Bruno Wacogne
email: bruno.wacogne@univ-fcomte.fr

Déborah Legrand

Charles-Louis Azzopardi

Christian Pieralli

Annie Frelet Barrand

Annie Frelet-Barrand

Optical Spectroscopy Methods to Monitor Cells and Bacteria Concentrations and to Detect Contamination during Cell Culture: Application to the Fabrication of ATMPs

Keywords: Optical Spectroscopy, Advanced Therapy Medicinal Product, Cell Growth Monitoring, Contamination Detection

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

Introduction

New treatment solutions for patients with no further therapeutic options have recently emerged. They are called ATMPs (Advanced Therapy Medicinal Products). Some of them are based on the use of "drug" cells derived from genetic modification or tissue and cell engineering. Cells acquire new physiological functions, biological characteristics or reconstruction properties to the expense of substantial manipulations. Indeed, natural processes of the body such as the use of stem cells for tissue regeneration, lymphocytes for cancer immunotherapy or apoptotic cells for anti-inflammatory purposes have inspired studies concerning these new biological drugs. However, the production of these drugs requires the implementation of complex technologies of cell sorting, amplification, genetic transduction, amplification-division, activation, and this at several stages of production in sterile clean room type environment and in complex facilities. Also, the time needed to complete the production and the complex quality control processes further increase the fabrication costs. A schematic description of the fabrication process of CAR-T cells is given in figure 1 (adapted from [START_REF] Wang | Clinical manufacturing of CAR T cells: foundation of a promising therapy[END_REF]. It also applies to other ATMP productions. [START_REF] Wang | Clinical manufacturing of CAR T cells: foundation of a promising therapy[END_REF] Basically, the patient's blood is sampled and cells of interest (T cells for figure 1) are extracted/isolated and transduced to acquire the desired therapeutic properties. At this step, only well-transduced T cells are conserved. Now, these genetically modified cells are amplified/expanded in a bioreactor for a period that can extend up to 1 week. Eventually, the ATMP can be either injected to the patient or cryo-preserved before injection. Among others, one of the main constraints that must be addressed is the following.

Working in a controlled environment and preserving the closed system as necessary is crucial to meet the requirement of no contamination of the products. Frequently, the absence of containers, reagents or materials adapted to the protocol makes it difficult. Quality controls (red stars in figure 1) imply repeated samplings during the production, especially to control cell multiplication (expansion phase) and to detect any possible bacterial contamination (throughout the process). This increases the risk of contamination, the time of completion and requires increased traceability. Because these evaluations are time consuming, the production process still continues in parallel. In some cases, the production is stopped after several days when a contamination is detected. Obviously, this actually increases the cost and delay or even stop the delivery of the drug to the patient.

The work presented in this paper addresses the constraint of closed system and real time control of what happens in the bioreactor during the expansion phase. The goal is to provide an easygoing/soft/simple method to monitor the cell growth and to detect contaminations as early as possible. In-line or real time measurement techniques have been widely studied either for cell culture or contaminant development monitoring, very rarely for both.

Various spectroscopic coupled to chemometric techniques were presented in Teixeria review (Teixeria, 2009) concerning cell culture. In addition, other methods based on ultrasonic measurements [START_REF] Melchor | In-bioreactor ultrasonic monitoring of 3D culture human engineered cartilage[END_REF] or capacitive techniques [START_REF] Lee | Real-time monitoring of 3D cell culture using a 3D capacitance biosensor[END_REF]) have been proposed. Impedance monitoring, either in a bulk system [START_REF] Cacopardo | Real-time cellular impedance monitoring and imaging of biological barriers in a dual-flow membrane bioreactor[END_REF] or in a microfluidic chip (Fong Lei, 2014) has also been presented. But, in these references, indications whether or not the proposed method can be adapted in a closed system configuration were not discussed.

Concerning bacteria detection or monitoring, different sensors have been proposed to detect Escherichia coli (hereafter E. coli) by Ikonen [START_REF] Ikonen | On-line detection of Escherichia coli intrusion in a pilot-scale drinking water distribution system[END_REF]. Modified Field Effect Transistors have been experimented to detect the same bacteria [START_REF] Thakur | Rapid detection of single E. coli bacteria using a graphene-based field-effect transistor device[END_REF]. Detecting several contaminants with a single device is challenging. However, it has been demonstrated by using optical absorption spectroscopy [START_REF] Theint | Development of an optical biosensor for the detection of Trypanosoma evansi and Plasmodium berghei[END_REF], fiber optic Fourier Transform Infra-Red spectroscopy [START_REF] Hassan | Detecting bacteria contamination on medical device surfaces using an integrated fiber-optic mid-infrared spectroscopy sensing method[END_REF], quartz crystal sensors [START_REF] Chang | Series quartz crystal sensor for remote bacteria population monitoring in raw milk via the Internet[END_REF] and electrochemistry [START_REF] Safavieh | High-throughput real-time electrochemical monitoring of LAMP for pathogenic bacteria detection[END_REF]. More generally, recent reviews concerning electrochemical biosensors [START_REF] Cesewski | Electrochemical biosensors for pathogen detection[END_REF] and impedimetric immunosensors (Leva-Bueno, 2020) for pathogen detection have been published.

Simultaneous cell monitoring and contaminant detection has only been the subject of very few papers. For example, Liu proposed advanced signal processing applied to Raman spectroscopy [START_REF] Liu | Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW)[END_REF]. Together with normal condition monitoring, authors demonstrated the detection of growth problems 5 hours after they stopped feeding the cells. They also detected effects of contamination with their monitoring algorithm. However, the nature of the contamination and the time required to detect it was not specified.

In this paper, we propose a proof of concept based on different optical spectroscopy methods to continuously monitor the evolution of cell concentration in a bioreactor and to issue an alarm signal shortly after a contamination occurred. The next section describes the experimental set-up, biological samples used in this study and spectroscopy methods employed. Section 3 presents the results obtained using several ways of exploiting information contained in transmission or absorption spectra of solution containing cells or bacteria (lymphocyte B cell and E. coli). Optical characterizations of the concentrations are based on colorimetric estimations of lymphocyte and E. coli solutions. These methods, together with the measurement of the maximum of the absorption spectra, can be used to monitor cell growth in real time. Other methods can be used for early detection of contamination. They are based on spectra shape analysis and Principal Component Analysis (PCA) respectively. Short discussions of these early results and aspects concerning socio-economic impacts will be given in section 4.

2

Materials and Methods

For this proof of concept, measurements are not performed in a closed system configuration. Adaptation of the method in this particular environment is shortly discussed in section 4.

2.1

Lymphocytes and E. coli Preparation.

Lymphocyte cell lines (Ramos, ATCC, USA) were cultured in X-Vivo (Lonza, Switzerland) with 5% FBS (Gibco™ 10270106) and 10% streptomycine/penicillin (100 µg/mL+100 UI/mL, CABPES01-0U, Eurobio) in a humidified 37°C, 5% CO2 incubator. Cells were recovered after 2-3 days culture by centrifugation at 700 g, 10 min, 25°C. Different cell concentrations (10

Experimental Set-up

The extremely simple experimental set-up is schematically presented in figure 2. The set-up was composed of a white light source (Ocean Optics HL 2000) connected to a cuvette holder (Avantes CUV-UV/VIS) via conventional step index optical fibers (Thorlabs M25L01). After propagation through the cuvette, light was launched into a spectrometer for transmission/absorption spectra acquisition (Ocean Optics QE-Pro).

Fluorimeter polymethacrylate cuvettes were filled up to 3 mL with solutions of cells or bacteria (Sigma-Aldricht C0793-100EA). Spectra were measured using the specific feature available in the SpectraSuite software from Ocean Optics. Reference was obtained with a cuvette filled with PBS only. After transfer to PC, data processing was performed using MATLAB™ R2014b version. Spectra were truncated between 450 nm and 1120 nm in order to remove noisy data due to the transmission calculation in the SpectraSuite software.

