Meriem Sahnoune

Nicolas Tokhadzé

Sarah Ech Cherif

El Kettani

Julien Devémy

Florent Goujon

Philip Chennell

Alain Dequidt

Christelle Goutaudier

Valérie Sautou

Patrice Malfreyt
email: patrice.malfreyt@uca.fr

Drug Interactions with Plasticized PVCs

Keywords: Drug adsorption, polymeric materials, potential of mean force, microscopic description, plasticized PVC surfaces, drug quantification, thermodynamic characterization

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

Polyvinylchloride (PVC) is the world's third-most widely produced thermoplastic material after polyethylene (PE) and polypropylene (PP). Its worldwide production volume is expected to reach 60 million metric tons in 2025. [START_REF]Polyvinyl Chloride (PVC) Market -Growth, Trends, COVID -19 Impact, and Forecasts[END_REF] The fields of applications are numerous and cover the following sectors such as construction, packaging, automotive, toys, footwear, floorings and medical devices. [START_REF] Czogala | Recent Attempts in the Design of Efficient PVC Plasticizers with Reduced Migration[END_REF] Light weight, softness, low manufacturing cost, transparency and recyclability are interesting properties that explain its extensive use. In order to make PVC adaptable to the final application by focusing on its flexibility, softness and brittleness, PVC is then mixed with plasticizers up to a concentration of about 40% to the PVC material.

For information purposes, PVC consumes almost 90% of all the plasticizers of the market. [START_REF] Li | An Atomistic Evaluation of the Compatibility and Plasticization Efficacy of Phthalates in Poly(Vinyl Chloride)[END_REF][START_REF] Wei | Plasticiser Loss from Plastic or Rubber Products Through Diffusion and Evaporation Plasticiser Loss from Plastic or Rubber Products Through Diffusion and Evaporation[END_REF] The most widely used type of plasticizers were phthalic acid esters known as phthalates and di(ethylhexyl phthalate) (DEHP) was the earliest and widest plasticizer used with about 50% of the phthalate production. [START_REF] Li | An Atomistic Evaluation of the Compatibility and Plasticization Efficacy of Phthalates in Poly(Vinyl Chloride)[END_REF] However, this plasticizer was categorized in 2008 as CMR 1B (carcinogenic, mutagenic or toxic to reproduction) [START_REF]the European Parliament and of the Council on classification, labelling and packaging of substances and mixtures, amending and repealing Directives 67/548/EEC[END_REF] and as an endocrine disruptor by the European Chemical Agency. [START_REF] Echa | Information on chemicals[END_REF] The issues of the potential toxicity and carcinogenic risks become particularly critical when PVC material is used in medical devices (MD). The used of DEHP in PVC medical devices is now restricted by the European Regulation N o 2017/245 on medical devices. [START_REF]745 of the European Parliament and of the Council of 5 April 2017 on medical devices[END_REF] Alternative plasticizers like diisononyl phtalate (DINP), di-(2-Ethylhexyl) phtalate (DEHT), 1,2-cyclohexane dicarboxylic acid diisononyl ester (DINCH) or tris(2-Ethylhexyl) trimellitate (TOTM) have therefore been used in the manufacturing of MDs. [START_REF]Scientific Committee on Emerging and Newly Identified Health Risks (SCENIHR) 2016. SCENIHR -opinions[END_REF] Since the plasticizers are relatively small molecules which are not chemically bound to the polymer matrix, they can interact in MDs both with the polymer matrix and the drug solution. Two other phenomena known as leaching and sorption may compete each other and lead to adverse effects for the patient. Leaching is defined by the migration of the plasticizer into the drug solution whereas the sorption relates to the adsorption of the drug to the polymer surface, absorption into the polymer matrix and permeation. [START_REF] Czogala | Recent Attempts in the Design of Efficient PVC Plasticizers with Reduced Migration[END_REF][START_REF] Jenke | Evaluation of Model Solvent Systems for Assessing the Accumulation of Container Extractables in Drug Formulations[END_REF][START_REF] Treleano | Investigation into the Sorption of Nitroglycerin and Diazepam into PVC Tubes and Alternative Tube Materials during Application[END_REF][START_REF] Jin | Diazepam Sorption to PVC-and non-PVC-Based Tubes in Administration Sets with Quantitative Determination using a High-Performance Liquid Chromatographic Method[END_REF] The potential adverse effects of leaching relates to the product's safety through either the toxicity of the leached substances or the degradation of some properties of the drug. [START_REF] Bernarda | Mi-29 gration of Plasticizers from PVC Medical Devices: Development of an Infusion Model[END_REF][START_REF] Bernard | Analysis of Plasticizers in PVC Medical Devices: Performance Comparison of Eight Analytical Methods[END_REF][START_REF] Bernard | on behalf of the ARMED Study Group, Effects of Flow Rate on the Migration of Different Plasticizers from PVC Infusion Medical Devices[END_REF][START_REF] Tokhadze | Impact of Alternative Materials to Plasticized PVC Infusion Tubings on Drug Sorption and Plasticizer Release[END_REF] Sorption can affect the efficacy and stability of a drug product via the loss of an important component, such as an active pharmaceutical ingredient (API) [START_REF] Kambia | Compatibility of Nitroglycerin, Diazepam and Chlorpromazine with a New Multilayer Material for Infusion Containers[END_REF][START_REF] Maiguy-Foinard | Influence of a Double-Lumen Extension Tube on Drug Delivery: Examples of Isosorbide Dinitrate and Diazepam[END_REF] or an excipient [START_REF] Masse | In Vitro Assessment of the Influence of Intravenous Extension Set Materials on Insulin Aspart Drug Delivery[END_REF] which leads to ineffective drug responses after administration of injectable drugs and makes it difficult to control the concentration of the delivered drug. Drug-polymer materials interactions represent therefore a major issue for the pharmaceutical industry in the delivery of insulin [START_REF] Hewson | Insulin Infusions in the Neonatal Unit: Delivery Variation due to Adsorption[END_REF][START_REF] Jakobsson | The Impact of Insulin Adsorption onto the Infusion Sets in the Adult Intensive Care Unit[END_REF] and other drugs. [START_REF] Treleano | Investigation into the Sorption of Nitroglycerin and Diazepam into PVC Tubes and Alternative Tube Materials during Application[END_REF][START_REF] Jin | Diazepam Sorption to PVC-and non-PVC-Based Tubes in Administration Sets with Quantitative Determination using a High-Performance Liquid Chromatographic Method[END_REF][START_REF] Tokhadze | Impact of Alternative Materials to Plasticized PVC Infusion Tubings on Drug Sorption and Plasticizer Release[END_REF][START_REF] Hacker | Falsely Elevated Cyclosporin and Tacrolimus Concentrations over Prolonged Periods of Time Due to Reversible Adsorption to Central Venous Catheters[END_REF][START_REF] Jin | Evaluation of Drug Sorption to PVC-and Non-PVC-based Tubes in Administration Sets Using a Pump[END_REF][START_REF] Jin | Evaluation of Tacrolimus Sorption to PVC-and non-PVC-Based Tubes in Administration Sets: Pump Method vs. Drip Method[END_REF][START_REF] Jin | Evaluation of Nitroglycerin and Cyclosporin A Sorption to Polyvinylchloride-and non-Polyvinylchloride-Based Tubes in Administration Sets[END_REF][START_REF] Haned | Migration of Plasticizers from Poly(vinyl chloride) and Multilayer Infusion Bags using Selective Extraction and GC-MS[END_REF] A number of factors have been identified to impact on sorption: [START_REF] Tokhadzé | Critical Drug Loss Induced by Silicone and Polyurethane Implantable Catheters in a Simulated Infusion Setup with Three Model Drugs[END_REF] the physicochemical properties of the drug itself (lipophilicity, pKa, isoelectric point, steric hindrance, concentration), the excipient composition, infusing process (flowrate, medical devices length), the physicochemical properties and chemical nature of the polymer material [START_REF] Stoffers | Feasibility Study for the Development of Certified Reference Materials for Specific Migration Testing: Part 1. Initial Migration Concentration and Specific Migration[END_REF][START_REF] Stoffers | Feasibility Study for the Development of Certified Reference Materials for Specific Migration Testing: Part 2. Estimation of Diffusion Parameters and Comparison of Experimental and Predicted Data[END_REF] such as polyethylene (PE), poylethylene terephthalate (PET), polyamide (PA) and the nature and amount of plasticizers. [START_REF] Treleano | Investigation into the Sorption of Nitroglycerin and Diazepam into PVC Tubes and Alternative Tube Materials during Application[END_REF][START_REF] Tokhadze | Impact of Alternative Materials to Plasticized PVC Infusion Tubings on Drug Sorption and Plasticizer Release[END_REF][START_REF] Jin | Evaluation of Drug Sorption to PVC-and Non-PVC-based Tubes in Administration Sets Using a Pump[END_REF][START_REF] Jin | Evaluation of Tacrolimus Sorption to PVC-and non-PVC-Based Tubes in Administration Sets: Pump Method vs. Drip Method[END_REF] A number of experimental procedures [START_REF] Jenke | Evaluation of Model Solvent Systems for Assessing the Accumulation of Container Extractables in Drug Formulations[END_REF][START_REF] Treleano | Investigation into the Sorption of Nitroglycerin and Diazepam into PVC Tubes and Alternative Tube Materials during Application[END_REF][START_REF] Jin | Diazepam Sorption to PVC-and non-PVC-Based Tubes in Administration Sets with Quantitative Determination using a High-Performance Liquid Chromatographic Method[END_REF][START_REF] Hacker | Falsely Elevated Cyclosporin and Tacrolimus Concentrations over Prolonged Periods of Time Due to Reversible Adsorption to Central Venous Catheters[END_REF][START_REF] Jin | Evaluation of Drug Sorption to PVC-and Non-PVC-based Tubes in Administration Sets Using a Pump[END_REF][START_REF] Jin | Evaluation of Tacrolimus Sorption to PVC-and non-PVC-Based Tubes in Administration Sets: Pump Method vs. Drip Method[END_REF][START_REF] Tokhadzé | Critical Drug Loss Induced by Silicone and Polyurethane Implantable Catheters in a Simulated Infusion Setup with Three Model Drugs[END_REF][START_REF] Genay | Experimental Study on Infusion Devices Containing Polyvinyl Chloride: To What Extent are They di(2-ethylhexyl)phthalate-free?[END_REF][START_REF] Ferchichi | Sorption of Paracetamol onto Biomaterials[END_REF] have been developed to determine the drug concentration before/after passing through the medical devices by using High Performance Liquid Chromatography (HPLC) methods to quantify the difference in drug quantities. Experimental procedures focusing on material analyses have also been investigated as an alternative approach but they have not been yet able to completely describe the molecular mechanisms involved in drug sorption to MDs.

