1.1. Progress in InChI Representation of Small Molecules, Polymers, Mixtures and Reactions
1.2. A Proposal for a Hierarchical Representation of Chemical and Structural Complexity of NMs
1.3. What We Intended the Case Studies to Teach Us
- distinguishes between NMs and groups similar NMs;
- enables extraction of the main characteristics of the NM directly from the representation. This is a machine-readable form usable by databases and literature-mining tools that can guide users to information on specific NMs that discriminates between bulk forms of materials or other NMs with other sizes, shapes, or surface modifications;
- allows users to merge relevant information from different sources, including data from other NMs with varying degrees of similarity to the material under study; and
- can be unequivocally decoded into structural information used to generate data-driven and physics-based computational models.
2. Materials and Methods
2.1. Experimental Case Studies
2.1.1. Case Study 1. Functionalized Gold NMs
2.1.2. Case Study 2. Graphene-Family NMs
2.1.3. Case Study 3. Complex Engineered (Doped and Multi-Metallics) NMs
2.2. Application Use Case Studies
2.2.1. Case Study 4. Encoding for Data FAIRness
2.2.2. Case Study 5. Nanoinformatics Applications
2.2.3. Case Study 6. NM Regulation
2.3. Identifying Essential NInChI Features by an Iterative Prioritization Process
3. Results and Discussion
3.1. Case Study 1. Functionalized Gold NMs
3.1.2. Specific Features of Functionalized Au NMs
- Chemical composition. The simplest NMs were spheroid Au cores with an organic surface ligand for stabilization. The size and shape of Au NPs were controlled by changing the synthesis conditions . Chemical information is processed from the centre out, starting with basic information about core composition, size, and shape, before progressing to surface characteristics and functionalities, and even extending to interactions with surrounding molecules.
- Thus, minimum information is the core composition and a representation of the stabilizing ligand.
- Size, shape and morphology. NMs are in general referred to by their core composition, with an associated physicochemical property and/or morphology, e.g., Au quantum dots, citrate-functionalized Au NMs, core-shell silver-gold NMs, etc. Their biological effects and physicochemical properties depend on the NM dimensions and shapes, e.g., spheres, rods, stars, cages, core-shell particles, etc. At the atomic level, a multitude of Au NM shapes can be differentiated . Au nanospheres or colloids can be synthesized in an aqueous HAuCl4 solution using different reducing agents, e.g., citrate, which produces nearly monodisperse nanospheres . The size of the nanospheres can be precisely controlled by varying the citrate/Au ratio, i.e., smaller amounts of citrate will yield larger nanospheres, and size variants differing by only few nm have been successfully synthesized [75,76]. Alternatively, Au nanorods are synthesized using templates, e.g., the electrochemical deposition of Au within the pores of nanoporous polycarbonate, or, alumina template membranes [77,78]. The pore diameter of the template membrane determines the diameter of the Au nanorod. The length of the nanorod can be controlled by the amount of Au deposited within the pores of the template . Au nanostars have a thin, branch-like structure exhibiting plasmonic properties  and enhanced near-infrared light-absorbing capabilities, with reduced toxicity . Octahedral solid core Au nanohexapods have been fabricated by reducing HAuCl4 with DMF in an aqueous solution containing Au octahedral seeds [81,82]. Case study 1 suggested that the core size, shape and nanotopography of the Au NMs (intrinsic properties) should be determined by direct imaging techniques such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy (AFM)  (see Table S1) [84,85,86,87,88]. However, there are alternative ways to report size and shape and their distributions. Since Au NMs are not completely monodisperse, it is important to determine the particle size distribution to determine how agglomerate size affects toxicity , or to assess the quality of synthesized Au NPs . Dynamic light scattering (DLS) is the most common sizing technique but has limitations, e.g., high polydispersity can distort the results . Thus, consensus approaches to describing size distributions may be needed, e.g., DLS, SEM/TEM, field flow fractionation coupled to online sizing detectors, centrifugal techniques, nanoparticle tracking analysis and tunable resistive pulse sensing . However, commonly the mean diameter is reported [84,92,93].
