Edouard Alphandéry
email: edouardalphandery@hotmail.com

A discussion on existing nanomedicine regulation

ABSTRACT:

Due to the large diversity of nanomaterials and to their partly unknown behavior in human, nanomedicine regulation is taking shape at a slow pace. Here, I discuss the following aspects linked with the emergence of such regulation: i), nanomaterial definition/classification, which may be extended beyond the sole notion of size or dimension and overcome the confusion arising from the distinction between their intentional and non-intentional use or exposure, ii) nanomaterial interacting behaviors that lead to the formation of aggregates or agglomerates depending on their interacting strength, opsonization following capture of biological material at their surface, and the apparition of assemblies of dispersed or soluble nanomaterials depending on their level of degradation in a given medium, iii) nanomaterial dose whose value depends on the method used for measurement and on the type of dose considered (injected versus effective), iv) the different steps of nanomaterial preparation whose most critical one is often sterilization that should remove bacterial contamination without affecting nanomaterial properties, v) the different characterization methods and properties that they measure, vi) the toxicological assessment of these materials that necessitates accurately selecting the toxicity tests and associated operating conditions, vii) nanomaterial biodistribution properties that notably depend on the frequency with which the organism is monitored, the types of studied tissues/organs, and the nature of the nanomaterial used for the study (transformed or not), viii) the risk analysis that should determine the severity of potential damages caused by a nanomaterial as a function of the probability of this damage.

KEYWORDS:

Nanomedicine, regulation, dose, preparation, biodistribution, toxicity, risk analysis, standard, ISO, OECD, nanoparticle, nano. INTRODUCTION Regulating the nanomedicine field is not an easy task because of the large diversity of nanomaterials, comprising among others polymers, liposomes, dendrimers, nanocrystals, nanocomplexes, nanoemulsions, nanoparticles, metal colloids, micelles, quantum dos, fullerene, carbon nanotube, liposome, and virosome. Indeed, all these materials have different properties such as various compositions, sizes, shapes, interactions, charges, resulting in a very broad range of activities and behaviors. A series of non-scientific communications have generalized certain behaviors, notably of toxicity, to a whole set of nanomaterials, whereas in fact they only apply to one type of nanomaterial examined in a specific situation. This has created some fears among the general population, often without a sound scientific basis. In addition, clinical data are often missing, making it difficult to evaluate the benefit/risk ratio of nanomaterials. Despite these difficulties, regulation governing the use of these materials in the medical field is slowly taking shape as discussed elsewhere, by describing the different available standards and guidelines [START_REF] Halamoda-Kenzaoui | Mapping of the available standards against the regulatory needs for nanomedicines WIREs Nanomed[END_REF][START_REF] Hafner | Nanotherapeutics in the EU: an overview on current state and future directions[END_REF], by presenting the principles of a harmonized approach of standardization [START_REF] Mühlebach | Regulatory challenges of nanomedicines and their follow-on versions: A generic or similar approach?[END_REF], by analyzing the nanomaterials already in the market to define the bases for new nano-regulation [START_REF] Sainz | Regulatoryaspectsonnanomedicines[END_REF], by highlighting the controversies in nanomaterial regulation [START_REF] Soares | Nanomedicine: Principles, Properties, and Regulatory Issues[END_REF], by debating about the link between regulation and innovation in nanomedicine [START_REF] Wacker | Dealing with nanosafety around the globe-Regulation vs. innovation[END_REF], by pointing out differences in nanomaterial regulation between various regions of the world [START_REF] Chang | Nanomedicine: Past, present and future -A global perspective[END_REF], and by listing different types of nano-drugs and regulatory issues associated with their entry to market [START_REF] Hafner | Nanotherapeutics in the EU: an overview on current state and future directions[END_REF].

Here, the different aspects of nanomedicine regulation are discussed starting from the analysis of the standards presented in Figure 1. They concern the definition and the classification of the nanomaterials, which essentially rely on an analysis of size or dimension [START_REF] Halamoda-Kenzaoui | Mapping of the available standards against the regulatory needs for nanomedicines WIREs Nanomed[END_REF]. Other important properties of these materials should probably also be taken into account. They discuss the different types of interactions between nanomaterials, as well as methods for measuring dose, preparation and characterization of nanomaterials, which can determine their behavior and benefit/risk ratio. The studies of toxicity and biodistribution of these materials as well as a risk analysis are necessary steps described in these standard that need to be undertaken to be able to start clinical trials and later obtain marketing authorization. Furthermore, while developing new standards for nanomaterials, the pitfalls encountered during nanomaterial development and characterization [START_REF] Alkilany | Misinterpretation in Nanotoxicology: A Personal Perspective[END_REF][START_REF] Crist | Common Pitfalls in Nanotechnology: Lessons Learned from NCI's Nanotechnology Characterization Laboratory[END_REF] should also be avoided. Nanomaterial standards directly impact the translation of these drugs into clinical trials, making their design important for the scientific community, the society as a whole, and more importantly the patients who could benefit from new and more efficient treatments provided by specific nano-therapies [START_REF] Hua | Current Trends and Challenges in the Clinical Translation of Nanoparticulate Nanomedicines: Pathways for Translational Development and Commercialization[END_REF].

NANOPARTICLE DEFINITION

According to existing standards, a material is considered nano-metric when at least one of its dimensions (internal or external), or surface structure, is at the nano-scale. This definition raises a number of concerns. Firstly, the size of a nanomaterial can't always be measured, for example when nanomaterials form aggregates from which individual nanomaterials can't be isolated or when they are comprised in an environment such as the interior of the organism, where nanomaterial characterization tools can't easily be used. Thus, this definition of a nanomaterial may exclude nanomaterials whose size can't be measured. In addition, the size of a nanomaterial depends both on the method employed to measure it and on nanomaterial environment or configuration. For example, nanomaterial size deduced from EM (electron microscopy) is usually smaller than that estimated by DLS (dynamic light scattering). It may be suggested to identify a nanomaterial not only from its size but from some of its other properties such as catalytic ones. Secondly, it was suggested to make a distinction between the internal and external dimension of a nanomaterial, without however clarifying what this distinction implies. One might argue that the external dimension may correspond to the dimension of the surface of a nanomaterial that is exposed to an external environment while the internal dimension may be that of the inner core of the nanomaterial. However, this view has some limitations. Indeed, it does not describe the transition between the internal and external dimensions of the nanomaterials, i.e. one does not know when and where the internal and/or external dimension(s) start(s). In addition, it is not clear what the terms ''nano-material surface structure'' refer to. They seem to describe an assembly of different materials rather than a single nanomaterial. Furthermore, this definition seems insufficient to fully define a nanomaterial. To be more precise, the definition could be complemented by specifying: i) the situation of a nanomaterial relatively to injection, i.e. before, during, or after its administration to a living organism, ii) nanomaterial environment, for example an inorganic, organic, solid, liquid, or gaseous medium, iii) nanomaterial methods used to measure its properties, and iv) other nanomaterial properties than only their sizes.

Another concern in nanomaterial definition is the distinction between intentional and unintentional nanomaterial production and use. Nanomaterials synthesized by animals such as birds or fishes or by bacteria may be considered as intentionally or unintentionally produced depending on whether one considers that an animal or a bacterium can have an intention (or not). When nanomaterials are produced by the organism of a human, for example by having free iron react with specific enzymes such as ferritin, it is not clear whether this would be considered as an intentional or unintentional process.

Another example of a confusing situation can be provided when one is willing to fabricate another type of material than a nanomaterial, but ends up by making a nanomaterial. In this case, it is difficult to know whether the nanomaterial is intentionally or unintentionally produced or used. The distinction between these two situations (intentional and unintentional) was probably introduced to determine how much control can be reached over these materials, since an intentional process should in principle result in a better control than an unintentional one. However, to address this concern, it may be better to directly attribute a certain level of control over a given nanomaterial, which would depend on how much knowledge one has gained on this material.

NANOMATERIAL CLASSIFICATION

The most simple classification of nanomaterials distinguishes between materials with three dimensions at nanometer scale (nanoparticles, quantum dots), two dimensions at this scale (nano-fiber, nano-rod, nanotube), or one dimension at this scale (Nano-leaf). Here again, the question arises about whether this classification exclusively by size or dimension is not too restrictive. To be broader, the classification could define the nano-power of a nanomaterial that would be measured not only according to the number of nano-metric dimensions, but also to other properties that arise due to nanomaterial specific sizes, such as their heating property or more generally medical activity.

COMMON PITFALLS TO BE OVERCOME

Nanomaterial studies are prone to a number of pitfalls. Regulation could indicate how to overcome them, especially by discussing the following aspects (8):

• Do nanomaterial studies need to focus on nanomaterials themselves, their environment such as their supernate, or both? A number of compounds such as endotoxins may be in equilibrium or diffuse between the surface of nanomaterials and their environment, in which case such environment may be studied, e.g. by measuring its endotoxin concentration.

• When nanomaterials degrade, for example by transforming into ions, should degradation products be studied? Specific standards indicate how to study products of degradation, but they don't describe the transition between nanomaterials and products of degradation. On the one hand, the nature of this transition could be specified by explaining where it comes from (a change in nanomaterial size, composition, charge etc…). On the other hand, kinetic of degradation could be undertaken under detailed conditions.

• Nanomaterial properties depend on the environment in which they are located. They are different in water, a culture medium, inside a cell, in tissue, in blood etc... Regulation could list a series of environments that are representative to those encountered by nanomaterials during their use, and in which nanomaterial properties could be studied.

• Nanomaterials can interfere with methods used for their characterization, due to their size or interaction (aggregation/agglomeration). Regulation could explain how to avoid or remove these interferences.

NANOPARTICLE INTERACTIONS

Nanomaterials form assemblies, in which they interact with each other and their environment. Hence, its interacting properties should be taken into account. Regulation seems to distinguish between three types of interacting properties, associated with aggregation/agglomeration, opsonization, or dispersion/sedimentation. They are discussed below.