A bottom-up approach from the chemical structures (drug, plasticizer and polymer material) to the desired properties of the material in terms of deterioration, loss of additives and drug sorption remains still a delicate matter. However, the progress made these last years on molecular modeling now make it possible to rationalize the experimental observations. [START_REF] Sahnoune | Understanding and Characterizing the Drug Soprtion to PVC and PE Materials[END_REF] The next step will be the development of novel plasticizers, excipients and polymer materials that limit the adverse effects aforementioned from interactions at the atomic scale. Indeed, molecular simulation is reaching a kind of maturity that allows it to use reliable and robust interaction potentials often developed from experimental data in molecular dynamics and Monte Carlo algorithms. The increase in computing power is at the origin of the production of atomistic sophisticated configurations representative of the experimental sample in terms of composition of the chemical species (polymers, drug, solvent, plasticizers). In addition, the use of statistical mechanics and advanced methodologies such as the calculation of free energy provides a direct route to obtain macroscopic properties of experimental interest and to understand the molecular properties that drive the adsorption. A comprehensive understanding of the drug adsorption on polymer material, currently leads us to combine experimental and molecular modeling approaches to mutually enrich the macroscopic and microscopic views of adsorption phenomena. This is exactly the approach we have proposed in a recent study [START_REF] Sahnoune | Understanding and Characterizing the Drug Soprtion to PVC and PE Materials[END_REF] on materials without additives. Indeed, we have shown 31 that paracetamol does not show significant adsorption on PE and unplasticized PVC materials with Gibbs free energies of adsorption less of than -25 kJ mol -1 whereas diazepam adsorbs on PE with an free energy of adsorption of about -35 kJ mol -1 . No loss of diazepam was observed when it was contact with unplasticized PVC tubings in line with a thermodynamic property of adsorption of about -20 kJ mol -1 .

The combination of experiments and molecular simulations led us to establish a threshold value of -30 kJ mol -1 above which no drug loss was detected. Nevertheless, experimental studies [START_REF] Jin | Diazepam Sorption to PVC-and non-PVC-Based Tubes in Administration Sets with Quantitative Determination using a High-Performance Liquid Chromatographic Method[END_REF][START_REF] Salloum | Impact of the Nature and Concentration of Plasticizers on the Ability of PVC to Sorb Drug[END_REF] have shown that diazepam adsorbs onto plasticized PVC films in static and clinical conditions of drug uses. To better represent the clinical conditions, we performed experimental studies in which diazepam and paracetamol were put in contact with different tubings. The sorption involves two stages : adsorption then absorption. [START_REF] Salloum | Impact of the Nature and Concentration of Plasticizers on the Ability of PVC to Sorb Drug[END_REF] Adsorption is the result of a weak interaction between the drug in solution and the PVC surface. This phenomenon is faster and reversible compared to the absorption which corresponds to the diffusion of the drug inside the material. Investigating the adsorption process is then required before a study of the absorption. Additionally, the absorption of drugs into the material is not always measurable 32 and will require adapted methodologies and long simulation times that remain inaccessible with an atomic description of the interactions. One alternative would be to develop coarse-grained models in mesoscopic simulations to investigate the absorption at longer time scales. This is outside the scope of this paper.

In this paper, we aim to address the question of the adsorption of two drugs (paracetamol and diazepam) onto plasticized PVCs surfaces. In a first step, we develop HPLC experiments to investigate the impact of the nature of the plasticizer on the adsorption of paracetamol and diazepam onto plasticized PVC tubings. In a second step, we propose an interpretation of the experimental results through the calculation of the Gibbs free energy profile of the adsorption process. These calculations will inform about the strength of the interaction between the drug and the PVC surface. We aim to correlate the loss by sorption of drugs measured by HPLC with the Gibbs free energy of adsorption obtained by advanced molecular simulations. In a third step, we will investigate the description of the interfacial region at the atomic scale and the mobility of drugs onto the PVC surfaces. Combining experiments and advanced molecular simulation approaches should allow a better understanding of the molecular interactions and microscopic effects that govern the adsorption of drugs onto these plasticized PVCs.

We propose to study 3 plasticizers (TOTM, DEHT, DINCH) (see Figure 1) whose effects are compared to PVC without additives. Modelling atomistic configurations representative of plasticized PVC surfaces is not so common in the literature. [START_REF] Li | An Atomistic Evaluation of the Compatibility and Plasticization Efficacy of Phthalates in Poly(Vinyl Chloride)[END_REF][START_REF] Özeren | Prediction of Plasticization in a Real Biopolymer System (starch) using Molecular Dynamics Simulations[END_REF][START_REF] Özeren | Ranking Plasticizers for Polymers with Atomistic Simulations: PVT, Mechanical Properties, and the Role of Hydrogen Bonding in Thermoplastic Starch[END_REF] The quality of the molecular potentials will be tested through the calculation of the partition coefficient of diazepam and paracetamol. Indeed, the octanol-water partition coefficient [START_REF] Garrido | 1-Octanol/Water Partition Coefficients of n-Alkanes from Molecular Simulations of Absolute Solvation Free Energies[END_REF][START_REF] Bhatnagar | Direct Calculation of 1-Octanol-Water Partition Coefficients from Adaptive Biasing Force Molecular Dynamics Simulations[END_REF][START_REF] Espinosa | Calculation of the Water-Octanol Partition Coefficient of Cholesterol for SPC, TIP3P, and TIP4P Water[END_REF] (log P) is a key-property for drug molecules to provide informations on the interactions of drugs in hydrophilic and lipophilic media. A good reproduction of this property can be used as an evidence of the quality of the atomistic models at least for solvent and drug molecules. The experimental measurements of this property will be carried out for diazepam in this study.

The paper is organized as follows: Section 2 contains the description of the experimental and computational procedures. In section 3, we discuss the experimental results in line with the macroscopic properties provided by molecular simulations and we characterize the drug adsorption by the description of local arrangements close to the PVC surface. We conclude in Section 4.

2 Experimental and computational procedures

Experimental studies

Experimental sorption studies were conducted to evaluate paracetamol and diazepam concentration variations after static contact with pure PVC tubings and PVC plasticized tubings.

Materials

The tubings used in this study are described in Table S1 of the Supporting Information, all were composed of PVC. Tubings made of PVC without additives are referred to as pure PVC. Other tubings are made with different plasticizers: DINCH, DEHT and TOTM. The mass fraction of plasticizer depend on the plasticizer used. All tubings were provided by Cair LGL (Lissieu, France). 60 mL polypropylene syringes (Pentaferte, Italy, ref 002022970) were used to fill tubings with solutions. For these solutions, the following API were used:

• Paracetamol, purity > 99%, pharmaceutical raw material (ref 1547095, Cooper Pharmaceutique, Melun, France)

• Diazepam, purity > 99%, chemistry grade (ref D08988-1G, Sigma-Aldrich Chimie SARL, Saint Quentin Fallavier, France)

Reagents

The following reagents were used: acetonitrile 99% purity (Fisher Chemical, United Kingdom); methanol 99% purity (Fisher Chemical, United Kingdom); formic acid 98% purity (Fluka, Germany) and monobasic potassium phosphate (Sigma-Aldrich, Germany), 1octanol 99% purity (Honeywell, Germany). All the reagents were of HPLC grade.

Sample preparation

APIs were diluted with sterile water to a standardized molarity of 0.07 mM, corresponding to 10.6 µg mL -1 for paracetamol solution, and 19.9 µg mL -1 for diazepam. To obtain complete dissolution, diazepam solutions were stirred at 50 o C for 48h.

Study design

Each tubing was first rinsed with the API solution, at a flow rate of 1200 mL/h using an electric syringe pump. After rinsing, the tubings were completely filled with the API solution, closed at both extremities using a lue-lock stopper, and then stored in a climatic chamber (Binder, model KBF240, Gmbh Tuttlingen, Germany) at 25 o C. The initial API concentration was quantified right after filling the syringe (T0), then evolution of the concentration was assessed at days 1 (T1), 2 (T2), 4 (T4), 7 (T7), and 14 (T14). For each analytical time, five independent tubings were used (n=5) and were fully emptied to collect samples. A total of 25 tubings was used for each condition. Quantification was performed with a liquid chromatography system (LC2010-Shimadzu) equipped with an UV detector. Analytical methods and validation data of each API are presented in previously published work. [START_REF] Tokhadze | Impact of Alternative Materials to Plasticized PVC Infusion Tubings on Drug Sorption and Plasticizer Release[END_REF]

Octanol/water partition coefficient

The octanol/water partition coefficient (log P) is determined using the shake-flask method according to a validated protocol for low solubility molecules. [START_REF] Ishak | Aqueous Solubility, Vapor Pressure and Octanol-Water Partition Coefficient of Two Phtalate Isomers, Dibutyl Phthalate and Di-Isobutyl Phthalate, Contaminants of Recycled Food Packages[END_REF] In this static mode proce-dure, equal quantities of octanol pre-saturated water and water pre-saturated octanol are slowly shaken into double coated vessel with a magnetic stirring bar, in order to avoid emulsion formation as far as possible. A known mass of paracetamol is first dissolved in one or other of the solvents. The vessel is then stirred for an extended time allowing equilibrium to be reached (up to 3 days). Each experiment is replicated twice. Temperature is strictly controlled at 298,1K. For analysis, each phase is sampled and paracetamol content is quantified by UV-vis absorption spectrometry using a precise calibration line.