Color Based Analysis of Transmission Spectra

The associated colors of the solutions were computed from transmission measurements. They do not actually correspond to the true colors of the solutions as discussed in section 4. However, they are expressed in different color spaces in order to study the evolution of colorimetric parameters with species concentrations. Tristimulus values (CIE XYZ) were calculated from spectral distribution with a CIE 1931 standard colorimetric standard observer 2° and a CIE standard illuminant D65. Color of the sample expressed in tristimulus color space was then converted in other color spaces: CIELAB (CIE Lab 1976), sRGB (standard RGB) and HSV. HSV coordinates were determined from sRGB. Color space conversion were performed using the open source Python package "Colour" dedicated to color science (Mansencal, 2020).

Analysis of absorption spectra

Absorption spectra used in section 3.3 were slightly smoothed using a cubic spline algorithm in order to maximize the R 2 of the spectra fittings. Principal Component Analysis was performed with smoothed and normalized spectra.

In this work, so-called "contaminated spectra" presented below are artificial and made by adding spectra of lymphocytes and E. coli. This aspect will be discussed in section 4.

3

Experimental Results

Measuring Concentrations of Both Species with Colorimetric Description of Transmission Spectra

Examples of transmission spectra recorded with lymphocytes and E. coli for different concentrations are given in figure 3. Transmission data are used because they are more consistent with a colorimetric description of the spectra than absorption data. As previously mentioned, colors of the solution are described in several color spaces. Among them, we arbitrarily chose the following color spaces: XYZ, Lab, sRGB and HSV. The goal is to describe each parameter of these color spaces as a function of species concentration. Figure 4 shows the evolution of these colorimetric components as a function of the lymphocyte concentrations. In this figure, "star markers" correspond to experimental values. When the experimental data can be used to determine species concentrations, mathematical fitting of the experimental data is performed and displayed as continuous lines in the figure. In other cases, fitting is not performed. XYZ components of the transmission spectra can easily be described by an exponential function. However, and as it is depicted on the figure 4 with the light red areas, fitting XYZ only works in a reduced range: [1×10 5 -9×10 5 ] cell/mL. For low concentrations, transmission is of the order of 100% and no real difference can be observed in the transmissions spectra corresponding to these concentrations. Conversely, for high concentrations, the transmission approaches 0% and no coherent information can be extracted from the spectra. The situation is slightly different for the Lab components. Here, only the "L" component is useful. It can be described with a linear regression. The "a" component is almost zero for any lymphocyte concentration and contains no exploitable information. The "b" component shows a non bijective evolution with the concentration. Therefore, it cannot be used to compute lymphocyte concentrations. For these two components, no attempts were made to describe their behavior. For the Lab space again, the useful measurement range is [1×10 5 -9×10 5 ] cell/mL. The 3 components of the sRGB space can be used to compute the concentration using linear regressions in the above mentioned useful range. The HSV space only shows 2 exploitable components: "S" and "V". They are described with linear regression in the same useful range.

The same analysis can be conducted with transmission spectra of E. coli. The result is given in figure 5. In this case, much less components can be used to compute bacteria concentrations. Indeed, for the XYZ and sRGB spaces, the evolutions of the components with the concentrations are non-bijective. This is also the case for the "L" and "V" components in corresponding color spaces. Concentrations can only be computed from the behavior of "b" and "S". Evolutions of the components with the concentration are linear. Again, the useful range of the usable colorimetric components is [1×10 5 -9×10 5 ] bact/mL. In order to define components offering the best accuracy when computing the concentration, we calculated the R 2 of each possible fitting. This is summarized in table 1. V linear 0,9936 1×10 5 -9×10 5 V NA NA NA R 2 values are all relatively high. The highest values are obtained with the "L" component of the lymphocyte transmission spectra (R 2 =0.9939) and with the "b" component of the E. coli transmission spectra (R 2 =0.9904). For these two examples, fitting functions 𝐿 𝑙𝑦𝑚 (𝑪) for the lymphocyte L component and 𝑏 𝐸.𝑐𝑜𝑙𝑖 (𝑪) for the E. coli "b" components are given by: 𝐿 𝑙𝑦𝑚 (𝑪) = 𝑚1. 𝑪 + 𝑚2

(1)

𝑏 𝐸.𝑐𝑜𝑙𝑖 (𝑪) = 𝑛1. 𝑪 + 𝑛2 (2)
Where m1 = -6.735×10 -5 , m2 = 93.56, n1 = 1.147×10 -7 and n2 = 0.1295

Using equations ( 1) and ( 2) allows estimating the species concentrations as presented in figure 6. Blue diamonds correspond to the real concentrations and red crosses correspond to the concentrations calculated using equations ( 1) and ( 2). Standard deviations reported in the figure correspond to 1.89×10 4 cell/mL for the lymphocytes (3.8% at the center of the concentration range) and 2.37×10 6 bact/mL for E. coli (4.7 % at the center of the concentration range). STDs obtained using fitting equations corresponding to exploitable color spaces shown in table 1 are reported in table 2. To summarize this section, concentrations of lymphocytes and E. coli can be measured over a large concentration range using the color associated to transmission spectra ([1×10 5 -9×10 5 ] cell/mL). Table 2 shows that the "L" component of the Lab color space leads to the lowest STD for cell concentration measurements (1.89×10 4 cell/mL, i.e. 3.78 % at the center of measurement range). For E. coli the lowest STD is obtained with the "b" component of the same Lab color space (2.37×10 6 bact/mL, i.e. 4.74 % at the center of measurement range). Aspects concerning the use of colorimetric analysis of transmission spectra will be shortly discussed in section 4.

Often in biology laboratories, concentration is measured using the absorption of a solution at a fixed wavelength. Measured solutions are diluted in order to obtain optical densities in a range which ensures a reliable measurement. In the next section, we show how the information contained in the whole absorption spectrum can be used to measure concentrations. This will also introduce section 3.3 where the possibility to exploit the shapes of absorption spectra can be used to detect culture contaminations.

3.2

Measuring Concentrations with the Shapes of the Absorption Spectra.

Examples of absorption spectra recorded with lymphocytes and E. coli for different concentrations are given in figure 7. The shapes of spectral absorptions of the two species are completely different. The idea is, for each species, to model the evolution of the shapes of absorption spectra as a function of concentration and to use this mathematical description to calculate the concentration in species from any measured spectrum. Indeed, the ulterior motive is to measure the absorption spectrum of the contents of the bioreactor continuously. For each recorded spectrum, the idea is to analyze the shape, to separate the part due to the contribution of lymphocytes from that due to E. coli and to calculate their respective concentrations. This would allow detecting a possible contamination in real time and stopping the fabrication of the ATMPs extremely early, i.e. before a large amount of money is lost and possibly reducing the final cost of the drug.

First, it is necessary to determine the evolution of the spectra of the two species as a function of the concentration. This is achieved using spectra mathematical fitting. We found a general equations which can be used for each of the species. We found that an exponential based function can be used to fit absorption spectra of lymphocytes while those of E. coli can be fitted with a 3rd order polynomial function. We then have:

𝑆𝑝𝑒𝑐 𝑙𝑦𝑚 (𝜆, 𝑪) = 𝑝1. 𝑒𝑥𝑝 (-( 𝜆-𝑝2 𝑝3 ) 2 ) + 𝑝4 (3)
𝑆𝑝𝑒𝑐 𝐸.𝐶𝑜𝑙𝑖 (𝜆, 𝑪) = 𝑞1𝜆 3 + 𝑞2𝜆 2 + 𝑞3𝜆 + 𝑞4

Here, λ is the wavelength, C is the concentration. The concentration dependence exists through the "pi" and "qi" parameters which can be expressed as follows.