Aggregates versus Agglomerates. Nanoparticles used in nanomedicine are assemblies of nanoparticles in weak or strong interactions with each other. In order to take into consideration these different interactions, assemblies of nanoparticles were divided between those of strongly bound or fused nanomaterials (aggregates) and those of weakly bound nanomaterials (agglomerates). To provide a physical meaning to the terms ''aggregates'' and ''agglomerates'', these assemblies have been described as having their external surface that is either similar to the sum of the surface areas of each individual nanomaterial (agglomerates) or smaller than the sum of the calculated surface areas of each individual nanomaterial (aggregates). These definitions raise some concerns. On the one hand, they are only valid if nanomaterials are sufficiently distant from each other for agglomerates or close to each other for aggregates. It would therefore be beneficial to consider a separating distance between nanomaterials, which could be lower than an average distance in the case of aggregates and larger than this distance in the case of agglomerates, as illustrated in Figure 2. This distance could be estimated for each nanomaterial according to its size, composition, charge, and surrounding environment. On the other hand, when nanomaterials are interacting with each other they are in a dynamic system that evolves over time and can often easily switch from an aggregated to an agglomerated state or vice versa from an agglomerated to an aggregated state. Thus, it would also be valuable to specify the condition in time at which the aggregate/agglomerate is measured or observed, i.e. at one or several different time(s), or in a situation of equilibrium or saturation reached over time. In the literature, to distinguish between an aggregated and agglomerated condition, a difference between aggregated and well-dispersed nanomaterials is often highlighted. The author believes that the words agglomerated and well-dispersed describe a relatively similar situation.



Opsonization. One of the main interactions of nanoparticles with the organism is the so-called opsonization [START_REF] Aggarwal | Nanoparticle interaction with plasma proteins as it relates to particle biodistribution, biocompatibility and therapeutic efficacy[END_REF][START_REF] Corbo | Personalized protein corona on nanoparticles and its clinical implications[END_REF][START_REF] Nguyen | Photoelectron Spectroscopy Studies of Nanoparticles Dispersed in Static Liquid[END_REF]. In this mechanism, nanoparticles are covered with opsonins that bind at nanoparticle surface following their administration. Opsonins are then recognized by defense cells, which possess receptors for them, e.g. scavenger receptors, resulting in nanoparticle phagocytosis and clearance. Opsonization, whose efficacy depends on several parameters such as nanoparticle shape or type of coating, is an important factor that determines nanoparticle biodistribution properties. A standard that would explain how to deal with opsonization, i.e. measure its presence and/or efficacy as well as determine if/how/when such mechanism should be avoided or sought for, is currently lacking in current regulation  Dispersibility versus solubility. Interaction between nanomaterials is also governed by their soluble or dispersed state. Current regulation makes a distinction between these two conditions, since nanomaterial toxicity profiles can dependent upon this state. However, this approach seems to oversimplify the real situation of nanomaterials, which is often in-between a soluble and insoluble state.

Indeed, nanomaterials considered as insoluble can disintegrate or lose some of their constituents which end up as ions in solution or in the body and are solubilized. On the other hand, a nanomaterial may consist of both soluble and insoluble parts, for example when it is made of a vesicle with an aqueous core.

DOSE MEASUREMENTS

The injected dose is an essential parameter in the determination of the benefit/risk ratio of a nanomaterial. The current standards suggest considering the following parameters for its estimation.

First, the administered volume can determine the quantity of nanomaterials in a targeted organ.

Although an increase in such volume can result in a larger quantity of nanomaterials in such organ, the opposite situation can also occur, e.g. when nanomaterials induce an immune response that removes nanomaterials from this organ. Second, the method used for dispersing a nanomaterial, e.g. a proper solvent, pH or ionic strength of the dispersing medium, or the correct dispersing method often chosen among sonication, stirring, or shaking, can be essential in determining the quantity or dose of nanomaterial that is effectively administered. The dispersing method should disperse nanomaterials without damaging or modifying them and enable a control over certain dispersion parameters such as the volume/temperature of dispersion, or the type or quality of dispersing medium [START_REF] Hartmann | Techniques and protocols for dispersing nanoparticle powders in aqueous mediais there a rationale for harmonization?[END_REF]. Third, the length of time between the step of homogenization of the nanomaterial suspension and that of administration should be optimized. Often when this lengths of time is increased, sedimentation and/or aggregation become(s) more important, especially for nanomaterials in liquid suspension. Fourth, the types of forces involved between nanomaterials, such as diffusion, convection and/or sedimentation forces, which depend on nanomaterial properties such as nanomaterial size, charge, composition, or environment, determine the way in which nanomaterials organize in a given medium, directly impacting their distribution before and after administration and therefore also the dose that is effectively injected or effective after injection. Fifth, the size distribution and by extension the distribution in other nanomaterial properties than sizes can also determine the quantity of injected nanomaterials that is efficient, e.g. a large size distribution may hamper the heating power of a nanomaterial, resulting in a percentage of nanomaterial that is efficient, which is lower for a wide than narrow size distribution.

Sixth, the effective dose can also depend on interactions between nanomaterial and the organism, especially cells, e.g. nanomaterials could be more cytotoxic when they are internalized than when they remain outside of cells.

Furthermore, the most widely used parameters to estimate the dose of nanomaterial are the mass and number of nanomaterials per unit volume. Standards also recommend measuring the surface of nanomaterials, which depends on nanomaterial size and size distribution. The author guesses (since it is not explicitly mentioned in standards) that such measurement is recommended to gather some indication about the catalytic power of a nanomaterial, since the latter is believed to essentially occur at nanomaterials surface. Such assumption is however only partly true. To provide an example when a metallic nanoparticle is heated by the application of an alternating magnetic field, the external source of energy couples with the magnetic moment of the nanoparticle core without necessarily directly involving nanoparticle surface.

A final consideration concerns the method used for dose measurement, which often inaccurately estimates nanomaterial dose at least for two reasons, the presence of aggregates/agglomerates that distorts the measurements and the conditions of nanomaterials used for the measurement (for example in powder) that can be far from their real situation in the organism.

PREPARATION OF NANOMATERIALS

Figure 3 summarizes the recommended rules for nanomaterial preparation, which are summarized below [START_REF] Piktel | Recent insights in nanotechnology-based drugs and formulations designed for effective anti-cancer therapy[END_REF]. First, to approach the situations that would occur in the individual, a formulation, which is close from its ready-for-injection stage, should preferentially be used for toxicity/efficacy assessments.

Second, in order to correctly disperse nanomaterials, it is advisable to use an appropriate coating agent, dispersion medium, and/or mixing method. Third, to prevent nanomaterial contamination by impurities, the presence of impurities or toxic contaminants in the media used for making the preparation should be minimized. Fourth, the preparation should be stable, i.e. the dissolution and/or degradation of nanomaterials over time should be minimized. This may be achieved by preventing the change of one of its properties, such as the loss of its coating, or of part of its composition, and by monitoring such changes. Fifth, the dispersing medium should be adapted by adjusting its ionic strength, calcium, magnesium, or anion concentration, pH, presence of organic additives, and identity or concentration of dispersing agents. Sixth, the properties of the preparation should be described before and after its administration to the organism. Seventh, a proper method of sterilization should be chosen, mainly among filtration, autoclaving, irradiation, and chemical treatments, which enables to get rid of endotoxins and contaminants without destroying the nanomaterials, [START_REF] França | Sterilization Matters: Consequences of Different Sterilization Techniques on Gold Nanoparticles[END_REF][START_REF] Li | Superparamagnetic Iron Oxide Nanoparticles as MRI contrast agents for Non-invasive Stem Cell Labeling and Tracking[END_REF][START_REF] Vetten | Challenges facing sterilization and depyrogenation of nanoparticles: Effects on structural stability and biomedical applications[END_REF]. Seventh, the stability of the nanomaterial over time should be ensured.

CHARACTERISATION OF NANOMATERIALS

Characterization of nanomaterials is an essential step in their evaluation [START_REF] Coty | Characterization of nanomedicines: A reflection on a field under construction needed for clinical translation success[END_REF]. A framework has been set in place by current regulation to ease such task, which includes a descriptor (description to identify a nanomaterial parameter), clarification (additional information about such parameter), pertinence  Shape, which corresponds to the contour of nanomaterial surface, can be various for the same nanomaterial, and is defined by specific words designing the type of shape, for example spherical, rectangular, or cubic.

(



Isotropy/anisotropy, which is measured by estimating either the ratio between the smallest and largest length of a nanomaterial for geometry anisotropy or by measuring the anisotropy constant for magnetic anisotropy.

 Surface area, which is defined as the surface of a nanomaterial accessible to a gas, liquid, or adsorbent surrounding the nanomaterial.



Composition, which includes a chemical compositions with information about the level of impurities and stoichiometry of a nanomaterial and a crystalline composition defining the parameters of the crystalline and spatial group of a nanomaterial.

 Surface charge or zeta potential, which is the electric charge at nanomaterial surface, where large values of zeta potential can stabilize nanomaterial dispersion by promoting repulsions between them and low zeta potential values can lead to stronger nanomaterial attraction than repulsion resulting in nanomaterial aggregation.



Solubility/dispersibility, which is the degree or concentration up to which a nanomaterial can be homogenously or uniformly dissolved or mixed in a solvent or material without forming a precipitate, aggregating, dissolving, or changing one of its property (size, shape, charge for example).

When feasible, an uncertainty should be associated with the values of the above parameters, which comes on the one hand from the different values of these parameters estimated when the measurements are repeated and on the other hand from the uncertainty associated with the measuring device.

In order to measure the above parameters, various methods of nanomaterial characterization have been recommended by current nanomaterial regulation, as summarized in table 1. They rely on:  A balance between different forces applied on nanomaterials in: i), analytical centrifugation (AC), where nanoparticles are inserted in an ultracentrifuge and exposed to centrifuge and diffusion forces while the formation of different fractions is monitored by an optical detection system, enabling the measurement of nanoparticle molecular weight, density, and diameter [START_REF] Carney | Determination of nanoparticle size distribution together with density or molecular weight by 2D analytical ultracentrifugation[END_REF], ii), aerosol particle mass analyzer (APMA), in which nanoparticles are placed inside a rotating cylinder in the presence of an applied voltage, and exposed to centrifugal and electrostatic forces, allowing the measurement of nanoparticle mass/charge ratio.



