Nishant Pandya

Eric Marsden

Pascal Floquet

Nadine Gabas

Sensitivity analysis of a model for atmospheric dispersion of toxic gases

Keywords: Industrial risk, Sensitivity analysis, Gas dispersion modelling

We study the relative influence of uncertainty in input parameters of an atmospheric dispersion model on the variation of the outputs. Sensitivity indices are calculated using the FAST and Sobol' methods on a passive gas dispersion scenario, using the PHAST tool. We assess both the individual and coupled influence of parameters.

Introduction

Recent changes to French legislation concerning the prevention of technological and natural risks require industrial sites to calculate the safety perimeters for different accident scenarios, based on a detailed probabilistic risk assessment. It is important that safety perimeters be based on the best scientific knowledge available, and that the level of uncertainty be minimised. A significant contribution to the calculation of the safety zones comes from the modelling of atmospheric dispersion, particularly of the accidental release of toxic products. One of the most widely used tools for dispersion modelling in several European countries is PHAST™ (DNV Software, UK). This software application is quite flexible, allowing the user to customize values for a wide range of model parameters. Users of the software have found that simulation results may depend quite strongly on the values chosen for some of these parameters. While this flexibility is useful, it can lead different users to calculate effect distances that vary considerably even when studying the same scenario [START_REF] Ccps | Center for Chemical Process Safety, Guidelines for Use of Vapor Cloud Dispersion Models, 2nd Edition[END_REF]. Sensitivity analysis techniques can be used to measure the effect on the variation in the model output due to variation in input parameters. Several sensitivity analysis (SA) methods exist, including one-at-a-time (OAT) method, fractional experiments, differential analysis, Fourier Amplitude Sensitivity Test (FAST) and Sobol'. Several sensitivity analysis of various dispersion software tools have been carried out using OAT method [START_REF] Bubbico | Accidental release of toxic chemicals: Influence of the main input parameters on consequence calculation[END_REF][START_REF] Dnv | UDM verification manual[END_REF][START_REF] Nair | Review of Models Used for Determining Consequences of UF6 Release: Model Evaluation Report[END_REF]. The objective of this study is to carry out a sensitivity analysis of PHAST dispersion modelling using global methods such as FAST and Sobol'. We have developed a methodology and applied it to a case study of a gas passive dispersion scenario.

Software tools

2.1. PHAST PHAST (Process Hazard Analysis Software Tool) is a comprehensive consequence analysis tool. It examines the process of a potential incident from the initial release to far field dispersion, including modelling of pool vaporisation and evaporation, and flammable and toxic effects. PHAST is able to simulate various release scenarios such as leaks, line ruptures, long pipeline releases and tank roof collapse in pressurised / unpressurised vessels or pipes. These source terms are combined with the PHAST dispersion model called UDM (Unified Dispersion Model) [START_REF] Dnv | UDM Theory Document[END_REF] to obtain desired consequence results: i) cloud behaviour ii) transition through various stages such as jet phase, heavy phase, transition phase and passive dispersion phase, iii) distance to hazardous concentration of interest and iv) footprint of the cloud at a given time. PHAST release and dispersion models are also available in the form of an Excel interface, called MDE Generic Spreadsheets™. Sensitivity studies can be easily carried out using these Spreadsheets, since they allow direct control of input parameters and output results, easy parameter variation and multiple runs (simultaneous simulation of various scenarios). PHAST v.6.53 has been used in this work.

SimLab

SimLab (Simulation Laboratory for Uncertainty and Sensitivity Analysis) is a software tool (JRC, Italy, 2006) designed for Monte Carlo (MC) analysis that is based on performing multiple model evaluations with probabilistically selected model input. The results of these evaluations are used to determine 1) the uncertainty in model predictions and 2) the input variables that give rise to this uncertainty. SimLab generates a sample of points based on the range and distribution of each input parameter specified by the user. For each element of the sample, a set of model outputs is produced by evaluating an internal or external model. In essence, these model evaluations create a mapping from the space of the inputs to the space of the results. This mapping is the basis for subsequent uncertainty and sensitivity analysis to calculate various sensitivity indices.

Sensitivity analysis (SA)

SA is the study of how the variation in the output of a model can be apportioned, quantitatively or qualitatively, to variation in the model parameters [START_REF] Saltelli | Sensitivity Analysis, John Wiley & Sons Publication[END_REF]. [START_REF] Saltelli | What is Sensitivity Analysis?[END_REF] proposes one possible way of grouping these methods into three classes: screening methods, local SA methods and global SA methods.

Screening methods

SA is the study of how the variation in the output of a model can be apportioned, quantitatively or qualitatively, to variation in the model parameters [START_REF] Saltelli | Sensitivity Analysis, John Wiley & Sons Publication[END_REF]. [START_REF] Saltelli | What is Sensitivity Analysis?[END_REF] proposes one possible way of grouping these methods into three classes: screening methods, local SA methods and global SA methods.

Local SA methods

They are useful for models which are computationally expensive to evaluate and/or have a large number of input parameters. Various strategies and methods have been discussed in several articles with illustrative examples: [START_REF] Bettonvil | Searching for important factors in simulation models with many factors: Sequential bifurcation[END_REF][START_REF] Campolongo | The use of graph theory in the sensitivity analysis of the model output: a second order screening method[END_REF][START_REF] Deana | Comparison of group screening strategies for factorial experiments[END_REF][START_REF] Morris | Input screening: Finding the important model inputs on a budget[END_REF]

