MEANS-InOut is developed by INRAE (French national institute of research for agriculture, food and environment) and Cirad (French agricultural research organisation for the sustainable development of tropical and Mediterranean regions). It is supported by ADEME (French agency for environment).

A survey was conducted on MEANS-InOut, a customised LCA tool dedicated to agriculture, to better understand the expectations and needs of users of such a tool. This study showed that MEANS-InOut customised LCA tool:  Was efficient to create agricultural LCI and evaluate systems (initial goals)  Lacked of efficiency for eco-design (secondary goal),  Was adapted to advanced LCA users,  Was difficult for beginners to handle: a need for support was identified.

Consultation with users can ensure the perpetuation of customised LCA tool.

Customised agricultural LCA tools provide a userfriendly interface for collecting data and focus on the most significant life cycle phases for assessment of agricultural systems :  A point of view on agricultural modelling  A framework to reduce the risk of errors  Simplification of LCA studies of agricultural production systems  Less flexibility than generic tools  Difficulties to handle new uses or research questions.

MEANS-InOut customised LCA tool:

 generates LCIs of agricultural systems.

 initially designed for users with limited LCA knowledge, and high agricultural knowledge.

15 users answered the survey, 8 of them were members of ACV Bio project.

Objectives of MEANS-InOut achieved for advanced LCA users: to facilitate LCA studies by generating agricultural LCIs.

Objectives of MEANS-InOut not fully achieved for LCA beginners: software is considered to be too complicated.

Possible explanation: lack of LCA experience before using the tool.

Beginners did not know the inherent complexity of LCA, and were surprised by the high level of hypothesis embedded. They asked for more detailed instructions and a simpler modelling. A training should be associated to the access of MEANS-InOut.

Key functionality missing for eco-design: parametrization of relevant levers for the studied system improvement.  Already available: feed formulation.  New functionalities developed to match the users needs since the survey (e.g. fuel consumption of agricultural operations). A special attention is needed because a compromise is difficult to find between more precise functionalities and simpler software.

The conclusions of the study should be taken with caution, given the very small sample size consulted.

  Figure 1: Main functions of MEANS-InOut customised agricultural LCA tool

Figure 2 :

 2 Figure 2: Use of MEANS-InOut within ACV Bio project

Table 2 :

 2 Level of satisfaction of MEANS-InOut users depending on their level in LCA practice

Acknowledgements


Dr Emmanuel Lansac

Isabelle Di Centa

Pichoy Danial

MD Olivier Bouchot

MD, Ph D Eric Arnaud Crozat

MD Rachid Hacini

MD Fabien Doguet

MD, Ph D, Roland Demaria

MD, Ph D, Jean Philippe Verhoye

MD Jerome Jouan

MD, Ph D Emmanuel Lansac¹

Isabelle Di Centa

MD Pichoy Danial¹

MD, Ph D Didier Chatel

MD Stephane Lopez

MD Thierry Folliguet

PhD Pascal Leprince

PhD Thierry Langanay

MD Christian Latremouille

PhD Georges Fayad

MD Jean Philippe Fleury

MD Jean Luc Monin¹

MD Leila Mankoubi

MD Milena Noghin

MD Alain Berrebi

MD, MD Sarah Pousset

MSc Aline Laubriet-Jazayeri

MSc Alexandre Lafourcade

Estelle Marcault

MSc Michel Kindo

Laurent Payot

MD Eric Bergoend

MD Cecile Jourdain Hoffart

MD Mathieu Debauchez

MD; Florence Tubach

Aortic valve repair versus mechanical valve replacement for root aneurysm: the CAVIAAR multicentric study

Keywords: aortic insufficiency, aortic root aneurysm, aortic valve repair, remodeling

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

INTRODUCTION

Aortic valve repair is now recognized as a good alternative to prosthetic valve replacement with low rate of valve-related events and an improved quality of life compared to valve replacement in monocentric studies (1-4). As a result, the latest 2017 guidelines for heart valve disease recommend a 'Heart Team discussion' for selected patients 'with pliable, non-calcified tricuspid or bicuspid' aortic valve insufficiency 'in whom aortic valve repair may be a feasible alternative to valve replacement' (Class IC indication). Furthermore, aortic valve repair using the re-implantation technique or remodeling with aortic annuloplasty, is recommended in young patients with aortic root dilatation and tricuspid aortic valve (Class IC indication) (5). However, most aortic valves are still replaced. Indeed, according to Euro Heart Survey for Valvular Heart Disease, despite two thirds of cases having a tricuspid or bicuspid dystrophic aortic insufficiency representing suitable candidates for aortic valve repair, only 21.5% had their valve repaired at that time (6). We developed the CAVIAAR technique, a standardized and physiological approach to valve repair, combining the physiological reconstruction of the aortic root according to the Remodeling technique, the systematic assessment of cusp effective height, and the introduction of a calibrated expansible external aortic ring (Extra Aortic Ring, CORONEO Inc, Montreal) (8-10). This multicenter prospective cohort study was designed to investigate if the CAVIAAR standardized valve repair technique was associated with an increased valve-related event free survival compared to mechanical composite valve and graft replacement, in patients undergoing surgery for aortic root aneurysm (bicuspid or tricuspid aortic valves). An early post-operative assessment showed that 30-day mortality was 3.8% (n=5) in both groups (p=1.00) and showed a trend toward fewer major adverse valve-related events (MAVRE) in the REPAIR group (11). This report presents the 4-year results of this prospective cohort study.

MATERIALS AND METHODS

Study

Study Design

The study was initially a randomized clinical trial. Randomization between the two procedures was done in the operating room, after assessment of aortic valve suitability for repair. Since only 5 patients were recruited within 13 months due to reluctance of the patients and physicians to randomization in this particular context, the study design was revised to a prospective cohort study. In the cohort design, choice of the surgical technique was based on patient, surgeon and referring cardiologist preference. This change allowed a good enrollment rate in the REPAIR group, but in the REPLACE arm, enrollment rate remained low, despite regular reminders sent to investigators. Indeed, centers favoring repair were more prone to include consecutive patients in the study than center favoring valve replacement. Therefore, from those centers, we included the 61 missing patients of the REPLACE group from an ad-hoc historical cohort study. After approval by the institutional review board, all 61 consecutive patients operated with mechanical composite valve and graft operation from May 2007 to October 2011 (inclusion period of the prospective CAVIAAR study) from three of the CAVIAAR study centers were screened for eligibility (exhaustiveness was ensured through systematic screening of the operative room agendas and hospital discharge electronic database (PMSI) for REPLACE surgery). Inclusion criteria were the same as those for the prospective CAVIAAR cohort study and were validated through the pre-operative echo, operative reports, and medical chart review. After October 2011, all patients were prospectively followed including the ad-hoc historical cohort of the REPLACE group.

Study Procedures

In the REPLACE group, the modified button composite valve and graft Bentall operation was performed using a mechanical aortic valve in all cases. Anticoagulation was performed with vitamin K antagonist aiming an INR range 2-3 as recommended in clinical practice. In the REPAIR group, valve repair procedure was standardized and consisted of combining the Remodeling of the aortic root with subvalvular aortic annuloplasty using an expansible, external aortic ring (Extra Aortic Ring, CORONEO Inc, Montreal) with effective height cusp assessment as previously described (Fig 1) (8-10,12). Aspirin for 3 months post operatively were given after a consensus decision among investigators of the trial, to cover suture anastomosis of coronary ostia.

Outcomes

The primary outcome of the CAVIAAR study is a composite criterion of all-cause mortality, re-operation (including re-exploration for bleeding, tamponade, mediastinitis, need for new permanent pacemaker in the 30-days post-operative period), thromboembolic or major bleeding events, infections (endocarditis or surgical site infections), and heart failure. Events of this outcome have been validated by an adjudication committee involving cardiac surgeons and cardiologists. The secondary endpoints are mortality and the components of the composite primary outcome. To fulfill the AATS/STS/EACTS guidelines for reporting mortality and morbidity after cardiac valve interventions, published in 2008 one year after the beginning of the trial, we added secondarily with the newly recommended MAVRE (Major Adverse Valve Related Events) outcome criterion defined as valve-related mortality (including sudden or unexplained death), valve-related morbidity (need for new permanent pacemaker within 14 days post operatively, thromboembolic or major bleeding events, endocarditis, valve-related re-operation) (13).

Data Collection

For all patients, clinical and echographic data were collected pre-and perioperatively, at discharge, and yearly thereafter. Notifications of all severe adverse events (including all components of the outcome criteria) to the study sponsor was mandatory, and this has been monitored for all patients.

Statistical Analysis

The study was sized to determine if valve repair was associated with a 45% decrease in the 3 year-event rate relative to the 32% rate assumed in the REPLACE group. With an 80% power and a 5% alpha risk, 123 subjects per group were needed. To account for a 5% loss to follow-up, an enrolment of 130 patients per group was scheduled. Patient characteristics were expressed as means (SD) or medians (interquartile range -IQR) for continuous variables, and as numbers and frequencies for categorical variables.

Parametric tests (chi square test for qualitative variables and Student t-test for quantitative variables) were used if the variable was normally distributed. In other cases, we used nonparametric tests (Fisher exact test for qualitative variables and Wilcoxon test for quantitative variables).

Treatment groups were assessed for imbalance across demographic, clinical, and echographic data using χ² tests (or Fisher's exact tests as appropriate) for categorical variables, and Student's t-test tests (or Wilcoxon tests as appropriate) for continuous variables.

To control for a range of potential confounders due to the non-randomized design, an inverse probability of treatment weighting (IPTW) was used and was based on propensity score to construct a weighted cohort of patients, who differed with respect to surgical approach, but were similar with respect to other measured characteristics (14). Each patient's propensity to undergo REPAIR was estimated using a logistic-regression model that included the following observed potential confounding baseline variables: age, gender, Body Mass Index, diabetes, hypertension, NYHA, calendar year of surgery, bicuspid or tricuspid aortic cusps, pre-operative AI grade, and associated cardiac procedures. To assess the balance of measured covariates between treatment groups, we used the absolute standardized difference (ASD) before and after IPTW. An ASD less than 0•1 was considered as evidence of balance.

Survival for the different endpoints was described with Kaplan-Meier curves. The hazard ratio for primary and secondary outcomes after REPAIR, as compared with REPLACE, was estimated using the inverse probability of treatment-weighted Cox proportionalhazards models. A post hoc analysis was performed to account for multiple events occurring in a single patient, (thus contributing to patient burden), by estimating the relative risk for primary and secondary outcomes after REPAIR, as compared with REPLACE, with weighted negative binomial regression models (Poisson model extension). In the REPAIR arm, risk factors of high AI grade (> 2) were identified with logistic regression analysis. Influence of aortic cusps type (bicuspid or tricuspid), on valve-related adverse events, was also investigated with logistic regression analysis. A sensitivity analysis was performed by excluding the 61 patients from the retrospective cohort of the REPLACE group.

All statistical analyses were performed with R software version 3.5.1 (https://cran.rproject.org/). Tests were deemed statistically significant at the α level of .05.

RESULTS

Patients

This article reports results from the analysis of the CAVIAAR study criteria up to 4 years after surgery (corresponding to a median [IQR] follow-up of 3.0 [2.7-3.3] years).