Computational procedures

The two APIs, the three plasticizers and the PVC chains were modeled by using the allatom general Amber force field (GAFF2). [START_REF] Wang | Development and Testing of a General Amber Force Field[END_REF] The atomic partial charges were calculated at the B3LYP/6-31++g(d,p) [START_REF] Becke | Density-Functional Exchange-Energy Approximation with Correct Asymptotic Behavior[END_REF][START_REF] Becke | Density-Functional Thermochemistry. III. The Role of Exact Exchange[END_REF] level using the Cioslowki's atomic polar tensor (APT) method. [START_REF] Cioslowski | General and Unique Partitioning of Molecular Electronic Properties into Atomic Contributions[END_REF][START_REF] Cioslowski | A New Population Analysis Based on Atomic Polar Tensors[END_REF][START_REF] Stephens | Theory of Vibrational Rotational Strengths: Comparison of a Priori Theory and Approximate Models[END_REF] These calculations were carried out using the Gaussian16 45 package. The water molecules were described by using the TIP4P2005 model. 46 The 1-octanol molecules were modeled using the united atoms TraPPE force field. [START_REF] Eggimann | An Online Parameter and Property Database for the TraPPE Force Field[END_REF] The GAFF2 model is quite universal and has been applied to many chemically different systems whereas the TIP4P2005 and TraPPE force fields have been shown to successfully reproduce a significant number of thermodynamic properties. We will use the calculation of the partition coefficient as a test of the quality of these force fields and their transferability.

The molecular dynamics simulations were performed with the LAMMPS package. [START_REF] Plimpton | Fast Parallel Algorithms for Short-Range Molecular Dynamics[END_REF] For the integration of the equation of motion we used the standard velocity-Verlet algorithm with a timestep of 2 fs. The SHAKE algorithm [START_REF] Ryckaert | Numerical Integration of the Cartesian Equations of Motion of a System With Constraints: Molecular Dynamics of n-Alkanes[END_REF] was used to constrain all the C-H bonds of all molecules, the O-H bonds and the HOH angle of water molecules. The Lennard-Jones crossing parameters were calculated using the Lorentz-Berthelot rules. The Nose-Hoover thermostat and barostat algorithms [START_REF] Nosé | Constant Pressure Molecular Dynamics for Molecular Systems[END_REF] were used to maintain the temperature at 300 K and the pressure at 1 atm. For the repulsion-dispersion interactions, the cutoff radius was fixed to 12 Å. The periodic boundary conditions were applied in the three directions. The electrostatic interactions were handled with the Particle-Particle-Particle-Mesh (PPPM) 3D method. [START_REF] Pollock | Comments on P3M, FMM, and the Ewald Method for Large Periodic Coulombic Systems[END_REF] The design of pure PVC was modeled using 9 atactic chains of 95 monomers each. Each chain was constructed step by step in order to propose random equilibrium chain configurations. The resulting configuration was relaxed in the NPT ensemble with increasing temperature up to 600 K, to maintain the system at this temperature for 1 ns, then cooling to 300 K with a ramp of 1 ns. The final configuration is then obtained after NVT runs at 300 K. The final density of pure PVC converged to 1.37 g cm 3 with fixed dimensions L x × L y = 50 × 50 Å. The design of plasticized PVCs used shorter PVC chains with 50 monomers to obtain a better homogenization with plasticizers. The number of molecules of plasticizers and PVC chains depends on the mass fraction of the corresponding tubing. These properties are given in Table S2 in the Supporting Information along with the simulated density of each system.

First, a box of plasticizers with homogeneous distribution was constructed, then the box was filled with PVC chains step by step to obtain random equilibrium of the mixture. Configurations were relaxed using the same method as for pure PVC. Systems were composed of a polymeric surface, one drug, and 4000 water molecules. Classical molecular dynamics simulations were performed with an equilibration period of 500 ps. Structural and thermodynamic properties were calculated during the acquisition phase of 40 ns.

The harmonic biasing method Umbrella sampling (US) [START_REF] Torrie | Nonphysical Sampling Distributions in Monte Carlo Free Energy Estimation: Umbrella Sampling[END_REF][START_REF] Simonson | Thermodynamics of Protein-Peptide Binding in the Ribonuclease-S System Studied by Molecular Dynamics and Free Energy Calculations[END_REF] was used for PMF profiles.

US is a technique for efficient sampling at values of the reaction coordinates for which the PMF is unfavorable by applying a bias along the reaction path. The Hamiltonian is then modified by the addition of a weighting function that takes a quadratic form where the spring constant k was equal to 0.11 kJ mol -1 Å-2 . We used the Weighted Histogram Analysis However, the values of the Gibbs free energy minimum remained unchanged within the standard deviations. The Gibbs free energy ∆G(z) was set to zero for the largest separation distance between the drug molecule and the polymeric surface. The standard deviations were estimated to be in the range of 2-3 kJ mol -1 in the region of the Gibbs free energy minimum by carrying out at least 5 independent calculations (see Figure S1 in the Supporting Information). Among the 5 simulations, we retain the PMF profile that corresponds to the deepest well and therefore to the strongest adsorption.

3 Results and discussion

Partition coefficients

The adaptive biasing force (ABF) method, [START_REF] Darve | Calculating Free Energies Using Average Force[END_REF][START_REF] Chipot | Free Energy Calculations: Theory and Applications in Chemistry and Biology[END_REF][START_REF] Darve | Adaptive Biasing Force Method for Scalar and Cector Free Energy Calculations[END_REF][START_REF] Comer | The Adaptive Biasing Force Method: Everything You Always Wanted to Know but Were Afraid To Ask[END_REF] described in the Supporting Information, was used to determine the free energies of solvation (∆G o solv ) in 1-octanol and hydratation (∆G o hyd ) of paracetamol and diazepam drugs. A comparison between ABF and US methods was shown in Figure S2 in the Supporting Information. The octanol-water partition coefficient (log P) is then calculated by

log P = ∆G o 2.303RT = ∆G o hyd -∆G o solv 2.303RT (1)
where R and T are the universal gas constant and temperature, respectively. Two interfacial systems were constructed : water/void and 1-octanol/void, with box dimensions 30 Å × 30 Å × 80 Å with 102 and 896 1-octanol and water molecules, respectively. The free energy of hydration ∆G o hyd was calculated by transferring the drug molecule from the condensed water phase to the gas water region and ∆G o solv from the transfer from the bulk liquid phase of 1-octanol to its vapor region. The liquid phase extends over a region of 30 Å in the z-direction. Lennard-Jones (LJ) interactions were truncated at 14 Å. For the determination of the PMF curve, the reaction coordinate was defined as the distance between the center of mass of the drug molecules and the condensed phase. The overall reaction coordinate distance was 35 Å starting from the center of the condensed phase to the center of the void.

To improve the sampling, the overall reaction pathway was divided into seven windows of 5 Å. To generate the initial configuration for each window, a 200 ps molecular dynamics simulation was performed where the solute was pulled at the beginning of the window in bins of width 0.2 Å. The same harmonic force used for the PMF profiles was set to keep the solute within the window.

The free energy of hydration was calculated by the difference between the mean free energy averaged over the slab of the water phase and that averaged over the slabs of the void. The free energy of solvation was calculated in the same way but considering the octanol phases. The curves of potential of mean force are shown in Figure 2. We considered only the slabs of the bulk phases in order to avoid the impact of the interface. The free energy values are given in Table 1 along with the calculated and experimental values [START_REF] Carlsson | Determination of Octanol-Water Partition Coefficients using a Micro-Volume Liquid-Liquid Flow Extraction System[END_REF][START_REF] Valko | Rapid Method for the Estimation of Octanol/Water Partition Coefficient (log P(oct)) From Gradient RP-HPLC Retention and a Hydrogen Bond Acidity Term (zetaalpha(2)(H))[END_REF][START_REF] Machatha | Estimation of the Ethanol/Water Solubility Profile from the Octanol/Water Partition Coefficient[END_REF][START_REF] Wattanasin | Zone Fluidics for Measurement of Octanol-Water Partition Coefficient of Drugs[END_REF][START_REF] Bahmani | A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs[END_REF][START_REF] Avdeef | Absorption and Drug Development : Solubility, Permeability and Charge State[END_REF] of the octanol-water partition coefficient. First, the partition coefficient of paracetamol we have measured in this work by using the shake-flask method at 298 K compares very well with those measured in batch conditions. [START_REF] Carlsson | Determination of Octanol-Water Partition Coefficients using a Micro-Volume Liquid-Liquid Flow Extraction System[END_REF][START_REF] Avdeef | Absorption and Drug Development : Solubility, Permeability and Charge State[END_REF] A mean deviation of 31% is observed with experimental values obtained in fast and dynamic conditions, [START_REF] Valko | Rapid Method for the Estimation of Octanol/Water Partition Coefficient (log P(oct)) From Gradient RP-HPLC Retention and a Hydrogen Bond Acidity Term (zetaalpha(2)(H))[END_REF][START_REF] Wattanasin | Zone Fluidics for Measurement of Octanol-Water Partition Coefficient of Drugs[END_REF] for which the equilibrium state was probably not reached. Second, the calculated partition coefficient of paracetamol (log P = 0.33) matches very well with the corresponding experimental coefficient (log P = 0.34) measured in this work with a deviation smaller than 3%. Concerning the partition coefficient of diazepam, we reach the same conclusion about the accuracy of the prediction by showing a deviation between experiments and simulations of 7%. In view of the statistical fluctuations of the calculation of the PMF curves and the range of experimental log P values, we can conclude that the molecular models used are of good quality. We also show a certain transferability of Amber, TIP4P2005 and TraPPE force fields. We can now extend the simulations to the interaction of these species with a PVC surface with confidence.

Experiments

We used the HPLC method to detect any loss by sorption of the paracetamol and diazepam solutions with pure PVC and plasticized PVC tubings. Figure 3 shows the evolution over time (in days) of the concentration of both drugs during static contact with different tubings.

First, Figure 3a shows that the paracetamol solution remained unchanged for 14 days regardless of whether PVC material was plasticized or not. These experiments indicate that there is no adsorption of paracetamol onto PVC tubings. Second, Figure 3b

Thermodynamic characterization

The thermodynamic characterization of the adsorption is achieved through the calculation of the potential of mean force which represents the change in the Gibbs free energy as a function of the separation distance between the drug and PVC material. These PMF curves inform us about the free energy changes as the drug molecule approaches the PVC surface from a distance beyond the range of interaction to contact. They model in some way the adsorption process. Figure 4a shows the free energy curves corresponding to the adsorption of paracetamol onto pure and plasticized-PVC surfaces. These curves exhibit negative Gibbs free energy minimum at a contact separation distance. The depth of the well is assimilated here to the Gibbs free energy of adsorption. For paracetamol, the values of Gibbs free energy of adsorption, listed in Table 2, fall into a range of -22 to -15 kJ mol -1 .