Global SA methods

Global SA techniques incorporate the whole range of variation and the probability density function of the input parameters to calculate their influence on the output. Many global sensitivity analysis techniques are now available, such as Fourier Amplitude Sensitivity Test (FAST) [START_REF] Saltelli | Sensitivity Analysis, John Wiley & Sons Publication[END_REF]Xu and Gertner, 2007a), regression-based methods [START_REF] Helton | A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling[END_REF]) and Sobol's method [START_REF] Sobol | Sensitivity estimates for nonlinear mathematical models[END_REF]. A survey of sampling-based methods has been presented by [START_REF] Helton | Survey of sampling-based methods for uncertainty and sensitivity analysis[END_REF]. Most of the global methods, such as FAST and Sobol' rely on the assumption of parameter independence [START_REF] Xu | Uncertainty and sensitivity analysis for models with correlated parameters[END_REF]. The quantitative measure of sensitivity is represented by Sensitivity Indices. The firstorder sensitivity index, S i of an input factor p i is the measure of the main (direct) effect of p i on the output variance. S ij (where i ≠ j), the second-order sensitivity indices, measures the interaction effect of p i and p j on the output variance. Other higher-order indices are defined in the same manner. The total sensitivity index, S Ti is the sum of all sensitivity indices involving factor p i [START_REF] Homma | Importance measures in global sensitivity analysis of nonlinear models[END_REF]. For example, the total sensitivity index of factor 1, S T1 for a model with 3 input factors is given as: S T1 = S 1 + S 12 + S 13 + S 123 Total indices are especially suited to apportion the model output variation to the input factors in a comprehensive manner. The FAST method calculates the first-order and total sensitivity indices, whereas Sobol' method, in addition to these, also provides all higher-order sensitivity indices to determine quantitatively the interaction between parameters. But with Sobol', as number of indices to be calculated is very high, computational cost and calculation time go high. The first-order (S i ) and total (S Ti ) indices can be interpreted as following:

• S Ti high: p i is an influent parameter • S i and S Ti both small: p i is not an influent parameter (neither alone nor in interaction with other parameters) • S i and S Ti nearly the same: no interaction of p i with other parameter • S i and S Ti very different: high interaction of p i with other parameter

Methodology

We have developed a method to carry out sensitivity studies by linking PHAST Spreadsheet and SimLab. Our method comprises of six steps, as shown in the Fig. 1.

: The description of input parameters defined by the user is sent to SimLab. : The set of sample element created by SimLab is saved in the controller. and : For each element of the sample, PHAST calculates the output, which is sent to the controller.

The set of PHAST outputs is transferred to SimLab. Depending on the selected sensitivity analysis method (FAST or Sobol'), SimLab calculates various sensitivity indices (1 st order, 2 nd order, …, Total order).

Case Study

We have studied a scenario of passive dispersion of air (as a pollutant) in the atmosphere. The base case is defined as a continuous release in the following conditions: duration of 360 000 s, release rate 0.05 kg/s, release height 50 m, temperature 298 K and release velocity 5 m/s. The ambient conditions are: Pasquill stability class D, wind speed 5 m/s, temperature 298 K, solar flux 500 W/m 2 , molecular weight 28.966, logarithmic temperature and linear pressure profiles. Dispersion is considered over land: dry soil, temperature 298 K and surface roughness of 0.1 m. Averaging time is set to 600 s. This case study can be calculated analytically by TNO passive dispersion equation [START_REF] Dujim | An evaluation of validation procedures and test parameters for dense gas dispersion models[END_REF]. In order to choose the relevant model outputs, the concentration profile of pollutant at the ground level as a function of the downwind distance from the release point is calculated with PHAST for the base case as shown in Figure 2. The following model outputs are considered for the analysis: Output 1. Ground level concentration at 200 m downwind distance Output 2. Ground level concentration at 10 000 m downwind distance Output 3. Maximum downwind distance at the ground level concentration of 0.1 ppm Output 4. Downwind distance at which highest ground level concentration is observed Output 5. Maximum crosswind distance at the ground level concentration of 0.1 ppm Fig. 2 shows relative good agreement between analytical and PHAST results. The description of the independent input parameters is given in Table 1. The values of remaining parameters are kept constant as defined in the base case.

Results and discussion

For each output, the objective of the FAST global analysis is to determine which parameters are influent/non-influent, to appreciate the relative influence of parameters and to assess qualitatively interaction between parameters. Table 2 shows the results of FAST method with sample size of 100 000 points. The interpretation of the above results is summarised in Table 3.

Table 3: Interpretation of FAST sensitivity analysis results

Outputs

Influent parameters in decreasing order

Interaction between parameters

Output1 Z0, Q, ua Z0, Q, ua Output2 Z0, Q, ua Z0, Q, ua Output3 Q, ua Q, Z0, ua Output4 Z0 - Output5 Q, Zr Q, Z0, ua
Sobol' method is used to determine quantitatively the interaction between parameters.

As an example, Fig. 3 shows a pie chart of all higher-order sensitivity indices relative to Z0 for the output C at 200m. Fig. 3 shows that 85 percentage of total sensitivity index of release height is composed of 39% main effect of Z0, 23% interaction effect between Z0 and ua, 12 % interaction effect between Z0 and Q and 10% interaction effect of Z0, Q and ua.

The main problems encountered in this work were: i) very long computational time and ii) absence of PHAST output for some of the sample elements (for output3 and output5).

Conclusion

We have developed a method to carry out sensitivity analysis of PHAST dispersion model and applied it to a passive gas dispersion scenario. In further work, we shall apply it to more complex dispersion scenarios. However, the treatment of dependent input parameters shall require modification in the sampling procedure.

Figure

  Figure 1: Methodology for sensitivity analysis

Figure 2 :

 2 Figure 2: Ground level concentration vs. downwind distance for the base case

Figure 3 :

 3 Figure 3: Sensitivity analysis results of SOBOL' for model output C at 200m and related to input factor Release height, Z0

Table 1 :

 1 Input Parameters

		Parameters	Type of distribution	Parameter range	Unit
						Min	Max
	Z0 Height of release	Continuous uniform	0	700 m
	Q	Release rate	Continuous uniform	0.005	50 kg/s
	ua	Wind speed	Continuous uniform	1	11 m/s
	tav Averaging time	Continuous uniform	18.75	3600 s
	ZR Surface roughness Discrete uniform	0.0001; 0.001; 0.01; 0.1; 1; 3 m
		1.E+00
			PHAST -Ground level conc. (ppm)
			Analytical -Ground level conc. (ppm)
			Model outputs
		1.E-01
		1.E-02 Concentration (ppm)	2
		1.E-03
			1
					4	3
		1.E-04
		1.E+01	1.E+02	1.E+03	1.E+04	1.E+05
					Downwind distance, X (m)