The temperature increase of a nanomaterial followed by: i), the measurement of the amount of energy released or absorbed by a nanomaterial as a function of temperature, nanomaterial specific heat capacity, the temperature or energy of melting, fusion, crystalline phase transition, precipitation, and denaturation of a nanomaterial (21), using differential scanning calorimetry (DSC), ii), the production of ions detected by a mass spectrometer, which enables the determination of nanomaterial concentration, [START_REF] Fabricius | ICP-MS-based characterization of inorganic nanoparticles-sample preparation and off-line fractionation strategies[END_REF], using Inductively Coupled Plasma Mass Spectrometry (ICP-MS).



The application of a uniform electric field on nanomaterials, inducing the separation of nanomaterials of different sizes, charges, or masses, followed by the measurement of nanomaterial electrophoretic mobility, often converted to a 'zeta potential', using: i), for nanoparticle in suspension, electrophoretic light scattering (ELS), which was further refined in capillary electrophoresis (CE) [START_REF] Chetwynd | Current Application of Capillary Electrophoresis in Nanomaterial Characterisation and Its Potential to Characterise the Protein and Small Molecule Corona[END_REF],

ii), for nanoparticle in a gas, differential mobility analyzer (DMA), where these techniques enable the determination of nanoparticle size and size distribution [START_REF] Flagan | Differential mobility analysis of aerosols : a tutorial[END_REF][START_REF] Guh | Electrospray-differential mobility analysis of bionanoparticles[END_REF].



The application of a vertical separating force onto a horizontal laminar flux of nanomaterials using field flow fractionation (FFF), which enables the separation of nanomaterials with different sizes, masses or compositions in individual fractions, where each fraction can be analyzed using a light scattering technique [START_REF] Williams | Field-Flow Fractionation: Addressing the Nano Challenge[END_REF].



Interactions between an electron beam and nanomaterials in: i), Auger electron spectroscopy (AES), where a beam of electrons with relatively low energy (<  2 keV) and penetration depth focused on nanomaterials provides information about nanomaterial surface, [START_REF] Baer | Application of surface chemical analysis tools for characterization of nanoparticles[END_REF], ii), Electron energy loss spectroscopy (EELS), where a beam of electrons is inelastically scattered by a nanomaterial, leading to a series of interactions such as inner shell ionization, from which the composition of the nanomaterial can de deduced, iii), Scanning electron microscopy (SEM), where the surface of a nanomaterial is exposed to a beam of electron, providing information about surface topography and composition, iv)

Secondary ion mass spectrometry (SIMS), in which a beam of electrons is focused on nanomaterial surface producing secondary ions whose mass/charge ratio provides the composition of the nanomaterial, iv), Transmission electron microscopy (TEM), in which a beam of electrons crosses a thin layer of nanomaterials, given access to nanoparticle size, size distribution, shape, geometry.



Interactions between X-rays and nanomaterials using: i), Energy-dispersive X-ray spectroscopy (EDX), where a nanomaterial is exposed to a beam of protons, electrons, or X-rays, leading to the emission of an X-ray, whose measured energy is characteristic of the emitting element and therefore enables nanomaterial composition to be determined, ii), Small-angle X-ray scattering (SAXS), which measured the difference in density between nanomaterials and leads to an estimate of nanomaterial size and mass, iii), X-ray photoelectron spectroscopy (XPS), where a nanomaterial is irradiated with a X-ray beam and the nanomaterial surface chemistry is determined from the properties of the emitted electrons, iv), X-ray diffraction (XRD), where the intensity of X-rays scattered by a nanomaterial enables the determination of nanomaterial composition and structure.



Interactions of light with nanomaterials, including: i), UV/Vis absorption spectroscopy from which the composition and concentration of a nanomaterial can be deduced, ii), fluorescence spectroscopy (FS), where the fluorescent properties of a nanomaterial can be correlated to its structure ii), Raman scattering imaging (RSI) in which light is scattered by nanomaterial inducing a shift in its frequency that can be related to nanomaterial composition, ii), infra-red absorption spectra of nanomaterials exposed to light with a broad spectrum of wavelengths, using FT-IR spectroscopy, giving access to the different types of bonds between a nanomaterial and the molecules at its surface iii), the creation of a nanomaterial grating under the application of an electric field that diffracts light using induced grating (IG), and enables the determination of nanomaterial sizes, iv), laser diffraction by nanomaterials, which can provide information about nanomaterial size and shape (28), v), analysis of light scattered by a single nanomaterial to estimate nanoparticle size and composition, using single particle light interaction (SPLI), vi), static light scattering (SLS) in which the molecular weight of a nanomaterial is deduced from the relation between the intensity of light scattered by a nanomaterial and its molecular weight and size, as determined by the Rayleigh theory, vii), dynamic light scattering (DLS), where nanomaterials suspended in liquid that are prone to Brownian motion are illuminated leading to a light scattering signal from which nanomaterial hydrodynamic size, size distribution, and zeta potential can be deduced [START_REF] Lim | Characterization of magnetic nanoparticle by dynamic light scattering[END_REF][START_REF] Pecora | Dynamic light scattering measurement of nanometer particles in liquids[END_REF], viii) nanoparticle tracking analysis (NTA), which works similarly to DLS except that it enables to determine the size of individual nanomaterials whereas DLS monitors the behavior of nanomaterials collectively.



The use of acoustic systems in: i), acoustic detection in acoustic spectroscopy (AS), where nanomaterials are exposed to light producing acoustic waves that are detected, hence revealing the presence and quantity of nanomaterials, [START_REF] Cook | Quantitative Photoacoustic Imaging of Nanoparticles in Cells and Tissues[END_REF], or ii) electroacoustic velocity (EV) [START_REF] Greenwood | Review of the measurement of zeta potentials in concentrated aqueous suspensions using electroacoustics[END_REF].



The adsorption of a gas at the surface of nanomaterials using the Brunauer-Emmett-Teller (BET) method, yielding an estimate of the specific surface area of a nanomaterial (33)



The formation in a mixture of nanomaterials and supersaturated gas of droplets larger than nanomaterials, which can be detected laser nephelometry [START_REF] Kulmala | The condensation particle counter battery (CPCB): A new tool to investigate the activation properties of nanoparticles[END_REF], using a technique called condensed particle counter (CPC).



The separation of nanomatetrials having different surface properties, charges, sizes, hydrophobicity/hydrophilicity, geometries using various types of liquid chromatography, such as size exclusion chromatography or high performance liquid chromatography, which is combined with a method such as mass spectroscopy to enable the identification of the nanomaterial composition, using liquid chromatography mass spectroscopy (LCMS).



The deposition of nanomaterial on top of quartz crystal, leading to a change in the oscillation frequency of the crystal and enabling the nanomaterial mass measurement with a resolution of up to ~ 2 ng per cm 2 , using a quartz crystal microbalance (QCM)



The flow of nanomaterials through a fluidic channel, which modifies the frequency of a contilver, providing an estimate of the buyant and dry mass as well as size of a nanomaterial, using a technique called resonant mass measurement (RMM).



The use of probe positioned above the surface of a nanomaterial to detect various nanomaterial properties using scanning probe microscopy (SPM). As an example of property, the force applied by the nanomaterial on the probe can be measured using atomic force microscopy (AFM).

Table 2 summarizes the different nanomaterial parameters that can be measured by these various methods of characterization.

The results of the characterization shall be included in a report that mentions the description of the nanomaterials using the measured parameters, nanomaterial preparation, the conditions under which nanomaterial measurements are made, the methods, equipment, and laboratory used to determine nanomaterial properties, the values of the measured parameters and their associated uncertainty, and the experimental protocols relying on identified standards.

TOXICOLOGIC EVALUATION OF NANOMATERIALS

The types of toxicity test that need to be carried out on a nanomaterial depend on the duration of interaction between the nanomaterial and the organism, the type and number of organs, cells, biological entity with which a nanomaterial is in contact, are listed below :

 Cytotoxicity, evaluated according to ISO 10993-5, is a toxicity induced at cellular level by nanomaterials [START_REF] Ivask | Methodologies and approaches for the analysis of cell-nanoparticle interactions[END_REF]. During its evaluation, as summarized in Figure 5, a number of parameters that can have an impact on it can be taken into consideration, such as: i), the type of test used to measure it, e.g.

nanoparticle cytotoxicity was shown to be slightly more pronounced with the MTT test than with neutral red assay, or alarm blue assay [START_REF] Costa | In vitro cytotoxicity of superparamagnetic iron oxide nanoparticles on neuronal and glial cells. Evaluation of nanoparticle interference with viability tests[END_REF], ii), the composition of nanomaterials, e.g. iron oxide nanomaterials appear to be relatively non-cytotoxic compared with other compositions iii), the type of coating surrounding the nanomaterials, e.g. oleic acid is reported to be less cytotoxic than silica (36), iv), the type of tested cells, by distinguishing between those inducing an absorption, such as phagocytizing cells (murine macrophages RAW264,7, human THP-1 cells as differentiated macrophages), and those not responsible for such mechanism (murine fibroblasts 3T3, murine fibroblasts L929, human keratinocytes HaCaT), v) the incubation time, vi), the presence (or not) of a serum surrounding cells, vi), cellular internalization, which was usually shown to increase with increasing quantity and duration of nanomaterial incubated, (37), vi), the quantity of nanomaterial incubated with cells, vii), the rate of nanomaterial sedimentation / aggregation, e.g. nanomaterials were reported to be more cytotoxic when they sedimented and/or aggregated, (  Sensibilisation, evaluated according to ISO 10993-10, using: i), the application of a patch containing nanomaterials at the surface of animal skin that releases nanomaterials followed by the monitoring of erythema and edema (tests BT, GPMT), ii) the local application of nanomaterials at the back of animal ears followed by the measurement of the proliferation of lymphocytes in lymph nodes (LLNA test).

 Irritation, assessed following ISO 10993-10, tested on rabbits in which 0.5g/0.5mL of nanomaterials is applied on their skin, the nanomaterial region is covered by a bandage for a few hours, and the presence of erythema and edema is monitored following removal of the bandage. Such presence can come from the large exposed surface that can capture these entities [START_REF] Li | pH-and NIR Light-Responsive Polymeric Prodrug Micelles for Hyperthermia-Assisted Site-Specific Chemotherapy to Reverse Drug Resistance in Cancer Treatment[END_REF]. The American pharmacopeia recommends treating nanomaterials at a sufficiently high temperature for a long enough time to reduce the concentration of endotoxins to an acceptable level (generally a minimum temperature of 250 °C for more than 30 min, USP 1995). Pyrogenicity can be measured using: i), the Limulus amebocyte lysate (LAL) test following ISO 29701 after removing the interferences between this test and nanomaterial [START_REF] Dobrovolskaia | Ambiguities in applying traditional Limulus Amoebocyte Lysate tests to quantify endotoxin in nanoparticle formulations[END_REF], ii), the monocyte activation test (MAT), or iii), the rabbit test.