𝑝1(𝑪) = 𝑎1. 𝑒𝑥𝑝 (-( 𝑪-𝑎2 𝑎3 ) 2 ) (5)
𝑝2(𝑪) = 𝑏1. 𝑪 + 𝑏2 (6)
𝑝3(𝑪) = 𝑐1. 𝑪 2 + 𝑐2. 𝑪 + 𝑐3 (7)
𝑝4(𝑪) = 𝑑1. 𝑪 2 + 𝑑2. 𝑪 + 𝑑3 (8)
𝑞1(𝑪) = 𝑒1. 𝑪 2 + 𝑒2. 𝑪 + 𝑒3 (9)
𝑞2(𝑪) = 𝑓1. 𝑪 2 + 𝑓2. 𝑪 + 𝑓3 (10)

𝑞3(𝑪) = 𝑔1. 𝑪 2 + 𝑔2. 𝑪 + 𝑔3 (11)
𝑞4(𝑪) = ℎ1. 𝑪 2 + ℎ2. 𝑪 + ℎ3 (12)
Coefficients used in equations ( 5) to [START_REF] Melchor | In-bioreactor ultrasonic monitoring of 3D culture human engineered cartilage[END_REF] are given in table 3. Figures 8 and9 show the evolutions of the pi and qi functions and equations ( 5) to [START_REF] Melchor | In-bioreactor ultrasonic monitoring of 3D culture human engineered cartilage[END_REF] are recalled in the figures. Obviously, pi and qi data can hardly be fitted with mathematical function. Only p4 and q4 data can efficiently be modelled (this aspect will be discussed in section 4). Despite this, shapes of the absorption spectra can be modelled with a relatively good accuracy.

For the lymphocytes, the STD is of the order of 0.3%. For the E. coli however, the STD is larger: between 4% to 6%. Functions Spec lym (λ, 𝐂) and Spec 𝐸.𝐶𝑜𝑙𝑖 (λ, 𝐂) are represented in figure 10. These functions are used to calculate species concentrations from the shapes of the experimental absorption spectra. The result is shown in figure 11. The standard deviation for the lymphocytes is 1.69×10 4 cell/mL. This is lower than what was obtained using the "L" components in the Lab color space (1.89×10 4 cell/mL). For E. coli obviously, we failed measuring concentrations from the shape of the absorption spectra. Indeed, the standard deviation (1.31×10 7 bact/mL) is more than 5 times larger than what was obtained using the "b" component in the Lab color space (2.37×10 6 bact/mL). Therefore, the initial idea consisting in studying the shape of an absorption spectrum in real time in order to early detect any contamination seems impossible when trying to compute the actual concentrations values. We now consider the evolution of the maxima of the lymphocyte absorption spectra with concentration. This evolution can be modelled with a second order polynomial function as depicted in figure 12(a). Here again, the useful range is [1×10 5 -9×10 5 ] cell/mL. Maxima of the absorption spectra can be modelled as follows.

𝑀𝑎𝑥 𝑙𝑦𝑚 (𝑪) = 𝑠1. 𝑪 2 + 𝑠2. 𝑪 + 𝑠3 [START_REF] Safavieh | High-throughput real-time electrochemical monitoring of LAMP for pathogenic bacteria detection[END_REF] Where: s1 = -7.557×10 -11 , s2 = 1.527×10 -4 and s3 = 16.74

Calculation of lymphocyte concentrations from the evolution of the spectra maxima is shown in figure 12(b). Here, a standard deviation as low as 1.39×10 4 cell/mL (2.78 % at the center of the range) is obtained. This is the lowest values obtained so far in this study. To summarize sections 3.1 and 3.2, concentrations of either lymphocyte or E. coli can be measured using white light spectroscopy. The best way to monitor lymphocyte concentration is to consider the evolution of the maxima of the absorption spectra. Concerning E. coli, the best method consists in measuring the "b" component in the Lab color space from the transmission spectra. Surprisingly, the evolution of the shape of the lymphocyte absorption spectra does not lead to a better accuracy than the simple measure of the absorption maximum. However, estimating the shape of the absorption spectra can be used for another purpose. Indeed, the above described methods can be used to monitor the evolution of the concentrations of both species when only one species is present. When considering spectra of lymphocyte and E. coli mixtures, it is extremely difficult to separate their respective contributions. The problem becomes insoluble when several types of pathogens are considered.

We recall that the main goal is to measure the lymphocytes concentrations in a close -system environment and in real time. The goal is also to be able to detect any possible contamination in the same experimental environment. This can be achieved by considering things differently. This is the subject of the next section.

Monitoring Cell Concentrations and Detecting Contaminations: a New Approach

Fitting the Shape of the Absorption Spectra.

As long as everything is normal (no contamination) during the expansion phase, cell concentration can be monitored using methods described above. Also, we know the equation which describes the shape of the absorption spectra (equation ( 5)). In case of contamination, the shape of the spectrum resulting from the contribution of the lymphocytes and the contaminant differs from the ones corresponding to lymphocyte alone. In this situation, equation ( 5) should no longer be usable to fit the shape of absorption spectrum.

Practically, during the expansion phase, absorption spectra are recorded and fitted with a function representing the shapes of the lymphocyte spectra when they are alone. At this stage, only the shape of the spectra is analyzed. Therefore, equation ( 5) can be used in a simpler way than what is presented in section 3.2 where coefficients are expressed as a function of the concentration. This function is called 𝐴𝑏𝑠 𝑙𝑦𝑚 (𝜆). Here, the function only depends on the wavelength and coefficients are adjusted only to obtain the highest R 2 . Therefore, an accurate fitting (high R 2 ) using 𝐴𝑏𝑠 𝑙𝑦𝑚 (𝜆) means no contamination, lymphocyte concentration is calculated using equation ( 13), content of the bioreactor is adjusted according to this concentration and fabrication continues. Conversely, an R 2 value less than a threshold to be determined is the sign of a contaminated culture. Fabrication must then be stopped. Figure 13 explain this process. As concentrations are not calculated, only the shape of the recorded spectrum is fitted.

The following equation can efficiently be used.

𝐴𝑏𝑠 𝑙𝑦𝑚 (𝜆) = 𝑎. 𝑒𝑥𝑝 {-( 𝜆-𝑏 𝑐 ) 2 } + 𝑑 (14)
Here, constraints were put to coefficients a, b, c and d. Otherwise, the fitting algorithm ("trust region" in Matlab™ Curfitting toolbox) always finds a set of parameters to describe even contaminated spectra. The fitting bounds and starting points are summarized in table 4. Figure 14 shows examples of absorption spectra fittings using equation ( 14). Spectra were slightly smoothed as mentioned above. In figure 14(a) fitting is performed with pure lymphocytes at 210 5 cell/mL. The R 2 value is quite high: R 2 =0.9986. Indeed, with pure lymphocyte spectra, R 2 coefficients are always greater than 0.99 in the above mentioned useful range [1×10 5 -9×10 5 ].

In figure 14(b) fitting is performed with the same lymphocyte concentration contaminated with 210 7 E. coli/mL. In this case and because the contamination modifies the shape of the recorded spectrum, R 2 decreases to 0.9737. Note that the so-called "contaminated spectra" are artificial and obtained by adding absorption spectra of both species as described in section 2.4.

For different lymphocyte concentrations in the useful range, figure 15 shows the evolution of R 2 with increasing concentrations of E. coli. Each curve corresponds to one lymphocyte concentration. It is observed that, for any lymphocyte concentrations, the contamination detection limit is of the order of 2.5.10 7 bact/mL. For this, we set a positivity threshold R 2 = 0.988 (arrows in the figure). Knowing that E. coli divides every 20 min and considering that the contamination is due to 1000 bact/mL, the warning signal can be issued 4h52 min post contamination. Using Principal Component Analysis.