Follow-up was complete in 92% of patients. The flow chart of the study is presented in Figure 1. 24 patients were intraoperatively excluded because the valve was either too calcified or retracted or with too large fenestrations, and thus not eligible for repair, thus non eligible to the CAVIAAR study. Patient baseline characteristics were previously reported in an early outcomes' publication, with selected demographic and clinical characteristics at baseline summarized in Table 1. Results of logistic-regression model that included observed potential confounding baseline variables used for IPTW is shown in Table S1. 3.8% of patients had Marfan syndrome in the REPAIR group and 4.6% in the REPLACE group (p=0.77).

Cusp repair was necessary in 63% of patients, with a median of 2 cusps repaired and the rate of cusp repair increased from 43.1% to 83.3% when systematic caliper use was included (after 2009). A second crossclamp was needed in 11 patients in the REPAIR group versus 1 patient in the CVG group (P = .003) (Table 1). In the REPAIR group, 7 had a bicuspid valve and 4 a tricuspid valve. Residual AI of grade 2 or greater dictated reclamping in 7 patients in the REPAIR group. Schafer's caliper was used in 2 patients. Five patients had cusp repair at the first clamp session. Cusp prolapse was found at reexploration, and the valve was successfully re-repaired in all instances. None of these patients had AI of grade 2 or greater at discharge, but during follow-up 2 patients were reoperated by an aortic valve replacement for severe AI recurrence Endpoints 130 patients had REPAIR and 131 REPLACE. These two groups differed in some Aspects (Table 1: NYHA status, number of cusps). After weighting on the Inverse Probability Weighting Treatment using propensity score estimated in 261 patients, the effect of REPAIR vs REPLACE was estimated in the weighted pseudopopulation, i.e. 2 groups likely to have similar profile and initial severity (Figure S1 and Table S1). Baseline characteristics after IPTW and after sensitivity analysis excluding the 61 patients from the retrospective cohort of the REPLACE group is presented in Table S2 and Figure S2. Up to 4 years, REPAIR did not significantly differ from REPLACE in terms of primary outcome (HR 0.66 IC95%[0.39; 1.12], p=0.12, IPTW-weighted) and MAVRE (HR 0.61 IC95% [0.36; 1.06], p=0.079, IPTW-weighted), but showed a trend in favor of REPAIR related to significantly less frequent valve related deaths (HR 0.09 IC95%[0.02; 0.34], p<0.001, IPTW-weighted) and major bleeding events (HR 0.37 IC95% [0.16; 0.85], p=0.02, IPTW-weighted) without an increased risk of valve-related re-operation (HR 2.10 IC95% [0.64; 6.96], p=0.22, IPTW-weighted) (Figures 2A,3). After sensitive analysis excluding the 61 patients from the retrospective cohort of the REPLACE group, REPAIR group did not significantly differ from REPLACE group in terms of primary outcome (HR 0.68 IC95% [0.33; 1.38], p=0.283, IPTW-weighted) and MAVRE (HR 0.56 IC95% [0.28; 1.15], p=0.116, IPTW-weighted) up to 4 years.

Overall survival was not significantly different between REPAIR (94.12 %; n=8) and REPLACE groups (89.71%; n=14) (HR 0.57 IC95% [0.19; 1.72], p=0.32, IPTW-weighted), however freedom from valve-related death was significantly lower in REPAIR (99.1%, n=1) compared to 94.3% (n=7) in REPLACE (HR 0.09 IC95% [0.02; 0.34], p<0.001, IPTW-weighted).

Causes of death in the REPAIR group (n=8) were as follows: 4 cardiac non-valve-related (2 heart failures, 1 acute myocardial ischemia, 1 tamponade), 1 cardiac valve-related (1 patient died of mesenteric ischemia after a re-operation for structural valve deterioration at 2 years), and 3 not cardiac related. Causes of death in the REPLACE group (n=14) were as follow: 1 cardiac non-valve-related (1 acute myocardial ischemia), 7 cardiac valve-related (3 sudden death, 1 endocarditis, and 3 fatal cerebral hemorrhages), and 6 non cardiac related (Figures 2A,4).

When accounting for multiple events occurring in a single patient, REPAIR group showed a not statistically significant trend toward less occurrence of primary outcome (RR 0.63 IC95% [0.39; 1.03], p=0.068) and occurrence of MAVRE was significantly less frequent in REPAIR group (RR 0.51 IC95% [0.31; 0.86], p=0.011), mostly related to less frequent major bleeding events (RR 0.34 IC95% [0.16; 0.73], p=0.005) compared to REPLACE (Figures 2B,4).

Major bleeding events occurred exclusively during the peri-operative period for the REPAIR group (re-operation for bleeding or tamponade), and was similar to REPLACE (HR 0.57 IC95% [0.25;1.29], p=0.18, IPTW-weighted), while hemorrhagic events during follow-up exclusively occurred in the REPLACE group 8.8% (n=12) versus 0 for REPAIR. Survival free of endocarditis was not different between both group (HR 0.33 IC 95% [0.07; 1.49], p = 0.15, IPTW-weighted) however when accounting for multiple events there was a trend toward more endocarditis in the REPLACE group compare to REPAIR (RR: 0.29 IC 95% [0.08; 1.03, p = 0.056).

Echocardiographic Analysis

Up to 4 years, 86.4% of patients in the REPAIR group had AI grade 0 or 1, 12.5% had a grade 2, and one patient had a grade 4 (Table 2). No risk factor for occurrence of an AI grade 2 was identified. The only risk factor of AI ≥ grade 3 was a reclamping at first operation (HR In the REPAIR group, systolo-diastolic expansibility was maintained throughout the pre-and post-operative, as well as the follow-up periods at the aortic annulus level (median 5%, 9%, and 5%, respectively), and sinuses of Valsalva level (median 2%, 3%, and 3%, respectively). The aortic ring annuloplasty reduced the native aortic annulus diameter (-20.7%) which remained stable over time. In the REPAIR group, left ventricular end diastolic diameter was reduced throughout the pre-and post-operative (-6.5%), and throughout the post-operative period and up to 4 years (-3%). In the REPLACE group, left ventricular end diastolic diameter was reduced throughout the pre-and post-operative (-13%), and throughout the post-operative period and up to 4 years (-3%).

In the REPLACE group, the mean gradient was significantly higher than in the REPAIR group in the post-operative period (median 10 versus 6 mmHg, p<0.001, IPTW-weighted), and remained similarly higher up to 4 years (median 9 versus 6 mmHg, p=0.003, IPTWweighted). Similarly, ejection fraction up to 4 years was significantly lower in the REPLACE compared to REPAIR group (median 60% versus 65%, p=0.004, IPTW-weighted). No significant difference was noted between the 2 groups regarding left ventricular end diastolic diameter (median 52.5 versus 52, p=0.811)

DISCUSSION

In this prospective cohort study of patients undergoing surgery for dystrophic aortic root aneurysm, the CAVIAAR standardized technique of valve repair was not associated with a significant improvement in the primary outcome up to 4 years (a composite criterion of mortality, re-operation, thromboembolic or major bleeding events, endocarditis or operating site infections, and heart failure), as compared to mechanical composite valve and graft replacement. However, REPAIR group had two third less frequent major bleeding events and ten times less frequent valve-related deaths than REPLACE (Figure 5). Moreover, when we accounted for multiple events occurring in a single patient, REPAIR group had one half the occurrence of MAVRE and two third less frequent major bleeding events than REPLACE. Finally, for most outcomes, the trend was in favor of REPAIR for fewer occurrences which is also combined to a better quality of life without oral anticoagulation daily management and valve noise.

Aortic valve sparing operation historical cohort showed excellent survival (90%), and freedom from valve-related re-operation (96%) at 20 years [START_REF] Te | REIMPLANTATION OF THE AORTIC VALVE AT 20 YEARS[END_REF]. Studies comparing aortic valve sparing versus replacement are mostly single center studies as the recent paper of Ouzounian and al. showing superiority of valve sparing root replacement compared to biological or mechanical composite valve and graft [START_REF] Ouzounian Maral | GRAFT PROCEDURES IN PATIENTS WITH AORTIC ROOT DILATION[END_REF]. Only one multicentric study was published from the National Marfan Foundation prospective aortic root replacement international registry, enrolling patients operated with various techniques of composite valve graft replacement (85% mechanical, 15% bioprosthetic) or valve-repair procedure (Remodeling and Reimplantation) [START_REF] Price | LONG-TERM OUTCOMES OF AORTIC ROOT OPERATIONS FOR MARFAN SYNDROME: A COMPARISON OF BENTALL VERSUS AORTIC VALVE-SPARING PROCEDURES[END_REF][START_REF] Lee | CLINICAL OUTCOMES OF ROOT REIMPLANTATION AND BENTALL PROCEDURE: PROPENSITY SCORE MATCHING ANALYSIS[END_REF][START_REF] Flynn Cd | SYSTEMATIC REVIEW AND META-ANALYSIS OF SURGICAL OUTCOMES IN MARFAN PATIENTS UNDERGOING AORTIC ROOT SURGERY BY COMPOSITE-VALVE GRAFT OR VALVE SPARING ROOT REPLACEMENT[END_REF][START_REF] De Meester | VALVE REPAIR IMPROVES THE OUTCOME OF SURGERY FOR CHRONIC SEVERE AORTIC REGURGITATION: A PROPENSITY SCORE ANALYSIS[END_REF][START_REF] Ouzounian Maral | GRAFT PROCEDURES IN PATIENTS WITH AORTIC ROOT DILATION[END_REF]. Results published at 1 year found no difference in survival, valve-related morbidity, or MAVRE between both groups [START_REF] Js | EARLY AND 1-YEAR OUTCOMES OF AORTIC ROOT SURGERY IN PATIENTS WITH MARFAN SYNDROME: A PROSPECTIVE, MULTICENTER, COMPARATIVE STUDY[END_REF].

The main strength of the CAVIAAR cohort comes from being the first multicenter prospective study enrolling a diverse population of patients with dystrophic aortic root aneurysm including Marfan patients, bicuspid or tricuspid valve irrespective of the degree of The CAVIAAR study also has limitations. First, this is not a randomized controlled trial.

Although randomization would have been ideal, attempts to randomize failed due to the reluctance of young patients, referring cardiologists, and surgeons to openly randomize in the operating room between two treatments implying completely different life styles, without taking into consideration patient preference as recommended by the guidelines.

Propensity score methods allowed us to take into account the non-randomized scheme and thus limit the indication bias. However, those receiving REPLACE had a greater percentage of patients with BAV and advanced NYHA status compared to the repair group while the repair group also had a higher percentage of those with 0 or 1 preoperative AI compared to the REPAIR group. This may relate to the choice or the surgeons to perform aortic valve repair in selected patients while those receiving REPLACE were a less selected population of historical controls. However, the effect of REPAIR as compared to REPLACE was estimated in the weighted pseudopopulation, to control for known confounders. figure S1 evidences the of covariates included in the propensity score after weighting, including NYHA status and the number of cusps. However, residual confusion cannot be excluded, a randomized controlled trial would have been ideal. Second, in the REPLACE arm, we included 61 patients from an ad-hoc historical cohort study from May 2007 to October 2011 (inclusion period of the prospective CAVIAAR study). However, all these patients were prospectively followed and all eligible to REPAIR in the CAVIAAR study. Third, the primary composite criterion of the study was proposed before current guidelines to report valve-related events defining the MAVRE which is more accurate to evaluate valve event-free survival. Therefore, MAVRE was a posteriori added to the CAVIAAR study as a secondary endpoint. Forth, some of the complications associated with REPLACE were centered on bleeding, and an INR of 2-3 was recommended in these patients. New mechanical valves with a lower INR target were not considered in the study but may result in a lower bleeding risk in patient undergoing replacement with one of these new mechanical valves. Finally, publication results were delayed compared to the end of follow due to extend time related to the complexity to collect multicentric data including monitoring on site. Ten years outcomes are planned to complete this mid-term follow up of the CAVIAAR trial.