In the context of physical adsorption governed mainly by van der Waals interactions, the values reported here with paracetamol are relatively weak. It means that the adsorption of paracetamol onto these PVC surfaces is thermodynamically possible but that the drug-PVC interaction is not strong enough to detect an adsorption of paracetamol on PVC tubings with our analytical procedure. In a preliminary study, [START_REF] Sahnoune | Understanding and Characterizing the Drug Soprtion to PVC and PE Materials[END_REF] we concluded that no drug loss by sorption can be observed when the Gibbs free energy of adsorption is greater than -30 kJ mol -1 . Experiments and simulations carried out here with plasticized PVC-tubings support this conclusion.

Figure 4b shows the free energy profiles ∆G(z) of the adsorption process of diazepam onto PVC surfaces. The PMF of the adsorption of diazepam onto the pure PVC surface shows a minimum of -21.5 kJ mol -1 . In line with the threshold value of -30 kJ mol -1 for ∆G o ads , PMF calculations predict then no adsorption onto unplasticized-PVC as confirmed by HPLC measurements. With plasticized-PVC surfaces, the situation is quite different. Indeed, the PMF curves highlight much deeper wells ranging from -55 to -33 kJ mol -1 . As a result, the simulations predict stronger interactions between diazepam and the plasticized-PVC surfaces and experiments confirm the strength of these interactions by significant drug losses by sorption when the drug is in contact with plasticized-PVC tubings (see Figure 3b).

The strength of these interactions remains typical of that involved in physisorption. The coupling between experiments and PMF calculations, that has proven successful on pure PE and PVC surfaces, is successfully extended to more complex surfaces such as plasticized-PVC-surfaces. We have to some extent highlighted a correlation between the strength of the drug-material interaction and the drug loss by sorption. Figure 4: Gibbs free energy profiles ∆G(z 1 ) of the interaction of a) paracetamol and b) diazepam drugs onto plasticized PVC surfaces in water as indicated in the legend. The profiles of the interaction with unplasticized PVC surfaces calculated in a previous work [START_REF] Sahnoune | Understanding and Characterizing the Drug Soprtion to PVC and PE Materials[END_REF] are given for comparison. z 1 represents the difference between the centers of mass of surface and drug.

In order to understand the key driving force for drug adsorption, we calculated the same PMF curves in the void. The free energy profiles are given in Figure S3 in the Supporting Information and the thermodynamical properties of adsorption are listed in Table S3 in the Supporting Information. Firstly, we can note that all the free energy profiles present deeper wells in the void than in water indicating that the net interactions between drug and PVCsurfaces are thermodynamically favored in the void. It means that this highly favorable drug-PVC surface interaction in the void is partly counterbalanced by unfavorable contributions in water. To further investigate the calculation of these positive free energy contributions, we developed a thermodynamic cycle in Figure S4 in the Supporting Information that calculates the difference in the free energy between void and water (∆G o,water ads -∆G o,void ads ). The calculation of these free energy differences requires the calculation of the surface tension of water, the work of adhesion of PVC surfaces and water (Table S4 and Figure S5 in the Supporting Information) and the Gibbs free energy of hydration of both drugs (see Table 1).

These different thermodynamical properties require adapted methodologies such as free energy perturbation, 65 potential of mean force and surface tension calculation. [START_REF] Ghoufi | Surface Tension of Water and Acid Gases from Monte Carlo Simulations[END_REF] The reader is redirected to the Supporting Information for a comprehensive presentation of the calculation of the work of adhesion between an unplasticized PVC surface and water (see Table S4 and Figures S4-S5). From this cycle, we can conclude that the less favorable Gibbs free energy of adsorption in water with respect to the void results from positive contributions coming from the desorption of water molecules and dehydration of drug during the adsorption process. In the case of pure PVC surfaces, we show that the difference in the free energy of adsorption between water and void matches reasonably well with the calculated desorption and dehydration contributions of the thermodynamic cycle taking into account different approximations and error bars of the free energy calculations. Roughly, the positive free energy due to the desorption of water molecules accounts for 5-15 kJ mol -1 and the dehydration of diazepam and paracetamol contributes about 40 and 22 kJ mol -1 , respectively. By using different methodologies of simulation, we show that we can interpret the difference of PMF curves in water and in the void and give an order of magnitude of these positive free energy contributions.

We now pay attention to the shape of the free energy profiles. From these curves, it is then possible to estimate the number of adsorbed molecules from the calculation of the l ads parameter as

l ads = ∞ surface exp - ∆G(z) R T -1 dz ( 2
)
where R is the universal gas constant and T the temperature set at 300K. The number of adsorbed drug molecules per unit area, at equilibrium is the same as the number of such molecules in a slab of solution of thickness l ads . Adsorption has a significant impact on the concentration in the solution if l ads is not negligible versus the volume/area ratio of the container, taking into account that roughness actually increases the effective area.

The values of l ads are given in Table 2. We establish here a strong correlation between the increase of l ads and a more favorable Gibbs free energy of adsorption but also with the experimental decrease of concentration of diazepam when it is contact with plasticized-PVC tubings. Indeed, the order of magnitude of l ads parameter for the interaction of diazepam with PVC-DINCH, PVC-DEHT and PVC-TOTM surfaces for which HPLC measurements show a total adsorption, is by no means comparable (at least two orders of magnitude) to systems with paracetamol and no detected adsorption. The atomic density profiles along the z-direction perpendicular to the surface can be calculated as

ρ(z) = 1 A N i=1 δ(z -z i ) (3)
where A = L x L y is the surface area, N the number of atoms to be considered, z i the z-position of the atom i and δ the Dirac delta function. The simulation cell is split into uniform slabs of volume V = A∆z perpendicular the surface normal. ∆z represents the slab width and ρ(z)

is the the atomic density profile in each slab at z-position. These profiles were calculated from standard unbiased molecular dynamics simulations. We checked that the generated equilibrium configurations correspond to those of the free energy minimum 31 obtained from the PMF calculations. Figure 5c and Figure 5d and Figure S7c and Figure S7d (Supporting Information) show however that the presence of TOTM molecules does not induce any major differences in the local arrangements of drugs at the interfacial region for either paracetamol or diazepam molecules. When the surface is covered by plasticizers, we observe a slight decrease of the peak of adsorption with diazepam and a slight increase of this peak with paracetamol.

However, this could mean that plasticizer molecules, which are not chemically attached to polymer chains, could in certain conditions leave the PVC material through migration but the duration of our simulations is not long enough to observe to this phenomenon. Typical configurations of diazepam absorbed onto plasticized PVC materials are given in Figure S10 in the Supporting Information.

The density profiles of Figures S7c andS7d of the supporting Information show that paracetamol adsorbs onto the PVC surface with oxygen atoms pointing more towards the water bulk and nitrogen atom toward the PVC in the case of pure PVC surface. This effect is mitigated with the presence of TOTM molecules. For the diazepam, it is rather the chlorine atom that points toward the PVC atoms with pure PVC material (see Figure 5b)). The reader is redirected to the Supporting Information for the density profiles calculated in the PVC-DINCH and PVC-DEHT surfaces.

These density profiles do not provide an unambiguous answer to the question of absorption of the drug into the PVC material. Indeed, the density profile calculated by Eq.( 3) is smoothed out due to local surface roughness. The profiles of Figure 5 and Figure S7 (Supporting Information) could suggest that the drug molecule has penetrated a little into the material, which would indicate the first stages of the absorption. To definitively conclude on this point, we calculated an intrinsic density profile [START_REF] Partay | A New Method for Determining the Interfacial Molecules and Characterizing the Surface Roughness in Computer Simulations. Application to the Liquid-Vapor Interface of Water[END_REF][START_REF] Jorge | A Critical Assessment of Methods for the Intrinsic Analysis of Liquid Interfaces: 2. Density Profiles[END_REF][START_REF] Lapshin | On Competitive Gas Adsorption and Absorption Phenomena in Thin Films of Ionic Liquids[END_REF] by considering the following expression

ρ(z ′ ) = 1 A N i=1 δ(z ′ -z i ) = 1 A N i=1 δ(z -z i + ξ(x i , y i )) ( 4
)
where ξ is the instantaneous position of the surface and x i and y i are the atomic coordinates in the plane parallel to the PVC surface. The calculation of ξ(x i , y i ) is done by moving down a (x, y) grid of fictitious spheres along the z axis, starting in the water region. Once a surface atom i is detected at a given distance from the probe sphere, ξ(x i , y i ) is set to the current z coordinate of the sphere. This definition makes it possible to better identify the molecules at the interface and to determine the boundaries of the solid-fluid interface.

The intrinsic density profiles, shown in Figure 6 were then calculated for the adsorption of diazepam onto pure PVC and plasticized PVC-TOTM surfaces.

These profiles show a region of non-overlap between water and surface atoms on the one hand and diazepam and PVC atoms on the other hand. This zone is explained by the excluded volume interaction of the Lennard-Jones potential. It is clear that this definition Figure 6: Intrinsic density profiles calculated by using eq.4 for the adsorption of diazepam onto a) the pure PVC surface and b) the plasticized PVC-TOTM surface. The choice of the probe sphere radius is set to 1.75 Å which is the size of a water molecule in terms of van der Waals radius. The Lennard-Jones parameter σ of the surface atoms is used to establish the proximity check, i.e. if a surface atom i is detected at a distance smaller than σ i /2 + 1.75 then the position ξ(x i , y i ) is set to z of the sphere. As a consequence, water molecules that are in the first layer close to the surface have a value of z ′ close to zero. We used a 80 × 80 grid, which allows for a fast calculation as well as a small enough resolution of 0.625 Å given the box dimensions along the x and y directions.

better considers the structure of the interface. A better ordering of water molecules is observed by showing a first adsorption peak due to the interaction of water molecules with PVC and plasticizers chains. Unlike the density profiles shown in Figure 5b, two water layers are clearly visible showing the impact of the surface on the structuring of water in the interfacial region. We also establish definitively that, for the duration of the simulation, diazepam does not penetrate into the PVC and PVC-TOTM surfaces, it remains outside the surface in the interfacial region in interaction with plasticizers, PVC and water molecules.

Diazepam interacts directly with the surface atoms without water molecules between them indicating that it loses half of its first hydration layer upon adsorption. The drug adsorption can also be investigated through the analysis of the mean square displacements of the drug onto the surface in the x, y and z directions. Figure 7a shows displacements in a plane parallel to the surface and Figure 7c in the direction normal to the surface. Parts b) and d) of Figure 7 display the sampling zones of both drugs in the plane parallel to the surface. We observe that diazepam covers an area of plasticizers (see Figure 7b) whereas paracetamol samples a small region of PVC surrounded by plasticizers as shown in Figure 7d. It is clear from Figure 7a that the diffusion of paracetamol along the x and y directions is larger than that of diazepam in line with a weaker interaction.