Analyzing the shape of the absorption spectra allows detecting a contamination when 2.5.10 7 bact/mL are present in the bioreactor. But it is possible to further reduce the time required to issue the alert signal using Principle Component Analysis (PCA). In what follows, spectra are smoothed and normalized as mentioned above. PCA is conducted in two steps.

The first step consists in verifying that populations of both lymphocytes and E. coli can be distinguished. Figure 16 (a) shows that plotting PC2 vs. PC1 allows separating biological populations. The black line represents the frontier between these populations. It is defined by either the analysis of the mean and variance of the two distributions or the minima of the coordinates of lymphocytes (circles) and maxima of E. coli (crosses). Taking into account these data, a straight line which separates the PC1-PC2 domain in two zones is defined [START_REF] Janné | Hierarchical principal component analysis (PCA) and projection to latent structure (PLS) technique on spectroscopic data as a data pretreatment for calibration[END_REF].

The second step consists in checking where a so-called "contaminated spectrum" is located in the PC1-PC2 space. Indeed, the 30 data used to generate figure 16(a) form a base for the pure lymphocyte and pure E. coli populations. Contaminated spectra correspond to all possible combinations of lymphocyte spectra added to E. coli spectra. They are processed one by one. Each contaminated spectrum is considered as a 31rst data in the above mentioned base before a new PCA is performed. This is iterated for the 225 possible combinations. Indeed, in this PCA study, all E. coli spectra are considered and not only those corresponding to concentrations above 1×10 7 bact/mL as it was the case up to now. The result is shown in figure 16(b) with the contaminated spectra marked with red squares. Contaminated spectra of contaminated culture are all situated in the E. coli region. This means that the bacteria detection limit is at least 1×10 6 bact/mL (the minimum E. coli concentration considered in this study). The warning signal can now be issued only 3h19min after a contamination with 1000 bact/mL occurred. Therefore, the in-line process proposed in Figure 13 can now be modified as presented in figure 17.

Discussion

Technical Aspects

Figures 4 to 6 show that analyzing transmission spectra using the associated color proves to be efficient to measure both species concentrations. Indeed, according the results obtained in this study, this is the best way to measure E. coli concentration in real time and possibly in a closed system environment. In a general manner (for both lymphocyte and E. coli), we note that the "H" (Hue) and "a" (scale between green and red) parameters remain constant while the "b" (from blue to yellow) evolves very little. This means that the color of the solutions is not changing (or very little) with the con-centration. The "L" (Luminance) and "V" (Value) parameters decrease with the concentration. This indicates that the intensity of the transmitted light decreases with the concentration. Although the color is not changing the associated color is reinforced with the concentration. This is observed with the increase of the "S" (Saturation parameter. "L" decreases but the color remains almost constant: this is confirmed by the fact that the "RGB" parameters decrease almost the same way with the concentration. Also, the relative values of the "RGB" parameters are constant which is consistent with a constant "H" parameter. However, it is somehow inappropriate to talk of color when working with transmission spectra of biological solutions. Indeed, measuring the true color of the solutions would imply using a calibrated light source and studying the spectrum of the light reflected by the sample solution. Determining the true color of the solutions is not our purpose. Decomposition of the measured spectra in different color spaces is only a mathematical means of determining concentrations. For example, we also studied the entropy of the spectra. Estimating the quantity of information contained in the spectra using their entropy makes possible to measure concentration but in a reduced useful range which will not be presented in this paper.

Figures 8 to 12 show that, during ATMPs production, analyzing the shape absorption spectra of lymphocytes allows monitoring the expansion phase. However, looking closer at figures 8 and 9 shows that most of the coefficients used to describe the shape of the absorption spectra cannot be efficiently fitted with mathematical functions. Indeed, for lymphocytes, only the p4 function can be efficiently described (this is the same for E. coli considering q4). This means that a better concentration determination could be obtained using p4 only instead of the whole spectrum shape. This was not investigated in this study but looking at equation (4), we understand that this p4 function somehow represents the baseline of the recorded spectra. Functions p2 and p3 describe the shape of the spectra while p1, together with p4 contribute to the amplitude of the spectra. This is why indeed, measuring the concentration using the maxima of the absorption spectra (equation ( 13)) is particularly accurate (figure [START_REF] Melchor | In-bioreactor ultrasonic monitoring of 3D culture human engineered cartilage[END_REF]).

This does not mean that the shape itself contains no useful information. It also provides powerful a tool to issue an alert signal less than 5 hours after a contamination with 1000 bact/mL occurred as it is shown in figure [START_REF] Thakur | Rapid detection of single E. coli bacteria using a graphene-based field-effect transistor device[END_REF]. Principal Component Analysis can be used to reduce the time required to issue a warning signal (figures ( 16)) the drawback being that it cannot be used to measure lymphocyte concentrations.

So-called "contaminated spectra" are artificial. They do not correspond to real lymphocyte and E. coli mixtures. These spectra consist of the addition of lymphocytes and E. coli spectra. Therefore, a bias could be introduced in the results presented here because adding absorption spectra may lead to artificial absorptions greater than 100%. The method described in section 3.3 is still valid because only the shape is considered and not the actual value of the maximum absorptions. We recall that the R 2 coefficient is only used to issue an alert signal. Equation depicted in figure 10(a) remains valid to monitor the expansion phase as long as no contamination occurs. Because it is performed using normalized spectra, results obtained using Principal Component Analysis are not affected. However, an ongoing and more realistic study involves real spectra recorded with real mixture of lymphocytes and E. coli.

Although they are not the only ones, we mentioned methods based on spectro(photo)metry in the introduction. One, based on Fourier Transform Infrared Spectroscopy (FTIR), was used to identify four types of bacteria [START_REF] Hassan | Detecting bacteria contamination on medical device surfaces using an integrated fiber-optic mid-infrared spectroscopy sensing method[END_REF]. This was not performed in solution but onto a sensor's surface. Also, Principal Component Analysis was used by the authors to successfully separate types of bacteria, to discriminate between Gram + and Gram -bacteria and to analyze mixtures. Another cited work presents the use of several sensors (Turbidity, particle counting, temperature, pH and spectrophotometry) to detect contaminants in drinking water [START_REF] Ikonen | On-line detection of Escherichia coli intrusion in a pilot-scale drinking water distribution system[END_REF]. However, methods described in this paper seem to be extremely difficult to automate. Also in this paper, analysis was performed using only 2 wavelengths which drastically reduces the amount of available information. Note that 2 reduced wavelength ranges were used by Hassan [START_REF] Hassan | Detecting bacteria contamination on medical device surfaces using an integrated fiber-optic mid-infrared spectroscopy sensing method[END_REF].

It should be noted that in our work, we exploit the total amount of information contained in the whole 450 nm to 1120 nm wavelength range (except for associated-color investigations) which represents over 900 data per spectrum with the spectrometer we used. Also, because measurements are performed in a cuvette and not on a biosensor's surface, the method can easily be used to provide a real time analysis of what happens in the bioreactor, in a closed-loop environment and without the need for any biosensor cleaning and/or regeneration.

From a practical point of view indeed, white light spectroscopy and conventional cuvettes makes possible an easy adaptation in a closed system configuration as illustrated in figure 18. In this work, only a contamination due to E. coli is considered. Cases of other bacteria or other types of containments like yeasts and fungi are currently being investigated. But as long as the shapes of the absorption spectra of contaminants are different enough from the ones of lymphocytes, the methods presented here should still be valid.

Socio-economic Impacts

The expansion phase lasts several days and each day increases the price of ATMPs. The quality control imposes regular samplings, themselves risk of contamination. Being able to monitor cell expansion and to perform quality controls without sampling is particularly interesting. In this way, expansion phase can be stopped rapidly which reduces the global cost of ATMPs. In this work, methods able to issue a warning signal about 3 hours after a contamination with 1000 E. coli/mL are described. ATMPs are likely to create a real therapeutic revolution in the coming years because they are designed to treat patients with pathologies that are currently incurable. It is right now difficult to estimate the number of pathologies that these medicines from the living can address and the number of patients likely to benefit.