Conclusion

Although primary outcome did not significantly differ between REPAIR versus REPLACE group of patients with dystrophic aortic insufficiency, the trend is in favor of REPAIR by a significant reduction of valve-related deaths and major bleeding without an increased risk of re-operation. Occurrence of MAVRE was also significantly reduced when taking into account all events in a single patient. This midterm result is in line with medical evidence guidelines recommending aortic valve repair for root aneurysm. In order to achieve very long follow-up of patient's outcomes, the CAVIAAR cohort was included into the AVIATOR registry from the Heart Valve Society (23).

  Abbreviations: MAVRE (major adverse valve-related events); CAVIAAR (Conservation

  Overview and Ethics Statement CAVIAAR (Conservation Aortique Valvulaire dans les Insuffisances Aortiques et les Anévrismes de la Racine aortique) study is a multicenter, prospective cohort study conducted in 22 centers in France, with an inclusion period from May 2007 through October 2011. Enrolled patients with aortic root aneurysms were operated either with the CAVIAAR technique consisting of Remodeling root repair and an associated expansible aortic ring (REPAIR), or with a mechanical composite valve and graft replacement (REPLACE). In both groups, consecutive patients were included. Inclusion criteria were adult patients electively operated for an aortic root aneurysm with pliable tricuspid or bicuspid valves regardless of aortic insufficiency (AI) grade, but with an operative indication according to current guidelines [5]. Aortic dissection, redo, urgent operations, patients with aortic stenosis, and patients with formal indication of treatment with anticoagulants (other than mechanical valve) were excluded. All patients provided written informed consent. The protocol was approved by the institutional review board (Committee of Protection of Persons (CPP) of Pitié Salpêtrière University Hospital, Paris, France, authorization n°20-07, given 21/03/2007). The study is registered on the clinicaltrial.gov website as NCT00478803. An independent data and safety monitoring committee provided safety oversight.

13. 6

 6 IC95% [1.91;96.68], p=0.019) and the pre-operative sinotubular junction diameter (HR 1.16 IC95% [1.04;1.29], p=0.005). There was no difference between bicuspid or tricuspid valve in the primary endpoint (HR = 0.95 [0.48,1.90], p=0.886), MAVRE (HR=1.00 [0.47,2.14], p=0.997), nor recurrence of AI grade 2 (HR = 0.40 [0.15,1.09], p=0.055) or 3 (HR = 0.52 [0.05,4.96], p=0.547). Only one patient with structural valve deterioration died during follow-up 18 months after surgery. This patient died of multiple organ failure.

Funding:Figure 1 :

 1 FIGURE LEGENDS

Figure 2 :

 2 Figure 2: Outcomes at 4-year follow-up. A: Inverse probability of treatment-weighted Cox

Figure 3 :Figure 4 :Figure 5

 345 Figure 3: Kaplan-Meier Survival curves at 4 year-follow-up for freedom from composite

3) p=0.18 †

			TOTAL	REPAIR	REPLACE
						p-value
			(N=261)	(N=130)	(N=131)
		II	87 ( 33.3%) 32 ( 24.6%) 55 ( 42.0%)
		III	30 ( 11.5%) 13 ( 10.0%) 17 ( 13.0%)
		IV	5 ( 1.9%)	1 ( 0.8%) 4 ( 3.0%)
	Atrial Fibrillation		10 ( 3.8%)	5 ( 3.8%)	5 (3.8%) p=1.0000**
	HTA		141 ( 54.0%) 70 ( 53.8%) 71 ( 54.2%)	p=0.95*
	AI Grade	0	29 (11.1%) 20 (15.4%) 9 (6.9%) p=0.0022**
		I	55 (21.1%) 37 (28.5%) 18 (13.7%)
		II	67 (25.7%) 30 (23.1%) 37 (28.2%)
		III	67 (25.7%) 26 (20.0%) 41 (31.3%)
		IV	43 (16
	Marfan Syndrome		11 (4.2%)	5 (3.8%)	6 (4.6%)	p=0.77*
	Bicuspid		115 ( 44.1%) 43 ( 33.1%) 72 ( 55%) p=0.0001*
	NYHA	I	139 ( 53.3%) 84 ( 64.6%) 55 ( 42.0%) p=0.002**
						26

.5%) 17 (13.1%) 26 (19.8)% Sinuses of Valsalva Diameter (mm), mean±SD (range) 50.2±9.0 (32-98) 50.0±9.7 (33-98) 50.5±8.2 (32-69) p=0.30 † †

		TOTAL	REPAIR	REPLACE
					p-value
		(N=261)	(N=130)	(N=131)
	Other Cardiac	64 ( 24.5%) 37 ( 28.5%) 27 ( 20.6%)	p=0.14*
	Procedures
	ECC Time (min)	156.1±49.2	183.1±38.7	129.1±43.5	p<0.0001 †
	mean±SD (range)	(65-315)	(114-315)	(65-314)
	AC Time (min)	123.8±38.1	147.7±30.1	99.8±29.2	p<0.0001 †
	mean±SD (range)	(50-137)	(103-237)	(50-180)
	Second CPB Run	12 (4.6%)	11 (8.5%)	1 ( 0.8%)	p=0.003*
	Reasons Hemostasis		3 (2.3%)	1 (0.8%)
	Residual		7 (5.4%)
	AI≥Grade 2
	CABG		2 (1.5%)
	Aortic Annulus	27.1 ±2.3	27.9±2.3	26.5±2.1	p<0.0001 †
	Diameter (Hegar,	(21-40)	(21-40)	(21-32)
	mm) mean±SD
	(range)

30 days Operative Mortality 10 (3.8%) 5 (3.8%) 5 (3.8%) p=1.00** Study Composite Criterion at 30 Days 54 ( 20.7%) 24 ( 18.5%) 30 ( 22.9%) p=0.38* Major Adverse Valve Related Events at 30

	17 ( 6.5%)	5 ( 3.8%) 12 ( 9.2%)	p=0.08
	Days

* †Student t test. † †Wilcoxon test. *χ² test. **Fisher's exact test. 552

Acknowledgments

We thank all the patients who are participating in this trial; the investigators at the participating centers, the members of data and safety monitoring committee, and particularly Naima Beldjoudi, Marine Naudin, Margaux Verdier, Julie Jeau, Oriane Froelich, Gaelle Laffargue and Marjorie Chossard, Said Lebbah.

Data are presented as number and percentage of patients unless otherwise specified; SD, Standard Deviation; NYHA, New York Heart Association; CABG, Coronary Artery Bypass Graft; PFO, Patent Foramen Ovale; ECC, ExtraCorporeal Circulation; AC, Aortic Crossclamping; CPB, CardioPulmonary Bypass; AI, Aortic Insufficiency 29 1

AI, from a large number of centers, thus providing a good external validation with results up to 4 years. The second strength is to compare two groups undergoing homogenous surgical techniques; that is, either a standardized approach to aortic valve repair (Remodeling root repair with ring annuloplasty) or a mechanical composite valve and graft replacement. Furthermore, multiple events occurring in a single patient were also analyzed as they significantly impact quality of life and patient burden (18). Occurrence of MAVRE taking into account all events in a single patient became significant favoring REPAIR. This finding shows also the limitation of statistical analysis taking into account only single events, while all events contribute to the patient burden.

CAVIAAR findings are in coherence with a single center report matching repair and replacement patients, using propensity score analysis and showing an improved survival rate at 9 years in the repair group (87%) versus replacement (60%) (p=0.007) [START_REF] De Meester | VALVE REPAIR IMPROVES THE OUTCOME OF SURGERY FOR CHRONIC SEVERE AORTIC REGURGITATION: A PROPENSITY SCORE ANALYSIS[END_REF]. Furthermore, recent meta-analyses on mechanical valve replacement in young patients (< 55 years) showed an early mortality of 6% and an annual late mortality of 2.0%/year after composite mechanical valve and graft replacement and an early mortality of 3.15%, with a late mortality of 1.55%/year for isolated mechanical valve replacement (19-21). A microsimulation to estimate life expectancy based on the age of the patient at surgery for mechanical valve replacement showed a 50% drop in life expectancy post-operatively when compared to general population. In contrast, recent analysis of aortic valve repair for isolated aortic insufficiency showed similar life expectancy compared to the general population (22).

Study limitations


Vincent Fernandez

Neal Fairley

Jonas Baltrusaitis

Surface Analysis Insight Note: Synthetic line shapes, integration regions and relative sensitivity factors

Keywords: XPS, line shape, background, relative sensitivity factor, quantification

Methods for estimating photoemission intensity from X-ray photoelectron spectroscopy data are examined. The role played by "synthetic" bell-shaped curves, integration intervals, background curves and the use of relative sensitivity factors (RSFs) in reporting percentage atomic concentration for a sample is presented. In particular, photoemission lines with differing energy distributions obtained from the NaCl sample surface are used to demonstrate how a comparison of photoemission intensities is dependent on the line shapes, background curves and appropriate use of RSFs.

Introduction

X-ray Photoelectron Spectroscopy (XPS) is an analytical technique widely used in chemistry, physics and engineering. [START_REF] Seah | The quantitative analysis of surfaces by XPS: A review[END_REF][START_REF] Salmeron | From Surfaces to Interfaces: Ambient Pressure XPS and Beyond[END_REF][START_REF] Bagus | The interpretation of XPS spectra: Insights into materials properties[END_REF][START_REF] Blomberg | In situ X-ray photoelectron spectroscopy of model catalysts: at the edge of the gap[END_REF][START_REF] Foelske-Schmitz | X-Ray Photoelectron Spectroscopy in Electrochemistry Research[END_REF] XPS provides insights into the chemical state of the corresponding atoms and the amount of substance measured via recording numbers of electrons with specific energy emitted for a sample due to scattering of bound-state electrons by photons. [START_REF]A Discussion on photoelectron spectroscopy -Electron spectroscopy for chemical analysis (e.s.c.a[END_REF] While the physics of the photoelectric effect ensures quantitative analysis is available, factors related to the measurement of count rates and calibration of instrumentation present challenges when attempting to understand results from samples analyzed by XPS. [START_REF] Shard | Practical guides for x-ray photoelectron spectroscopy: Quantitative XPS[END_REF] In this work, some of these challenges are considered and highlighted by an analysis of data recorded from a relatively simple NaCl sample that demonstrates a level of care that should be given to understanding XPS data.