- The structural representation of a NM must include size, size distribution, shape, and morphology distinguish one NM from another.
- Dimension and thickness of coating or shell. This property relates to chemical composition and morphology and may be difficult to determine. Examples include NMs where silica or polymer beads are coated with Au of variable thickness, creating Au nanoshells [94,95]. The diameter of the nanoshell is determined by the diameter of the underlying core, and the shell thickness is controlled by the amount of Au deposited on the surface of the core . By varying the composition and dimensions of the chemical layers, nanoshells can be fabricated with surface plasmon resonance (SPR) peaks ranging from the visible to the near-infrared region, i.e., 700–900 nm . Similarly, nanocages are hollow, porous Au NMs ranging in size from 10 to >150 nm. Silver nanostructures can be used as a sacrificial template and transformed into Au nanostructures with hollow interiors via galvanic replacement, e.g., a reaction between truncated silver nanocubes and aqueous HAuCl4 [96,97]. Au nanocages have been created with controllable pores on the surface . The dimension and wall thickness of the nanocage is controlled by adjusting the molar ratio of silver to HAuCl4 . Gold nanocages also be heated by light (photothermal effect) .
- These examples show that a shell formalization is needed that captures the dimensions and features of each shell in a sequential manner based on distance from the core, i.e., core-shell1-shell2 etc.
- Au NM surface characteristics and functionalities—Experimental determination of all relevant physicochemical NM surface characteristics such as roughness, charge density, oxidation, etc. is often infeasible so capturing these properties in a NInChI appears beyond the scope. This is less a limitation for intentionally synthesized conjugates. For example, PEGylation involves coating NMs by grafting, entrapping, adsorbing, or covalently binding to the NM surface to enhance its stabilization [99,100]. Covering Au NMs with polyethylene glycol (PEG) or its derivatives modifies binding of plasma proteins, interaction with opsonins, and clearance by the reticuloendothelial system . Similarly, nanoflares are Au conjugates functionalized with oligonucleotide sequences complementary to a specific nucleic acid target (messenger RNA) hybridized to short sequences that fluoresce when bound to a target .
- These examples indicate the need to describe organic coatings or biomolecules. However, quantitative and qualitative description of the coating entity (density, thickness, purity, orientation, bonding) may be beyond the scope of the NInChI, at least for now.
- Au NM interactions with surrounding molecules—The surface characteristics of Au NMs determine their life span and fate within the body and their toxicity [99,100,102,103]. As noted above, Au NP toxicity is affected by the type of particle coating , with polymer coatings increasing the stability and prolonging the NM circulation in the blood by reducing binding of opsonizing proteins . Surface characteristics can influence the electrostatic and hydrophobic interactions between particles (i.e., agglomeration) and clearance by opsonization toxicity [99,100,102,103]. A protein corona can form on particles in vivo  that influences biodistribution, biokinetics and toxicity. An overview of toxicity studies of different Au NMs and their functionalizations is given in Table S3 which identifies the need for detailed structural representation of the ligands and surface functionalization.
- Although relevant to the biological behavior of the NMs, protein corona and interactions with the environment are extrinsic properties or transformations that are beyond the scope of the proposed NInChI. They may, however, be suitable for a future extension by analogy with the RInChI.
3.1.3. Conclusions on Relevant Features of a NInChI
3.2. Case Study 2. Graphene-Family NMs
3.2.2. Specific Features of Graphene-Family NMs
- Graphene introduces new structural features necessary to differentiate between members. These include edge-structures, impurities, and defects (Tiers 2 and 3 in Figure 1). MInChI may allow inclusion of impurities in a NInChI, while edges and defects could be defined as new categories. RInChI may facilitate grouping of synthesis-route specific properties if they are reproducible.
- These examples further elaborate formalization of hollow NMs representations.