Only a few ATMPs are available on the market. The enormous production cost also led to marketing authorization cancellation of some of them (Glybera, Sipuleucel-T, ChondoCelect). At the date of manuscript writing, only the following treatments are available (note the price for a single treatment):

 KYMRIAH ($ 475,000)  YESCARTA ($ 373,000)  Strimvelis ($ 594,000) Included in these costs are losses due to fabrications, which are found to be contaminated during the final conformity test and those due to late-stage fabrication stops due to the scheduling of controls at precise times. Having a real-time and closed system monitoring and quality control method is of great interest in terms of research, industrial manufacturing and more importantly in terms of benefit to the patients.

Conclusion

In this paper, we have presented spectroscopic methods to continuously perform a quality control during the expansion phase of ATMP production. These methods are based on analyzing the transmission or absorption optical spectra of the content of a bioreactor. Colorimetric based techniques can be used to monitor lymphocyte multiplication during the expansion phase of ATMP production. They can also be used to monitor E. coli proliferation in other applications. However, the colorimetric method cannot be used to simultaneously monitor the concentration of lymphocytes and bacteria when a contamination occurs. The originality of this work is that we do not need to measure the contaminant concentration but only to detect its presence. Consequently, spectral shape analysis can be used not only to monitor the lymphocytes multiplication but also to issue an alert signal about 4h52min post contamination. Warning can even be issued earlier by using Principal Component Analysis. The warning signal can then be issued 3h19min post contamination. However, Principal Component Analysis cannot be used alone as it fails to measure lymphocyte concentrations when no contamination is detected. Note that the warning delays are calculated for a contamination due to 1000 E. coli/mL. Advantages of such methods can be summarized as follows.

 Regular sampling of the content of the bioreactor is no longer required. This further reduces the risk of sampling related contaminations.  The use of planned sampling at a fixed date and time without even knowing whether contamination is avoided.  The goal being to stop the production as soon as a contamination is detected and not to identify the pathogen responsible for it, this identification can be performed later.  As long as the culture is normal, the lymphocyte expansion is monitored in real time.

Because contamination is detected extremely early the production can be stopped instantly which greatly contribute to reduce the production cost.

Indeed, in order to guarantee access of ATMPs to the largest number of patients, a new conception of the current mode of production and qualification of these living drugs is necessary. Currently, our studies are focused on the validation of these above described methods considering other types of pathogens.

Fig. 1 .

 1 Fig. 1. Fabrication process of CAR-T cells. Adapted from[START_REF] Wang | Clinical manufacturing of CAR T cells: foundation of a promising therapy[END_REF]

Fig. 2 .

 2 Fig. 2. Description of the experimental set-up.

Fig. 3 .

 3 Fig. 3. Examples of transmission spectra of lymphocytes and E. coli. (a) Lymphocytes at 6×10 4 and 5×10 5 cell/mL, (b) E. coli at 8×10 6 and 1×10 7 bact/mL.

Fig. 4 .

 4 Fig. 4. Evolution of the colorimetric components with lymphocyte concentrations. (a) XYZ space, (b) Lab space, (c) sRGB space and (d) HSV space.

Fig. 5 .

 5 Fig. 5. Evolution of the colorimetric components with E. coli concentrations. (a) XYZ space, (b) Lab space, (c) sRGB space and (d) HSV space.

Fig. 6 .

 6 Fig. 6. Calculating the species concentrations from colorimetric parameters. (a) Lymphocytes concentrations using the "L" component in the Lab color space. (b) E. coli concentrations using the "b" component in the same color space.

Fig. 7 .

 7 Fig. 7. Examples of absorption spectra of lymphocytes and E. coli. (a) Lymphocytes at 4×10 5 and 5×10 5 cell/mL, (b) E. coli at 2×10 7 and 3×10 7 bact/mL.

Fig. 8 .

 8 Fig. 8. Evolution of the pi functions with the lymphocyte concentration (left hand side). Examples of experimental absorption spectra fitted with the Spec lym (λ, 𝐂) function (right hand side).

Fig. 9 .

 9 Fig. 9. Evolution of the qi functions with the E. coli concentration (left hand side). Examples of experimental absorption spectra fitted with the Spec 𝐸.𝐶𝑜𝑙𝑖 (λ, 𝐂) function (right hand side).

Fig. 10 .

 10 Fig. 10. Theoretical evolutions of the absorption spectra with species concentrations. (a) Lymphocytes, (b) E. coli. Adapted from (Wacogne, 2020).

Fig. 11 .

 11 Fig. 11. Calculating the species concentrations from the shapes of the absorption spectra. (a) Lymphocytes concentrations. (b) E. coli concentrations. Adapted from (Wacogne, 2020).

Fig. 12 .

 12 Fig. 12. Calculating the lymphocyte concentrations from the absorption maxima. (a) Evolution of the maxima with the lymphocyte concentrations. (b) Fitting concentrations using a second order polynomial function.

Fig. 13 .

 13 Fig. 13. In-line process to detect contaminations and to adjust the bioreactor composition as a function of the lymphocyte concentration.

Fig. 14 .

 14 Fig. 14. Examples of spectra fitting using equation (14). (a) Lymphocyte concentration = 210 5 cell/mL, R 2 = 0.9986. (b) Lymphocyte concentration = 210 5 cell/mL, E. coli concentration = 210 7 bact/mL, R 2 = 0.9737.

Fig. 15 .

 15 Fig. 15. Evolution of the R 2 coefficient with the concentration in E. coli for different lymphocyte concentrations. The legend corresponds to lymphocyte concentrations in cell/mL (Wacogne, 2020).

Fig. 16 .

 16 Fig. 16. Principal Component Analysis. Blue circles: lymphocytes, green crosses: E. coli, red squares: contaminated cultures and black line: separation between lymphocytes and E. coli. (a) ACP for pure lymphocyte and E. coli. (b) The same as (a) with contaminated cultures. Adapted from (Wacogne, 2020).

Fig. 17 .

 17 Fig. 17. In-line process to detect contaminations and to adjust the bioreactor composition based on Principal Component Analysis.

Fig. 18 .

 18 Fig. 18. Possible integrations of the method in a closed system environment. (a) Using a derivation. (b) Using a sterilized reflection probe coupled to a mirror. Adapted from (Wacogne, 2020).

Table 1 .

 1 Summary of the methods used to describe the evolution of the lymphocyte and E. coli concentrations, type of function used to fit the experimental data, values of the fitting R 2 and useful ranges. "pol deg 2": second order polynomial function, "exp": exponential function, "linear": linear regression. NA: not applicable. Light red highlighted rows correspond to non-exploitable components.

		Lymphocytes				E. coli
	Metho d	Fit type	R 2	Range (cell/mL)	Method	Fit type	R 2	Range (bact/mL)
	X	exp	0,9881 1×10 5 -9×10 5	X	NA	NA	NA
	Y	exp	0,988 1×10 5 -9×10 5	Y	NA	NA	NA
	Z	exp	0,9873 1×10 5 -9×10 5	Z	NA	NA	NA
	L	linear 0,9939 1×10 5 -9×10 5	L	NA	NA	NA
	a	NA	NA	NA	a	NA	NA	NA
	b	NA	NA	NA	b	linear	0,9904 1x10 7 -9×10 7
	R	linear 0,9903 1×10 5 -9×10 5	R	NA	NA	NA
	G	linear	0,992 1×10 5 -9×10 5	G	NA	NA	NA
	B	linear 0,9936 1×10 5 -9×10 5	B	NA	NA	NA
	H	NA	NA	NA	H	NA	NA	NA
	S	linear 0,9662 1×10 5 -9×10 5	S	linear	0,9901 1x10 7 -9×10 7

Table 2 .