NANOMATERIAL BIODISTRIBUTION

Nanomaterial biodistribution properties depend on their intrinsic properties such as their size, their chemical composition, their charge, their hydrophobicity/hydrophilicity, their shape, their geometry, their tendency to form aggregates/agglomerates. They are also function of the conditions under which the biodistribution tests are carried out, i.e. their mode of administration, the choice of animal species, nanomaterial environment such as injection medium, the injected dose, the types of examined samples/tissues, and the frequency of the examinations. Finally they are influenced by the characterization method, which is employed. A first remark concerns the large number of these parameters, which can lead to different biodistribution profiles for the same nanomaterial tested under different conditions. To the author knowledge, a standard specifying a set of parameters that should be used for biodistribution studies is lacking. Biodistribution behaviors are difficult to generalize to all nanomaterials because of the differences between these materials. However, some studies have attempted such generalization by examining the size effect. They suggested that nanomaterials smaller than 15 nm distribute over the entire body, nanoparticles of intermediate size (between 15 nm and 200 nm) accumulate in mononuclear phagocytic system (SPM) organs such as liver and spleen, while nanoparticles larger than 200 nm are preferentially found in spleen. However, their analysis mainly relies on a presumed size-dependent diffusion of nanoparticles through the orifices, which give access to various organs. They don't seem to fully take into consideration other mechanisms such as immune reactions that can play an important role in distribution properties and are influenced by other factors than size such as nanomaterial surface properties. Furthermore, the method used to evaluate the biodistribution properties is also subject to unresolved discussions in current standards. First, if the material used for such studies is not identical to that injected in humans, for example if it is associated with a fluorescent or radioactive substance to facilitate its detection, is it still considered that its behavior is representative to that expected in a human? More generally, what level of modification of a nanomaterial can be acceptable for carrying out a valid biodistribution study? The same type of consideration surrounds the choice of the animal species that must have a metabolism comparable to that of a human. In this respect, the choice of the rodent model is still questionable. Second, should one focus primarily on the modification of the nanomaterial in the body, or rather on the effect induced by the presence of nanomaterials on the organism? Both aspects require different methods of characterization. Third, how frequently should the study be performed? Nanomaterials can furtively diffuse through certain organs, making their presence undetected if organs are not often enough monitored. Fourth, to which types of nanomaterials do the parameters describing nanomaterial kinetic of elimination and biodistribution, such as clearance and half-life, apply? Most of the time, nanomaterials gradually degrade over time in the body. Thus, when measuring one of these parameters, it characterizes a nanomaterial during its transformation, for example changing size between before and after administration or even, in a more extreme case, being completely dissolved. What is the level of transformation after which one considers that the nanomaterial studied is no longer the same? A transformation threshold value could be introduced such as an acceptable decrease in size or percentage of variation of certain properties, beyond which the nanomaterial would be considered different from the original one. Above this threshold, a value of a kinetic parameter could no longer be attributed to a nanomaterial.

RISK ANALYSIS

The aim of the risk analysis [START_REF] Dekkers | Towards a nanospecific approach for risk assessment[END_REF][START_REF] Halappanavar | Promise and peril in nanomedicine: the challenges and needs for integrated systems biology approaches to define health risk[END_REF], carried out according to ISO15499, ISO14971, ISO10993-22, is to estimate the probability and the severity of possible damages caused by nanomaterials depending on the level of exposure and the foreseen clinical application of these materials. The different aspects of a risk analysis are summarized in Figure 6. They should enable a comparison between the damages that they might cause with well-established harms induced by similar materials. Damages could come from the misuse of nanomaterials. Their severity essentially depends on how long nanomaterials remain in the organism, the way in which they are administered, the quantity of nanomaterials that is absorbed by the organism, and the duration of nanomaterial interaction with biological material. They can result from the use of an incorrect method of characterization, which fails to provide relevant characteristics, lacks sensitivity, or evaluates the properties of nanomaterials in an unsuitable configuration, which is not representative of its condition during its use. In addition to being enriched by the analysis of the state of the art on a given material, the risk analysis should consider factors that yield damages such as wrongly determined properties of nanomaterials, erroneous sample preparation, false measurement of nanomaterials, or inappropriate methods used for evaluating the bio-distribution and toxicity of these materials. The risk analysis relies in large part on the evaluation of the toxicity and biodistribution properties of nanomaterials as a function of nanomaterial doses. To achieve a proper risk analysis, the type of tests carried out on nanomaterials should be function of the mode of action of these materials in the foreseen medical indication. The risk management process is summarized in Figure 1 of ISO10993-22.

CONCLUSION

In this article, I have presented different aspects of current nanomaterial regulation, which concerns the definition, fabrication, characterization, interactions of these materials as well as their toxicity, biodistribution, and risk assessment. It appears important that such regulation covers the various aspects of nanomaterial development. In additions to the previously mentioned unresolved questions, it seems that the following aspects have not been fully addressed by current regulation:



The definition of some criteria to help choosing the most relevant characterization and toxicity assessment methods among the large number of different ones presented in standards,



The determination of specific conditions in which these methods should be employed since they can have strongly impact the study outcomes,

FIGURES AND TABLES:

Figure 1: List of standards that apply to nanomedicine, which were taken into consideration to write this manuscript. Table 1: The mechanisms of action, drawbacks and advantages and parameters measured for different nanomaterial characterization methods.

Table 2:

The characterization methods used to measure the various nanomaterial parameters.

  , viii) diffusion versus sedimentation, xi) the production of radical oxygen species that can induce the production of cytokines and chemokines involved in pro-inflammatory responses , xii) degradation of nanomaterials leading to the release of certain atoms / ions from nanomaterials. During cytotoxicity assessment, it is necessary to take into consideration the interferences between nanomaterials and both calorimetric/fluorescent agents whose luminescence can be decreased by light absorption from nanomaterials and the compounds of the cellular growth medium.Genotoxicity, whose method of evaluation are described 10993-3, include : i) the in vitro micronucleus test in which the number of cells with two nucleus in the presence of CytoB is counted(OECD LD 487), ii),the search for DNA alterations when the thymidine kinase gene expresses hypoxanthine-guanine phosphoriboxyl transferase (HPRT) or xanthine-guanine phosphoriboryl transferase (XPRT) using mammalian cells (OECD 476), iii) the monitoring of DNA or chromosome damages when the genes L5178Y and TK6 are used to express mouse lymphoma (OECD 490), iv) the in vivo alkaline comet assay in which DNA strand breaks are looked for in cells or nuclei isolated from animal tissues (OECD489), v) the in vivo micronucleus test where micronucleus are searched in erythrocytes of mammals (OECD 474), vi) mammalian in vivo chromosome aberration test in which chromosome aberrations are monitored in bone marrow cells of animals (OECD 475), vii) TGR gene mutation assays in which mutations are measured in rodent tissues (OECD 488).  Cancerogenicity can be evaluated according to OCDE451, OCDE 453, and ISO 10993-3, using rodents receiving nanomaterials frequently during their life while monitoring the apparition of tumors as well as other side effects.  Toxicity for reproduction, which may be assessed according to ISO 10993-3, ODCE 421, ODCE 414, ODCE 415, ODCE 416, or ODCE 422, using female rodents or rabbits receiving nanomaterials a few days after coupling followed by the monitoring of toxicity effects on fetus developments.  Immuno-toxicity, assessed following ISO/TS 10993-20, EHC 180, EHC 236, through: i) the detection of the first signs of immunosuppression/immunostimlation following repeated administration of nanomaterials in vivo, ii), the monitoring of nuclear factor kappa B produced by immune cells, or iii) the evaluation of nanomaterial phagocytosis, chimotaxis, or of the production of nitric acid by macrophages.



  Hemocompatibility, assessed according to ISO 10993-4, measures thrombosis, coagulation, plaquette agregation, leucocyte activation, hemolysis, activation of complement system, using the coculture model of endothelial and monocyte cells.Systemic toxicity, evaluated following ISO 10993-11, measures the potential toxicity of nanomaterials towards organs, using rodents receiving a unique (acute toxicity), or repeated (chronic toxicity) nanomaterial administration. The frequency and dosage of the administration are adapted depending on the foreseen clinical protocol. To assess toxicity, the variations in corporal mass or water/food consumption of the animals are measured. The macroscopic or clinical pathologies are monitored. Toxicity is also examined through histological analysis, variations in masses, or analysis of organs, tissues, or blood. Certain specific nanomaterial properties can have an impact on systemic toxicity such as: i), their faculty (or not) to cross physiological barriers, ii), their potential dissolution in contact with organs or tissues, iii), their possible accumulation in organs/tissues. Specific care should be given to reticulum endoplasmic system (liver and spleen), kidney, brain, bone marrow, where some nanomaterials have been shown accumulate. Pyrogenicity, assessed according to ISO 29701, ISO 10993-11, USP 85, USP 151, ANSI/AAMI ST72, could be due to the presence of endotoxins such as lipopolysaccharides at nanomaterial surface.

Figure 2 :

 2 Figure 2: A schematic picture showing the different types of interactions between nanomaterials,

Figure 3 :

 3 Figure 3: A schematic picture summarizing the recommendations for nanomaterial preparation.

Figure 4 :

 4 Figure 4: A schematic picture showing the different parts of a nanomaterial that need to be

Figure 5 :

 5 Figure 5: A summary of the different aspects to be considered to carry out cytotoxicity assessment of a

Figure 6 :

 6 Figure 6: A declination of the different steps to lead a risk analysis, based on an analysis of the severity

  various types of molecules or entities associated or in interaction with such material, such as antibody, protein, lipid, drug, targeting agent, as shown in Figure4. It needs to distinguish between properties of interacting and non-interacting nanomaterials. It also has to define the variability in nanomaterial properties between one batch and another, the method and conditions used for characterization, e.g. nanomaterial inside or outside its packaging, in powder, liquid, or gas form, homogenized or not, before or after its administration, at various concentrations, pH, temperature, redox state, or exposed to various types of stress. The different nanomaterial properties, which can influence their toxicity/efficacy or behavior in vivo, are listed below:Size/size distribution, evaluated by an estimate of the spherical diameter, length, width, surface, volume of a nanomaterial, and distribution of these parameters.

toxicological relevance of a parameter), measureande (nanomaterial variable that is measured). It shall not only determine the properties of a nanomaterial as a whole, but also lead to a description of the different constitutive parts of such material, such as its central part, coating, corona, surrounding medium, or



Aggregate/Agglomerate, which are nanomaterial assemblies in strong (aggregates) or weak (agglomerates) interaction, characterized by their size and frequency relatively to those of noninteracting nanomaterials.