Table 2

 2

				: FAST sensitivity analysis results
	z0			tav	zr		Q		ua
	S i	S Ti	S i	S Ti	S i	S Ti	S i	S Ti	S i	S Ti
	Output1 0.054 0.881 0.001 0.097	0	0.182 0.003 0.277 0.006 0.367
	Output2 0.276 0.658 0.161 0.103	0	0.098 0.105 0.351 0.173 0.496
	Output3 0.005 0.226 0.009 0.304 0.004 0.305 0.283 0.879 0.067 0.617
	Output4 0.772 0.835	0	0.006 0.163 0.229	0	0.006	0	0.007
	Output5 0.031 0.595 0.001 0.282 0.002 0.331 0.285 0.899 0.009 0.496


Louis-Félix Nothias

Stéphanie Boutet-Mercey

Xavier Cachet

Erick De

La Torre

Laurent Laboureur

Jean-François Gallard

Pascal Retailleau

Alain Brunelle

Pieter C Dorrestein

Jean Costa

#,+ Luis M Bedoya

Fanny Roussi

Pieter Leyssen

José Alcami

Julien Paolini

Marc Litaudon

David Touboul

Environmentally-Friendly Workflow Based on Supercritical Fluid Chromatography and Tandem Mass Spectrometry Molecular Networking For the Discovery of Potent Anti-Viral Leads From Plants

ABSTRACT.

A supercritical fluid chromatography-based targeted purification workflow using tandem mass spectrometry and molecular networking was developed to analyze, annotate and isolate secondary metabolites from complex mixture. This approach was applied for targeted isolation of new antiviral diterpene esters from Euphorbia semiperfoliata whole plant extract. The analysis of bioactive fractions revealed that unknown diterpene esters, including jatrophane esters and phorboids esters, were present in the samples. The purification procedure using semi-preparative-supercritical fluid chromatography led to the isolation and identification of two jatrophane esters (13 and 14) and four 4-deoxyphorbol esters (15-18). Compound 16 was found to display antiviral activity against chikungunya virus (EC50 = 0.45 µM), while compound 15 was found to be a potent and selective inhibitor of HIV-1 replication in a recombinant virus assay (EC50 = 13 nM). This study showed that supercritical fluid chromatography-based workflow and molecular networking can facilitate and accelerate the discovery of bioactive small molecules by targeted molecules of interest, while minimizing the use of toxic solvents.

The secondary metabolites produced by various organisms such as plants, bacteria, fungi, and insects, represent a unique reservoir of chemical diversity, often endowed with potent biological properties. 1 If metabolomics can help deciphering structural diversity from a complex mixture, 2,3 the discovery of new bioactive molecules is still a very challenging task. The isolation of bioactive natural products is generally achieved by a bioassay-guided purification procedure. 1 However, this procedure is not always successful, often leading to isolation of known molecules. The whole process is timeconsuming, and generally uses significant amount of toxic organic solvents. Last decades, hyphenated analytical methods such as liquid chromatography coupled to mass spectrometry (LC-MS) or NMR (LC-NMR) have been used as dereplication tools to detect the presence of known bioactive molecules prior to start any isolation procedure. 3,4 However, despite the development of efficient LC-MS-based method for generic and comprehensive profiling of secondary metabolites in natural extracts, 5 the annotation of new and known analogues in a systematic and untargeted way remains a challenging task.

For these reasons, the development of more efficient approaches is highly required to discover new bioactive molecules. To overcome the current limitations of existing methodology, these new approaches should be able (i) to prioritize biomass selection by facilitating the annotation of analogues, and the detection of unique chemical structure, (ii) to accelerate isolation of bioactive compounds, (iii) to minimize potential degradation of bioactive compounds, and finally (iv) to minimize the use of toxic chemicals reducing the environmental impact of the overall process. Supercritical CO2 (sCO2) is now widely used in industry allowing the extraction of natural products with a reduced environmental impact [6][7][8][9] or the separation of chiral compounds at analytical or preparative scales. 10 Over the last few years, supercritical fluid chromatography (SFC) is gaining back of interest because manufacturers (Agilent Technologies, Waters, and Shimadzu, in particular) marketed user-friendly and robust analytical and preparative systems. Compared to LC, SFC offers a largest choice of stationary phases exhibiting different selectivity allowing better chromatographic resolution and shortened retention times due to the low viscosity and high diffusivity of sCO2. Moreover, up-scaling from analytical to (semi)preparative scale is achievable while preserving a good chromatographic resolution. Although terpenes constitute a large class of non-polar compounds, publications dealing with their separation and isolation process using sCO2 are scarce, except for monoterpenes. 11,12 Like HPLC system, 13 SFC-based system can be coupled with tandem mass spectrometers in order to provide structural information on biomolecules. While bioinformatics tools are now well implemented for metabolomics, 14 or proteomics, 15 only few of them can really empower the information covered in MS/MS data of natural compounds. Among them, MS/MS molecular networking (MS 2 MN) has emerged as an efficient tool to explore MS/MS data since its availability on Global Natural Product Social molecular networking web-platform (GNPS). [16][17][18][19][20] MS 2 MN is a graph-based approach that relies on the assumption that structurally related molecules share similar fragment ions in their respective MS/MS spectra. 16,21 Thus, MS 2 MN allows establishing spectral similarity relationships represented as a spectral networks, so-called molecular networks (MNs). The emergence of this approach unleashed the potential of untargeted MS/MS analysis by allowing the visualization of spectral groups in an untargeted way.

Moreover, MNs can be seeded with MS/MS data of molecular standards used to seed the MNs and propagate annotation, 18 leading to the dereplication of not only identical molecules but also analogues organized as molecular series. 17 Recent studies successfully employed MS 2 MN annotation in LC-MS/MS-based metabolomics and natural product isolation, 20,17,18 but none of them was based on SFC-MS/MS data. [22][23][24][25] We will expose our efforts to develop an efficient and environmentally friendly SFC-MS 2 MN-based workflow for the discovery and isolation of new bioactive molecules from a complex natural extract. The study of E. semiperfoliata plant extract endowed with strong antiviral activity 26,27 offers an interesting subject to optimize and evaluate our approach. In fact, several chromatographic fractions showing antiviral potency were not studied yet, 28 while phorboids diterpene esters were reported from this species but never evaluated. 29 Moreover, thanks to the described MS/MS fragmentation behavior of some jatrophane esters previously isolated from this species, this study also aimed at exploring how diterpene ester fragmentation pattern drive molecular network clustering.