 2 Values of the Standard Deviation for the different fitting possibilities.

		Lymphocytes			E. coli
	Method	Fit type	STD (cell/mL)	Method	Fit type	STD (bact/mL)
	X	exp	4,19×10 4	X	NA	NA
	Y	exp	3,84×10 4	Y	NA	NA
	Z	exp	4,27×10 4	Z	NA	NA
	L	linear	1,89×10 4	L	NA	NA
	a	NA	NA	a	NA	NA
	b	NA	NA	b	linear	2,37×10 6
	R	linear	2,39×10 4	R	NA	NA
	G	linear	2,17×10 4	G	NA	NA
	B	linear	1,94×10 4	B	NA	NA
	H	NA	NA	H	NA	NA
	S	linear	4,51×10 4	S	linear	2,41×10 6
	V	linear	1,94×10 4	V	NA	NA

Table 3 .

 3 Coefficients used in equations (5) to[START_REF] Melchor | In-bioreactor ultrasonic monitoring of 3D culture human engineered cartilage[END_REF].

	p1(C)	a1=14.32	a2=4.110 5	a3=6.14510 5
	p2(C)	b1=7.2110 -5	b2=647
	p3(C)	c1=110 -10	c2=-1.910 -5	c3=325.7
	p4(C)	d1=-510 -11	d2=1.310 -3	d3=6.791
	q1(C)	e1=1.210 -23	e2=-1.610 -15	e3=1.210 -9
	q2(C)	f1=-3.310 -20	f2=5.110 -12	f3=-1.710 -6
	q3(C)	g1=2.810 -17	g2=-5.510 -09	g3=-8.310 -4
	q4(C)	h1=-110 -14	h2=2.410 -6	h3=1.234

Table 4 .

 4 Lower bounds, upper bounds and starting points used to fit absorption spectra with equation (14).

	Coefficient	Lower bound	Upper bound	Starting point
	a (%)	0	200	100
	b (nm)	600	750	675
	c (nm)	0	500	250
	d (%)	0	200	100

Acknowledgements. This work was supported by the MiMedi project funded by BPI France (grant No. DOS0060162/00) and the European Union through the European Regional Development Fund of the Region Bourgogne-Franche-Comte (grant No. FC0013440).


K Charrière

C L Azzopardi
email: cl.azzopardi@femto-st.fr

M Nicolier
email: mnicolier@chu-besancon.fr

T Lihoreau

F Bellivier
email: frank.bellivier@inserm.fr

E Haffen

B Wacogne
email: bruno.wacogne@univ-fcomte.fr

Functional analysis to drive research and identify regulation requirements: an example with a lithium monitoring device

Keywords: Functional analysis, medical device, lithium monitoring

Medical device development is often understood as a linear process with design stages occurring sequentially. First stages are usually performed in order to specify the future device definition through interviews/meetings of the end-users, researchers and manufacturers. Because the medical device is original, these first stages mainly involve end-users and researcher. However, regulation constraints and economic reality sometimes makes manufacturers hesitant to base the industrial development on this initial basis. Functional analysis, well known by manufacturers, is a method used to accurately define the final functions of a medical device. In this conference, we estimate that the functional analysis can be put to profit in a more efficient way if researchers and end-users get familiar with it prior to the interview/meeting stages. Although the results of such knowledge democratisation is not demonstrated here, we present the function analysis conducted on a lithium monitoring device according to this multidisciplinary approach. We also show that function analysis can be used not only to drive research actions but also to identify regulation requirements.

INTRODUCTION

Research and development actions in technologies for health are usually driven by discussions and experience exchanges between practitioners, researchers and industrial partners. Because the need to be addressed involves innovations that have never been studied before, first discussions are often led by User driven medical device development is now widely applied in different collaborative formats likes Living Labs [START_REF] Korman | Living Labs: overview of ecological approaches for health promotion and rehabilitation[END_REF] or Hacking Health organizations [START_REF] Chowdhury | Hacking Health: Bottom-up Innovation for Healthcare[END_REF]. These approaches have been more or less conceptualized a while ago. In a well-organized and well-documented paper (Money, 2011), it is recall that user-centred usability engineering methods have already been proposed to improve the Medical Device Design and Development (MDDD) [START_REF] Gosbee | Human factors engineering and patient safety[END_REF]. Money and co-workers describe the MDDD using 4 main development stages, each of them involving distinct actions. They can be summarized as follows. Stage 1 identifies the user's need and involves enquiries and interviews, both dealing with contextual and usability issues. This forms the basis for the initial concept which is further defined during stage 2 which uses reduced focus groups deepening the initial concept ideas. This produces requirements documents leading to stage 3, fully oriented to the device manufacturing. The device can then be evaluated during stage 4 in order to check whether or not the users' requirements are satisfied. We assume that prototyping actions are present between stages 2 and 3 or completely included in stage 3.

In (Money, 2011), what manufacturers think of the user-driven approach is analysed through interviews. Instead of trying to rewrite their conclusion, we simply reproduce the main points they report. "The findings reveal that despite standards agencies and academic literature offering strong support for the employment formal methods, manufacturers are still hesitant due to a range of factors including: perceived barriers to obtaining ethical approval; the speed at which such activity may be carried out; the belief that there is no need given the 'all-knowing' nature of senior health care staff and clinical champions; a belief that effective results are achievable by consulting a minimal number of champions. Furthermore, less senior health care practitioners and patients were rarely seen as being able to provide valuable input into the process".

More generally, what is mentioned above also applies to researchers who often misunderstand both the manufacturer economic constraints and patients/practitioners possibilities. Indeed, some technical or scientific solutions can be incompatible with a practical use or they can lead to an overexpensive device. In these 2 cases other and more realistic solutions must be explored, otherwise, conditions to be safe, efficient and to match with the local regulation will be difficult. Indeed, since the very beginning of the MDDD, discussions between end-users, researchers and manufacturers must be somehow oriented/guided towards realistic solutions. For this a conceptual tool must be employed and this tool should ideally be known (even partially) by all the stakeholders.

Functional analysis is a technology design tool already well known by manufacturers but very rarely by researchers and the reduced number of end-users interviewed during the above mentioned stage 2. We believe that functional analysis should be pedagogically presented to end-users and researchers prior to any enquiry. This pedagogical effort should be clearly codified to allow beginners quickly understanding main issues of this method and how it can be used to drive the research and development actions and to identify the corresponding regulation issues. In this way interviews conducted to set-up the initial prototype design can be focused on functional analysis key points and the "expert" users can be efficiently involved in stage 3 of the MDDD. At the end, we think that what was called "end-users' wishes" at the beginning of this introduction will become "end-users' highlights", that researchers will directly focus on realistically translatable solutions and that manufacturers will be less hesitant to these data into account.

In this conference, we do not present a retrospective study on how this pedagogic effort is put to profit to enhance MDDD, but we describe the functional analysis which has been conducted in the frame of the H2020 R-LiNK project (grant agreement n°754907) according to this multidisciplinary concept. The goal is to present how this analysis accounts for end-users' requirements, how it drives the research actions to be privileged and how it allows identifying regulation requirements. In the next section, we rapidly presents the main goals of the R-LiNK project. The functional analysis is described in section 3. A short discussion is then proposed in section 4 before some conclusive remarks are given.

THE R-LINK PROJECT

The consortium of this European project led by Pr. Franck Bellivier (INSERM UMR-S1144) is composed of 22 European partners including research institutes, hospitals, clinical investigation centers and companies.