- As surface functionalization and doping by heteroatoms can alter the properties of CNTs, (see also case study 3) a mechanism to capture the bonding modality in a NInChI would be useful, (Tier 4 in Figure 1).
3.2.3. Conclusions on Relevant Features of a NInChI
- Graphene. The proposed NInChI should indicate: the size of the graphene layer(s); the number of layers (single, bi-, tri-, n- layers); the topology of the structure if applicable (zig-zag, armchair); surface/edge functionalization and bonding mode; impurity information; and heteroatom doping. Although defects affect the properties of graphene, incorporation of information about them may be too difficult, especially for the non-intentional defects.
- Fullerenes. InChI notations for Cn (C60–C90) are already well established. However, to represent fullerenes as part of graphene-family materials and properly identify derivatives and surface modifications, NInChI could add as additional extensions that describe structural changes (i.e., the identity of surface functionalizations).
- CNTs. Graphene may be considered the parent material for CNTs, which are essentially folded (rolled) graphene sheets. Consequently, the NInChI will share common attributes with graphene sheet(s), such as: surface and edge functionalization; heteroatomic doping; and information on impurities. Additionally, information on the number of walls within the CNT should be provided (e.g., SWCNT, DWCBT or MWCNT). CNTs also require more extensive description of their surface and morphology. Therefore, the NInChI should be extended to include information on: the nanotube chirality defined by (n,m) notation (ideally each layer in the MWCNT should have its chirality defined); outer and inner diameters; length; surface charge; and specific surface area. CNTs exhibit additional, higher-level morphological properties to be included (to some extent) in the NInChI, e.g., the end-capping of nanotubes and their shape. End-capping could be partially covered by the edge functionalization parameter described above. However, this refers to the substances or heteroatoms bonded to the edges of CNTs, and should be distinguished from the outer carbon-capping of the tube structure. Due to the complexity of the capping parameter (studies show that the curvature of the cap can alter the properties of the CNT), only basic information on whether the nanotube is closed or open could be accommodated. Finally, information on the shape of the tubular structure should be included in the NInChI notation e.g., straight, branched, helical, waved, and more . This could be accommodated by shape classes that group similar types of tubes.
3.3. Case Study 3. Complex Engineered (Doped and Multi-Metallics) NMs
3.3.2. Specific Features of Complex Engineered NMs
- These structures highlight a need to capture information on crystal structures, which may be mixtures of phases, and on amounts of dopants and distribution in the NMs. These features map to Tier 1 in Figure 1.
3.3.3. Conclusions on Relevant Features of a NInChI
3.4. Case Study 4. Encoding for Data FAIRness
3.4.2. Specific Use Cases—Nano-Related Data Management, Analysis and “FAIRification”
- NInChI will enhance FAIRness of NMs datasets by providing a higher level of indexing based on information in the different layers. Specific NMs can be found based on size, shape, surface coating etc. as encoded in the NInChI. Similarly, scientific interoperability of datasets will be enhanced by exclusion of non-relevant datasets.
- If a NInChI is used to establish similarity of NMs (across batches, following storage or ageing, etc.) its representation should include additional methods for determining the relevant properties to ensure direct comparability/interoperability. We therefore argue for a NInChI encoding sufficient information to quickly gauge similarity that is adequate for most applications that integrates measurements from multiple batches and samples of NMs.
- NInChIs can support the integration of computational NMs and their associated simulation datasets into nanosafety databases, with transparency around the origin of NMs datasets (experimental versus computational), through inclusion of notation to indicate in silico NMs.
3.4.3. Conclusions on Relevant Features of a NInChI
3.5. Case Study 5. Nanoinformatics Applications
3.5.2. Specific Use Cases—Calculating Nanodescriptors and Enabling Read-Across
- NInChI supply information for simulation, and link input and output data and simulation parameters as part of an automated nanoinformatics workflow. All model data can be stored and linked together with the corresponding NInChI. This allows data retrieval based on specific NM queries. Simulation parameters will also be stored as meta-data for a given NM and modelling method (Figure 6). An example of the three components for the nanodescriptors calculations based on atomistic simulations is given in Table 2.