We also observe that the presence of plasticizers tends to slightly slow down the diffusion of the drug. This also correlates with the regions sampled by the drug molecules. In the direction perpendicular to the surface (Figure 7c), we see the same trends while verifying that mobility along this axis is reduced by at least a factor of 5 due to the adsorption.

The local environment of the adsorbed molecule can be analyzed using spatial distribution functions (SDF). [START_REF] Svishchev | Structure in Liquid Water: A Study of Spatial Distribution Functions[END_REF] The positions of the surrounding atoms of the molecule are calculated with respect to an internal frame defined by three central atoms of the molecule, leading to a scalar field representing the density of each atom type relative to the orientation of the molecule frame. We can then visualize the higher probability of finding a given atom type around the molecule by calculating an isosurface for a chosen threshold value. Figure 8 shows the SDF of both drugs in water and interacting with PVC-TOTM surfaces.

Figures 8a and8b show the distributions functions around paracetamol and diazepam in bulk water conditions. On one hand, the hydrogen bonds exhibit a characteristic structuration, with successive hydrogen and oxygen layers starting from the alcohol (paracetamol only) and carbonyl functions. Indeed, diazepam and paracetamol give 2.2 and 3.4 hydrogen bonds, respectively. The calculation of hydrogen bonds is based upon the following definition : the X-H..O distance is required to be less than 2.5 Å and the H-X...O angle to be less than no overlap for diazepam (see Figure 8d), we can conclude that the diazepam does not rotate on itself and always points the same face to the surface: this rigid configuration allows the diazepam to conserve its hydrogen bond with water molecules. Indeed, the number of hydrogen bonds for adsorbed diazepam is 2.1. In contrast, Figure 8c shows that the PVC and TOTM isosurfaces show different positions around the paracetamol, which is the result of possible rotations for the adsorbed molecule. Upon adsorption, paracetamol loses 0.4 hydrogen bond. These distribution functions confirm that the drug molecule maintain interactions with water, plasticizers and PVC chains upon adsorption even if the comparison with the functions calculated in bulk water confirm a quantitative dehydration of both drugs.

Conclusions

A combined experimental and theoretical study was carried out to investigate the sorption of paracetamol and diazepam onto plasticized PVC materials. Both APIs were studied at the same molar concentration, alone without excipients and that therefore the results are comparable. HPLC experiments establish that diazepam adsorbs strongly onto plasticized PVC tubings whereas it does not adsorb onto a pure PVC tubing. Concerning paracetamol, experiments indicate no adsorption whether or not the tube is plasticized.

The quality of the force field was tested on the calculation of the partition coefficient.

Experiments were carried out to determine this coefficient for diazepam. The adsorption process was also investigated through the calculation of the profile of Gibbs free energy along the direction normal to the PVC material. The simulations show that the adsorption of diazepam and paracetamol onto PVC-materials are thermodynamically favored indicating a physical adsorption through mainly van der Waals interactions. Nevertheless, the order of magnitude of the free energy of adsorption range from -55 to -15 kJ mol -1 which corresponds to totally different amounts of adsorbed molecules.

The comparison of the simulated free energy of adsorption with HPLC measurements confirms the threshold value 31 of about -30 kJ mol -1 above which we did not detect any drug loss by sorption. We have highlighted a correlation between the strength of the interaction between the drug and PVC surface through the calculated free energy of adsorption and the drug loss by sorption experimentally determined. Since the adsorption process is accompanied by dehydration of drugs and desorption of water molecules, we assess these unfavorable free energy contributions by applying different advanced simulation approaches ranging from the calculation of the potential of mean force in the void to the calculation of the work of adhesion between the PVC surface and water. Roughly, in the case of the adsorption of unplasticized PVC surfaces, the contribution to the total free energy of the desorption of water molecules is within 5-15 kJ mol -1 whereas the dehydration of drugs contributes to about 40 and 22 kJ mol -1 for diazepam and paracetamol, respectively. Interestingly, we es-tablish here that the simulations performed on more sophisticated systems with the presence of plasticizers within the PVC material are able to energetically characterize the sorption process and help the interpretation of experiments.

We took advantage of the molecular simulations to explore the atomistic description of the interfacial region that remain inaccessible to experiments. The density profiles of water, PVC chains, plasticizer molecules and drug show an adsorption of the drug in line with all the negative values of free energy of adsorption. The analysis of the different arrangements of molecules at the surface of the material does not allow to interpret the differences in the strength of the drug-surface interaction but identify the adsorption of drugs in terms of contact areas, hydration and location of plasticizer molecules. However, it was possible to conclude that the simulations show only a drug adsorption and not an absorption by considering that the simulation time is limited to a hundred nanoseconds. The spatial distribution functions propose an overall view of the interaction areas around the adsorbed drug onto the PVC material. To complete the microscopic analysis of the adsorption, we show that the strongest values of free energy of adsorption correspond to a slight slowdown in the mobility of the drug not only in the direction normal to the surface but also in the plane parallel to the surface.

To conclude, we show here that advanced simulation approaches and experiments are complementary and must be used together to interpret and characterize both energetically and microscopically the drug adsorption to polymer materials. We can hope in the years to come to design plasticized polymer surfaces that would be able to prevent drug sorption.

results. SimatLab is a joint public-private laboratory dedicated to the modeling of polymer materials and supported by Michelin, Clermont Auvergne University (UCA), CHU Clermont and CNRS. The authors also thank CAIR LGL for their financial support for the discussion about the MEDSIM project and for providing the tubings used in this study. Computations have been performed on the supercomputer facilities of the Mésocentre Clermont Auvergne University.

Figure 1 :

 1 Figure 1: Conformations of the plasticizers (DEHT, DINCH and TOTM) and drugs (paracetamol and diazepam). The hydrogen, carbon, oxygen, nitrogen and chlorine atoms are represented in white, grey, red, blue and green, respectively.

  Method (WHAM)[START_REF] Kumar | The Weighted Histogram Analysis Method for Free-Energy Calculations on Biomolecules. I. The Method[END_REF] to recover the unbiased free energy profile ∆G(z). The calculations were performed in the Np N T statistical ensemble over 200 ns with constant surface S = L x L y = 50 × 50 Å2 where p N = 1 atm is the normal component of the pressure. The PMF profiles corresponded to the Gibbs free energy profile ∆G(z) along a chosen reaction coordinate. The reaction coordinate was defined as a z-coordinate : the difference between the z-positions of the center of mass of the drug molecule and the polymer surface. The maximum value of the reaction coordinate was set to 28 Å. For each studied system, the average position (x f , y f ) of the drug molecule during the standard molecular dynamic simulation was calculated. To reduce the sampling region of the PMF, the x and y transition coordinates were constrained on the domains [x f -4; x f + 4] and [y f -4; y f + 4]. The position z of the minimum of the PMF can change with respect to the roughness of the surface at the atomistic level.

Figure 2 :

 2 Figure 2: Hydration (solid lines) and solvation (dotted lines) free energy profiles calculated for paracetamol and diazepam where r is the difference between the centers of mass of drug and liquid phase.

Figure 3 :

 3 Figure 3: Evolution of a) paracetamol and b) diazepam concentrations in static contact with PVC tubings, compared to initial concentrations. The error bars on the concentrations values are less than 1% and cannot be read with the scale used.

3. 4 Figure 5 :

 45 Figure 5: and b) atomic density profiles of PVC, plasticizers, water and diazepam molecules. c) and d) density profiles of some atoms of the diazepam molecule to be red on the right scale. See Figure S6 in the Supporting Information to identify the positions of atoms of diazepam. Panels a) and c) correspond to the adsorption of diazepam onto the unplasticized PVC surface whereas panels b) and d) represent the adsorption of diazepam onto the plasticized PVC-TOTM surface.

Figure 5 and

 5 Figure S7 (Supporting Information) show the atomic density profiles of PVC, TOTM plasticizers, water at the interfacial region, for diazepam and paracetamol drugs, respectively. The atoms of the drugs used for the calculation of these density profiles are shown in Figure S6 in the Supporting Information. The density profiles obtained with the DINCH and DEHT plasticizers are given in Figures S8-S9 in the Supporting Information. Even though the PMF calculations show free energy of adsorption relatively different (-14.8 for paracetamol against -54.4 kJ mol -1 for diazepam), the density profiles of Figure 5a and Figure S7a show an adsorption of the drug with distributions totally restricted to the interface. These profiles show that the drugs form a monolayer with an average thickness of 5 Å, corresponding roughly to the size of the molecules. The density profiles of Figures 5b and Figure S7b also establish that the plasticizer molecules show a homogeneous distribution within the PVC material and that some plasticizers sample the PVC-water interface. This means that as the drug molecules adsorb onto the surface they interact with PVC, water and also plasticizers as clearly shown by Figure 5d and Figure S7d.

Figure 7 :

 7 Figure 7: (a) x and y and (c) z components of mean square displacements versus time of adsorbed drug molecules on pure PVC and PVC-TOTM surfaces. (b) and (d) positions of diazepam and paracetamol molecules along the x and y directions. The blue color represents the positions of the drug, red the positions of the plasticizers and green the PVC molecules.

30 oFigure 8 :

 308 Figure 8: Spatial distribution functions of a) paracetamol and b) diazepam molecules and water molecules in bulk water conditions. Spatial distribution functions of water, PVC and TOTM atoms around the adsorbed c) paracetamol and d) diazepam molecules as indicated in the legend. The isosurfaces representing oxygen and hydrogen water atoms are shown in red and white, respectively. PVC and TOTM atoms (all types) are shown in blue and yellow, respectively. We limit the calculation to a distance of 8 Å in order to include only the first neighbors.

Table 1 :

 1 Gibbs free energies of solvation and hydration calculated from the PMF curves. ∆G and the partition coefficient were calculated by using Eq.1. The experimental values of the partition coefficient are reported for comparison. A variation of 0.1 in log P represents a difference in the free energies of solvation and hydration of about 0.6 kJ mol -1 . a value determined in this work, b values resulting from works of Refs.59-64

	∆G o hyd	∆G o solv	∆G	log P	log P
	(kJ mol -1 ) (kJ mol -1 ) (kJ mol -1 )		Exp.
			Paracetamol
	-45.5	-47.4	1.9	0.33±0.1 0.34 ± 0.02 a
					0.34-0.52 b
			Diazepam
	-78.2	-93.0	14.8	2.6±0.2	2.8-3.0

Table 2 :

 2 Gibbs free energy of adsorption (kJ mol -1 ) obtained from the minimum of ∆G(z 1 ). The statistical fluctuations are within 2-3 kJ mol -1 for the Gibbs free energy minimum (see FigureS1of the Supporting Information) and within about 10% for the adsorption length l ads .