The setting of quality control tests that could ensure reproducibility and accuracy of all protocols [START_REF] Peltonen | Practical guidelines for the characterization and quality control of pure drug nanoparticles and nano-cocrystals in the pharmaceutical industry[END_REF], especially during fabrication, characterization, toxicity, and efficacy assessments of nanomaterials,



The listing of reference materials [START_REF] Roebben | Reference materials and representative test materials to develop nanoparticle characterization methods: the NanoChOp project case[END_REF] used to compare the properties of a given nanomaterial with those of a similar material with well-established behaviors,



The description of specific conditions for the up-scaled production of nanomaterials [START_REF] Park | Measurement of inherent material density of nanoparticle agglomerates[END_REF]46), which could result in significant changes of nanomaterial properties, where standards could clarify the extend up to which such modification would be acceptable. from the region of Paris (A1401025Q), the ANR (''Agence Nationale de la Recherche'') Méfisto, as well as the Universities Paris 6 and Paris 11. We also would like to thank the Nomis Foundation and Markus Reinhard for their support.

DECLARATION OF INTEREST:


Mathieu Moog
email: mathieu.moog@sorbonne-universite.fr

Sofiane Schaack

Fabio Pietrucci

A Marco Saitta

Unsupervised Exploration of MoS 2 Nanoclusters Configurations : Structures, Energetics, and Electronic Properties

We propose an efficient method to explore the configuration space of nanoclusters by combining together ab initio molecular dynamics, metadynamics and data clustering algorithms. On one side, we employ collective variables sensitive to topological changes in the network of interatomic connections to map the configuration space; on the other, we introduce an automatic approach to select, in such space, representative structures to be optimized. In this way, we show that it is possible to sample thoroughly the set of relevant nanocluster geometries, at a limited computational cost. We apply our method to explore MoS 2 clusters, that recently raised a sizable interest due to their remarkable electronic and catalytic properties. We demonstrate that the unsupervised algorithm is able to find a large number of low-energy structures at different cluster sizes, including both bulk-like geometries and very different topologies. We are thus able to recapitulate, in a single computational study on technologically-relevant MoS 2 clusters, the results of all previous works that employed distinct techniques like genetic algorithms or heuristic hypotheses. Furthermore, we found several new structures not previously reported. The ensemble of MoS 2 cluster structures is deposited in a publicly accessible database.

Introduction

In recent years, molybdenum-sulfur materials have been intensely studied by the scientific community for a range of applications, 1 from hydrodesulfurization catalysts to transistors. [START_REF] Yoon | How good can monolayer MoS2 transistors be?[END_REF][START_REF] Radisavljevic | Single-layer MoS2 transistors[END_REF][START_REF] Yin | Single-layer MoS2 phototransistors[END_REF] Interesting forms of this material go from the bulk to monolayers to nanostructures such as inorganic fullerenes [START_REF] Pietsch | The quest for inorganic fullerenes[END_REF] and nanoplatelets, [START_REF] Bertram | Nanoplatelets made from MoS2 and WS2[END_REF][START_REF] Zhang | Synthesis and characterization of a molybdenum disulfide nanocluster[END_REF][START_REF] Helveg | Atomic-scale structure of single-layer MoS2 nanoclusters[END_REF][START_REF] Li | Electronic properties of MoS2 nanoparticles[END_REF] either recently synthesized or sought after.

For all these reasons, it is desirable to understand the formation mechanisms of Mo n S m nanostructures as well as their relationship with extended forms in terms of structure and properties. Consequently, this has been the focus of a number of experimental [START_REF] Lauritsen | Size-dependent structure of MoS2 nanocrystals[END_REF][START_REF] Helveg | Atomic-scale structure of single-layer MoS2 nanoclusters[END_REF] and theoretical studies. [START_REF] Bertram | Experimental and theoretical studies on inorganic magic clusters: M4X6 (M=W,Mo, X=O,S)[END_REF][START_REF] Seifert | MonS2n+x clustersmagic numbers and platelets[END_REF][START_REF] Gemming | Transition metal sulfide clusters below the cluster-platelet transition: theory and experiment[END_REF][START_REF] Gemming | Electronic and geometric structures of MoxSy and WxSy (x=1,2,4; Y=1-12) clusters[END_REF][START_REF] Murugan | Ab initio study of structural stability of Mo-S clusters and size specific stoichiometries of magic clusters[END_REF][START_REF] Murugan | Assembling nanowires from Mo-S clusters and effects of iodine doping on electronic structure[END_REF][START_REF] Murugan | Atomic structures and magnetism in small MoS2 and WS2 clusters[END_REF][START_REF] Wang | Probing the smallest molecular model of MoS2 catalyst: S2 Units in the MoSn-/0 (n=1-5) Clusters[END_REF][START_REF] Singh | Novel cage clusters of MoS2 in the gas phase[END_REF][START_REF] Mayhall | Molybdenum oxides versus molybdenum sulfides: geometric and electronic structures of Mo3Xy-(X=O,S and y=6,9) clusters[END_REF][START_REF] Laraib | First principles modeling of Mo6S9 nanowires via condensation of Mo4S6 clusters and the effect of iodine doping on structural and electronic properties[END_REF][START_REF] Patterson | Structure of Molybdenum and Tungsten Sulfide MxSy+ Clusters: experiment and DFT Calculations[END_REF][START_REF] Hafizi | Stable iso-mers and electronic, vibrational, and optical properties of WS2 nano-clusters: A firstprinciples study[END_REF][START_REF] Hafizi | Stable iso-mers and electronic, vibrational, and optical properties of WS2 nano-clusters: A firstprinciples study[END_REF][START_REF] Wang | Small stoichiometric (MoS2)n clusters with the 1T phase[END_REF] In some cases, W n S m and W n O m clusters have been studied in parallel with Mo n S m [START_REF] Bertram | Experimental and theoretical studies on inorganic magic clusters: M4X6 (M=W,Mo, X=O,S)[END_REF][START_REF] Gemming | Transition metal sulfide clusters below the cluster-platelet transition: theory and experiment[END_REF] due to the chemical similarity between the corresponding metals and chalcogens. Investigating different stoichiometries and cluster sizes provided indeed useful information: so called "magic clusters" seem to indicate a tendency to favor over-saturation in sulfur and monolayer-like structures with growing size, rather than 3D arrangements. [START_REF] Bertram | Nanoplatelets made from MoS2 and WS2[END_REF] In most of the previous studies, the search for the possible structures of stable clusters was based on the enumeration of chemically-intuitive geometries, using heuristic arguments or starting from the bulk material as reference. This approach clearly is at high risk of missing important structures as hinted at by the typically large energy gap between the most stable candidate found and the others. Moreover it lacks objectivity and transferability to other systems. In fact, the importance of efficient and unbiased structural search algorithms in condensed matter in general and in nanostructures in particular is well appreciated. [START_REF] Rossi | Searching for low-energy structures of nanoparticles: a comparison of different methods and algorithms[END_REF][START_REF] Pickard | Ab initio random structure searching[END_REF][START_REF] Oganov | How Evolutionary Crystal Structure Prediction Works and Why[END_REF] In this respect, two recent studies used evolutionary algorithms to explore the configuration space of MoS 2 [START_REF] Wang | Small stoichiometric (MoS2)n clusters with the 1T phase[END_REF] (although the search was supplemented in this case by arbitrary humanprovided structures ex post) and WS 2 clusters. [START_REF] Hafizi | Stable iso-mers and electronic, vibrational, and optical properties of WS2 nano-clusters: A firstprinciples study[END_REF] In this work we propose an alternative sampling strategy, based on ab-initio MD simulations, accelerated with metadynamics. [START_REF] Laio | Escaping free-energy minima[END_REF] Finite-temperature enhanced sampling approaches have become widespread as they allow to explore generic transformation mechanisms in chemistry, physics and biology, irrespective of the height of free-energy barriers. [START_REF] Pietrucci | Strategies for the exploration of free energy landscapes: unity in diversity and challenges ahead[END_REF][START_REF] Camilloni | Advanced simulation techniques for the thermodynamic and kinetic characterization of biological systems[END_REF] However, their application to nanostructures is still sporadic, albeit successful. [START_REF] Pietrucci | Graph theory meets ab initio molecular dynamics: atomic structures and transformations at the nanoscale[END_REF][START_REF] Pietrucci | Fate of a graphene flake: A new route toward fullerenes disclosed with ab initio simulations[END_REF][START_REF] Pavan | Sampling the energy landscape of Pt 13 with metadynamics[END_REF][START_REF] Pavan | Metallic nanoparticles meet metadynamics[END_REF][START_REF] Tribello | Analyzing and driving cluster formation in atomistic simulations[END_REF] A problem related to the exploration of configuration space with enhanced sampling consists in finding suitable unsupervised procedures to select a limited number of representative geometries from finite-T trajectories, which are further relaxed to identify local energy minima, thus providing candidates for stable clusters. Here we address this task exploiting a density clustering procedure initially introduced for protein structure classification, [START_REF] Daura | Peptide folding: when simulation meets experiment[END_REF] obtaining in this way an automated protocol. Both for metadynamics and for classification, the atomic struc-ture is described by topological collective variables ("social permutation invariant" SPRINT coordinates) [START_REF] Pietrucci | Graph theory meets ab initio molecular dynamics: atomic structures and transformations at the nanoscale[END_REF] that are able to track the changes of interatomic bond networks in a range of condensed-matter systems. These variables have been effectively employed in the literature both for exploration [START_REF] Pietrucci | Fate of a graphene flake: A new route toward fullerenes disclosed with ab initio simulations[END_REF][START_REF] Zheng | Car-Parrinello molecular dynamics+metadynamics study of high-temperature methanol oxidation reactions using generic collective variables[END_REF][START_REF] Wang | Formation of the H2SO4• HSO-4 dimer in the atmosphere as a function of conditions: a simulation study[END_REF][START_REF] Balan | Firstprinciples study of boron speciation in calcite and aragonite[END_REF][START_REF] Fu | Assessing generic collective variables for determining reaction rates in metadynamics simulations[END_REF] and classification of structure. [START_REF] Lai | Stochastic atomistic simulation of polycyclic aromatic hydrocarbon growth in combustion[END_REF][START_REF] Johansson | Soot precursor formation and limitations of the stabilomer grid[END_REF][START_REF] Johansson | Formation and emission of large furans and oxygenated hydrocarbons from flames[END_REF] Overall, our methodology has several advantages. It requires a very limited number of parameters: essentially the typical interatomic distances required to define SPRINT coordinates, the shape of the metadynamics repulsive Gaussians, and the granularity of the density clustering (see Methods section for details). For this reason, we expect this protocol to be readily transferable to many other types of nanostructures. In addition it also provides an identification of transition pathways, which can be of great interest for experimental synthesis, between different metastable structures, a "by-product" inherent to the use of MD trajectories. Finally this method has a limited computational cost on nowadays hardware.