Carefully used modern XPS instruments usually produce quality reproducible XPS signals from most materials. However, multiple factors impact transforming these signals into the elemental composition that many analysts seek. Such factors include those related to the sample, the instrument, data collection, and the analysis approach. When a material is known and uniform over the analysis depth, several scientifically justifiable assumptions and corrections [START_REF] Briggs | Practical Surface Analysis by Auger and X-Ray Photoelectron Spectroscopy[END_REF] to the measurement of photoemission correction may be used to obtain an accurate analysis. However, most samples are of unknown composition and do not have uniform composition over the analysis depth of XPS. Routine or practical quantitative analysis using XPS is dependent on relative sensitivity factors used to estimate the response of signal intensity to photoemission and many analyses implicitly assume that the composition over the analysis volume is uniform. [START_REF] Cumpson | Angle-resolved XPS and AES: Depth-resolution limits and a general comparison of properties of depth-profile reconstruction methods[END_REF] Therefore, the percentage atomic concentrations calculated from XPS seldom have absolute accuracy better than 10 to 15%. However, with significant care and knowledge of the sample results can be precise (reproducible) and highlight differences among samples. Hence practical quantification by XPS is dependent on the existence of relative sensitivity factors (RSFs) used to estimate the response of signal intensity to photoemission. [START_REF] Wagner | Empirical atomic sensitivity factors for quantitative analysis by electron spectroscopy for chemical analysis[END_REF][START_REF] Scofield | Hartree-Slater subshell photoionization cross-sections at 1254 and 1487 eV[END_REF] Indeed, libraries of RSFs are offered by all manufacturers of XPS instrumentation and these RSFs are provided with the understanding that quantification by these means is limited to certain sample composition, structure and morphology. However, despite uncertainty in quantification results obtained by XPS for surface properties of heterogeneous samples, XPS is often the only non-destructive means of gaining information about a sample.

The question emerges on why to calculate percentage atomic concentration from XPS measurements. The percentage atomic concentration provides a means of normalizing peak intensities concerning the measurement process by XPS. Measurement performed under different conditions, such as simply altering the sample height concerning the apparatus, can alter the absolute intensity of photoemission peaks. The relative intensity for these same peaks from identical samples measured under differing conditions is, in theory, unaltered.

Hence presenting the intensity of emission peaks relative to the sum of all emission peaks intrinsic to percentage atomic concentration offers a means of demonstrating two measurements or two samples are identical. More importantly, the percentage atomic concentration for two measurements or samples that differ provides the basis from which a skilled analyst begins to understand XPS data. The difficulty in computing accurate percentage atomic concentration from XPS data is further emphasized when emission peaks are adjusted for relative sensitivity. The fact that not all photoemission peaks from a given element occur in XPS spectra with the same intensity or FWHM suggests that a scale factor used to correct for differences in photoelectron peak intensity, when computing raw intensity from data, will also depend on a consistent method for defining the integration interval limits as well as the curve [START_REF] Tougaard | Practical algorithm for background subtraction[END_REF][START_REF] Végh | The analytical form of the Shirley-type background[END_REF] used to model inelastically scattered background signal within the interval. The need for consistency in how emission peaks are integrated is further complicated when peak models are required to separate signals from different sources of emission that overlap within spectra.

The problem posed by measuring emission via components in a peak model compared to measuring emission by integrating data over an energy interval is that the latter is by definition over a finite interval, whereas in the case of the former, synthetic line shapes derived from Gaussian and Lorentzian functions [START_REF] Major | Practical guide for curve fitting in x-ray photoelectron spectroscopy[END_REF][START_REF] Major | A detailed view of the Gaussian-Lorentzian sum and product functions and their comparison with the Voigt function[END_REF] are defined in terms of the area over an infinite energy range. One might think that a solution is to truncate synthetic line shapes to the interval used to integrate intensity from data. However, such an approach introduces inconsistencies between components within a peak model depending on how close a component is positioned concerning the limits of the integration interval. That is, adopting this strategy results in a component in the center of an interval that integrates to a different area from a component close to the limits of the interval. Further, the relative error in intensity for the area measured by components compared to the area measured based on integrating data depends on the line shapes used by components in a peak model. The exponential decay in intensity in the case of a Gaussian is significantly more rapid than the quadratic decay associated with Lorentzian line shapes. As a consequence, the energy interval width required to capture the same area for a Lorentzian as a Gaussian is wider for a Lorentzian than would be required for a Gaussian. This is illustrated in Figure 1 where the full-width corresponding to varying proportions of height for a Lorentzian and Gaussian are plotted concerning the fractional height parameter we introduce, n. The convention of characterizing a bell-shaped curve by the full width at half maximum (FWHM), for the Lorentzian and Gaussian, plotted in Figure 1b, would suggest these are two curves that have similar width characteristics. However, if the width is measured by full width a one-fifth maximum (n=5), one would observe that the Lorentzian and Gaussian are not of similar width character. This suggests that the integration interval can and will have a different effect on the calculated area.

These concepts of component area relative to one another and the area from integration intervals can be explored by using photoemission data from NaCl. Specifically, Cl 2s transition provides a photoemission peak with a mostly Lorentzian shape, while Cl 2p is a doublet where both peaks are closer to a Gaussian. In this Insight, data measured from NaCl will be used to explore why quantification by XPS is not straightforward, even for a homogeneous material such as NaCl. The corresponding Youtube video that delineates the underlying concepts of Cl 2p/Cl s2 based quantification is available under Supporting Information.

Experimental Section

The experiment performed on a Kratos Axis Nova measured two spectral regions with energy intervals corresponding to Cl 2s and Cl 2p photoemission from NaCl powder. Repeat measurements for Cl 2s and Cl 2p spectra were acquired using a single scan in energy and dwell time per data channel of 0.133 s. A total of 101 spectra per photoemission line were recorded. Charge compensation is used throughout these measurements, which were performed using FoV1 lens mode, slot aperture with analyzer pass energy 10 eV and monochromatic X-ray source, photon energy 1486.6 eV. The apparent binding energy for Cl 2s photoemission is 267 eV and for Cl 2p is 196 eV. Instrumental response due to kinetic energy of recorded electrons is flat for the energy interval spanning Cl 2s and Cl 2p. Data analysis is performed using synthetic line shapes in CasaXPS. [START_REF] Fairley | Systematic and collaborative approach to problem solving using X-ray photoelectron spectroscopy[END_REF] High energy resolution spectra were recorded using the energy resolution provided by the pass energy 10 eV to allow the doublet components of Cl 2p to be resolved in energy and to permit the energy, for which the rise in background signal associated with photoemission from Cl 2p, to be easily identified. Pass energy 10 eV also ensures the recorded shape for Cl 2s is predominately the natural line shape for Cl 2s photoemission, which is Lorentzian in character. These characteristics of spectral line shapes are central to the discussion that follow.

FoV1 defined for the University of Nantes Kratos Axis Nova operating in pass energy 10 eV results in an intensity transmission change between kinetic energy for Cl 2s and Cl 2p of about 2%. In the context of uncertainties in Scofield cross-sections and estimates for escape depth correction, a 2% change in transmission between Cl 2s and Cl 2p is considered a flat transmission response. Over an energy range corresponding to a survey (1000 eV) measurement the transmission response of FoV1, pass energy 10 eV cannot be described as flat. The performance of the instrument, however, can be demonstrated in FoV2 and pass energy 160 eV scan shown in Figure 2 with the quantification performed using Scofield RSFs and escape depth correction with no transmission correction. For this special mode of operation, the resulting quantification shows stoichiometric quantitites of Na and Cl as evidenced by Na 1s, Na 2s and Cl 2p peaks.

Results and Discussion

Quantification and RSFs

Quantification by XPS involves separating photoemission signal from background signal and then selecting appropriate RSFs. These steps are illustrated in Figure 3 where the signal, representing the summation of all 101 spectra measured over the energy interval in which the Cl 2p signal, is present and performed in three ways. Cl 2p is a doublet, therefore two energyresolved photoemission peaks provide two ways to partition the signal. The doublet is modeled in Figure 3 using two components, each of which is assigned RSFs calculated by Scofield. [START_REF] Scofield | Hartree-Slater subshell photoionization cross-sections at 1254 and 1487 eV[END_REF] A third method for linking Cl 2p signal to an RSF is via integration over the entire doublet. The total RSF obtained by summing the two spin-orbit split RSFs used for each component is applied to the signal integrated over the combined Cl 2p doublet structure. Each component is identified as distinct through the j quantum number (1/2 and 3/2) subscript to Cl 2p. The total intensity for the Cl 2p doublet in Figure 3 is calculated by integrating intensity after subtracting a flat linear background. In each case, the RSF calculated by Scofield would allow the amount of substance to be calculated for chlorine by the inclusion of any one of these three measures for photoemission intensity, Cl 2p 1/2 , Cl 2p 3/2, or Cl 2p region. Thus, if the RSF adjusted signal is compared using a percentage atomic concentration calculation, a comparison for these three should logically all report 33.3%, as shown in Figure 3. This result in Figure 3 thus demonstrates that for Cl 2p there is little difference in outcome for the amount of substance if measured by either of these three estimates for chlorine. The scenario for Cl 2p in Figure 3 is precisely the outcome hoped for when analyzing XPS data using peak models or integration regions. However, a similar analysis for Cl 2s reveals a problem not encountered during an analysis of Cl 2p.

The reason the results shown in Figure 3 for Cl 2p are comparable between the region and components to the peak model is line shapes used to model doublet signal are constructed from a generalized Voigt function where the Gaussian width relative to the generalized Lorentzian is broad. [START_REF] Sherwood | The use and misuse of curve fitting in the analysis of core X-ray photoelectron spectroscopic data[END_REF] The width of the Gaussian indicated by the parameter value 870 used in LA(1,1.12,870) suppresses the spread of intensities away from the maximum intensity in the line shape that would otherwise exist due to the contribution of the generalized Lorentzian. Further, the FWHM for these doublet Cl 2p components is about 1 eV, which is narrow compared to the total width for the integration region.

By comparison, the FWHM for the component used to model Cl 2s photoemission in Figure 4 is 2.3 eV and the line shape LA(95) is a true Voigt function. [START_REF] Moeini | Definition of a new (Doniach-Sunjic-Shirley) peak shape for fitting asymmetric signals applied to reduced graphene oxide/graphene oxide XPS spectra[END_REF] The characteristic shape for LA(95) is determined by a narrow Gaussian compared to the width of the Lorentzian in the convolution integral. Hence, LA(95) allows the signal to spread outside the region limits used to compute the results shown in Figure 4, Figure 5 and Figure 6. It can be seen in Figure 4 that there is a discrepancy of 6.3 % between the intensities measured using the same RSF of 1.69 for the Cl 2s component and the Cl 2s region. We refer the reader to Figure 1 for the importance of the characteristics of a line shape, where a comparison is made between the spread of intensity over energy is made between Gaussian and Lorentzian functions. Based on these two curves in Figure 1 The role for the component with line shape LA(95) is to estimate the intensity of Cl 2s signal, while these two other components provide shapes appropriate for modelling background signal. One of these additional components makes use of the line shape TP(1,1,0), which for the purposes of the peak model provides a linear background that models background signal not emanating from Cl 2s photoemission. The second component is designed to model inelastic scattered signal from Cl 2s photoemission. A sigmoid line shape computed from the line shape LA(95) provides a component with shape equivalent to shapes computed directly from data for photoemission peaks. This sigmoid component defined by SB(0)LA( 95) permits the peak model to fit LA(95) at binding energies where inelastic scattered Cl 2s intensity begins to contribute to background intensity, while zero-loss signal is still appreciable. . SB(0) sigmoid line shape description as well as TP shape are further described in the videos available under Supporting Information.