RESULTS AND DISCUSSION

Development of a SFC-MS/MS Method for the Analysis of Diterpene Esters.

In order to determine the best conditions of separation, various parameters, i.e. stationary phases, the mobile phase composition and flow-rate, back pressure and oven temperature, were optimized using a test-mixture of seven jatrophane esters standards (3-8, and 11 in Figure 1). Hypercarb ® columns demonstrated excellent and similar selectivity. These two stationary phases bear hydrophobic surfaces and highly delocalized π-electron system.

Because this column gave better peak widths and symmetries, Hypercarb ® was selected for further optimization. Optimal separation (Figure 2, thumbnail L) was obtained by decreasing the particle size down to 3 µm and adjusting the gradient of the mobile phase as described in the experimental section. The relative standard deviation on retention times over five successive injections was always below 1 % indicating that no alignment is necessary for data retreatment. Carry-over was estimated to be below 1 %.

A B C D E F G H I J K L
Molecular Networking with SFC-MS/MS data. The ethyl acetate extract of the whole plant of E. semiperfoliata showed significant antiviral activities against CHIKV and HIV replication, and some of the isolated jatrophane esters were shown to display selective activities. 28,32 In the present study, MS 2 MN was used to explore the molecular content of bioactive fractions obtained previously from this extract. 28,32 Analysis by SFC-MS/MS were performed on standard compounds andfractions, and data were further analyzed by on GNPS web platform. 19 Fractions F7, F8 and F9 exhibiting potent antiviral activity against CHIKV virus replication (EC50 = 1.2, 9.4 and < 0.8 µg.mL -1 , respectively) were selected. The MS/MS data were acquired using a data-dependent acquisition method to collect collisioninduced fragmentation spectra for the four most intense ions observed in the range m/z 300-800. 19 MS/MS spectra of previously isolated compounds 1-12 were included in the dataset as references to annotate some clusters, and to optimize MS2MN parameters.

The Δm/z between two nodes in the MN were annotated as molecular differences using a table of elemental compositions (elements C, H, O, N, and Na only), [START_REF] Hoffmann | Mass Spectrometry: Principles and Applications[END_REF] according to mass errors below 0.01 Da.

The use of default parameters of the GNPS workflow led to the generation of molecular network nodes that were found to be artefactual. Indeed, some consensus MS/MS spectra clusterered by MS-Cluster, were of chimeric nature. The chimeric MS/MS spectra are due to the clustering of MS/MS spectra produced by different, but related molecules, with a cosine score above 0.7, and precursor ions within the precursor ion mass tolerance (2.0 Da). After optimization of molecular networks parameters including the use of a precursor ion mass tolerance of 0.01 Da, and a product ion tolerance of 0.0075 Da, the number of these chimeric features could be drastically reduced.

Furthermore, to achieve a satisfactory clustering of diterpene esters using referencestandard compounds, molecular networks were generated using 12 minimum matched peaks, and by removing fragment ions below 10 counts from the MS/MS spectra. Metabolomic Standards Initiatives. [START_REF] Sumner | [END_REF] Moreover, annotation (level 2) was propagated A third cluster (red color) showed MS/MS spectra evoking diterpene backbone fingerprint of phorbol esters (m/z 311, 293 and 283), 32,33 but was interpreted as unknown jatrophane esters, because these spectra contained more than four neutral loss of acyls. Indeed, the maximum number of ester groups found in phorbol derivatives usually does not exceed three, 32 Thus, these molecules were annotated as modified jatrophane esters of group C. Comprehensive inspection of each node in the main MN, revealed that multiple nodes (in grey) were a result of consensus MS/MS spectra produced during the MS-clustering process. These nodes are composed of nearly identical MS/MS spectra of related molecules possessing the same precursor ion, but different diterpene backbone fingerprint and retention time. The second MN (green color) contained MS/MS spectra reminiscent of compounds with the same apparent diterpene backbone fingerprint as jatrophane esters of group B (m/z 313, 295 and 285) 32,33 but the MS/MS spectra of these compounds differed from the jatrophane esters of group B by the presence of only two or less loss of acyl-groups. Moreover, the MS/MS spectra of the ion at m/z 523.266 was consistent with a structure of 4-deoxyphorbol ester, which was previously isolated by Appendino et al. 29 (Figure 4). This MN was tentatively annotated as 4-deoxyphorbol esters according to these observations.

Although the clustering of diterpene esters in MS/MS molecular networks appeared to be driven by (i) the type of diterpene esters, and (ii) their functionalization, this approach is not sufficient to establish the molecular structure of these annotated compounds. We first aimed at selecting the best fraction for a targeted SFC-MS-based purification procedure. In order to give information on the relative distribution of nodes between the three fractions, the data-dependent MS/MS acquisition method were achieved with no exclusion parameters. In Cytoscape, a first "fraction layout" using the number of scan per fraction was generated to map the relative distribution of each node as pie chart (Fig

S2A, in Supporting Information

). A second fraction layout using the sum of the precursor ion intensity per fraction was generated using advanced output options of GNPS workflow (Fig. S2B, in Supporting Information). No significant differences were observed between both approaches. The fraction layout showed that the MN annotated

Semi-preparative SFC-based Purification of Molecules Detected by Molecular

Networking. Based on the above MS 2 MN analysis, fraction F9 was selected for further purification of 4-deoxyphorbol esters and jatrophane esters of group C using a carbongraphite semi-preparative column (Figure 5A). The UV chromatogram at 230 nm was Compounds 13 and 14 are heptaesters 2,3,5,7,8,9,15-heptahydroxylated-oxojatropha-6( 17),11E-diene, closely related to euphodendroin and amygdaloidin. 34,35 In their 1 H NMR spectra, important signal broadness resulting from a slower conformational exchange rate was observed. This phenomenon, caused by the hindering effects of ester groups, 36,37 was solved by increasing the analysis temperature as described in SI.