We showcase the efficiency of our method by applying it to three different cluster sizes:

Mo 2 S 4 , Mo 3 S 6 and Mo 4 S 8 .

Methods

We started by running long exploratory ab initio Born-Oppenheimer MD simulations using the CPMD 45 code accelerated by metadynamics [START_REF] Laio | Escaping free-energy minima[END_REF] as implemented in the PLUMED plugin. [START_REF] Bonomi | PLUMED: a portable plugin for free-energy calculations with molecular dynamics[END_REF] We used DFT in the Perdew-Burke-Ernzerhof approximation, 47 employing Goedecker-Teter-Hutter pseudopotentials to describe core electrons, 48,49 a 1 fs timestep and a kinetic energy cut-off of 120 Ry with a convergence of 10 -5 Ry.

The metadynamics trajectories were stored every 5 fs. For each cluster size, several independent trajectories were generated, starting from random initial atomic positions in a cubic periodically-repeated box of 12 Å side with the objective which launched until they failed or reached at least 100ps, in order to explore the space from different conditions. In all we launched 4 simulations for Mo 2 S 4 , 8 for Mo 3 S 6 and 18 for Mo 4 S 8 (in this case several simulations stopped under 20ps being trapped in high energy states). By adding together all trajectories of the same cluster sizes, their total lengths reached 250 ps for Mo 2 S 4 , 900 ps for Mo 3 S 6 and 1200 ps for Mo 4 S 8 adding up to a overall length of 2.35ns.

In metadynamics the exploration of configuration space is accelerated by means of a history-dependent potential built as a sum of Gaussian repulsive functions deposited along the trajectory projected on collective variables (CV). The efficiency of the algorithm is therefore highly dependent on the choice of CV, that must be able to track all possible structural transformations of interest. In this work we used SPRINT collective variable, [START_REF] Pietrucci | Graph theory meets ab initio molecular dynamics: atomic structures and transformations at the nanoscale[END_REF] based on graph-theory and able to capture the "social" behavior of atoms, in terms of the topology of the network of interatomic bonds. Starting from the interatomic distance matrix of the system, a sigmoid function decaying from 1 to 0 is applied to each distance d ij between atoms i and j:

δ(d ij ) = 1 -( d ij d 0 ) m 1 -( d ij d 0 ) n (1)
with the parameters set to d 0 = 4.5 Å, m = 8 and n = 24 based on the pair correlation function of an unbiased molecular dynamics simulation of a cluster, computed using a short (2ps) and unbiased molecular dynamics simulations, in order to include up to the second nearest neighbor.

The resulting adjacency matrix is diagonalized, and SPRINT CVs are defined based on the largest eigenvalue and the corresponding eigenvector as:

S i = √ N λ max ν max,sorted i (2)
where N = number or atoms.

The resulting positive values, one per atom, are sorted (keeping Mo and S separated) in order to enforce invariance by permutation of atoms of the same type. We applied Ndimensional Gaussian-shaped repulsive potentials of height 0.04 kJ.mol -1 and width σ = 0.7, added every 10 fs. Those parameters were chosen after some short (less than 2ps) molecular dynamics test simulations, in which that we did manage both to fill the initial potential wells and we saw drops in bias potential, indicating exploration of new wells corresponding to potential stable structures.

The atomic trajectory projected in the N -dimensional SPRINT space is then classified using the algorithm proposed by Daura et al. [START_REF] Daura | Peptide folding: when simulation meets experiment[END_REF] as implemented in the piv clustering code. [START_REF] Gallet | Structural cluster analysis of chemical reactions in solution[END_REF] The algorithm depends on a single parameter, a cut-off distance (d c ) in SPRINT space defining whether two points are neighbor, and thus determining the granularity of the clustering. We adopted d c =0. The resulting candidate structures were then first partly optimized with a kinetic energy cutoff of 60 Ry for the wavefunction and 720 Ry for the density and a convergence criterion on the forces of 10 -3 a.u. Next, duplicates and structures failing to relax were deleted. The remaining structures were further relaxed with a kinetic energy cutoff of 120

Ry for the wavefunction and 1440 Ry for the density, with convergence criterion of 10 -3 a.u.

on the forces in order to obtain the final geometries, binding energies, HOMO-LUMO gaps and magnetizations. The relaxations were run with quantum espresso 51 with the PBE [START_REF] Perdew | Generalized gradient approximation made simple[END_REF] functional and Rappe-Rabe-Kaxiras-Joannopoulos ultrasoft pseudopotentials 52 in a cubic Finally, in order to provide an estimate of the cost of the method, we computed the average computational cost of a 100 ps metadynamics simulation (figure 1) as a function of the cluster size. Indeed, we expect that the cost of the metadynamics outweigh the cost of the relaxations, and therefore is the determining factor. From figure 1, it is clear that the cost of the method steeply increases with the size of the clusters, and is in general too expensive for local workstation but largely accessible for current supercomputers. Note that the cost of a single step varies significantly over a 100 ps simulation, notably because metadynamics forces the system to visit states of progressively higher energy, therefore figure 1 gives a rough idea of the cost of those simulation.

Results

Following the procedure outlined in the methods section, we singled-out more than 109 stable forms: 14 clusters for Mo 2 S 4 , 27 for Mo 3 S 6 and 68 for Mo 4 S 8 . In order to sort the structures in terms of stability, we computed the binding energy using:

BE = nE(M o) + mE(S) -E(M o n S m ) n + m (3)
where E(M o) and E(S) are the energies of a molybdenum and sulfur atom, respectively, computed in the same conditions as the cluster and E(M o n S m ) is the energy of the Mo n S m cluster. This definition has the advantage to allow energetic comparison of clusters with different number of atoms. For each n we rank the structures in increasing BE, as n.BE rank;

for example, the 5 th most stable structure of the Mo 3 S 6 family will be referred to as 3.5.

The eight most stable structures and their binding energy relative to the most stable candidate are shown at the top of figure 234, while the binding energy, HOMO-LUMO gap and total magnetization for each stable structure is presented in the lower part of the same figure.

The observations of the most stable structures confirms what has been found elsewhere: [START_REF] Wang | Small stoichiometric (MoS2)n clusters with the 1T phase[END_REF] for n=3 and n=4 the most stable candidate has the form of the monolayer 1T phase of MoS 2 .

We observe a clear tendency for clusters to form platelet-like structures instead of core-shell ones. It is also noteworthy that we find several structures predicted using an evolutionary algorithm on WS 2 . [START_REF] Hafizi | Stable iso-mers and electronic, vibrational, and optical properties of WS2 nano-clusters: A firstprinciples study[END_REF] In addition we found several motifs (figure 6) shared by many of the stable structures A few structures, although less energetically favored (figure 5), do stand out from the others, where we can observe: a ring-like structure (2.13 ); the beginning of an MoS 2 1D structure (3.10 ); but also a structure with a S-S-S chain on an otherwise stable structure (4.20 ); and an interesting platelet-like structure that does not match the proposed structure of phase 1H or 1T ( 4.21 ). We also found some structures such as ( 2.10 ) or ( 3.10 ) that presented an interesting tetrahedron motif with two S atoms and two Mo atoms, bonded by their Figure 5: Interesting structures that are not amongst the eight most stable structures of each family n. Green and orange numbers and blue dots have the same meaning as the labels in figure 2. Blue, violet and green squares refer to the motifs in figure 6.

Discussion

From an energetic point of view, although the difference in methodology makes it difficult to make straightforward comparison with other works, we do find the same general trend with growing size [START_REF] Gemming | Electronic and geometric structures of MoxSy and WxSy (x=1,2,4; Y=1-12) clusters[END_REF][START_REF] Murugan | Ab initio study of structural stability of Mo-S clusters and size specific stoichiometries of magic clusters[END_REF][START_REF] Murugan | Atomic structures and magnetism in small MoS2 and WS2 clusters[END_REF] (figure 7): larger structures are favored and the binding energy of the clusters as a function of their size seems to be increasing toward that of the bulk (figure 7).

We find (figure 7) that the structures of the smaller clusters are generally more spaced out than those of the larger clusters in binding energy, which also shows when computing the number of structures within 2 eV (in absolute energy) of the most stable structure for each cluster size: we find 6 structures for Mo We observe, in general, the same energy ranking as in previous works, [START_REF] Wang | Small stoichiometric (MoS2)n clusters with the 1T phase[END_REF] with one exception where structure 4.3 is found to be more stable than 4.2. [START_REF] Wang | Small stoichiometric (MoS2)n clusters with the 1T phase[END_REF] However, in this case, the differences in energy are small: 0.22 on the one hand [START_REF] Wang | Small stoichiometric (MoS2)n clusters with the 1T phase[END_REF] and 0.27 eV in our case and can be attributed to either small differences to the use of different DFT functionals.