Figure 5 illustrates how using a Shirley background -which forces different choices for the integration limits -results in a poor comparison between the area calculated by fitting LA(95) to data in Figure 4 and the area calculated in Figure 5. The resulting areas are 54.8 and 45.2 % while their contributions should be equal. Hence, Shirley's background underestimates the area of Cl 2s. The use of Scofield cross-sections and escape depth correction accounts for the comparison between Cl 2s and Cl 2p intensity measured by components fitted to data in Figure 6. If it is assumed Scofield cross-sections are computed correctly, then the 1:1:1 ratio shown in Figure 6 calculated from components in peaks models with line shapes selected appropriately, suggests a basic use of integration regions will introduce quantification errors unless RSFs are adjusted to account for the use of a given background and integration interval. Indeed, these considerations account to some extent for the variation in RSFs supplied by different instrument manufacturers and also empirical RSFs calculated from standard materials.

The quantification tables presented in Figure 3 and Figure 4 differ from the quantification table in Figure 5 in the following sense. Figure 3 and Figure 4 tables are attempting to demonstrate precision in calculated areas based on integration regions and components fitted to data, while the table in Figure 6 should be viewed in terms of results with the accuracy expected for quantification by XPS. Since the results presented in Figure 3 and Figure 4 are concerned with the precision of the calculated area for photoemission, these results may be deemed entirely significant to the theme of this Insight paper. By contrast, the ratio for Cl 2s and Cl 2p intensity reported in Figure 6 is subject to uncertainties introduced through RSFs, instrumental factors and factors relating to electron scattering due to sample properties. Therefore, the table in Figure 6 is only an indication that steps leading to the comparison made between Cl 2s and Cl 2p are feasible rather than accurate in a theoretical sense. In general, the apparent accuracy shown in Figure 6 is not typical for XPS and must surely include an element of luck in achieving results as tabulated.

Peak Model Construction

The previous section presents an argument in support of measuring photoemission peak intensity through the use of fitting curves to data, at least for cases where photoemission peaks are Lorentzian in character. An argument will now be made to suggest fitting curves to data provides insight into sample properties and identifies possible measurement artifacts. The latter is not necessary for successful use of XPS when understanding samples, but being aware that shapes in data are due to instrumental factors rather than the physics or chemistry of the sample can help to avoid over-interpretation of XPS data. In particular, the spectra in Figure 3 and Figure 4 are constructed from data saved at acquisition time as 101 spectra with relatively poor signal-to-noise. The assumption when summing these rapidly acquired spectra to form a single spectrum with good signal-to-noise is that all acquisition cycles are equivalent and data may be summed without any pre-processing to account for shifts in energy. Performing a Principal Component Analysis (PCA) [START_REF] Garland | A study of in situ reduction of MoO3 to MoO2 by X-ray Photoelectron Spectroscopy[END_REF][START_REF] Bargiela | Towards a reliable assessment of charging effects during surface analysis: Accurate spectral shapes of ZrO2 and Pd/ZrO2 via X-ray Photoelectron Spectroscopy[END_REF] on the set of 101 spectra measured from Cl 2p photoemission immediately identifies that not all spectra in the sequence of measurements are aligned in energy. Observing the energy calibration for each scan differs through the experiment (Figure 7) is an explanation of why the peak model in Figure 3 involves line shapes with asymmetry. The evidence in Figure 7 would suggest stability of the sample and instrument was only achieved at some point after measurement cycle 40.

Insight into sample properties may be gained by considering the construction method for the peak model in Figure 4. The use of the line shape LA(95) in fitting data in Figure 4 is contingent on the background approximation. Specifically, positioning in terms of width and intensity on a flat background greatly alters the line shape which best fits these data in Figure 4. Thus, rather than arbitrarily defining the flat background by eye, a component is added to the peak model which allows the intensity for a flat background to be determined through optimization. Then, scanning LA(m) line shape parameter m while fitting both line shape and flat background component allows a mathematical determination for the line shape LA(95) as most appropriate for these data. The peak model in Figure 4 also includes a component designed to model the background response to the inelastic scattering of Cl 2s electrons. The heuristic for the use of the Shirley component is a response in the background to Cl 2s signal is delayed in energy until bound states for electrons in ions are available to induce energy loss in photoelectrons. The flat background beneath Cl 2p is evidence that energy loss due to the scattering of photoelectrons from Cl 2p is delayed. However, the width of Cl 2s photoemission is such that energy loss for Cl 2s photoelectrons occurs within the higher binding energy wing of this Lorentzian-shaped peak. The energy interval used in Figure 4 is possible because of the inclusion in the peak model a component that accommodates inelastic scattering of Cl 2s photoelectrons. While not conclusive, introducing these background components suggest the Lorentzian shape for Cl 2s is a feasible solution for modeling photoelectrons. The peak model therefore equally suggests a band gap for NaCl which would be ignored in an analysis making use of the Shirley background in Figure 5. The region defined in Figure 5 also implies a line shape, based on a Shirley background to identify the Cl 2s signal, which would require a line shape that accommodates distortions in the backgroundsubtracted signal due to the use of a Shirley background [START_REF] Moeini | Definition of a new (Doniach-Sunjic-Shirley) peak shape for fitting asymmetric signals applied to reduced graphene oxide/graphene oxide XPS spectra[END_REF] .

Conclusions

The analysis performed for photoemission signal from chlorine ions in NaCl demonstrates the importance of understanding how the choice of line shapes, background curves and RSFs influence results presented as percentage atomic concentration for XPS data. Quantification by XPS is discussed and considered from a practical perspective. The conclusion offered by this work is widely known but seldom stated, namely, that for practical uses of XPS, the percentage atomic concentration measured by XPS is a means to compare samples rather than a literal measure for a sample composition. The example comparing chlorine photoemission from Cl 2s and Cl 2p supports this conclusion by showing how line shapes and background curves greatly influence the accuracy possible in estimating photoemission intensity.

In general, accurate XPS quantification of the chemical composition is difficult to obtain unless great care has been taken already before the acquisition planning of the experiment and the processing of the data obtained. This also is relevant even when a comparison among samples of similar types is performed requiring consistent measurement approaches.

Validation of the analysis method for different peaks of the same element, such as the results presented in this work, is a useful way to test and verify XPS acquisition procedures, recognizing that for samples with contamination or other surface layers the comparisons may not apply. Figure 3. Cl 2p doublet spectrum formed by summing all 101 spectra acquired. The intensity is estimated using fitting two components to data and also calculated for Cl 2p using an integrating signal directly over the energy interval defined by the region marked in grey. The background which is defined by the integration region marked in grey is a horizontal line. Intensity is separated into two spin-orbit split components to the peak model and calculated directly from data via the integration region so that RSF-adjusted intensity from these three sources can be compared. Scofield cross-sections are used for all three measures for intensity, where the spin-orbit split components are assigned RSFs to allow either component to be used in a quantification calculation alone. The region's RSF is the total RSF for Cl 2p so these three RSF corrected intensities should in theory all appear in the ratio 1:1:1 when presented as percent atomic concentration. Binding Energy (eV) Binding Energy (eV) Figure 5. Cl 2s data with integration interval defined to allow the use of a Shirley background curve to estimate the shape of inelastically scattered photoemission signal. The table compares the corrected area for Cl 2s calculated from the integration region making use of the Shirley background shown to the corrected area for Cl 2s measured using the component curve in Figure 4. Using a Shirley background to calculate Cl 2s photoemission intensity significantly underestimates the intensity for Cl 2s compared to an estimate based on fitting the component curve LA(95) to data. Binding Energy (eV) Binding Energy (eV) Figure 7. Apparent binding energy determined for Cl 2p 3/2 signal. The abscissa is the measurement cycle index for each spectrum in the experiment in chronological order. These Cl 2p 3/2 peak binding energies are determined by fitting the peak model in Figure 3 to each spectrum. The trend in binding energy shows a movement with the time that is only 0.1 eV but represents evidence of systematic changes in energy for Cl 2p data that potentially alter shapes observed for the spectrum in Figure 3.

195

  it is clear a curve with Lorentzian characteristics includes signal over a far wider energy interval than if the curve favored a Gaussian shape. Hence the importance of observing the different characters of line shapes used in peak models for Cl 2s and Cl 2p The peak model for Cl 2s photoemission in Figure 4 makes use of three components. Two of these components are required to permit the use of the component with line shape LA(95).

Figure 1 .Figure 2 .

 12 Figure 1. Comparison of energy widths for Gaussian and Lorentzian curves measured at different proportions of height. FWHM corresponds to n = 2, where both curves have been normalized to unity (1 eV) energy spread at FWHM. Inset shows differences in full width at the same n. Note that Gaussian full width does not change as dramatically with full width as that for Lorentzian, as shown in the inset, suggesting a much smaller evolution of the integrated area under the peak for different background regions.

Figure 4 .

 4 Figure 4. Cl 2s photoemission modeled using line shape with a strong Lorentzian character. The background to these data is computed to optimize the figure-of-merit achieved by adjusting the line shape parameter m for the line shape LA(m). The resulting relationship between the photoemission peak measured using a component to a peak model with line shape LA(95) and intensity measured by integration of signal minus background curve is a 6.3% increase in signal measured by component compared to the region. Comp corresponds to the peak model while Region corresponds to the spectral region.

Figure 6 .

 6 Figure 6. Cl 2p 3/2 and Cl 2p 1/2 components shown in Figure 3 are quantified against component calculated in Figure 4 using SB(0)LA(95) and TP(1,1,0) to fit Cl 2s data.

Acknowledgments

This work by J.B. was supported as part of UNCAGE-ME, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0012577. The CNRS is acknowledged for financial support to the Thematic Workshop (N° 1317144) held at the Station Biologique, Roscoff, France. We are indebted to two anonymous reviewers for their suggestions on how to improve the manuscript.

Author contributions

Vincent Fernandez: Investigation (lead); Writingreview and editing (equal). Neal Fairley: Investigation (supporting); Methodology (lead); Writingoriginal draft (lead); Writingreview and editing (equal). Jonas Baltrusaitis: Methodology (supporting); Supervision (lead); Writingreview and editing (equal).

Supporting information

A tutorial on the spectral manipulation of the data described in this work using CasaXPS [START_REF] Fairley | Systematic and collaborative approach to problem solving using X-ray photoelectron spectroscopy[END_REF] software is presented in the form of tutorial videos. Specifically, the TP line shape description is presented at https://www.youtube.com/watch?v=YWuakB-4O7U. SB(0) sigmoid line shape description is provided at https://www.youtube.com/watch?v=f95lL6ZSFog. Finally, Cl 2s/Cl 2p quantification by line shapes is shown at https://www.youtube.com/watch?v=Rn8sNt42CqY.

Competing interests.

The authors declare no competing interests.


Déborah Hirt
email: deborah.hirt@univ-paris5.fr

F Mentré

A Tran

E Rey

S Auleley

D Salmon

X Duval

J M Tréluyer

Effect of CYP2C19 polymorphism on nelfinavir to M8 biotransformation in HIV patients

Keywords: HIV, nelfinavir, CYP2C19, pharmacokinetics, NONMEM

Aims : To evaluate the effect of CYP2C19 polymorphism on nelfinavir and M8 pharmacokinetic variability in HIV infected patients and to study the link between pharmacokinetic exposure and short term efficacy and toxicity. Methods: Nelfinavir (n=120) and M8 (n=119) concentrations were measured in 34 protease inhibitor naive-patients. Two weeks after initiating the treatment, blood samples were taken before, 1, 3 and 6 hours after drug administration. Genotyping for CYP3A4, 3A5, 2C19 and MDR1 was performed. A population pharmacokinetic model was developed to describe nelfinavir-M8 concentration time-courses and to estimate interpatient variabilities. The influence of individual characteristics and genotypes were tested using a likelihood ratio test. Estimated mean (C mean ), maximal (C max ) and trough (C trough ) nelfinavir and M8 concentrations were correlated to short term virological efficacy and tolerance using Spearman nonparametric correlation tests.