The main objective of R-LiNK is to identify the eligibility criteria for treatment with lithium in bipolar disorder type 1 (BD1) patients in terms of response, safety and tolerability. Research actions conducted to find lithium response biomarkers involve multidisciplinary research fields like "omics" investigations, blood analyses, nuclear magnetic resonance imaging and activity assessment. The functional analysis presented in this paper is related to treatment adherence as explained below.

Origin of the project

Bipolar disorders (BD) are prevalent mental disorders and a leading cause of suicide. Bipolar disorders are lifelong lasting, with an episodic course of the illness in most cases. Mood stabilizers are the mainstay of treatment of BD and lithium is the gold standard [START_REF] Sani | Treatment of Bipolar Disorder in a Lifetime Perspective: Is Lithium Still the Best Choice?[END_REF].

Indeed, a substantial minority of individuals remain asymptomatic for years on lithium (about 20%) but most show only partial response and up to one third do not respond [START_REF] Burgess | Lithium for maintenance treatment of mood disorders[END_REF]. Furthermore, in current clinical practice, lithium exposure is poorly controlled using laboratory tests. First, it is usually verified only once or twice a year. Second, adherence to chronic treatment is known to be poor. Meanwhile, the prescription of lithium remains delicate. For the treatment to be effective, serum concentrations 10-14 h after the last dose taken must reach 0.5 to 1.0 mEq/L. If plasmatic concentration exceeds 1.2 mEq/L, toxic effects are likely to occur [START_REF] Amdisen | Serum Lithium Determinations for Clinical Use[END_REF][START_REF] Baldessarini | Chemotherapy in Psychiatry[END_REF][START_REF] Bauer | The Essential Guide to Lithium Treatment[END_REF][START_REF] Tondo | Clinical use of lithium salts: guide for users and prescribers[END_REF].

Therefore, there is an important medical need to first, provide the patients/practitioners with a simple tool which could allow patients to become actor of her/his treatment, hence improving the adherence to treatment and second, to increase the frequency with which the lithium level can be assessed in a noninvasive manner. To this end, the device is intended to be used at home by patients, so it's a class C in vitro medical device, according the European regulation (EU 2017/746). The idea is not to replace laboratory lithium dosing techniques but to create a simple home-based lithium level indicator so that the patient can check if her/his lithium level is below, within or above the therapeutic window. A rapid analysis of the home-based usability possibilities led us to consider the detection of lithium in saliva. Also, because it will be used at home by patients or by non-specifically trained medical staff, the device must be carefully designed and a complete functional analysis must be performed.

FUNCTIONAL STATEMENT OF THE NEED

Functional specifications documents are well codified, even if different methodologies can be used to write them. They can be separated in three parts: ▪ need analysis ▪ functional analysis ▪ technical specification Before and during constitution of functional specifications, studies are carried out to better understand the needs of potential customers.

Analysis of the need

Analysis of the need is an essential phase because it dictates the direction of the future work. The needs and the objectives should be clearly identified and formalized.

To do this, different tools can be used; one of them is the APTE® method (see for example (APTE, no date)). This method starts from the expression of a need, without considering any technical solutions. It constitutes the first phase of design leading to the edition of the functional specifications. Three questions have to be considered.

▪ To whom is the "product" useful? ▪ What does the "product" acts on? ▪ What is the purpose of the "product"?

Expression and characterization of the need for R-LiNK monitoring device

In our project, the "product" is the monitoring device.

To whom is the "product" useful? The R-LiNK monitoring device is useful to patients suffering from bipolar disorder and the medical staff. What does the "product" acts on? It acts on patients' saliva to assess lithium levels. What is the purpose of the "product"? It aims at improving treatment adherence and to avoid relapse by self-monitoring and self-management. Furthermore, it aims at minimizing (avoid if possible) the risk of lithium overdose. This is illustrated in figure 1. This first expression of the need is of course essential but not sufficient. Indeed, the need will be correctly addressed only if the expected performances of the device are obtained. Therefore, the need has to be clearly characterized specifying qualification and quantification to estimate measurable quantities.

For the R-LiNK monitoring device, the need will be satisfied if the device allows lithium detection between 0.5 and 4 mM, with 0.2 mM accuracy and improve treatment adherence, among others.

Validation of the need

Here, the questions to be answered are defined as follows. What is the origin of the need or why is the device wanted? What can change the need or even makes the need disappear?

The need of detecting lithium with a simple monitoring device comes from practitioner facing some clinical issues.

First, as described above, 30-55% of individuals selected for treatment with lithium will not have the predicted outcome. One possible cause to the nonefficacy of the treatment is the non-adherence to treatment. Second, lithium can be toxic and the therapeutic window is limited, between 0.5 and 1.2 mM in plasma with toxic effects above the maximum value.

These two issues will not disappear but technological or pharmaceutical developments may change clinical practices. One major evolution has been identified: emergence of new medicine for bipolar disorder leading to stopping the use of lithium therapy.

Taking these elements into account, it is therefore unlikely that the need for a new lithium monitoring device disappears completely.

Establishment of service functions and constraints

Since the need has been validated, the functional analysis can continue and a list of functions can be established. First, it's important to better understand what a function is. Looking normative definition issued from the French Association for Standardization (AFNOR), a function is an action of a product or one of its constituents expressed exclusively in terms of purpose. Therefore, it is required to disregard technological solutions. In order to help defining these functions, an interactions diagram of the device with its environment can be drawn. The diagram defines the limits of the device and specifies the life situation being studied. This is presented in figure 2. In this figure, bubbles represent the physical elements of the environment that have impact on the device. Lines characterize functions linking the system to its environment. Main function: ▪ MF1: the device indicates the level of lithium in the biological sample. Constraint functions: ▪ CF1: the device is suitable for all users ▪ CF2: the device is suitable for home use ▪ CF3: the device respects the regulations in force ▪ CF4: the device integrates the European market at a reasonable cost Note that the market is not a physical element, but cost is really a constraint to take in account at the beginning of the development. That's why we chose to include this notion in our functional analysis of the need.

Justifications of functions

To ensure the function is really necessary, it's possible to ask, as for the need: what is the purpose, the origin and the probability to disappear for each function?

For the R-LiNK device, the purpose of MF1 is to improve treatment adherence by self-monitoring and to minimize or avoid the risk of lithium overdose or under dosing. New medicine for bipolar disorder leading to the stopping lithium therapy can make the function disappear. Eliminating the function is unlikely. The function is validated.

The purpose of CF1 is to meet the expectations of user comfort whatever the patient's physical condition is because: the patient can use the product easily and quickly so they can monitor themselves without demotivation, the patient is looking for effective products that provide him with a certain comfort of use. Eliminating the function is unlikely. The function is validated.

The purpose of CF2 is to meet the expectations of user comfort whatever the place of use because patients have to regularly monitor themselves at home or at their place of holiday, during their work travels. Eliminating the function is unlikely. The function is validated.

The purpose of CF3 is to be able to commercialize the device and to assure the user that the product has been approved and that he can therefore use. The products sold must be certified by standards of quality and safety. Eliminating the function is unlikely. The function is validated.

The goal of CF4 is to enable the use of the device throughout the European Union at a reasonable cost because all patients are concerned and reimbursement rules are not identical for the entire European community. Eliminating the function is unlikely. The function is validated.

Functional analysis and usability

At this stage of the functional analysis, details can be provided to really meet the end-users' need. It is often made by brainstorming or interview with the various stakeholders. In our project, these investigations led to the conclusions given below.

▪ The device must measure lithium quickly (5 minutes if patients have to wait for results, more if the result is recorded). ▪ The device must to detect lithium in the therapeutic window (ideally 0.5 mM to 5 mM, with an accuracy of 0.2 mM in saliva). ▪ The device must be non-invasive (use of saliva instead of blood). ▪ The device has to deliver the results in an understandable manner. ▪ The device must be easy to use, compact and mobile. ▪ Finally, the device must be easily stored after use.