Despite of differences in computational approaches, we find a general agreement with other studies on the magnetic nature of a cluster (magnetic vs non-magnetic), although we find some important differences in the values of the reported magnetization. Comparing our results for the most common structures in the literature for the three sizes (structures 2.1, 3.3 and 4.2 ) we find an agreement with all other works for the two largest structures (3.3 and 4.2 ) which are reported as non-magnetic [START_REF] Murugan | Ab initio study of structural stability of Mo-S clusters and size specific stoichiometries of magic clusters[END_REF][START_REF] Murugan | Atomic structures and magnetism in small MoS2 and WS2 clusters[END_REF][START_REF] Wang | Small stoichiometric (MoS2)n clusters with the 1T phase[END_REF] while for the smallest cluster we find magnetization of 2µ B 16,18,25 while we report 0.3µ B . It is quite likely that this discrepancy is due to the difference of methods used to take into account spin polarization. For the HOMO-LUMO gap we find a general agreement on the order of magnitude of the gap, although some discrepancies are also observed here: for cluster 2. We note that the approximation level of electronic structure description employed in this study (DFT-PBE) is a good compromise between energetic accuracy and computational cost, given our approach based on long molecular dynamics trajectories.In this respect, our study is focused on the prejudice-free exploration of a large number of cluster structures (we provide the geometries in Supporting Information as a reference for the scientific community), whereas a very accurate evaluation of energetics and electronic properties demands expensive, higher-level quantum chemistry approaches, that go beyond the scope of this work.

Conclusion

In summary, we demonstrates that ab initio molecular dynamics in combination with enhanced sampling, data clustering and graph theory-inspired coordinates (SPRINT) capturing the topology of the interatomic network are a powerful approach for the unsupervised exploration of the configuration space of small clusters.

Indeed, in the case of Mo n S 2n clusters we recapitulated most the existing literature and discovered more than 50 new stable structures, without making any guess about their geometry nor any assumption about their symmetry. We remark the excellent agreement of the structural forms found in this study with those observed in a previous study 24 using a very different exploration approach based on evolutionary algorithms.

In perspective, the unsupervised exploration of the configuration space of larger structures and/or different stoichiometries of Mo n S m clusters will constitute a very interesting field of research. Moreover, our proposed method could be seamlessly applied to other types of nanostructures and chemical compositions.

Figure 1 :

 1 Figure 1: Average computational cost of 100ps (in CPU hours) of simulation as a function of the size of the cluster .

(

  indicated by colored squares in figures 2-5) : a Mo-centered tetrahedron with S atoms on the edges; a "core" of 3 or 4 Mo atoms bonded to each other and capped with sulfur atoms and finally a S-capped 5-members rings composed of two non-bonded S atoms and three Mo atoms.

Figure 2 :

 2 Figure 2: Most stable configurations for Mo 2 S 4 clusters. Green and orange numbers refers to the references where the configurations were found, in MoS 2 (green) or WS 2 (orange) clusters, and asterix indicate the structure identified as the most stable in the respective papers. Blue dots indicate a bulk-like structure. Blue, violet and green squares refer to the motif in figure 6.

Figure 3 :

 3 Figure 3: Most stable configurations for Mo 3 S 6 clusters. Green and orange numbers and blue dots have the same meaning as the labels in figure 2. Blue, violet and green squares refer to the motifs in figure 6.

Figure 4 :

 4 Figure 4: Most stable configurations for Mo 4 S 8 clusters. Green and orange numbers and blue dots have the same meaning as the labels in figure 2. Blue, violet and green squares refer to the motifs in figure 6.

Figure 6 :

 6 Figure 6: Geometric units appearing in several of the stables MoS 2 structures: the Mocentered tetrahedron (left), the Mo-core (center) and the S-capped 5 members ring (right)

Figure 7 :

 7 Figure 7: Evolution of the binding energy (eV) of the most stable configuration with increasing size (in MoS 2 units), with the binding energy of the bulk for comparison.

  3 for Mo 2 S 4 , 0.6 for Mo 3 S 6 and 1.2 for Mo 4 S 8 . Those parameters were chosen in order to generate a manageable number of candidate structures, and were scaled with the dimension of the SPRINT collective variables. The classification resulted in 254 Mo 2 S 4 clusters, 130 Mo 3 S 6 and 178 Mo 4 S 8 clusters.

  2 S 4 , 15 for Mo 3 S 6 and 35 for Mo 4 S 8 . However we do note a very large absolute energy difference (0.97 eV) between the most stable and second most stable configurations of Mo 3 S 6 clusters, as opposed to 0.17 eV for Mo 2 S 4 and 0.20 eV for Mo 4 S 8 .

  1 the reported value of the HOMO-LUMO gap are 1.64 eV, 15 0.35 eV,[START_REF] Murugan | Ab initio study of structural stability of Mo-S clusters and size specific stoichiometries of magic clusters[END_REF][START_REF] Murugan | Atomic structures and magnetism in small MoS2 and WS2 clusters[END_REF] 1.66 eV,[START_REF] Wang | Small stoichiometric (MoS2)n clusters with the 1T phase[END_REF] while our reported value is 1.74 eV; for structure 3.3, reported values are 0.7 eV[START_REF] Murugan | Atomic structures and magnetism in small MoS2 and WS2 clusters[END_REF] 2.11 eV 25 against our reported 2.7 eV. Finally in Mo 4 S 8 , for structure 4.2 we find a HOMO-LUMO gap of 2.10 eV, while the literature contains values of 0.38[START_REF] Murugan | Ab initio study of structural stability of Mo-S clusters and size specific stoichiometries of magic clusters[END_REF][START_REF] Murugan | Atomic structures and magnetism in small MoS2 and WS2 clusters[END_REF] and 1.54 eV.

Acknowledgement

This work was granted access to the HPC resources of TGCC, CINES and IDRIS under the allocations 2018-A0040901387 and 2018-A0050910143 attributed by GENCI (Grand Equipement National de Calcul Intensif)

This work was also granted access to the HPC resources of the HPCaVe centre at Sorbonne Université.

Supporting Information Available

All structures related to this papers are provided in xyz format (one per cluster size), along with the computed binding energies, absolute and total magnetization and HOMO-LUMO gap which are given in a dat file (per cluster size).

All files are grouped inside supp info.zip.

This material is available free of charge via the Internet at http://pubs.acs.org/.


Julia Taeubner

Katharina Wimmer

Martine Muleris

Olivier Lascols

Chrystelle Colas

Christine Fauth

Triantafyllia Brozou

Joerg Felsberg

Jasmin Riemer

Michael Gombert

Julia Cmmrd

Taeubner

Sebastian Ginzel

Jessica I Hoell

Arndt Borkhardt

Michaela Kuhlen
email: michaela.kuhlen@med.uni-duesseldorf.de

Diagnostic challenges in a child with early onset desmoplastic medulloblastoma and homozygous variants in MSH2 and MSH6

Keywords: CMMRD, desmoplastic medulloblastoma, MSH2, MSH6, whole-exome sequencing, germline

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Constitutional mismatch repair deficiency (CMMRD) caused by biallelic germline mutations in one of the four mismatch repair (MMR) genes confers a high risk for a broad spectrum of malignancies primarily including haematological malignancies, brain tumours and tumours of the Lynch syndrome (LS) spectrum. Typically, patients develop tumours in the first two decades of life and survivors of the primary malignancy are at high risk to develop a second cancer. [START_REF] Wimmer | Diagnostic criteria for constitutional mismatch repair deficiency syndrome: suggestions of the European consortium 'care for CMMRD' (C4CMMRD)[END_REF] Therefore, timely diagnosis of CMMRD is important for appropriate anticancer treatment 2,3,4,5

, surveillance and genetic counselling of parents regarding the recurrence risk in siblings as well as their own risk for LS-associated tumours. About two-thirds of CMMRD patients present with pigmentary alterations, most frequently café-au-lait macules (CALM) reminiscent of neurofibromatosis type 1 (NF1)

, but also a variety of other unspecific pigmentation anomalies. Here, we report on a child with desmoplastic medulloblastoma (DMB) and peculiar skin pigmentation who meets the criteria for CMMRD testing. [START_REF] Wimmer | Diagnostic criteria for constitutional mismatch repair deficiency syndrome: suggestions of the European consortium 'care for CMMRD' (C4CMMRD)[END_REF] Whole exome sequencing (WES) revealed homozygous variants of uncertain significance (VUS) in MSH2 and MSH6. The diagnostic strategy allowing us to assign the clinical significance of these variants and finally to confirm CMMRD in the patient is presented.

Results

Case report

The 13 months old girl suffering from vomiting for several weeks presented with ataxia, convergent strabism and developmental regression at hospitalization. Magnet resonance imaging of the brain showed massive hydrocephalus caused by a large infratentorial tumour (49x41x85 mm) with both solid and cystic parts (Figure 1D). Pathologic analysis of the resected tumour revealed desmoplastic medulloblastoma with a balanced c-Myc and n-Myc status and ß-Catenin negativity. Metastases to the cerebral fluid or spinal cord were excluded. According to the HIT (Hirn-Tumor) guidance protocol, treatment with vincristine, cyclophosphamide, high-dose methotrexate, carboplatin and etoposide and intraventricular methotrexate was initiated and fairly well tolerated under supportive care. Radiotherapy was omitted due to the patient's age. One year after diagnosis, the child is well and in complete remission.

The patient had a very peculiar skin phenotype with a heterogeneous spectrum of pigmentary alterations including CALM and inguinal freckling reminiscent of NF1 as well as other hyper-and hypo-pigmented skin patches, atypical dermal melanocytosis/Mongolian spots and naevus-like spots (Figure 1A-B). Neither the healthy three-year-old sister nor the parents showed these alterations of skin pigmentation. The parents having both Roma background reported non-consanguinity, a pregnancy with a third child who died in utero for unknown reasons and a family history without cancer diagnosis in the three preceding generations (Figure 1C).

Identification of homozygous variants in MSH2 and MSH6

The family was included in our prospective study ´Germline predisposition in children with cancer´.

According to the study protocol, a trio-based WES analysis, including patient and both parents, was performed.

Analysis of the WES data for causative variants in the MMR genes revealed a MSH2 missense variant (c.274C>G, p.(Leu92Val)) and a MSH6 3-bp deletion (del) (c.2426_2428delTAG, p.(Val809del)) both present in a homozygous state in the patient and heterozygous in both parents (Figure 2A).