From the analysis of ESI-HRMS, 1 H and 13 C NMR data, it was deduced that compounds 15-18 were 4-deoxyphorbol esters. Compound 15 was identified through comparison of its 1 H-NMR and HRESIMS data with those previously reported. 29 Additional 13 C NMR data are provided in Table S2 (Supporting Information). Compound 17 is a 4,20dideoxyphorbol ester possessing a C-20 aldehyde group. The tendency of phorbol esters to easily oxidize and produce an α,β-unsaturated carbonyl was previously reported by Schmidt and Hecker. 38 Thus, 17 could be a direct oxidation product of 15.

Remarkably, our results showed that MS 2 MN could distinguish MS/MS spectra of macrocylic diterpene esters from phorbol esters. Thus, by overcoming this limitation, 32,33 MS 2 MN have to be considered as a powerful tool for MS/MS-based profiling of diterpene esters from Euphorbia(ceae).

Evaluation of the Antiviral Activity of the Isolated 4-Deoxyphorbol Esters.

Compounds 13-18 were evaluated for their anti-CHIKV potential by using a chikungunya virus-cell-based assay, whereas the four 4-deoxyphorbol esters (15-18) were evaluated for antiviral activity using recombinants HIV-1 (NL4.3-Ren) in MT-2 cells (Table S4, in Supporting Information). Due to the low amount of isolated, compounds 13 and 14 were not evaluated for anti-HIV activity. Their activity was compared to the 12-O-tetradecanoylphorbol-13-acetate (TPA), a phorbol ester known for his potent tumor-promoter activity, and prostratin (12-deoxyphorbol-13-acetate), 39 a promising non-tumor-promoter antiviral diterpene ester, which is currently under clinical trial evaluation in the US. [START_REF]Moving Ahead: Prostratin Experiments Requested by the FDA[END_REF] Considering the 4-deoxyphorbol esters series, compound 18 was found to be the most potent and selective inhibitor of CHIKV replication (EC50 around 440 nM and a selectivity index (SI) > 172). Structure-activity relationships (SARs) were consistent with those already described for the anti-CHIKV activity of phorbol esters, 41 such as requirement of an α-configuration of H-4, and the deleterious effect of a carbonyl group at C-20. In addition, results reported herein showed that the replacement of a tiglyl group by an acetyl group at C-12 greatly increases the selectivity for anti-CHIKV activity chains were key features for tumor-promoting activity. 42,43 Thus, as it is the case for prostratin, compounds 15 and 18 could be devoid of any tumor-promoting activity, making these compounds very attractive for further biological investigation. These results highlight that Euphorbia plants are a remarkable source of potent antiviral diterpene esters. 27,[START_REF] Nothias-Scaglia | [END_REF][44][45][46] In the present study, an SFC-MS/MS based workflow was developed to analyze the diterpene esters content of E. semiperfoliata. Data were analyzed through MS 2 MN, leading to (i) the dereplication of known jatrophane esters, (ii) the efficient annotation of analogues, and (iii) the identification of new diterpene esters that were potentially involved in the bioactivity found for the extract. A MS-targeted SFC-purification was carried out leading to the isolation and identification of two new jatrophane esters (13 and 14), and one known (15), along with three new 4-deoxyphorbol esters (16-18).

Compounds 15 and 18 exhibited potent and selective antiviral activities against HIV-1 replication, and at a lesser extent against CHIKV replication.

The discovery of new bioactive natural products can thus be accelerated using a SFC-MS/MS-based workflow, while decreasing the chemical risk and reducing the environmental impact of the whole process. Moreover, the present study brings a unique insight on MS 2 MN annotation, by investigating the fragmentation pattern responsible of the clustering of MS/MS spectra in the molecular networks. We demonstrated that MS 2 MN is an efficient tool to explore the molecular content of bioactive fractions, allowing annotation, dereplication of analogues, and prioritization of fractions to be purified. From a methodological perspective, further developments, such as upstream extraction/fractionation using supercritical fluids, would permit a complete eco-friendly workflow, raising metabolomics and natural product chemistry to a Green Era. Molecular networks (MS 2 MN). Molecular networks (MNs) were created using the Data Analysis workflow on GNPS platform (http://gnps.ucsd.edu). 19 The parameters used are detailed in Supporting Information. For the clustering of consensus MS/MS spectra, parameters were set for DE with a parent mass tolerance of 0.01 Da (allowing an error of 25 ppm at m/z 400 and 12 ppm at m/z 800 for the precursor ion), a product ion tolerance of 0.0075 Da allowing a maximum error of 25 ppm at m/z 300 and 12 ppm at m/z 600). The fragment ions below 10 counts were removed from MS/MS spectra.

EXPERIMENTAL SECTION

MNs networks were generated using 12 minimum matched peaks and a cosine score of 0.7. MNs were visualized using Cytoscape 3.4.0 software. 47

Figure 2 .

 2 Figure 2. Extracted-ion chromatograms of jatrophane esters 3-8 and 11 obtained by SFC-MS using 11 different stationary phases (A-K), and the optimized method using Hypercarb column (L).Optimal separation (Figure2, thumbnail L) was obtained by decreasing the particle

Figure 3 .

 3 Figure 3. Annotation of the MS 2 MN of antiviral fractions F7-F9 derived from E. semiperfoliata extract showing jatrophane esters (JE) of group A (JE-A, 1-7), and JE of group B (JE-B, 8-9), and their putative analogues. New compounds, jatrophane esters (JE) of group C (JE-C, 13, 14) and 4-deoxyphorbol esters (4-dPE, 15-18), were targeted upstream during the initial interpretation of MS 2 MN of SFC-MS/MS analyze, and were further isolated by semiprep-SFC. MSI-1 and MSI-2: Metabolomic Standard Initiative identification level.

  spectra with a DSF (m/z 311, 293, 283) and presenting three or more acyls spectra with a DSF (m/z 313, 295, 285) and presenting less than two acyls MS/MS spectra with a DSF (m/z 313, 295, 285) and presenting three or more acyls MS/MS spectra with a DSF (m/z 327, 309, 299) and presenting three or more acyls fingerprint (DSF) (m/z 321, 299, 281) Unknown DSF (m/z 319, 297, 279) Chimeric or unassigned MS/MS spectra with a

Figure 4 .

 4 Figure 4. MS/MS spectrum of the precursor ion at m/z 523.27 (compound 16), putatively annotated as 4-deoxyphorbol diester with tiglyloxy and isobutyryloxy moieties.

as 4 -

 4 deoxyphorbol esters was over-represented in the most active fractions F7 and F9.The cluster of jatrophane esters of group C was also specific to F9 indicating that 4deoxyphorbol esters and jatrophane esters of group C could be related to the potent anti-CHIKV activity of F9.