Results: A one-compartment model with first-order absorption, elimination and metabolism to M8 best described nelfinavir data. M8 was modelled by an additional compartment. Mean pharmacokinetic estimates and the corresponding inter-subject variabilities were: absorption rate 0.17 h -1 (99%), absorption lag time 0.82 h, apparent nelfinavir total clearance 52 L/h (49%), apparent nelfinavir volume of distribution 191 L, M8 elimination rate constant 1.76 h -1 and nelfinavir to M8 m m V CL 0.39 h -1 (59%) in *1/*1 patients and 0.20 h -1 in *1/*2 or *2/*2 patients for CYP2C19*2. Nelfinavir C mean was positively correlated to glycemia and triglycerides increases (p=0.02, p=0.04).

Conclusions: The rate of metabolism of nelfinavir to M8 was reduced by 50% in patients with *1/*2 or *2/*2 genotype for CYP2C19 compared to those patients with *1/*1 genotype.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

Nelfinavir is an HIV protease inhibitor, substrate of the transporter P-glycoprotein and metabolized via CYP2C19, CYP3A4 and CYP3A5 enzymes. Pharmacokinetic studies showed a wide interindividual variability of nelfinavir concentrations, some of this variability may be caused by variant drug metabolism or transporter genes. For CYP3A4*1B and CYP3A5*3 polymorphism, results from 3 studies were in agreement, showing no difference in nelfinavir concentrations between patients with these different genotypes. However, for MDR1 and CYP2C19 polymorphism, contradictory studies were found, showing either no impact on nelfinavir concentration or modified concentrations which could influence virologic response.

WHAT THIS STUDY ADDS -

Patients with an *1/*2 or *2/*2 genotype for CYP2C19 had a nelfinavir to M8 biotransformation divided by 2 compared to *1/*1 patients. We did not evidence any influence of MDR1 polymorphism on nelfinavir absorption.

Introduction

Nelfinavir is a protease inhibitor commonly used as a part of the highly active antiretroviral therapy for human immunodeficiency virus infected patients. The use of protease inhibitor-based regimen led to substantial decrease in viral load and restoration of immune function in most HIV-positive individuals, permitting a decline in death rates, and reductions in the incidence of opportunistic infections [1,[START_REF] Hammer | A controlled trial of two nucleoside analogues plus indinavir in persons with human immunodeficiency virus infection and CD4 cell counts of 200 per cubic millimeter or less[END_REF].

Nelfinavir bioavailability is between 70 and 80 % when administered with food [3]. In the intestine, P-glycoprotein (P-gp) restricts the entry into the body of nelfinavir which is a substrate of this transporter [4]. The volume of distribution is 2 to 7 L/kg of bodyweight. Nelfinavir is metabolized into the active metabolite hydroxyl-tertbutylamide (M8) via the CYP2C19 enzyme, and both drugs are metabolized via CYP3A4 [START_REF] Lillibridge | The role of polymorphic CYP2C19 in the metabolism of nelfinavir mesylate[END_REF][START_REF] Zhang | Circulating metabolites of the human immunodeficiency virus protease inhibitor nelfinavir in humans: structural identification, levels in plasma, and antiviral activities[END_REF]. Nelfinavir is the only HIV protease inhibitor that has an active metabolite (M8) present in potentially therapeutic concentrations [START_REF] Zhang | Circulating metabolites of the human immunodeficiency virus protease inhibitor nelfinavir in humans: structural identification, levels in plasma, and antiviral activities[END_REF].

There is a wide interindividual variability in the disposition of this drug, and some of this variability may be caused by variant drug metabolism or transporter genes. Some studies were already performed to study the impact of CYP3A4, CYP3A5, CYP2C19 and MDR1 polymorphism on nelfinavir pharmacokinetics. A single-nucleotide polymorphism in the 5' regulatory region of CYP3A4 gene (A-392 G) named CYP3A4*1B is the most common variant; it has been associated in vitro with enhanced CYP3A expression [START_REF] Tran | Pharmacokinetics and toxicity of docetaxel: Role of CYP3A, MDR1, and GST polymorphisms[END_REF]. The CYP 3A5*3 polymorphism (A6986 G) leads to an inactive truncated protein [START_REF] Hustert | The genetic determinants of the CYP3A5 polymorphism[END_REF]. However Fellay et al. [START_REF] Fellay | Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1: a pharmacogenetics study[END_REF], Saitoh et al. [START_REF] Saitoh | An MDR1-3435 variant is associated with higher plasma nelfinavir levels and more rapid virologic response in HIV-1 infected children[END_REF] and Haas et al. [START_REF] Haas | Pharmacogenetics of long-term responses to antiretroviral regimens containing Efavirenz and/or Nelfinavir: an Adult Aids Clinical Trials Group Study[END_REF] did not evidence any differences in nelfinavir concentrations between patients with these different genotypes. Concerning the effect of MDR1 polymorphism, conflicting results have been found. Two singles nucleotide polymorphism in MDR1-gene G2677A/T in exon 21, and C3435T in exon 26 were shown to be associated with variation in P-gp expression. Fellay et al. [START_REF] Fellay | Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1: a pharmacogenetics study[END_REF] showed an increase in median nelfinavir concentration for patients MDR1 3435 from TT, CT to CC genotypes. Saitoh et al. [START_REF] Saitoh | An MDR1-3435 variant is associated with higher plasma nelfinavir levels and more rapid virologic response in HIV-1 infected children[END_REF] found that children with CT genotype for MDR1 3435 had higher 8h postdose nelfinavir concentration compared to those with other genotypes. However Haas et al. [START_REF] Haas | Pharmacogenetics of long-term responses to antiretroviral regimens containing Efavirenz and/or Nelfinavir: an Adult Aids Clinical Trials Group Study[END_REF] did not evidence any influence of the MDR1 polymorphism in exon 26 and 21 on nelfinavir AUC. For CYP2C19 gene, CYP2C19*2 (G681A point mutation in exon 5) is the most common variant in Caucasian, which has no enzyme activity. Haas et al. [START_REF] Haas | Pharmacogenetics of long-term responses to antiretroviral regimens containing Efavirenz and/or Nelfinavir: an Adult Aids Clinical Trials Group Study[END_REF] found in 348 HIV infected adults, that patients *1/*2 (AG) or *2/*2 (AA) had significantly higher nelfinavir and nelfinavir plus M8 AUC 0-12h than *1/*1 (GG) genotype and tended to have a better virologic response. However Fellay et al. [START_REF] Fellay | Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1: a pharmacogenetics study[END_REF] in 123 adults, Saitoh et al. [START_REF] Saitoh | An MDR1-3435 variant is associated with higher plasma nelfinavir levels and more rapid virologic response in HIV-1 infected children[END_REF] in 71 children and Burger et al. [START_REF] Burger | The effect of the CYP2C19*2 heterozygote genotype on the pharmacokinetics of nelfinavir[END_REF] in 24 adults did not find any effect of the CYP2C19 genotype on nelfinavir concentrations in plasma.

The aims of this study were to evaluate the influence of genetic polymorphism on pharmacokinetic parameters (MDR1 on absorption, CYP2C19 on nefinavir to M8 biotransformation and CYP3A4 on nelfinavir and M8 metabolism) and to correlate concentrations to short term virological efficacy and toxicity.

Methods

Patients

The COPHAR2-ANRS 102 study was an open, multicenter, prospective trial of HIV1 infected adults who started a treatment with an antiretroviral combination of at least three drugs : 2 nucleoside reverse transcriptase inhibitors plus one protease inhibitor; either nelfinavir, or indinavir or lopinavir. In our group, all patients were administered nelfinavir.

Patients older than 18 years, infected by HIV-1, protease inhibitor-naive were eligible.

The Ethical Review Committee of the Bicêtre Hospital, Paris, France reviewed and approved the study protocol. All participants provided written informed consent.

These adults were administered nelfinavir as 1250 mg twice daily (BID), only one patient had 1500 mg twice daily and one had a 625 mg twice daily. Nelfinavir was given using the new formulation of 625 mg tablets [START_REF] Kaeser | Assessment of the bioequivalence of two nelfinavir tablet formulations under fed and fasted conditions in healthy subjects[END_REF]. A 250 mg tablet was added for the patient who received 1500 mg BID. A blood sample was taken for genotype before initiating the treatment and two weeks later patients underwent 4 blood samplings, before, 1, 3 and 6 hours after drug intake for pharmacokinetic analysis.

For each patient, time elapsed between administration and sampling times were carefully recorded. For modeling, we assumed that patients were at steady state with a dosing interval τ of 12 h. The trough concentration was the concentration measured the day before drug intake and the 3 other concentrations were measured after drug intake.

Short term efficacy was studied using HIV RNA levels at day 0 and week 2. Short term tolerance (fasten cholesterol, triglyceride and glycemia) was analyzed, based on measurements performed 4 weeks before and 4 weeks after initiating the treatment.

A questionnaire for adherence was also used. One adherence covariate was analyzed, corresponding to the yes/no answer at the question "During the last 4 days, did you forget or delay deliberately or not your antiretroviral drug intake?" Analytical method Nelfinavir and M8 plasma concentrations were measured by specific high performance liquid chromatography. The 4 participant laboratories were crossvalidated before starting the study. Results of the blind interlaboratory quality control at three concentrations for nelfinavir and for M8 were within 15% of the target values from medium and high values and within 20% for low values. Lower limits of quantification (LOQ) were 100 ng/mL for nelfinavir and 25 ng/mL for M8 depending on the method used.

Genotyping

All Genotypes were performed in the same laboratory. Total deoxyribonucleic acid (DNA) was extracted from plasma samples by use of the QIAamp DNA Blood Mini Kit (Qiagen, Courtaboeuf, France). Genotyping for CYP2C19*2 was performed by a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method with allele-specific primers, as described by de Morais et al. [START_REF] Morais | Identification of a new genetic defect responsible for the polymorphism of (S)mephenytoin metabolism in Japanese[END_REF]. Genotyping for CYP3A4*1B was determined by polymerase chain reaction (PCR) followed by direct sequencing. PCR was performed by use of a GenAmp PCR System 9700 (Applied Biosystems, Courtaboeuf, France) according to a previously published method [START_REF] Dally | The CYP3A4*1B allele increases risk for small cell lung cancer: effect of gender and smoking dose[END_REF]. Amplified DNA was purified by use of the QiaQuick DNA Purification System (Qiagen) and sequenced by use of BigDye Terminator chemistry and an ABI PRISM 3100 genetic analyzer (Applied Biosystems). Genotyping of CYP3A5*3 and *6 was performed by real-time polymerase chain reaction by use of TaqMan MGB probe technology (Applied Biosystems). MDR1 polymorphisms in exons 21 and 26 were determined by use of previously published methods [START_REF] Cascorbi | Frequency of single nucleotide polymorphisms in the P-glycoprotein drug transporter MDR1 gene in white subjects[END_REF]. For each genotyping analysis, at least 2 positive controls were used: 1 homozygous for the wild-type allele and 1 heterozygous and, when available, 1 homozygous for the mutated allele. These controls are DNAs that have been sequenced. describing the nelfinavir to M8 biotransformation, and the elimination rate constant for M8 (k em ). Since nelfinavir was orally administered, only k a , Tlag, V/F and k were identifiable for nelfinavir. For M8, since no urinary concentration data were available, and because no literature data were used to fix V m /F, only Fk m /V m and k em could be determined. Therefore, the model was reparametrized using an apparent clearance for nelfinavir (CL T /F = k x V/F) and an apparent nelfinavir to M8 biotransformation clearance (CL m /F = k m x V/F). The vector of identifiable parameters used in the population analysis was therefore ka, Tlag, CL T /F, V/F, m m V CL and k em .