- NM information encoded in the NInChI will enable grouping of NMs based on compositional and structural similarity, and enable generation of computational nanodescriptors to encode the whole NM.
3.5.3. Conclusions on Relevant Features of a NInChI
3.6. Case Study 6. NM Regulation
- RNs are often assigned purely on the basis of the chemical structure, which often leads to ambiguities due to multiple substances with different RNs corresponding to the same chemical structure. This is similar to the multiple to-be-registered substances that correspond to one reference substance defined in IUCLID6 .
- For NMs, there is the additional, more severe problem that no RNs exist for the different nanoforms, defined in EU chemicals legislation (Registration, Evaluation, Authorisation and Restriction of Chemicals or REACH) as NMs with the same core chemistry but different sizes, shapes, coatings. For these it is suggested that the bulk or micron-sized materials RN be used. ECHA are currently addressing this in IUCLID6 by specifying different assessment entities within a single substance registration.
- Commonly encountered mixtures of known or unknown composition and even whole classes of molecules like classes of enzymes receive a single RN.
3.6.2. Specific Use Cases—Nanoforms Concept and NInChI as Solution for Regulators
- Fitting the needs of the nanoforms concept of REACH and ECHA. According to REACH Regulation (Annex R.6-1, ), a nanoform is a form of a natural or manufactured substance containing particles, in an unbound state or as an aggregate or agglomerate where, in which 50% or more of the particles have one or more dimensions in the range 1–100 nm. This includes fullerenes, graphene flakes and single wall carbon nanotubes with one or more external dimensions below 1 nm. A set of similar nanoforms is a group of nanoforms for which hazard, exposure, and risk assessment can be performed jointly. A justification must be provided to show variations within these boundaries does not significantly affect hazard, exposure, and risk assessment of the similar nanoforms in the set. A nanoform can only belong to one set of similar nanoforms. ECHA developed a stepwise approach following the steps outlined by the OECD guidance on grouping of chemicals . ECHA requires information on the nanoform that includes composition of the substance, impurities or additives, surface treatment and functionalization (chemical coating and surface treatment(s) applied to the particles). It also includes physical parameters such as size distribution, shape aspect ratio and other morphological characterization data, crystallinity, information on assembly structure including (e.g., shell-like structures or hollow structures, if appropriate), and specific surface area (e.g., porosity). The user has to measure each property (using a standardized protocol), report the method and the results in IUCLID . A set of nanoforms can exhibit a range of values for each property provided that the range does not impact the nanoform’s risk. The regulation requires that at least 50% of particles (number distribution) is within the range of 1 to 100 nm, with further information on the particle size distribution (e.g., d10, d50, d90 values). The registrants must define the boundary defining the set of similar nanoforms, for example by specifying the minimum d10 and maximum d90. A set of nanoforms should exhibit similar dissolution rates, toxicokinetic behaviors, fate and bioavailability and ecotoxicological parameters.
- By using a structural representation, rather than a chemistry-unaware substance identifier, in Tiers 1-3 (Figure 1) encoding composition, size/shape and surface coating of nanoforms, NInChI will support the differentiation of individual nanoforms (assessment entities) independent of their inclusion in identifier repositories controlled by third-party organizations like the CAS registry.