	Surface	∆G o ads	l ads	∆G o ads	l ads
		(kJ mol -1 ) (µm) (kJ mol -1 ) (µm)
		paracetamol	diazepam
	PVC	-16.8	0.1	-21.5	0.8
	PVC-DINCH	-21.0	0.7	-33.4	84
	PVC-DEHT	-22.1	1.0	-48.4	2×10 4
	PVC-TOTM	-14.8	0.06	-54.4	4×10 5

Acknowledgement

The authors acknowledge the financial support received from the Auvergne Rhônes Alpes regional council through the program "Pack Ambition Recherche" (MEDSIM-2019). The authors would like to thank colleagues of SimatLab for stimulating discussions about the

Supporting Information Available

Supporting Information Available: description of the tubings used, description the e-ABF and US PMF methods, determination of the statistical fluctuations, PMF in the void, calculation of dehydration and desorption free energy contributions, density profiles, snapshots. This material is available free of charge via the Internet at http://pubs.acs.org.


Mohammed Moufid

Nezha El

Carlo Tiebe

Damien Ali

Hamada Fakra

Benachir Matthias Bartholmai

Nezha El Bari

Benachir Bouchikhi
email: benachir.bouchikhi@gmail.com.

Pollution parameters evaluation of wastewater collected at different treatment stages from wastewater treatment plant based on E-nose and E-tongue systems combined with chemometric techniques

Keywords: Wastewater, Electronic nose, Voltammetric electronic tongue, Water quality control, Chemometric methods

Pollution parameters evaluation of wastewater collected at different treatment stages from wastewater treatment plant based on E-nose and E-tongue systems combined with chemometric techniques

I. Introduction

Detecting and controlling chemical pollution in the environment has become a major challenge for developed and emerging countries in the modern world. Every year, great quantity of organic and inorganic chemical pollutants are released into the air, sea, and soil, posing potential health risks to plants, animals, and humans [START_REF] Wilson | Review of electronic-nose technologies and algorithms to detect hazardous chemicals in the environment[END_REF][START_REF] Okereafor | Toxic metal implications on agricultural soils, plants, animals, aquatic life and human health[END_REF]. The pollution of natural water resources by heavy metals is one of the most worrying issues [START_REF] Egbueri | Heavy metals pollution source identification and probabilistic health risk assessment of shallow groundwater in Onitsha, Nigeria[END_REF][START_REF] Abdeldayem | A preliminary study of heavy metals pollution risk in water[END_REF]. Environmental pollution monitoring is necessary to protect the public and mitigate the many potential negative effects on environmental quality and human health. The unpleasant odor of wastewater and its negative impact on aquatic ecosystems has even led to strict requirements for wastewater treatment [START_REF] Abbasi | Quality and cost analysis of a wastewater treatment plant using GPS-X and CapdetWorks simulation programs[END_REF].

Conventionally, wastewater quality is generally determined by the levels of various factors related to the main pollutants, including chemical oxygen demand (COD), biological oxygen demand (BOD), ammonium, orthophosphate, nitrate, conductivity, and pH [START_REF] Mustafa | Recent studies on applications of aquatic weed plants in phytoremediation of wastewater: A review article[END_REF][START_REF] Koyuncu | Domestic wastewater treatment by real-scale electrocoagulation process[END_REF][START_REF] Jega | Physicochemical properties of tagangu seasonal river receiving abattoir wastewater discharge, Aliero, Kebbi State, Nigeria[END_REF]. These parameters provide essential information on wastewater quality. They are useful for demonstrating that wastewater meets discharge requirements [START_REF] Innocenzi | Case study on technical feasibility of galvanic wastewater treatment plant based on life cycle assessment and costing approach[END_REF][START_REF] Schellenberg | Wastewater discharge standards in the evolving context of urban sustainability-The case of India[END_REF]. Chromatographic techniques such as high performance liquid chromatography (HPLC) and gas chromatographymass spectrometry (GC-MS) are most commonly used for the determination of compounds in wastewater [START_REF] Oliveira | Pharmaceuticals residues and xenobiotics contaminants: Occurrence, analytical techniques and sustainable alternatives for wastewater treatment[END_REF]. However, some of these techniques are time consuming and most of them require considerable economic resources. Some wastewater parameters, such as BOD, require 5 to 20 days to obtain measurement results, making these tests unsuitable for automated monitoring and control [START_REF] Almutairi | Method development for evaluating the effectiveness of hydrocarbons on BOD, UBOD and COD removal in oily wastewater[END_REF]. Thus, operating costs may somewhat limit the applicability of these traditional techniques.

For these reasons, portable, easy-to-use, and inexpensive systems are in high demand to monitor and control processes, increase their pollutant removal capacity and achieve consistent and stable operation. This would allow meeting the requirements of wastewater treatment at minimal cost. Due to the simplicity and portability of E-noses and E-tongues, as well as the possibility of implementing them in on-line automated equipment, they can be used as complementary systems to analytical methods. According to the definition of the International Union of Pure and Applied Chemistry (IUPAC), an E-nose and E-tongue are multi-sensory systems, which consist of a number of sensors and use advanced mathematical procedures for signal processing, based on multivariate analysis techniques In this study, the capability of an E-nose based on six semiconductor gas sensors, and an E-tongue configured as five working electrodes for qualitative analysis of wastewater is verified. The highlight and novelty of this work is to verify for the first time the capability of a fabricated E-nose and E-tongue to study the physicochemical parameters of wastewater and to compare the results obtained by each measurement system. The wastewater measurement data are processed qualitatively by chemometric techniques, such as Principal Component Analysis (PCA), Discriminant Function Analysis (DFA), Hierarchical Cluster Analysis (HCA), and Support Vector Machines (SVMs). In the case of quantitative analysis, Partial Least Squares Regression (PLSR) was performed to predict the concentration of physicochemical parameters of the wastewater using the data collected from the E-nose and E-tongue against their actual concentrations.

Materials and methods

Wastewater sampling and analytical methods

To study the composition of wastewater, 30 samples were collected from a WWTP in the city of Meknes in Morocco at different treatment points (Fig. 1). The collected samples are P1: wastewater samples collected at a location where solids and grit are removed, P2: wastewater samples collected at a location where oil and grease are removed, P3: wastewater samples collected in the bioreactor, and P4: distilled water. All collected samples were kept in the dark and brought refrigerated (4°C) to the laboratory for electronic sensing analysis.

Parallel analytical tests were carried out to determine the pH, the conductivity, the contents of total organic carbon (TOC), calcium (Ca), magnesium (Mg), Iron (Fe), manganese (Mn), potassium (K), sodium (Na), silicon (Si), phosphate (PO4), sulfate (SO4), nitrate (NO3), chloride (Cl), fluoride (F), and bromide (Br).

Techniques used for physicochemical parameters analysis

The wastewater was sampled with 20 mL amber glass bottles. After sampling, no stabilization with inhibitors took place. The samples were diluted 1:10 with distilled water for further analysis. The cations were determined by inductively coupled plasma optical emission spectrometry (ICP-OES) (iCAP 6000, Thermo-Fisher). The anions were analyzed by ion chromatography (883 Basic IC plus, Metrohm) with a Metrosep A Supp 5 -100/4.0 column (Metrohm). In addition to the analysis of the anions and cations, the pH value and the conductivity were determined with a portable pH meter (HQ Series Portable Meters, Hach) and the total organic carbon (TOC) with a TOC cuvette test (Hach).

Electronic setups and signals acquiring 2.3.1. E-nose setup and signal acquisition

The electronic nose used in this work was equipped with an array of six metal oxide semiconductor (MOS) gas sensors. An overview of the system is presented in the supplementary information (Fig. S1). The sampling section consisted of a 2 L Tedlar® bag connected to a micro air pump with a flow rate of 250 mL/min. Gas sensors model MQ were chosen based on their sensitivity to most VOCs that can be found in the environment. These gas sensors and their target gases are MQ-3 (alcohols, benzene, CH4, hexane, LPG, Co), MQ-4 (CH4, LPG, H2, alcohol), MQ-5 (LPG, CH4, H2, alcohol), MQ-8 (H2, alcohol, LPG, CH4, CO), MQ-9 (CO, LPG, CH4), and MQ-135 (NH3, NOx, Alcohol, Benzene, smoke, CO2). It also contains a relative humidity sensor and a temperature sensor to monitor the environmental conditions during each measurement. All sensors were installed in a 270 cm 3 Teflon chamber.

A data acquisition unit, consisting of a hardware data acquisition card, namely NI-USB 6212 from National Instruments, and a software application in the form of a graphical user interface (GUI) running in a computer, was used.

The Normalized conductance ratio (G-G0/G0) was chosen as the sensor response signal for the E-nose. In this expression, G represents the conductance of the sensor when exposed to the gas sample and G0 represents the conductance when the sensor is exposed to synthetic air.

Before and after each E-nose measurement, synthetic air is injected into the sensor chamber for 5 minutes to clean the sensors' surface so that the signal is at baseline. During the E-nose analysis, the headspace of the wastewater sample was pumped for 5 minutes into the sensor chamber at a flow rate of 250 mL/min. A total of 40 water and wastewater samples were used in this study. For each sample, 60 ml aliquots were measured to assess reproducibility. Four features were extracted from the E-nose responses, which are:

• The conductance difference ∆G = (Gs-G0). Here, Gs is the average conductance value during the last sixty seconds of the measurement and G0 is the average conductance value during the first sixty seconds of the measurement.

• The area under the curve (AUC) calculated between the first sixty and the last sixty seconds of the measurement. It was estimated based on the trapezoidal rule.

E-tongue setup and data acquisition

The E-tongue system consists of five working electrodes, a counter electrode (platinum), and a reference electrode (Ag/ AgCl, diameter 2 mm, saturated in 3 M KCl). The five working electrodes are made of gold (Au), glassy carbon (GC), platinum (Pt) from CH Instruments, Texas, USA (diameter of 2 mm and length of 5 mm), copper (Cu), and palladium (Pd) from BAS Inc, Tokyo, Japan (diameter of 1.6 mm and length of 6 mm). These working electrodes respond in the form of voltammograms related to the electrochemical properties of the samples.