Figure 5 .

 5 Figure 5. MS-targeted purification procedure by SFC. (A) Extracted ion chromatogram (EIC) of Fraction 9 for all ions between m/z 400 and m/z 720. The m/z of the targeted and isolated compounds based are indicated. B) The corresponding UV chromatogram at 230 nm. (C) Semi-preparative SFC-UV chromatogram of fraction F9 at 230 nm.

  (Figures 5B) during the transposition from the analytical to the semipreparative scales. The conditions of separation were optimized to achieve an adequate collection profile (Figure5C). The purification of F9 (200 mg) was achieved in 25 runs using a fully automatized batch mode (t ≈ 26 h). A total of 1.5 L of ethanol was used for this purification step. For comparison, the LC purification procedure for the standards was extremely time-consuming (three weeks), and consisted of at least 4 steps in normal phase and reverse phase LC to obtain pure compounds. During this last procedure, the solvent consumption was of approximately 35 L of ethyl acetate, 30 L of methanol, 15 L of heptane, 10 L of acetone, 15 L of acetonitrile, and 10 L of water. In addition, the various solvent evaporation steps were time and energy consuming, especially when large amount of water had to be evaporated. This clearly illustrates the efficient of SFC for the purification of natural products from a complex extract (1 day vs 15 days), while avoiding about 100 L of toxic solvent.The major peaks at tR 9.5 and 13.2 min correspond to compounds 13 (m/z 693.2510, [C35H42O13+Na] + , Δm/z = 0.5 ppm), and 8 (m/z 637.2610, [C33H42O11+Na] + , Δm/z = 2.3 ppm), respectively The others chromatographic peaks collected at tR 18.3, 13.2, and 16.9 min afforded the 4-deoxyphorbol ester 16 (m/z 523.2675, [C29H40O7+Na] + , Δm/z = 0.7 ppm) and its epimer 15 (m/z 523.2677, [C29H40O7+Na] + , Δm/z = 1.0 ppm), and compound 17 (m/z 521.2510, [C29H38O6+Na] + , Δm/z = 1.0 ppm), respectively. Sub-fraction F9-1 (Figure 5C) was collected based on detection of ions belonging to the cluster of jatrophane of group C. It was further purified using a second semi-preparative SFC leading to the isolation of compound 13 at m/z 675.2611 (rt = 6.8 min, [C32H44O14+Na] + , Δm/z = 2.6 ppm), compound 14 at m/z 717.2711 (rt = 3.8 min, [C34H46O15+Na] + , Δm/z = 3.3 ppm), and compound 18 at m/z 483.2352 (rt = 4.9 min, [C26H38O6,+Na] + , Δm/z = 1.4 ppm). It should be noted that the MS signal of the ion at m/z 483.2352 is about 1000 times lower than the signal of the major peak at m/z 693.2510 suggesting that the method is efficient for the isolation of abundant and minor secondary metabolites. Structural Elucidation of Diterpene Esters Isolated by Semiprep-SFC. Although MS/MS from molecular networks shows the type of modified forms of the diterpenes are present, it cannot provide a full structure. Therefore, the structural elucidation of compounds 13-18 (Figure 3) was achieved using a combination of NMR spectroscopy, ESI-HRMS/MS, and X-ray crystallography (Tables S1-S3, Figures S3 in Supporting

(15 vs 18 ).

 18 Compound 15 displayed selective inhibition of HIV-1 with an EC50 of 13 nM and SI > 3900. Compound 18 also exhibited a potent and selective antiviral activity with EC50 of 54 nM and a SI > 900. Both compounds showed more potency than prostratin (IC50 = 226 nM, SI > 221). The other 4-deoxyphorbol esters were significantly less potent (low µM range). The C-4 β-configuration looks crucial (15 vs 16) for an antiviral potency, while an oxidation at C-20 (15 vs 17) proved to be deleterious. The substitution of a tiglyl group by an acetyl group at C-12 (15 vs 18) did not modify the activity. Compounds 15 and 18 differ from TPA by the presence of C-12 and C-13 short acyl side chains and by absence of a C-4 hydroxy group. Previous evaluation of tigliane-type diterpenoids for tumor-promoting activity indicated that long C-12 and C-13 acyl side