The following equations describe nelfinavir and M8 plasma concentrations: where CA=0 or 1. Patients with a missing value for a covariate retained during the first statistical analysis were excluded of the covariate population modeling. The basic model was fitted again with the patients with all covariate values and the covariate could then be tested.

[ ]           - - -         - × × = × - × - × - × - τ τ a a T T k t k F V F CL t F V F CL T a a e e e e x F V F CL k F V D k N 1 1 [ ] ( ) ( ) ( ) ( )                                   -         - - + -         - - +         -         -         - × × × × = × - - × - × - × - × - em a em T τ k xt k a em a T τ k t k T em T a τ F V F CL t F V F CL m m a k k k F V F CL e 1 e k k k F V F CL e 1 e F V F CL k F V F CL k e 1 e V V CL D k F M8
A covariate was kept if its effect was biologically plausible; it produced a minimum reduction of 3.84 in the objective function value (OFV) and a reduction in the variability of the pharmacokinetic parameter, assessed by the associated intersubject variability. An intermediate model with several covariates was then obtained.

All the selected covariates were added one by one and kept if responding to the cited 3 criteria. At the end of this ascendant modeling, the final model was obtained. A backward elimination phase was finally performed by deleting each covariate from the final model in order to calculate the p value, using a likelihood ratio test.

For evaluation of the goodness-of-fit, the following graphs were performed: observed and predicted concentrations versus time, observed concentrations vs population predictions, weighted residuals vs time and weighted residuals vs predictions. Similar graphs using individual predictive POSTHOC estimation were displayed. Diagnostic graphics were obtained using the R program [START_REF] Ihaka | R: a language for data analysis and graphics[END_REF].

Validation.

Nelfinavir and M8 steady-state concentration profiles were simulated and compared with the observed data thanks to visual predictive check in order to evaluate the performance of the model. More precisely, the vector of pharmacokinetic parameters from 1000 patients was simulated using the final model. Each vector parameter was drawn in a normal distribution with a variance corresponding to the ISV previously estimated. A simulated residual error was added to each simulated concentration.

The simulations were performed using NONMEM. The 5 th , 50 th and 95 th percentiles of the simulated concentrations at each time were then overlaid on the observed concentration data using R program and a visual inspection was performed.

Links between concentrations and short term response / toxicity.

For each patient, mean (C mean,N ), maximal (C max,N ) and trough (C trough,N ) nelfinavir plasma concentrations and the sum of nelfinavir + M8 trough (C trough,NM8 ) plasma concentrations were derived from the estimated individual pharmacokinetic parameters. The efficacy was studied following the difference in log viral load between the day of initiation treatment and week 2. The significance of the viral load decrease was first tested using a wilcoxon non-parametric paired test. With respect to efficacy, the links between C trough,N , C trough,NM8 and the difference in HIV-1 RNA level between day 0 and Week 2 was evaluated using correlation Spearman tests. A Wilcoxon non-parametric test was also performed on decrease in viral load between patients having or not a C trough below the lower limit of therapeutic range (1500 ng/mL, limit used in the COPHAR 2-ANRS 111 trial).

Toxicity was analyzed from the difference between 4 weeks after and before treatment initiation in total cholesterol, high density lipoprotein cholesterol, triglyceride, glycemia and from appearances of diarrheas (grade 2) between treatment initiation and week 4. The significance of these differences was tested using a Wilcoxon nonparametric paired test. Then we performed correlation Spearman tests between C mean,N , C max,N , C trough,N and difference in total cholesterol, high density lipoprotein cholesterol, triglyceride and glycemia. We also used Wilcoxon non-parametric tests to compare these differences between patients having or not a C trough over the upper limit defined in the therapeutic index (5500 ng/mL, limit used in the COPHAR 2-ANRS 111 trial).

We also assessed the relationship between the genetic polymorphisms remaining in the final population model and C mean,N , C max,N , C trough,N and the relationship between these genetic polymorphisms and the efficacy and toxicity outcomes previously described, using Wilcoxon non-parametric tests.

Results

Demographic data

Thirty four patients were included in the nelfinavir arm. All these patients were available for pharmacokinetic evaluation. A total of 120 nelfinavir concentrations and 119 M8 concentrations were collected. One nelfinavir and 13 M8 concentrations were lower than the LOQ, so they were set to half of the LOQ. Inter-subject variability was described by multiplicative model. The available data were not sufficient to estimate inter-subject variability for Tlag, V/F and k em , and fixing the variance of these random effects to zero had no influence on the objective function values (OFV). Residual variabilities were best described by proportional error model. The addition of a correlation between nelfinavir and M8 residual variabilities (r= 0.37 (40%)) decreased OFV by 8.45 units.

Covariates were first tested on Bayesian Empiric estimates of Ka, CL/F and CL m /V m from the basic model. The most significant covariate was for CYP2C19 genotypes on CL m /V m , a significant difference was found between wild type (*1/*1, GG) and other patients (*1/*2, AG or *2/*2, AA) (p=0.01). The coadministration of combivir ® increased significantly k a (p=0.02). Four patients did not have a genotype for the CYP2C19, so they were excluded from the covariate modeling and a basic model was fitted again with the remaining 30 patients. Then, CYP2C19 genotype was first Links between concentrations and short term response / toxicity. The values of the following parameters: HIV-1 RNA level, total cholesterol, HDL cholesterol, glycemia and triglycerides were available for 30 patients as basal, and as 2 or 4 weeks treatment which allowed to calculate their variation and to test the significance of the difference.

The viral load decreased significantly after 2 weeks of treatment (table 3). However the significant decrease in HIV-1 RNA between day 0 and Week 2 was not correlated to C trough,N, nor to C trough,NM8 and was not different between patients with a C trough below or above the lower limit of therapeutic range of 1500ng/ml. Total cholesteol increased significantly after 4 weeks of treatment, contrarily to HDL cholesterol (Table 3). Nelfinavir C trough,N , C max,N and C mean,N were not significantly correlated with total nor with HDL cholesterol evolution. Glycemia increased after 4 weeks of treatment (p=0.05) and its evolution was significantly positively correlated to nelfinavir C mean,N and C trough,N (p=0.02 and p=0.03). Although triglycerides increase was not significant, its evolution was significantly positively correlated to nelfinavir C mean,N (p=0.04) (Fig 4). No patient had a C trough over the therapeutic index upper limit of 5500 ng/mL. No grade 2 diarrhea was recorded.

We could not evidence any significant differences in C trough,N C trough,NM8 , nor in short term efficacy or toxicity between patients *1/*1 and patients *1/*2 or *2/*2 for CYP2C19 gene.

Discussion

The concentrations of nelfinavir and of M8 were satisfactorily described by a onecompartment model with first-order absorption and elimination for nelfinavir with an additional compartment for M8 linked with a first order rate constant. This joint model was already used in adults [START_REF] Panhard | COPHAR 1-ANRS study group. Population pharmacokinetic analysis for nelfinavir and its metabolite M8 in virologically controlled HIV-infected patients on HAART[END_REF][START_REF] Hirt | Pregnancy-related effects on nelfinavir and M8 pharmacokinetics: a population study in 133 women[END_REF]. The following results support the use of this pharmacokinetic model: Nelfinavir mean plasma clearance was consistent with previously reported values: CL T /F = 52 L/h compared to 37.3, 35.5 and 44.9 L/h obtained in Panhard et al. [START_REF] Panhard | COPHAR 1-ANRS study group. Population pharmacokinetic analysis for nelfinavir and its metabolite M8 in virologically controlled HIV-infected patients on HAART[END_REF], in our previous [START_REF] Hirt | Pregnancy-related effects on nelfinavir and M8 pharmacokinetics: a population study in 133 women[END_REF] and in Jackson et al. [START_REF] Jackson | A population pharmacokinetic analysis of nelfinavir mesylate in human immunodeficiency virus-infected patients enrolled in a phase III clinical trial[END_REF] studies.

Nelfinavir to M8 biotransformation and M8 elimination were consistent with our 2 previous studies (in women and in children [START_REF] Hirt | Pregnancy-related effects on nelfinavir and M8 pharmacokinetics: a population study in 133 women[END_REF][START_REF] Hirt | Agerelated effects on nelfinavir and M8 pharmacokinetics: a population study with 182 children[END_REF]) and with Panhard et al. study [START_REF] Panhard | COPHAR 1-ANRS study group. Population pharmacokinetic analysis for nelfinavir and its metabolite M8 in virologically controlled HIV-infected patients on HAART[END_REF], CL m /V m =0.39 h -1 compared to respectively 0.65, 0.58 and 0.36 h -1 and k em = 1.76 h -1 compared to 3.3, 1.88 and 1.93 respectively.

Nelfinavir to M8 biotransformation was reduced in patients *1/*2 or *2/*2 for the CYP2C19 genotype compared to the wild type *1/*1 genotype which is consistent with Burger et al. [START_REF] Burger | The effect of the CYP2C19*2 heterozygote genotype on the pharmacokinetics of nelfinavir[END_REF] and Haas et al. [START_REF] Haas | Pharmacogenetics of long-term responses to antiretroviral regimens containing Efavirenz and/or Nelfinavir: an Adult Aids Clinical Trials Group Study[END_REF] studies who found a significantly lower M8 to nelfinavir AUC ratio in patients with the mutation than in wild type patients. In our study, no patients had liver dysfunction, and no one was taking concurrent medications that are potential inhibitors of CYP2C19, so the decrease in nelfinavir to M8 biotransformation could only be attributed to CYP2C19 polymorphism. A major aim of population pharmacokinetics is to determine which measurable pathophysiological factor can cause changes in the dose-concentration relationship.

In this study, only the CYP2C19 genotype was found to influence nelfinavir and M8 pharmacokinetics. Nelfinavir is metabolized exclusively by CYP2C19 into M8 [START_REF] Lillibridge | The role of polymorphic CYP2C19 in the metabolism of nelfinavir mesylate[END_REF].