Detailed information on the E-tongue system are provided in the additional information (Fig. S2). For E-tongue measurements, a potential is first applied to the working electrodes. Then, electro-active species in solution are reduced or oxidized at the electrodes' surfaces and the resulting current is measured.

For analysis with the E-tongue, the electrodes were immersed in a beaker of wastewater samples and interfaced with a portable potentiostat (PalmSens BV, the Netherlands) using a relay box. In effect, a constant potential is applied to the reference electrodes and a variable potential is measured on the working electrode. In addition, the current between the counter electrode and the working electrode is measured. The cyclic voltammetry technique was applied for electrochemical measurements over a potential range of -0.2 to 0.6 V and a scan rate of 50 mV/s. Forty water and wastewater samples were used in this study. For each sample, ten aliquots of 60 mL were measured to assess reproducibility. Two features were extracted from the E-tongue responses, which are:

• The change in current Δ𝐼 = (𝐼max -𝐼min) calculated as the difference between the maximum and minimum current values.

• The area (A) between the oxidation and reduction phases in the voltammograms. This area is estimated by the trapezoidal method.

Multivariate analysis techniques

Data processing is carried out using multivariate analysis techniques, such as unsupervised (PCA, HCA) and supervised (DFA, SVMs) methods.

PCA is an unsupervised linear method often used to analyze data obtained from multisensory systems. It is used for dimension reduction and allows a clear visualization of the dataset and simplifies its interpretation. The mechanism of PCA is based on the representation of the data in a new orthogonal coordinate system. Indeed, the axes are organized according to the variance of the data, keeping as much information as possible in the first axes [START_REF] Bro | Principal component analysis[END_REF].

DFA is a statistical method that determines Discriminant Functions (DF) using one or overall independent variables that maximize distance between groups and minimize distance within groups. The DFA is performed to check whether the variables used are enough to obtain a clear discrimination between the studied clusters [START_REF] Poulsen | Discriminant function analysis[END_REF].

HCA is a statistical procedure, providing a better alternative of accurate representation and classification of high-dimensional data, and it used the full dimensionality of the data to create a classification dendrogram. The aim of performing HCA is to separate data into specific groups based on similarity or distances between different observations. HCA results can be presented as a dendrogram, in which distances between observations are determined in different observations [START_REF] Köhn | Hierarchical cluster analysis[END_REF].

Multi-class SVMs is a non-linear supervised learning technique. It can be built using two strategies. The first one is by using "one-vs.-one" or "one-vs.-all" approaches. In the second, all the data are considered. In this study, the "one-vs.-one" method is applied to differentiate between water and wastewater samples [33].

Results and discussion

Physicochemical parameters determination of water and wastewater samples

The concentrations determination of pollution parameters of some influent wastewater samples were illustrated in Table 1. It can be seen from this table that the measured parameters do not exceed the concentration limit mentioned by the World Health Organization (WHO), which confirms that the Meknes wastewater treatment plant respects the standard concentration for the different components [START_REF] Rajendran | Assessment of physico-chemical parameters of river Cauvery in and around Nerur[END_REF][START_REF] Pant | Ground water quality in the Kathmandu valley of Nepal[END_REF][START_REF]ASTM D2579-93e1, Standard Test Method for Total Organic Carbon in Water (Withdrawn 2002)[END_REF][START_REF] Singh | Physicochemical parameters and alarming coliform count of the potable water of Eastern Himalayan state Sikkim: An indication of severe fecal contamination and immediate health risk[END_REF][START_REF]Guidelines for drinking-water quality[END_REF][START_REF] Von Gunten | Bromate formation during ozonization of bromide-containing waters: interaction of ozone and hydroxyl radical reactions[END_REF].

The pH refers to the measurement of hydrogen ion activity in the solution. Determination of pH plays an important role in the wastewater treatment process. Acidic (low pH) or basic (high pH) conditions alter the rate of microbial growth, the function of metabolic enzymes, and can stop the growth of most microorganisms that require a pH between 6.5 and 8.5. As seen in the table, the pH values measured for all the studied samples are acceptable except for P3.

Conductivity is the ability of water to carry an electric current. The increase in conductivity of water samples is due to the increased mobility of ions. In fact, a higher conductivity value indicates that there are more chemicals dissolved in the water. From the table, it is noticed that the water samples have a low conductivity value compared to the wastewater samples. This is due to the low concentration of physicochemical parameters found in the water samples compared to the wastewater samples.

Total Organic Carbon (TOC) refers to the amount of carbon present in any organic compound. TOC measurement is a mandatory requirement for wastewater treatment companies in many countries around the world. The TOC concentration of water is a basic indication of the extent of organic contamination and the purity of the water. High organic content means increased growth of microorganisms, which contribute to the depletion of oxygen reserves. In this study, the TOC of the wastewater is over 300 mg/l but the water is only 10.2 mg/l.

To clearly see the differences in the physicochemical parameters of the analyzed samples, Fig. 2 shows a plot of the data as a histogram graph. It can be seen from this figure that the wastewater samples have high concentration for all physicochemical parameters compared to water sample (control). As remarked, the compound, which is present at highest concentration in the three wastewater samples is chloride. Indeed, it is also remarked that there are different concentrations of each physicochemical parameter from site to site.

E-nose and E-tongue responses

Figure 3 shows the normalized conductance of the sensor array of the E-nose exposed to the headspace of water and wastewater samples. Indeed, Figures 3(a,b,c) show the plot of conductance change of sensor responses of wastewater influent collected from P1, P2, and P3, respectively. However, Fig. 3(d) shows typical responses of the six-sensor array of distilled water collected from P4. According to Fig. 3, the normalized conductance increases with the exposure time. This can be explained by the presence of reducing chemicals on the sensing surface of n-type semiconductor gas sensors. As seen, distilled water (control) has the lowest response compared to the wastewater samples. In addition, it is noted that the MQ-9 gas sensor has the highest response for all analyzed samples.

Figure 4 shows the electrochemical responses of the E-tongue system towards wastewater samples of P3. As can be seen, the amplitudes of the oxidation and reduction currents differ according to the analyzed samples. It should be noted that the intensity of the voltammogram corresponding to copper electrode (Cu) is higher than the others.

Data analysis results

Qualitative analysis results

PCA was conducted on the E-nose and E-tongue database (Fig. 5). Figure 5(a) reports, in a two-dimensional plot (PC1-PC2), the electronic nose data coming from the measurements corresponding to the analyzed samples. As one can see, the variances explained by the first and the second principal components were 73.88% and 19.78%, respectively. In this plot, the analysed samples appear to be well separated. As well as electronic nose results, PCA performed on E-tongue data points out the existence of four clusters based on their collection point in the WWTP (Fig. 5(b)). The variance explained by the first two principal components were 72.91% and 14.32%, respectively. DFA is one of the best classification methods, as this procedure maximizes the variance between categories and minimizes the variance within categories to optimize resolution. To confirm the PCA results, DFA was applied to the same database gathered from the E-nose and E-tongue. Figure 6 (a,b) shows the DFA plot corresponding to the E-nose and E-tongue database with a score of 98.1%, and 99.9% of the data variance, respectively. The first two discriminant functions (DF1 and DF2) show a very satisfactory classification of the wastewater samples, without overlap of the four groups.

HCA is an unsupervised technique and was used in this study to confirm the results obtained by PCA and DFA. Figure 7 shows the HCA results of the studied samples by using the E-nose and E-tongue database, respectively. From Fig. 7(a), at a distance d = 7000, it appears an overlap between all samples except for those of P2. Indeed, at a distance d = 4000, good separation between headspace of samples of P1, P3, and P4 was achieved. Furthermore, from Fig. 7(b), at a distance d = 190, the dendrogram illustrates that the samples from P3 and P4 are clearly separated from the other samples. Furthermore, samples corresponding to P1 and P2 were overlapped. Moreover, at a distance d = 87, the samples from P1 and P2 were also separated to each other except of one sample from P1 which overlapped with P2. Therefore, by using various grouping hypothesis, it can be concluded that water and wastewater samples are well discriminated using the E-nose and E-tongue systems.

Table 2 shows the SVMs confusion matrixes of the recognition of water and wastewater samples using the E-nose and E-tongue systems. Therefore, the SVMs model provided 100% and 97.5% success rate in the recognition and classification of analyzed samples using E-nose and E-tongue, respectively. It can be noticed that the E-nose system is able to distinguish all samples according to their collection point, without any overlapping. By using the E-tongue, only one error is reported for the recognition of the samples studied. Therefore, the SVMs results confirm the previous results obtained using the PCA, DFA, and HCA methods. Based on these results, it is argued that the E-nose and E-tongue systems are very promising and capable of discriminating water and wastewater samples.

Quantitative prediction for the contents of physicochemical parameters

Partial least squares regression based on E-nose and E-tongue data

The versus the E-nose and E-tongue data, respectively. One latent variable and a leave-one-out cross-validation method were used to build these models. The calibration curve shown in Fig. 8(a) represents the concentration of phosphate predicted by the E-nose versus the real concentration of phosphate found in each sample. Besides, the calibration curve shown in Fig.

8(b)

represents the concentration of phosphate predicted by E-tongue versus the real concentration of phosphate found in each sample. The PLS regression yielded a calibration model with good correlation between experimental and estimated concentrations of phosphate using E-nose and E-tongue with correlation coefficients of about 0.99 for training and testing sets. These results prove that the proposed E-nose and E-tongue systems can predict the concentration of phosphate in water and wastewater samples.

The same behavior was observed for the prediction of the concentration of the other physicochemical parameters with good correlation coefficients R by using both systems. The calculation of correlation coefficient (R), as well as Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV) for training and testing sets were described in Table 3 for E-nose and E-tongue.

The results depicted in this table show that the correlation coefficient R is higher than 0.91 for all parameters, except for iron (Fe) which remains 0.84. Furthermore, the RMSEC and RMSECV values are lower than 9.34 and 11.29, respectively. These results demonstrate that there is a good calibration between the predicted and real concentration of physicochemical parameters using E-nose and E-tongue signals.