  Material, Extraction, and Fractionation. The whole plant of E. semiperfoliata Viv. (= synonym Euphorbiq qmygdqloides ssp semiperfoliqtq A.R.Sm) was collected by L.-F. N. in November 2011 near Bocca di Vergio, at an altitude of 1200 m, in the Niolu region of Corsica (France) (GPS coordinates: 42°17′26.999′′ N, 8°54′2.894′ E) and identified by L.-F. N. and Marie-José Battesti. A voucher specimen (LF-023) was deposited at the Herbarium of the University of Corsica. The whole plant of E.semiperfoliata was air-dried, ground (dry weight, 370 g), and extracted three times with 5 L of ethyl acetate (EtOAc) at 40 °C and 100 bar using a Zippertex static high-pressure, high-temperature extractor. The EtOAc extract was concentrated under vacuum at 40°C to yield 24.7 g of residue (ρ = 6.7% w/w). The residue was mixed with Celite and subjected to flash LC on a silica column (Versapak, 80 × 150 mm, 20-45 µm, 385 g, acetone-n-heptane 0 to 100% in 60 min, 100 mL/min), leading to ten fractions (F1-F10).General Experimental Procedures.SFC-MS. Supercritical fluid chromatography (SFC) analyses were performed using a 1260 Infinity Analytical SFC system from Agilent Technologies (Waldbronn, Germany) equipped with an Aurora A5 Fusion module, generating supercritical CO2 from gaseous CO2. A back-pressure regulator (BPR) was used to fix the pressure at 150 bar after the column. Samples were kept at 4°C and 1 µL was injected. SFC was coupled to a diode array detector and to a quadrupole time-of-flight mass spectrometer (Agilent 6540 Q-TOF, Agilent Technologies) equipped with an electrospray ionization source operating in positive mode. Source conditions are provided in Supporting Information. A blank (pure ethanol) was injected after each sample analysis. A first set of columns was purchased from Agilent Technologies (Massy, France) including ZORBAX Rx-SIL RRHT (Si, 100 x 2.1 mm, 1.8 µm particles), ZORBAX SB-CN RRHT (CN, 100 x 2.1 mm, 1.8 µm particles), Pursuit pentafluorophenyl (PFP, 150 x 2.0 mm, 3.0 µm particles), Eclipse ZORBAX Eclipse Plus C18 RRHT (C18, 150 x 2.1 mm, 1.8 µm particles) and Pursuit XRs Diphenyl (DPH, 250 x 2.0 mm, 3 µm particles). A second set of column was provided by Waters (Guyancourt, France) including ACQUITY UPC² BEH 2-EP (2-ethylpyridine) (2-EP, 100 x 2.1 mm, 1.7 µm particles), ACQUITY UPC² Torus 1-AA (1-Aminoanthracene), ACQUITY UPC² Torus 2-PIC (2-Picolylamine) and ACQUITY UPC²Torus DEA (Diethylamine) (150 x 2.1 mm, 1.7 µm particles). Finally, a Discovery ® Zr-Carbon column (Zr-Carbon, 150 x 2.1 mm, 3 µm particles) was purchased from Supelco Saint-Quentin Fallavier, France) and Hypercarb ® column (Carbon, 100 x 2.1 mm, 3 µm particles) from Thermo Scientific (Courtaboeuf, France). The column oven was maintained at 30°C. The porous graphitic carbon column (Hypercarb ® ) was finally selected after column screening. The elution gradient was optimized using CO2 (solvent A) and ethanol with 0.1 % formic acid (solvent B). The optimal gradient was: 0-3 min at 3 % solvent B, 3-13 min linear gradient from 3 to 10 % solvent B, 13-17 min from 10 to 20 % solvent B, 17-20 min isocratic plateau at 20 % solvent B, 21-23 min reequilibration at 3 %, at a constant flow rate of 1.5 mL/min. SFC-MS/MS method. Untargeted MS/MS analyses were performed using an optimized data-dependent acquisition (DDA) mode consisting of a full MS scan from m/z 100 to m/z 1000 (scan time: 100 ms), followed by DDA of MS/MS spectra of the four most intense ions (Top-4, 250 ms each) from m/z 300 to m/z 800, with a minimum intensity of 2000 counts. No exclusion rule was used to use the number of scan as pseudo-quantitative proxy in MN 2 MS. Collision energy was fixed at 30 eV according to the optimized collision-induced dissociation (CID) conditions (Figure S1, in Supporting Information).

  through the network by inspection of the MS/MS spectra of nodes connected to reference standards. Indeed, it has been shown that groups A (blue color) and B (purple color) of jatrophane esters have different diterpene backbone fingerprint, with

	characteristic fragment ions at m/z 327, 309 and 299 (group A), and m/z 313, 295 and
	285 (group B). 32 Jatrophane esters of group A possess a ketocarbonyl at C-14 and an acyl
	group or an hydroxy group at C-2, while jatrophane esters of group B possess an acyl
	group or an hydroxy group at C-14, and no additional acyl group at C-2. The main
	differences observed in elemental composition between nodes annotated as analogues
	were 2H, O, CH2, C2H2O, suggesting structural variation of the functional groups, such as
	a double bond, hydroxylation, length of acyl chain, or additional acetylation,
	respectively.

and Environmentally-Friendly Workflow to Target and Purify Secondary Metabolites MS/MS molecular networking Targeted purification by preparative SFC

  Da of the precursor m/z. MS/MS spectra were window filtered by choosing only the top 6 peaks in the +/-50Da window throughout the spectrum, and every fragment ions below 10 count in the MS/MS spectra were removed. The data was then clustered with MS-Cluster with a parent mass tolerance of 0.01 Da and a MS/MS fragment ion tolerance of 0.0075 Da to create consensus spectra. A network was then created where edges were filtered to have a cosine score above 0.7 and more than 12 matched peaks.Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top 10 most similar nodes. The spectra in the network were then searched against GNPS's spectral libraries. The library spectra were filtered in the same manner as the input data. All matches kept between network spectra and library spectra were required to have a score above 0.7 and at least 6 matched MM007 HF copper rotating-anode generator, equipped with Osmic confocal optics, and a Rapid II curved Image Plate. Crystallographic data for 16 have been deposited to the Cambridge Crystallographic under the reference 1491995. 11E-diene (13, JE-C1): Amorphous powder; [a]D 25 -95 (c 0.1, MeOH). For 1 H and 13 C NMR spectroscopic data, see Table S1; HRESIMS m/z 675.2613 [M + Na] + (calcd for C32H44O14, Dm/z theoritical = + 1.4 ppm); (HRESIMS/MS deposit to GNPS spectral library, ref: CCMSLIB00000531511); See S12-S19 in Supporting Information. 11E-diene (14, JE-C2): Amorphous powder; [a]D 25 -130 (c 0.5, MeOH). For 1 H and 13 C NMR spectroscopic data, see Table S1; HRESIMS m/z 717.2733 [M + Na] + (calcd for C 32 H 45 O 13 , Dm/z theoritical = + 0.1 ppm); (HRESIMS/MS deposit to GNPS spectral library, ref: CCMSLIB00000531512); See 20-S29 in Supporting Information. 4a-deoxyphorbol 12-tiglate-13-isobutyrate (15, 4-dPE A): Amorphous powder; [a]D 25 + 15 (c 0.1, MeOH). For 1 H and 13 C NMR spectroscopic data, see Table S2; ESI-HRMS m/z 523.2674 [M + Na] + (calcd for C29H40O7, Dm/z theoritical = -0.9 ppm); (ESI-HRMS/MS deposit to GNPS spectral library, ref: CCMSLIB00000531515); See S30-S37 in Supporting Information. 4a-deoxyphorbol 12-tiglate-13-isobutyrate (16, 4a-dPE A): Colorless crystal; [a]D 25 + 30 (c 0.1, MeOH). For 1 H and 13 C NMR spectroscopic data, see Table S2; ESI-HRMS m/z 523.2675 [M + Na] + (calcd for C29H40O7, Dm/z theoritical = -0.7 ppm); (ESI-HRMS/MS deposit to GNPS spectral library, ref: CCMSLIB00000531518); See S38-S45 in Supporting Information. The authors declare no competing financial interest. Institut Jean-Pierre Bourgin (IJPB), INRA, UMR 1318, ERL 3559 CNRS, AgroParisTech, Saclay Plant Sciences, 78000, Versailles, France. Laboratoire de Pharmacognosie, UMR 8638 COMETE CNRS, Faculté de Pharmacie, Université Paris Descartes, Sorbonne Paris Cité, France ACKNOWLEDGMENT This work has benefited from an "Investissement d'Avenir" grant managed by Agence Nationale de la Recherche (CEBA, ANR-10-LABX-25-01)). Financial support was provided to LFN and PD by the National Institutes of Health (NIH) grant GM097509. JA was supported by The SPANISH AIDS Research Network RD12/0017/0015 that is included in Acción Estratégica en Salud, Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica 2008-2011, Instituto de Salud Carlos III, Fondos FEDER and Instituto de Salud Carlos III-FIS: PI12/00506. EDLT was supported by a fellowship of the Instituto de Salud Carlos III (ISCIII-FIS Nº. FII4CIII / 00014). Authors would like to thank Odile Thoison (CNRS-ICSN) for her precious help with semipreperative-SFC experiments.