The ExAC-database lists the MSH2 variant with an overall minor allele frequency of G=0.00007. This variant located in the functionally relevant N-terminal MutS-like protein domain (Figure 2B) affects a highly conserved amino-acid and is predicted to be deleterious by several programs (Polyphen, SIFT, MutationTaster).

The InSiGHT group database lists this MSH2 variant once with unknown pathogenicity (InSiGHT class 3).

No data on the frequency in unselected individuals is available for the 3-bp del in MSH6. It is predicted to lead to loss of a valine (Val) in a moderately conserved region of the core domain of MSH6. In the InSiGHT database, this variant (2 entries) was classified as variant with unknown pathogenicity (InSiGHT class 3).

Since, both variants were classified as VUS, neither of them could unequivocally confirm the diagnosis of CMMRD. Therefore, we performed additional tests in order to unravel whether the clinical phenotype of the child is caused by CMMRD and to render indications whether homozygosity of one or both of the VUS is disease causing.

Diagnostic workup

Many inactivating MSH2 missense variants lead at least to expression loss of the heterodimerisation partner MSH6. Therefore, we performed immunohistochemical (IHC) staining on the tumour tissue, which showed nuclear expression of all MMR proteins in neoplastic and non-neoplastic cells (Supplement Figure 1). gMSI testing performed according to the protocol developed by Ingham et al. [START_REF] Ingham | Simple detection of germline microsatellite instability for diagnosis of constitutional mismatch repair cancer syndrome[END_REF] did not reveal increased gMSI ratios in the patient's lymphocytes and, hence, could not confirm CMMRD in the child. Therefore, we next performed methylation tolerance and ex vivo microsatellite instability (evMSI) assays using a lymphoblastoid cell line (LCL) of the patient (Supplementary Material and Methods). These cells displayed a methylationtolerance phenotype characteristic of MMR-deficient cells (Figure 3A). Furthermore, the cell line from the patient achieved the evMSI phenotype as demonstrated by a 1bp-del in the NR27 microsatellite in the LCL in comparison to peripheral blood lymphocytes (Figure 3B). Taken together, both assays clearly confirm CMMRD in the patient.

Discussion

Diagnosing CMMRD has important implications for the management of paediatric cancer patients and their families. Therefore, coming to a definite diagnosis in a suspected patient is of utmost importance. In the era of large-scale sequencing the major challenge is correct classification of variants that affect functions or are being neutral. This case illustrates an effective diagnostic strategy to definitely confirm (or refute) CMMRD in patients in whom only MMR gene VUS are identified.

IHC staining of the MMR proteins and analysis of MSI are the preferred diagnostic methods to substantiate suspected CMMRD. However, due to the non-truncating nature of the VUS identified in MSH2 and MSH6, which is compatible with expression of a non-functional protein, IHC was not informative in our patient.

This limitation of IHC has already been shown in other CMMRD patients. [START_REF] Bodo | Diagnosis of Constitutional Mismatch Repair-Deficiency Syndrome Based on Microsatellite Instability and Lymphocyte Tolerance to Methylating Agents[END_REF][START_REF] Mork | Identification of a novel PMS2 alteration c.505C>G (R169G) in trans with a PMS2 pathogenic mutation in a patient with constitutional mismatch repair deficiency[END_REF][START_REF] Sjursen | A homozygote splice site PMS2 mutation as cause of Turcot syndrome gives rise to two different abnormal transcripts[END_REF] It is known that standard MSI testing developed for LS-associated tumours often fails to show MSI in brain tumours of CMMRD patients.

7

Therefore, we performed gMSI testing. This simple and fast assay detects altered dinucleotide-microsatellite alleles present at low level in non-neoplastic cells of individuals with CMMRD due to biallelic PMS2 or MLH1 mutations. CMMRD due to bi-allelic MSH2 mutations is rare and data are therefore limited, but all cases of MSH2-associated CMMRD analysed so far also showed increased gMSI ratios. [START_REF] Ingham | Simple detection of germline microsatellite instability for diagnosis of constitutional mismatch repair cancer syndrome[END_REF][START_REF] Bodo | Diagnosis of Constitutional Mismatch Repair-Deficiency Syndrome Based on Microsatellite Instability and Lymphocyte Tolerance to Methylating Agents[END_REF] However, since the assay relies on the analysis of dinucleotide microsatellite markers, it is insensitive towards MSH6-associated CMMRD.

3 Hence, a negative gMSI result, as observed in our patient, is compatible with a constitutive MSH6 defect, but speaks against a defect in PMS2, MLH1 or MSH2. Another approach is based on the evaluation of MSI using mononucleotide markers and methylation tolerance -another functional characteristic of MMR-deficient cells -, in immortalized lymphoblastoid cells (i.e. ex vivo) of suspected patients. [START_REF] Bodo | Diagnosis of Constitutional Mismatch Repair-Deficiency Syndrome Based on Microsatellite Instability and Lymphocyte Tolerance to Methylating Agents[END_REF] This method demonstrated unequivocal results in a case-control study, i.e. abnormal results for both assays were found in all CMMRD patients, whereas normal results for both assays were found in the control cohort. [START_REF] Bodo | Diagnosis of Constitutional Mismatch Repair-Deficiency Syndrome Based on Microsatellite Instability and Lymphocyte Tolerance to Methylating Agents[END_REF] This assay could finally confirm CMMRD also in our patient.

The preserved expression of MSH2 and MSH6 and, in particular, the negative gMSI result speaks against an inactivation of MSH2 in the patient rendering the MSH2 variant p.(Leu92Val) unlikely the disease causing variant. However, these results are compatible with MSH6-associated CMMRD and, consequently, a deleterious effect of the MSH6 variant p.(Val809del). Hence, it is possible that the MSH2 variant is a rare benign variant that happens to be genetically linked to the disease causing mutation in MSH6 (distance between the two variants is ~39kb). This notion is corroborated by data from the UMD database, which also reports this MSH2 variant as "likely neutral" and together with a different clearly disease-causing MSH6 mutation in a patient with LS. Ultimately however, functional studies would be needed to come to a final conclusion which of the two variants contributed to which extend to the clinical phenotype of the patient.

Upon confirming CMMRD, our patient was integrated into a cancer surveillance program as proposed by the European consortium `C4CMMRD`, the international biallelic MMR deficiency consortium and the consensus statement of the American Association for Cancer Research. [START_REF] Durno | Oncologic surveillance for subjects with biallelic mismatch repair gene mutations: 10 year follow-up of a kindred[END_REF][START_REF] Tabori | Clinical Management and Tumor Surveillance Recommendations of Inherited Mismatch Repair Deficiency in Childhood. Clinical cancer research : an official journal of the[END_REF] According to the InSiGHT-database the MSH6 variant was also found in patients tested for LS. We therefore assume that this variant increases the risk for LS-associated tumours in adult heterozygous carriers and strongly recommended that the parents are enrolled in a LS surveillance protocol even in absence of LS-associated tumours in their family history. The sister who did not show pigmentation alterations or any other signs of CMMRD was not tested for the MSH6 variant.

A very particular skin pigmentation phenotype was crucial for suspecting CMMRD in our patient. The girl not only has a large number of hyper-and hypopigmented skin areas, which are frequently reported in CMMRD patients, but also several Mongolian spots. With this very heterogeneous skin pigmentation phenotype she resembles a recently published CMMRD patient with atypical dermal melanocytosis [START_REF] Polubothu | Atypical dermal melanocytosis: diagnostic clue in constitutional mismatch repair deficiency syndrome[END_REF] and a patient published by Scott et al. [START_REF] Scott | Medulloblastoma, acute myelocytic leukemia and colonic carcinomas in a child with biallelic MSH6 mutations[END_REF] These patients indicate that also multiple Mongolian spots may belong to the CMMRDassociated skin features.

In conclusion, our case illustrates that WES may be a valid and rapid method to confirm the diagnosis.

However, paediatric oncologists and molecular geneticists should be prepared that the interpretation of sequencing data may be challenging and require thorough functional analysis to come to a valid conclusion.

Titles and legends to figures

Figure 1 .

 1 Figure 1. A-B Variegated skin pattern with CAL spots, hypo-pigmented areas, and Mongolian and

Figure 2 .

 2 Figure 2. A WES reveals a homozygous missense variant in MSH2 (c.274C>G, p.Leu92Val) and a 3-bp deletion in MSH6 (c.2426_2428delTAG, p.Val809del) in DNA extracted from peripheral blood of the patient. Both variants are heterozygous in the parents. B The MSH2 variant is located in the MutS_I protein domain and the MSH6 variant is located in the MutS_III protein domain. C Sanger sequencing confirmed homozygosity of the two variants in the patient. Both variants are present in a heterozygous state in the parents.

Figure 3 .

 3 Figure 3. A The lymphoblastoid cell line from the patient displayed methylation tolerance. Survival (%) of immortalized lymphoid cells derived from the patient (dark circles) and from an MMR wild-type control (open squares) after two (full lines) or three (dotted lines) rounds of MNNG treatment. B Immortalized lymphoid cells from the patient displayed ex vivo microsatellite instability. Electrophoretograms of fluorescent amplification products for NR21, NR27, and BAT26 microsatellites, respectively. The length of the predominant allele (in base pairs) and the fluorescence intensity are indicated in the box below each profile. A 1bp deletion (red arrow) occurred at the NR27 locus in the lymphoblastoid cell line (LCL) derived from the patient compared with the respective peripheral blood lymphocytes (PBLs).

Acknowledgments: This work was supported by the Elterninitiative Kinderkrebsklinik e.V. Duesseldorf. The authors wish to thank Dr. Claudia Potthoff and Dr. Stefan Balzer for their contribution in sample acquisition, Mrs. Silke Furlan and Mrs. Katayoun Alemazkour for technical assistance, Mrs. Fanny Bouchlis and Mrs. Brigitte Litra for functional assays and Dr. Joerg Schaper for providing the radiological image. We thank the Biological Resource Center, Hôpital Cochin, APHP-Paris France, for the lymphoid cell line establishment.

Supplementary information is available at xxx´s website

Informed consent

The study was approved by the local ethics committee and written informed consent was obtained from the parents.

Author Contributions

Conflict of interest:

The authors declare no conflict of interest.