In addition, the R-values obtained by the E-tongue are higher compared to those obtained by the E-nose for all parameters (except for calcium (Ca), manganese (Mn) and fluoride (F) in the testing set). Furthermore, the RMSEC values obtained by E-tongue are lower than the ones obtained by E-nose for all parameters with the exception of calcium (Ca) and silicon (Si) in the testing set. Moreover, the RMSECV values obtained by E-tongue are lower compared to those obtained by E-nose for all parameters with the exception of calcium (Ca), Magnesium (Mg), Iron (Fe), Manganese (Mn), Silicon (Si), Phosphate (PO4) and Fluoride (F) in the testing set.

Based on these outcomes, it can be said that the results obtained by E-tongue are better than Enose for the prediction of physicochemical parameters.

HCA results

Figure 9 shows the results of the HCA as a dendrogram obtained by processing the data corresponding to the calibration of the E-nose (Fig. 9(a)) and the E-tongue (Fig. 9

Conclusion

Wastewater is known to have many negative impacts on the environment and human health. In general, sensing systems that are easy to handle, on-site, affordable and capable of analyzing wastewater are rare in the literature. In the present study, the use of detection systems, such as an E-nose and an E-tongue that meet these requirements was highlighted. The effectiveness of the developed E-nose, consisting of six tin dioxide (SnO2) sensors, and an Etongue, consisting of five working electrodes made of noble and non-noble metals, in distinguishing samples from different stages of wastewater treatment was therefore investigated. Their ability to distinguish between water and wastewater samples was the main objective of this study. The second was to differentiate wastewater at different points in a WWTP. Thus, the prediction of some physicochemical parameters of the studied samples was carried out using both systems. Furthermore, the ability of the proposed E-nose and E-tongue to classify water and wastewater samples was proven by applying multivariate analysis techniques, such as PCA, DFA, HCA, and SVMs. A strong agreement was found between the satisfactory discrimination obtained by the two systems and the different concentrations of physicochemical parameters found in each sample. Furthermore, using PLS regression, a good calibration between the E-nose and E-tongue as a function of the concentrations of the physicochemical parameters was obtained with a correlation coefficient higher than 0.91 for both systems. The results obtained by HCA when applied to the calibration of the data reveal that the E-tongue is better than the E-nose in predicting the concentrations of the physicochemical parameters. These results indicate that the E-nose and E-tongue systems can be used as inexpensive, portable, easy-to-use tools for wastewater analysis.

Tables

Figure captions

Highlights

• E-nose and E-tongue were used for qualitative analysis of water and wastewater.

• ICP-OES and ion chromatography were used for quantitative analysis of the samples

• Good discrimination of wastewater samples was obtained using chemometric techniques.

• PLSR was used to determine the relationships between parameters and systems responses.

• E-nose and E-tongue were able to predict the levels of wastewater parameters.

Graphical abstract

  [13-15]. They use semi-selective sensors that produce a signal that can be associated either with a specific compound or with a qualitative aspect of the samples [16]. Despite the use of non-specific sensors, their combination offers enormous potential for information [17]. When these systems are subjected to a sample containing different compounds, they generate an output pattern that represents a synthesis of all the components contained in the analyte [18]. Considerable research efforts have been devoted to the development of E-noses and Etongues as well as their application in various fields, including the environment, agriculture, biomedicine, cosmetics, food, military, pharmaceuticals, and various areas of scientific research [19-22]. There are several potential applications of E-noses in environmental pollution monitoring. They are used to detect fires in chemical storage facilities, to identify environmental pollutants in diesel and gasoline exhaust [23], to monitor odors in and around livestock farms [24], to assess odor nuisance from landfills [25], to characterize odors emitted from various stages of a wastewater treatment plant [26], to survey industrial odors and gaseous emissions [27], and to analyze odors from poultry farms [28]. The main application of Etongues in the environmental field is the detection of physicochemical parameters in wastewater [29]. However, the ability of E-noses to predict the concentrations of physicochemical parameters in wastewater has never been investigated.

  PLSR model was evaluated by comparing the values of Root Mean Squared Error of Calibration (RMSEC) and Root Mean Square Error of Cross-Validation (RMSECV) that represent model and cross-validation errors, respectively [40]. If they are similar, the model could be selected as having good prediction ability [41]. PLS regression was performed to model the relationship between the physicochemical parameters with different concentrations

  (b)) versus the concentrations of physicochemical parameters. As shown in the dendrogram in Fig. 9(a), at d = 53, the headspace of wastewater samples (P4) is clearly separated from the others. Similarly, at d = 28, the P2 samples are also separated. However, the samples from P1 overlapped with those from P3. In the case of the dendrogram in Fig 9(b), at d = 25, the wastewater samples of P3 and P4 are clearly separated from each other but the samples of P1 and P2 overlapped. Interestingly, at d = 15, the P1 and P2 samples are well separated from each other. These results reveal that the calibration results obtained by the E-tongue are better than those obtained by the E-nose.

FiguresFig

  Figures

Fig. 1 :

 1 Fig. 1: Sampling points in a wastewater treatment plant of Meknes city.

Fig. 2 :

 2 Fig. 2: Histogram of the physicochemical parameters determined in the water and wastewater

Fig. 3 :

 3 Fig. 3: Normalized conductance evolution of sensors array exposed to headspace of water and

Fig. 4 :

 4 Fig. 4: Cyclic voltammograms corresponding to wastewater samples collected from bioreactor

Fig. 5 :

 5 Fig. 5: PCA plot performed for visualization of water and wastewater samples distribution

Fig. 6 :

 6 Fig. 6: DFA plot displaying patterns of water and wastewater samples based on: (a) E-nose

Fig. 7 :

 7 Fig. 7: HCA dendrogram results of water and wastewater samples measured using: (a) E-nose

Fig. 8 :

 8 Fig. 8: PLS prediction models of phosphate from the water and wastewater samples performed

Fig. 9 :

 9 Fig. 9: Dendrogram obtained by HCA of water and wastewater samples performed by the

Table 1 :

 1 Results of physicochemical parameters determination of water and wastewater samples.

						Maximum limit for
	Component (mg/L)	P1	P2	P3	P4
						potable water (WHO)
	pH	8.29	8.48	8.68	8.08	6.5-9.5 [34]
	Conductivity (µS/cm)	1390	1169	1497 120.1	500 [35]
	Total organic carbon (TOC) (mg/L) > 300 > 300 > 300 10.2	2 to 200 [36]
	Calcium (Ca) (mg/L)	80.43 57.90 88.09 22.56	75 [37]
	Magnesium (Mg) (mg/L)	42.87 54.99 54.80 7.94	50 [37]
	Iron (Fe) (mg/L)	1.51	0.62	0.35	0.04	0.1 [37]
	Manganese (Mn) (mg/L)	0.02	0.28	0.22	0.03	0.1 [34]
	Potassium (K) (mg/L)	16.47 12.53 22.13 2.65	Not available
	Sodium (Na) (mg/L)	74.93 72.07 105.20 1.98	200 [38]
	Silicon (Si) (mg/L)	9.42	3.78	8.78	1.77	Not available
	Phosphate (PO4) (mg/L)	13.57	7.19	15.69 0.22	Not available
	Sulfate (SO4) (mg/L)	49.24 45.25 71.53 0.57	200 [34]
	Nitrate (NO3) (mg/L)	1.56	0.64	0.49	0.3	50 [37]
	Chloride (Cl) (mg/L)	132.27 124.53 157.89 4.76	200 [37]
	Fluoride (F) (mg/L)	0.31	0.22	0.21	0.21	1.5 [37]
	Bromide (Br) (mg/L)	0.11	0.12	0.11	0	0.025 [39]

Table 2 :

 2 SVMs classification results of water and wastewater samples carried out using Enose and E-tongue signals with a success rate of 100% and 97.5%, respectively.

			E-nose signals			E-tongue signals
		P1	P2	P3	P4	P1	P2	P3	P4
	P1	10				10
	P2		10			1	9
	P3			10				10
	P4				10				10

Table 3 :

 3 Correlation coefficient (R), as well as Root Mean Square Error of Calibration (RMSEC) and Root Mean Square Error of Cross Validation (RMSECV) for training and testing sets using the data from both systems. Mn 0.93 0.03 0.05 0.98 0.02 0.03 0.97 0.02 0.03 0.97 0.02 0.09 K 0.97 1.45 1.98 0.99 1.01 1,60 0.99 0.4 0.41 0.99 0.45 1.38 Na 0.98 6.21 9.23 0.99 5.08 8,07 0.99 0.59 0.66 0.99 0.75 1.27

			E-nose signals		E-tongue signals
	Components	R	Training set RMSEC	RMSECV	R	Testing set RMSEC	RMSECV	Training set R RMSEC	RMSECV	R	Testing set RMSEC	RMSECV
	Ca 0.95 3.84 4.81 0.99 1.78 2.70 0.99 1.15 1.75 0.97 2.81 8.64
	Mg 0.95 2.45 3.05 0.98 1.44 2.29 0.98 1.42 1.56 0.98 1.14 5.99
	Fe 0.84 0.23 0.27 0.96 0.11 0.16 0.96 0.11 0.11 0.96 0.11 0.37
	Si	0.98 0.65 0.88 0.99 0.16 0,35 0.99 0.34 0.5 0.99 0.46 1.94
	PO4 0.99 0.68 1.07 0.99 0.65 1,02 0.99 0.32 0.48 0.99 0.45 2.12
	SO4 0.96 5.49 7.07 0.98 3.47 5,47 0.99 0.64 0.74 0.99 0.7 1.09
	NO3 0.99 0.51 0.63 0.99 0.35 0,55 0.99 0.14 0.16 0.99 0.21 0.42
	Cl 0.98 9.34 11.29 0.99 5.76 9,35 0.99 1.75 2.06 0.99 0.84 3.08
	F	0.91 0.02 0.02 0.99 0.01 0.01 0.98 0.01 0.01 0.97 0.01 0.04

Acknowledgements

The work was supported by the Ministry of Higher Education, Scientific Research and Executives training (CNRST of Morocco) and federal Ministry of Education and Research (Germany) for funding this research, under the Framework program of Moroccan-German scientific research cooperation, grant agreement n° PMARS N°2015-87. Furthermore, this work has been funded in part by CANLEISH under H2020-MSCA-RISE-2020 project, grant agreement number: 101007653: "Non-invasive volatiles test for canine leishmaniasis diagnosis". The authors would like to thank Mr. Khalid Mimich of the Autonomous Company of Water and Electricity Distribution of Meknes (RADEM) for providing wastewater samples.

Supplementary information