	corresponding to isotopologues 13 C were manually discarded. Allegro Layout Weak GRAPHICAL ABSTRACT
	A force-directed layout modulated by cosine score factor was used for data visualizations. Semi-quantitative estimation was provided by representing the size of the node by the sum of precursor ion intensities and by mapping the number of scan for each fraction as pie-chart diagram. MS/MS data were deposited to MassIVE Public GNPS dataset (http://gnps.ucsd.edu, MSV000079856). Molecular networks were created using the online workflow at GNPS. The data were filtered by removing all MS/MS peaks within data visualizations. In Cytoscape, molecular networks with less than 3 components were +/-17 peaks. Default parameters: http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=61a9770e433043a6a88ab125a2cffb 7c and optimized parameters: http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=90e3d8b4fe7545fd93c16e950d1686 f7 Relative Distribution Mapping in Molecular Networks. Using the advanced output option of Data Analysis GNPS workflow, "Create cluster bucket", a fraction layout was created. The output bucket table was imported into Cytoscape. In Cytoscape, the size of the node was modulated by the total sum of precursor ion intensities, and the piechart diagram represent the sum of precursor ion intensities in each fraction (Figure S7) or the number of scan (Figure S8). Visualisation of Molecular Networks in Cytoscape. Data were visualized using Cytoscape 3.4.0 software. 47 A force-directed layout (Allegro Weak Clustering, http://allegroviva.com/allegrolayout2) modulated by cosine score factor was used for clustering parameters: Algorithm tuning: scale (75%) and gravity (circular, 50%). Edge weighting parameters were set as default except weight transform (Cosine, inverse log : x => -Log10(x). Prevent nodes from overlapping. Semi-preparative-SFC and Purification Procedure. A semi-preparative SFC (semiprep-SFC) system from Waters ® (Milford, Massachusetts, USA) was used for compound isolation. It consists of a SFC fluid delivery module, an autosampler with a 48-vial plate, a SFC analytical-2-prep oven with a 10-column selection valve, a SFC BPR SuperPure Discovery Series from Thar and a cooling bath of type Neslab RTE7 Structural elucidation. Optical rotations were measured using a JASCO P1010 polarimeter at 25 °C. The UV spectra were recorded using a Perkin-Elmer Lambda 5 spectrophotometer. 2D NMR spectra were recorded using a Bruker 500 MHz instrument (Avance 500), and using a Bruker 300 MHz instrument (Avance 300) for 13 C NMR spectra. CDCl3 was used as solvent. Crystallographic data were collected using a Rigaku diffractometer constituted by a Compound properties (2R,3R,4S,5R,7S,8R,9S,13R,15R)-3,5,7,9,14,15-Hexaacetoxy-8-hydroxy-14-oxojatropha-6(17),(2R,3R,4S,5R,7S,8R,9S,13R,15R)-3,5,7,8,9,14,15-Heptaacetoxy-14-oxojatropha-Supercritical fluid chromatography (SFC) coupled to tandem mass spectrometry (MS/MS) OH O OH OTigl OiBu 6(17),Present Addresses Efficient
	not considered, in order to focus one main spectral families. Couple of nodes

controlled by a Digital One thermoregulator from Thermo Scientific. The system is coupled to a PDA 2998 and a DEDL 2424 from Waters. The autosampler was equipped with a 100 µL injection loop. A porous graphitic carbon column (Hypercarb, 150 x 10 mm, 5 µm particles was used. An automated six-vessels collection module was employed with a solvent flow set to 3 mL/min. The BPR was fixed at 150 bar, the oven temperature at 40°C, and ethanol was added as co-solvent. The instrument was controlled by Superchrom and data were processed using Chromscope (Thar, Pittsburgh, PA, USA). ○

丄

4a,20-dideoxyphorbol 12-tiglate-13-isobutyrate (17, 4,20-ddPE A): Amorphous powder; [a]D 25 + 20 (c 0.1, MeOH). For 1 H and 13 C NMR spectroscopic data, see Table S3; For 1 H and 13 C NMR spectroscopic data, see Table S3 Antiviral Assays. The anti-Chikungunya virus (CHIKV) bioassays were achieved according to the protocols previously described. [START_REF] Nothias-Scaglia | [END_REF] Anti-HIV bioassays were performed as detailed in the Supporting Information. 48 ASSOCIATED CONTENT Supporting Information. The Supporting Information is available free of charge on the ACS Publications website at DOI:XXXX. LC-ESI-HRMS/MS data, and MS/MS spectra of compounds are available on GNPS spectral library (http://gnps.ucsd.edu). 1D and 2D NMR spectra are shown in Supporting Information. Crystallographic information file (CIF) for compound 16 is provided. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION

Corresponding Authors

* Tel: + 33 1 69 82 30 85. E-mail: marc.litaudon@cnrs.fr. * Tel: + 33 1 69 82 30 94. E-mail: david.touboul@cnrs.fr Notes