Characterization of service functions and constraints

Based on all these indications, the functions are then defined with criteria, levels, and tests to be performed. This is summarized in table 1. Note that in table 1, we inserted a column titled "flexibility". This estimates how negotiable can be the results expected in columns "criteria" and "levels". Flexibility is defined as follows:

▪ F0 means zero flexibility, imperative level ▪ F1 means low flexibility, little negotiable level ▪ F2 means good flexibility, negotiable level ▪ F3 means strong flexibility, very negotiable level.

After this functional analysis of the need, technological solutions can be envisaged. At this stage, it is essential to think about the solutions objectively and without restrictions. To do this, a Functional Analysis System Technique (FAST) is useful (FAST1, FAST2, FAST3, no date). Among other methods, the use of FAST allows a highly multidisciplinary consortium to speak the same language. Structured analysis and design technique, also termed SADT [START_REF] Ross | Applications and Extensions of SADT[END_REF], may be used too, but we think it is more adapted when the technical solution is already chosen. Identification of these functions should ideally take place during the stage 1 of the MDDD described in the introduction of this document. The identification of the items presented in the second columns should be made during stage 2. The third and fourth columns should be filled during the end of stage 2 or the beginning of stage 3 depending on where the prototyping actions are performed. The idea of this conference is not to go through each items depicted in figure 4. However, we shortly describe how the main function analysis lead to "usability" solutions. The MF1 function is analyzed as follows.

▪ In order to assess the lithium level in the biological sample it is necessary to put the saliva in contact with the sensor's reagents. ▪ For this, the user must be able to collect the saliva. ▪ In order to collect the saliva, different technical solutions can be used: use an existing saliva sampling device (Salivette® type) or spit in a recipient.

Here, we recall that the scientific or technological solutions are listed in column 4 but the final choice is not yet made. This is because, in the frame of our functional analysis, the goal is not yet to select the final solutions but to identify all of them as we already mentioned above while writing that we should disregards technological solutions.

Indeed, when the FAST diagram is finalized, practical work can start. This is the prototyping phase which should take place during stage 2 or 3 of the MDDD method already mentioned. Choice of the technical solutions or specialty areas where research efforts should be put can now be finalized in accordance with the end-users' "highlights" and regulation constraints. Because our device is intended to be used in the European community, the regulation documents listed in figure 4 correspond to a part a part of required standards to comply with the European Regulation.

We have seen that the functional analysis allows defining what a medical device should be in order to meet requirements of end-users, researchers and manufacturers in accordance with the regulation constraints. The FAST diagram summarizes these aspects. Including this diagram in the general MDDD plane proposed elsewhere, we understand that the functional analysis takes place at the beginning of the development, mainly during stages 1 and 2.

In order to be efficiently conducted, the functional analysis must involve all the stakeholders which includes not only manufacturers who already know this kind of analysis but also end-users and researchers. For this, a pedagogic effort must be made which is not in the scope of this conference.

However, if we only stick to this understanding, there is a risk that the functional analysis is considered as a background task which extends over the beginning of the MDDD process. The consequence could likely be that important information highlighted during the functional analysis are under-estimated during the prototyping phase, if not simply forgotten. It is therefore crucial that actors try to consider the development of a medical device not like a process linear in time but as a whole.

The linear perception of technological developments is nowadays probably due to the importance that the TRL scale has gained these last decades. This "Readiness Technology Level" scale was originally proposed by the NASA to improve technology developments. However, the NASA proposed a more general and less linear development model with the so-called CML scale, namely the "Concept Maturity Level" scale [START_REF] Ziemer | Exploring Mission Concepts with the JPL Innovation Foundry A-Team[END_REF]. Readers interested in this method can refer to (CML1, CML2, no date). The CML scale has recently been adapted to the medical device development [START_REF] Béjean | Organizing Medtech Innovation with Concept Maturity Levels[END_REF]. The development is now not only described but also driven through 3 main dimensions: need, science and technology, programmatic.

We think that the functional analysis can efficiently be used to link the above mentioned 3dimensional development descriptions.

CONCLUSION

In this paper, we have presented how function analysis can be used to drive research and development actions and to identify regulation constraints during the development of medical devices. An example of functional analysis conducted to design a home-based lithium monitoring device is given. It is shown that function must be identified and characterized and that technological solutions and regulation constraints arise from this analysis. At this stage, scientific or technological techniques used in the final medical device are not yet chosen. They will be chosen during the subsequent prototyping phase according to the identified regulation constraints.

But, beyond the only description of a functional analysis, we pointed out that an efficient design of a medical device implies controlled discussion between end-users, researchers and manufacturers. In order to ensure these fruitful discussions and exchanges of experience, a common innovation frame must be adopted. The idea here is that functional analysis can be this common frame to the condition that it is pedagogically explained and presented to stakeholders less familiar with it than manufacturers. Functional analysis can then be regarded as a common thread in the design and development process.

This methodology is currently applied to the lithium monitoring device developed in the frame of the H2020 R-LiNK project and results of this development will be available soon.

Figure 1 :

 1 Figure 1: R-LiNK monitoring device: expression of the need.

Figure 2 :

 2 Figure 2: R-LiNK monitoring device interaction diagram for the "normal use" life situation. Two types of functions exist: the main function and constraint functions. The main function reflects actions performed by the device, the constraint functions reflect an adaptation of the device to its environment. Therefore, the environment has to be defined for a specified life situation. In our R-LiNK example, the diagram represents the life situation "normal use". Environment of the R-LiNK monitoring device consists of: ▪ users ▪ biological sample ▪ user's place ▪ European standards (IVD MD 2017/746) ▪ European market

  FAST allows, among other things, organizing and understanding relationship between functions. It helps identifying missing or redundant functions. All functions can be analyzed in the same way. The method consists in asking, for each of them: why do you do [Function], how do you do [Function] and when you do [Function] is there another function that occur together with or as a result of [Function]? This can be formalized using the codified diagram presented in figure 3.

Figure 3 :

 3 Figure 3: general principle for establishing a FAST diagram.The FAST diagram corresponding to the device developed in the frame of the R-LiNK project is presented in figure4. In this figure, the first column (on the left hand side) corresponds to functions. Identification of these functions should ideally take place during the stage 1 of the MDDD described in the introduction of this document. The identification of the items presented in the second columns should be made during stage 2. The third and fourth columns should be filled during the end of stage 2 or the beginning of stage 3 depending on where the prototyping actions are performed. The idea of this conference is not to go through each items depicted

Figure 4 :

 4 Figure 4: FAST diagram obtained for the home-based lithium monitoring device of the R-LiNK project.

Table 1 :

 1 Definition of the functions.

	N°	Function	Criteria / standard	Levels	Flexibility	Tests
	MF1	Monitor the level of salivary lithium	Indicate the level of lithium in saliva comprehensively for the patient	0,5 mM to 5 mM 100 µL saliva Without contamination Accuracy 0.2 mM	F1	Performance tests in the laboratory on artificial samples then on real calibrated samples. Tests in real conditions of use
	CF1	Suitable for all users	The patient easily uses the device without errors	Pass the aptitude to use tests	F0	Formative and summative evaluation tests
	CF2	Suitable for home use	The device works in the patient's environment: home, holidays	Pass the aptitude to use tests	F0	Formative and summative evaluation tests
	CF3	Respect the regulations in force	Complies with the new (auto-monitoring) European IVMD regulation	UE 2017/746	F0	Respect the regulation in force
	CF4	Integrate the European market at a reasonable cost	The cost is not a hindrance for the patient or the institutions	Consumable 10 € Cartridge reader 300 €	F1 F2	Estimated costs by item, including raw materials and an estimate of manufacturing costs

ACKNOWLEDGEMENTS R-LiNK has received funding from The European Union's Horizon 2020 Research and Innovation program Under Grant Agreement N° 754907.