Nelfinavir and M8 are described as being equally active [START_REF] Zhang | Circulating metabolites of the human immunodeficiency virus protease inhibitor nelfinavir in humans: structural identification, levels in plasma, and antiviral activities[END_REF]. Haas et al. [START_REF] Haas | Pharmacogenetics of long-term responses to antiretroviral regimens containing Efavirenz and/or Nelfinavir: an Adult Aids Clinical Trials Group Study[END_REF] found in 348 HIV infected adults, that patients *1/*2 or *2/*2 had significantly higher nelfinavir and nelfinavir + M8 AUC 0-12h than *1/*1 genotype and tended to have a better virologic response. We could not evidence a significant difference in nelfinavir or nelfinavir + M8 concentrations between the 2 genotypes. In this study, as we could estimate individual pharmacokinetic parameters, we could quantify the effect of CYP2C19 polymorphism directly on these parameters and have a mechanistic approach of the process. We found that the rate of metabolism of nelfinavir to M8 was reduced by 50% in patients with *1/*2 or *2/*2 genotype for CYP2C19 compared to those patients with *1/*1 genotype. Moreover, as shown in Figure 2, M8 concentrations were lower in patients *1/*2 or *2/*2 than in *1/*1 patients for CYP2C19, whereas nelfinavir concentrations were similar (Figure 2). This suggests an increase in nelfinavir elimination by CYP3A4 which compensate the decreased elimination via CYP2C19. Concerning CYP3A4 and MDR1 genes, in agreement with precedent studies [START_REF] Fellay | Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1: a pharmacogenetics study[END_REF][START_REF] Saitoh | An MDR1-3435 variant is associated with higher plasma nelfinavir levels and more rapid virologic response in HIV-1 infected children[END_REF][START_REF] Haas | Pharmacogenetics of long-term responses to antiretroviral regimens containing Efavirenz and/or Nelfinavir: an Adult Aids Clinical Trials Group Study[END_REF], we did not evidence any difference between wild type and mutated groups for nelfinavir and M8 pharmacokinetics.

Powderly et al. [START_REF] Powderly | Predictors of optimal virological response to potent antiretroviral therapy[END_REF] showed that change in viral load over the first 4 weeks of treatment was predictive of virological response over 48 weeks of treatment.

Hoetelmans et al. [START_REF] Hoetelmans | The effect of plasma drug concentrations on HIV-1 clearance rate during quadruple drug therapy[END_REF] in 29 HIV infected, antiretroviral-naïve patients using a quadruple drug regimen (nelfinavir, saquinavir, stavudine and lamivudine) showed that median nelfinavir concentration ratio was positively correlated with the elimination rate constant (k) of HIV-1 clearance (k = slope of the curve describing initial log viral load as a function of time). We could not establish a significant relationship between nelfinavir concentrations and the reduction in HIV RNA level after 2 weeks of treatment in our PI naïve patients. The main difference with Hoetelmans et al. study was that the decrease in HIV RNA was much lower in our study: viral load was divided by 1.80 in 14 days, corresponding to an elimination rate constant for HIV-1 clearance of 0.13 day -1 . In Hoetelmans study similar basal viral load were measured but k was 0.29 day -1 , indicating that median viral load was divided by 4 after two weeks of treatment.

Few studies evaluated cholesterol, triglycerides and glycemia early changes as a function of nelfinavir plasma concentrations. As Périard et al. [START_REF] Periard | Atherogenic dyslipidemia in HIVinfected individuals treated with protease inhibitors. The Swiss HIV Cohort Study[END_REF] who found that total cholesterol increased slightly but significantly (1.2 ± 0.2 mmol/L) after 4 weeks of treatment in 21 HIV-1 infected patients, we found a significant increase in total cholesterol in our 30 patients. However no relationship could be evidenced between this increase and nelfinavir plasma concentration. Similarly Reijers et al did not found any relationship between elevated cholesterol and plasma nelfinavir drug exposure, although the occurrence of elevated cholesterol was frequent i.e in 35% of their patients on quadruple regimen (stavudine, lamivudine, saquinavir and nelfinavir). Furthermore Reijers et al. [START_REF] Hoetelmans | The effect of plasma drug concentrations on HIV-1 clearance rate during quadruple drug therapy[END_REF] found that nelfinavir concentrations were not higher in hypercholesterolemic (>6.2 mmol/L), nor in hypertriglyceridemic (>4.5 mmol/L) patients. In our study, no patient had a triglycerides rate above 4.5 mmol/L, but a significant positive correlation was found between triglycerides rate and nelfinavir C mean,N (p=0.04). We also found that nelfinavir C mean,N and C trough,N were significantly positively correlated with glycemia evolution (between 4 weeks after and before initiating the treatment) (p=0.02 and p=0.03).

The rate of metabolism of nelfinavir to M8 was reduced by 50% in patients with *1/*2 or *2/*2 genotype for CYP2C19 compared to those patients with *1/*1 genotype, without any significant modifications on nelfinavir trough concentrations, efficacy or toxicity. In these Pi-naïve patients, efficacy could not be related to nelfinavir plasma concentrations but triglycerides and glycemia increased with nelfinavir exposure.

For this analysis, Déborah Hirt was supported by a CRES (contrat de recherche stratégie) from INSERM.

  Modelling strategy and population pharmacokinetic model Data were analyzed using the nonlinear mixed effect modelling software program NONMEM (version V, level 1.1, double precision) with the DIGITAL FORTRAN compiler [17]. The first-order conditional estimation (FOCE) with interaction method was used. A one-compartment model with first-order absorption, elimination and metabolism to M8, best described nelfinavir data. M8 was modelled by an additional compartment (Fig 1). Parameters of the model were the bioavailability (F), the absorption rate constant (k a ), the absorption lag time (Tlag), the volumes of distribution of nelfinavir and M8 (V and V m ), the total elimination rate constant for nelfinavir (k corresponding to k e +k m in Fig 1), the metabolic rate constant (k m )

  Figure1

Figure 2 Observed

 2 Figure 2 Observed (points) and predicted (lines) plasma concentrations of nelfinavir (top) and M8 (bottom) vs. time: for CYP2C19 wild type, i.e. patients GG for CYP2C19*2 (empty points and dashed lines) and for CYP2C19 mutated patients, i.e. AG or AA for CYP2C19*2 (full points and lines). For nelfinavir, full and dashed lines are superposed.

Figure 3

 3 Figure 3Evaluation of the final model: comparison between the 5 th (dash line), 50 th (full line) and 95 th (dash line) percentile obtained from 1000 simulations and the observed data (points) for nelfinavir (top) and M8 (bottom).

Figure 4

 4 Figure 4Correlation between C mean,N and the change in glycemia or triglycerides rate between week 4 and week -4.

  as age, bodyweight, body mass index, albumine and orosomucoid or using the Wilcoxon test for categorical ones such as sex, CDC stade

	em a T with t = delay-Tlag if delay >Tlag MDR1 exon21, MDR1 exon 26), adherence and the co-administration of combivir ® em a T and (drug combining AZT and 3TC). Co-medication was analyzed in 2 classes: patients who were taking combivir ® against other co-medications. Genetic polymorphism was analyzed in two different ways: wild type against heterozygote plus homozygote t = delay-Tlag + of virus infection (C/non C), genotypes (CYP3A4*1B, CYP3A5*3 and *6, CYP2C19*2, mutated or wild type plus heterozygote against homozygote mutated. Covariates that
									CL	=	θ	CL	×	(	median(CO) CO	)	β	CL CO	, where θ CL is the typical value of
	clearance for a patient with the median covariate value and CL CO β is the estimated
	influential factor for the continuous covariate. Categorical covariates (CA) were tested
	as,	CL	=	θ	CL	/(1	+	β	CL CA	×	CA)

τ if delay<Tlag, where t is the calculated time between start of absorption and sampling, Tlag is the estimated absorption lag time, delay is the recorded time elapsed between drug administration and blood sampling and τ is the time interval between 2 administrations.

When nelfinavir or M8 concentrations were below the LOQ, we set them to half of the LOQ. Several error models were investigated (i.e. multiplicative and additive error models) to describe residual variability. Proportional model was used for inter-subject variability (ISV). Data for nelfinavir and M8 were then fitted jointly. Only significant ISVs on pharmacokinetic parameter were kept, i.e. a minimum of 3.84 unit decrease using a likelihood ratio test in a backward elimination procedure. From the POSTHOC option of NONMEM applied on this basic model, Empirical Bayes estimates of each parameter were obtained. The effect of each patient covariate was tested on these estimates, using Spearman nonparametric correlation test for continuous covariates such were found to have an effect on a pharmacokinetic parameter with a p value lower than 0.10, were retained for inclusion in the population model. They were then added one by one to the basic pharmacokinetic model, the most significant at first. Continuous covariates (CO) were tested according to the following equation, using CL for example,

Table 1

 1

	summarizes patient characteristics:

Table 1 .

 1 Patient characteristics at baseline

		Median	Min	Max	Nb missing values
	Age (year)	31	19	63	0
	Bodyweight (kg)	67.25	51	88.5	0
	BMI (kg/m²)	22.92	17.14	32.47	0
	orosomucoid ( g/L ) Albumin ( g/L )	0.825 40	0.59 28.7	1.47 47	5 9
	Sex	women : 20	men : 14	0
	CDC Stage	stage A or B : 29	stage C : 5	0
	Combivir coadministration Good adherence	Yes : 24 Yes : 15		No : 10 No : 11	0 8
	GENOTYPES	Wild type	Heterozygotes Homozygote mutants	Nb missing values
	MDR1 exon 26	13	14	3	4
	MDR1 exon 21	22	5	4	3
	CYP3A4*1B	12	3	15	4
	CYP3A5*3	8	10	15	1
	CYP2C19*2	17	11	2	4

Table 2 .

 2 Population PK parameters (and relative standard error in %) of

	nelfinavir and M8 from basic and final models
						Basic model	Final model
						Estimate (RSE%) Estimate (RSE%)
	Structural model
	ka (h -1 )	0.16 (25)	0.17 (27)
	Tlag (h -1 )	0.83 (6)	0.82 (6)
	V/F (L)	176 (12)	191 (15)
	CL T /F (L/h)	51.3 (10)	52 (10)
	m m CL (h -1 ) V	0.26 (40)	0.39 (35)
	k em (h -1 )	1.56 (38)	1.76 (30)
	β	m m V CL CYP2C19	/	0.98 (49)
	Statistical model
	ω Ka (%)	102 (37)	99 (42)
	ω	CL T	F	(%)	50 (24)	49 (26)
	ω	m m CL V	(%)	65 (30)	59 (29)
	σ NFV (%)	31 (15)	32 (15)
	σ M8 (%)	48 (22)	49 (22)
	r σ NFV -σ M8	/	0.37 (40)

Table 3 :

 3 Evolution of short term efficacy (during the 2 first weeks of treatment) and toxicity (from 4 weeks before to 4 weeks after initiating the treatment) and significance of this evolution.

	Basal Value	Value at week 2 or 4	Variation	test p
	Median	Range	Median	Range	Median	Range
	HIV RNA (log 10 copies/mL) 4.86 2.95 -6.04 Glycemia 4.95 3.70 -6.20 (mmol/L) Total cholesterol 3.95 2.00 -6.60 (mmol/L) HDL cholesterol 1.17 0.46 -1.92 (mmol/L) Triglycerides (mmol/L) 0.90 0.29 -1.91	2.93 4.85 4.90 1.28 1.23	1.60 -4.57 4.20 -6.80 2.50 -7.70 0.39 -2.20 0.44 -2.45	1.80 0.21 0.81 0.01 0.20	1.25 -2.73 -1.5 -1.7 -1.49 -3.7 -1.53 -1.69 -0.68 -1.17	< 10 -4 0.05 10 -4 0.17 0.20

Acknowledgments:

The authors thank the study participants and the participating clinicians at each site, Agence Nationale de Recherche sur le SIDA (ANRS, essai 111) for financial support, and Roche for providing nelfinavir, Dr Agnes Certain for organizing the drug supply to all centers. Steering committee: principal investigator: D. Salmon-Céron, X. Duval, Statistics: F. Mentré; other members S. Auleley, M. Biour, M.J. Commoy, B. Diquet, C. Goujard, C. Katlama, C. Lascoux, M. Legrand, A. Métro, G., Peytavin, E. Rey, A.M. Taburet, J.M. Tréluyer.