- NInChI as a solution for the regulatory needs—Moving from CAS to NInChI. CAS RNs are completely arbitrary, contain no intrinsic information, and are easily misused as there is no way to validate them other than locating them in a database. Case study 4 highlighted some of the challenges presented by NMs regarding identification and linking of datasets to the specific NMs, batch-to-batch variability, NM ageing and transformation, and integrating data sets with confidence in the absence of unambiguous identifiers. Thus, for NMs there is a clear need for a representation like NInChI to replace or augment the CAS RN, or other chemistry-unaware identifiers, to increase confidence in datasets used in weight of evidence, grouping, read across, and QSAR modelling approaches. Using a NInChI to validate datasets, by checking the consistency of the data for the object under investigation, would significantly boost the quality of QSAR and read-across models, and the confidence with which they can be applied in nanosafety. Thus, in terms of addressing regulatory needs, NInChI will enable:
- Distinguishing of different nanoforms for registration (importance of standardized identifiers and structural representations, unified data management processes, etc.). The tiers in Figure 1 have been mapped to the information requirements included in Annex R.6-1, including shape, size and surface coating considerations and, as such, NInChIs will be an important means to differentiate individual nanoforms. By adapting the MInChI extension that includes ranges, we envisage incorporating a set of nanoforms into a single NInChI by providing ranges within which specific NM properties can vary while their toxicities remain the same, thus providing boundaries for a set of NMs. While this is not included explicitly in the current examples, an extension in a subsequent iteration of the NInChI would be possible.
- NMs information included in the NInChI will support grouping of NMs based on both compositional and structural properties.
- Read across and grouping using NInChI for predictions, as described in detail in case study 5. While QSARs are well established for small molecules, their acceptance for regulatory purposes is still limited, mainly due to dataset uncertainty and often poor documentation of models in the QSAR model report forms (QMRFs). NInChIs will make it straightforward to update existing QMRFs with the NInChIs of all NMs that were used as part of the training and test sets. Using the NInChIs will enable extraction of NM structural information from databases and visual presentation. This will aid expert evaluation and interpretation of QSAR models for grouping strategies, determine whether it is applicable to the NMs under evaluation, and allow independent assessment of predictions and structural similarity. The notation that describes NMs from the center outwards will also allow a simplified graphical representation of the NMs key elements, potentially as a 2 D representation of the 3 D structure, as shown schematically in Figure 7.
- NM information included in the NInChI supports verification and validation of grouping hypotheses based on simplified visualization of chemical and structural information.
3.6.3. Conclusions on Relevant Features of a NInChI
3.7. NInChI—A Proposal for a Layered Approach for Uniquely Identifying NMs
- It should be a unique representation, where a specific NM is always represented by the same NInChI and a NInChI is always associated with the same NM or group of closely related NMs. The latter modifies the one-to-one relationship of InChIs described in the introduction by accepting the stochastic nature of many NMs, discussed in more detail below.
- The structure should be optimal for extraction of specific information by a computer but also by a trained person (i.e., should have a degree of human interpretability)
- It be compatible with other notations in the InChI universe, reusing concepts or extensions of these notations or even incorporating their complete representations as part of the NInChI.
- InChI, PInChI or MInChI to represent the chemical composition (without the leading version number)
- morphology layer (prefix m): abbreviations are used for specific morphologies, e.g., sp: sphere, sh: shell, ro: rod, tu: tube
- size layer (prefix s): specified in scientific notation in meters, e.g., 2x-9 where x can be r: radius, d: diameter, l: length, t: thickness, ranges can be given separated by “:”
- crystal layer (prefix k)
- chirality layer (prefix w for carbon nanotubes)
3.8. NInChI Alpha—Demonstration of Worked Examples of NMs InChIs
- Purity ≥ 99%
- Rutile form, or rutile with up to 5% anatase, with crystalline structure and physical appearance as clusters of spherical, needle, or lanceolate shapes
- Median particle size based on number size distribution ≥ 30 nm
- Coated with silica, hydrated silica, alumina, aluminium hydroxide, aluminium stearate, stearate, stearic acid, trimethoxycaprylylsilane, glycerin, dimethicone, dimethicone/methicone copolymer, simethicone;
3.9. Prototype of a NInChI Generation Service
Conflicts of Interest
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|Required Simulation Input||Information Encoded in the NInChI|
|Core material chemistry||Au (Gold)|
|Size||20d-9 (20 nm)|
|NInChI||Simulations Input||Simulations Output|
|Core/size/shape/polyform||The structure (i.e., coordinates of all atoms) of a NM and input parameters (Buckingham and Coulomb force field parameterization)||Structural and energetic descriptors of the NM|
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