaterials with the body. However, the human body possesses compensation mechanisms when exposed to toxics and a huge disadvantage of this model is not to considered them. Moreover, they are less time consuming, more cost-effective, simpler and provide an easier control of the experimental conditions (Kroll et al., 2009; Fadeel et al., 2013b).

Their main drawback is the difficulty to reproduce all the complex interactions in the human body between sub-cellular levels, cells, organs, tissues and membranes. They use specific cells to achieve specific endpoints. In addition, in vitro assays cannot predict the physiopathological response of the human body when exposed to nanomaterials (Kroll et al., 2009; Fadeel et al., 2013b).

Another issue regarding the use of this approach is the possibility of interaction between nanomaterials and the reagents of the assay. It is likely that the reagents used in the in vitro assays interfere with the nanomaterial properties. High adsorption capacity, optical and magnetic properties, catalytic activity, dissolution, and acidity or alkalinity of the nanomaterials are some of the examples of properties that may promote this interaction (Kroll et al., 2009).

Many questions have been raised by the regulators related to the lack of consistency of the data produced by cytotoxicity assays. New assays for a correct evaluation of the nanomaterial toxicity are, thus, needed. In this context, new approaches have arisen, such as the in silico nanotoxicology approach. In silico methods are the combination of toxicology with computational tools and bio-statistical methods for the evaluation and prediction of toxicity. By using computational tools is possible to analyse more nanomaterials, combine different endpoints and pathways of nanotoxicity, being less time-consuming and avoiding all the ethical questions (Warheit, 2008; Raunio, 2011).

Quantitative structure-activity relationship models (QSAR) were one the first applications of computational tools applied in toxicology. QSAR models are based on the hypothesis that the toxicity of nanomaterials and their cellular fate in the body can be predicted by their characteristics, and different biological reactions are the result of physicochemical characteristics, such as size, shape, zeta potential, or surface charge, etc., gathered as a set of descriptors. QSAR aims at identifying the physicochemical characteristics which lead to toxicity, so as to provide alterations to reduce toxicology. A mathematical model is created, which allows liking descriptors and the biological activity (Rusyn and Daston, 2010; Winkler et al., 2013; Oksel et al., 2015).

Currently, toxigenomics is a new area of nanotoxicology, which includes a combination between genomics and nanotoxicology to find alterations in the gene, protein and in the expressions of metabolites (Rusyn et al., 2012; Fadeel et al., 2013a).

Hitherto, different risk assessment approaches have been reported. One of them is the DF4nanoGrouping framework, which concerns a functionality driven scheme for grouping nanomaterials based on their intrinsic properties, system dependent properties and toxicological effects (Arts et al., 2014, 2016). Accordingly, nanomaterials are categorized in four groups, including possible subgroups. The four main groups encompass (1) soluble, (2) biopersistent high aspect ratio, (3) passive, that is, nanomaterials without obvious biological effects and (4) active nanomaterials, that is, those demonstrating surface-related specific toxic properties. The DF4nanoGrouping foresees a stepwise evaluation of nanomaterial properties and effects with increasing biological complexity. In case studies that includes carbonaceous nanomaterials, metal oxide, and metal sulfate nanomaterials, amorphous silica and organic pigments (all nanomaterials having primary particle sizes smaller than 100 nm), the usefulness of the DF4nanoGrouping for nanomaterial hazard assessment has already been established. It facilitates grouping and targeted testing of nanomaterials, also ensuring that enough data for the risk assessment of a nanomaterial are available, and fostering the use of non-animal methods (Landsiedel et al., 2017). More recently, DF4nanoGrouping developed three structure-activity relationship classification, decision tree, models by identifying structural features of nanomaterials mainly responsible for the surface activity (size, specific surface area, and the quantum-mechanical calculated property lowest unoccupied molecular orbital), based on a reduced number of descriptors: one for intrinsic oxidative potential, two for protein carbonylation, and three for no observed adverse effect concentration (Gajewicz et al., 2018)

Keck and Mller also proposed a nanotoxicological classification system (NCS) (Figure ) that ranks the nanomaterials into four classes according to the respective size and biodegradability (Mller et al., 2011; Keck and Mller, 2013).

Nanotoxicological classification (reproduced with permission from Keck and Mller, 2013).

Due to the size effects, this parameter is assumed as truly necessary, because when nanomaterials are getting smaller and smaller there is an increase in solubility, which is more evident in poorly soluble nanomaterials than in soluble ones. The adherence to the surface of membranes increases with the decrease of the size. Another important aspect related to size that must be considered is the phagocytosis by macrophages. Above 100 nm, nanomaterials can only be internalized by macrophages, a specific cell population, while nanomaterials below 100 nm can be internalized by any cell due to endocytosis. Thus, nanomaterials below 100 nm are associated to higher toxicity risks in comparison with nanomaterials above 100 nm (Mller et al., 2011; Keck and Mller, 2013).

In turn, biodegradability was considered a required parameter in almost all pharmaceutical formulations. The term biodegradability applies to the biodegradable nature of the nanomaterial in the human body. Biodegradable nanomaterials will be eliminated from the human body. Even if they cause some inflammation or irritation the immune system will return to the regular function after elimination. Conversely, non-biodegradable nanomaterials will stay forever in the body and change the normal function of the immune system (Mller et al., 2011; Keck and Mller, 2013).

There are two more factors that must be taken into account in addition to the NCS, namely the route of administration and the biocompatibility surface. When a particle is classified by the NCS, toxicity depends on the route of administration. For example, the same nanomaterials applied dermally or intravenously can pose different risks to the immune system.

In turn, a non-biocompatibility surface (NB) can activate the immune system by adsorption to proteins like opsonins, even if the particle belongs to the class I of the NCS (Figure ). The biocompatibility (B) is dictated by the physicochemical surface properties, irrespective of the size and/or biodegradability. This can lead to further subdivision in eight classes from I-B, I-NB, to IV-B and IV-NB (Mller et al., 2011; Keck and Mller, 2013).

NCS is a simple guide to the evaluation of the risk of nanoparticles, but there are many other parameters playing a relevant role in nanotoxicity determination (Mller et al., 2011; Keck and Mller, 2013). Other suggestions encompass more general approaches, combining elements of toxicology, risk assessment modeling, and tools developed in the field of multicriteria decision analysis (Rycroft et al., 2018).

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Nanomedicine: Principles, Properties, and Regulatory Issues

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