Arnaud Chalin

Benjamin Lefèvre

Christelle Devisme

Charlotte Pronier

Virginie Carrière

Vincent Thibault

Laurence Amiot

Michel Samson
email: michel.samson@inserm.fr

Benjamin Lefevre

Serum CXCL10, CXCL11, CXCL12, and CXCL14 chemokine patterns in patients with acute liver injury

Keywords: Acute Liver Failure, ALI: Acute Liver Injury, ALT: Alanine Aminotransferase, AST: Aspartate Aminotransferase, BRAK (CXCL14): Breast and Kidney cancer, HBV: Hepatitis B Virus, HCC: Hepatocellular Carcinoma, HCV Acute liver injury, CXCL10, CXCL11, CXCL12, CXCL14

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

Introduction

Acute liver injury (ALI) may have different origins, including viral infection, alcohol consumption, and drug intake. Patients may spontaneously recover, depending on the severity of the insult and the etiology. However, in some cases, the injury can lead to acute liver failure (ALF). ALF leads to multiorgan system failure and the loss of hepatic function. The incidence of ALF is fewer than 10 cases per million people per year in the developed world, but in the absence of liver transplantation, mortality is up to 50% [START_REF] Bernal | Acute liver failure[END_REF], making ALF a significant public health problem.

Inflammation that results from tissue injury contributes to the pathogenesis of most acute liver diseases and is triggered by host-derived microbial and danger signals [START_REF] Szabo | Inflammasome activation and function in liver disease[END_REF]. Inflammation is characterized by increased blood flow and vascular permeability, with the accumulation of fluid and leukocytes in the injured tissue, and may be detrimental for patients if not managed.

Cytokines and chemokines mediate inflammation by attracting and activating immune cells and orchestrating their interaction [START_REF] Charo | The many roles of chemokines and chemokine receptors in inflammation[END_REF]. Chemokines play an important role in inflammation.

Numbering more than 50, they are categorized into four families: CC, CXC, CX3C, and C [START_REF] Charo | The many roles of chemokines and chemokine receptors in inflammation[END_REF]. Among them, CXCL10 (IP-10), CXCL11 (I-TAC), and CXCL12 (SDF-1) are well described in the field of liver pathogenesis. Indeed, CXCL10 and CXCL11 are both interferon-γ inducible chemokines involved in chronic liver injury and are linked to liver fibrosis and inflammation through their common receptor, CXCR3, suggesting a strong role during evolution of the disease [START_REF] Marra | Roles for chemokines in liver disease[END_REF][START_REF] Itoh | Clinical significance of elevated serum interferon-inducible protein-10 levels in hepatitis C virus carriers with persistently normal serum transaminase levels[END_REF][START_REF] Patel | Multiplex protein analysis to determine fibrosis stage and progression in patients with chronic hepatitis C[END_REF][START_REF] Helbig | Differential expression of the CXCR3 ligands in chronic hepatitis C virus (HCV) infection and their modulation by HCV in vitro[END_REF][START_REF] El Raziky | IP-10 Serum Level in Chronic Hepatitis C Virus Patients: Relation to Fibrosis and Response to Combined Interferon/Ribavirin Therapy[END_REF][START_REF] Helbig | Expression of the CXCR3 ligand I-TAC by hepatocytes in chronic hepatitis C and its correlation with hepatic inflammation[END_REF][START_REF] Larrubia | Role of chemokines and their receptors in viral persistence and liver damage during chronic hepatitis C virus infection[END_REF][START_REF] Semenov | the Role of Cytokines and Chemokines in Laboratory Diagnostic of Chronic Viral Hepatitis C[END_REF][START_REF] Deng | Selected Cytokines Serve as Potential Biomarkers for Predicting Liver Inflammation and Fibrosis in Chronic Hepatitis B Patients With Normal to Mildly Elevated Aminotransferases[END_REF]. CXCL12 is a strong lymphocyte chemoattractant that interacts with CXCR4, a pathway involved in many chronic liver diseases [START_REF] Liepelt | Stromal cell-derived factor-1 (SDF-1) as a target in liver diseases[END_REF]. Apart from its chemotactic effect, activation through the CXCL12/CXCR4 axis leads to fibrogenesis through the activation and proliferation of hepatic stellate cells, as well as regeneration by recruiting mesenchymal stem cells from bone marrow to the injured liver [START_REF] Hong | Hepatic stellate cells express functional CXCR4: role in stromal cell-derived factor-1alpha-mediated stellate cell activation[END_REF][START_REF] Saiman | CXCL12 induces hepatic stellate cell contraction through a calciumindependent pathway[END_REF][START_REF] Liu | Contribution and Mobilization of Mesenchymal Stem Cells in a mouse model of carbon tetrachloride-induced liver fibrosis[END_REF][START_REF] Ling | Stromal Derived Factor-1/CXCR4 Axis Involved in Bone Marrow Mesenchymal Stem Cells Recruitment to Injured Liver[END_REF][START_REF] Gehling | Mobilization of hematopoietic progenitor cells in patients with liver cirrhosis[END_REF][START_REF] Ding | Divergent angiocrine signals from vascular niche balance liver regeneration and fibrosis[END_REF][START_REF] Wald | Involvement of the CXCL12/CXCR4 pathway in the advanced liver disease that is associated with hepatitis C virus or hepatitis B virus[END_REF]. Furthermore, both CXCL12 and CXCR4 are upregulated in cirrhotic patients infected with hepatitis C virus (HCV) or hepatitis B virus (HBV), demonstrating the fundamental role of this axis in chronic injury [START_REF] Wald | Involvement of the CXCL12/CXCR4 pathway in the advanced liver disease that is associated with hepatitis C virus or hepatitis B virus[END_REF]. CXCL11 and CXCL12 share a more recently discovered common receptor, CXCR7, which is involved in cell survival, cell adhesion, and tumor development [START_REF] Burns | A novel chemokine receptor for SDF-1 and I-TAC involved in cell survival, cell adhesion, and tumor development[END_REF]. CXCL14 (BRAK) was initially identified from breast and kidney and is constitutively expressed in normal tissue [START_REF] Hromas | Cloning of BRAK, a novel divergent CXC chemokine preferentially expressed in normal versus malignant cells[END_REF]. No direct receptor has been identified for CXCL14 and it has been reported to interact with CXCR4, but with no modulatory effect [START_REF] Otte | CXCL14 is no direct modulator of CXCR4[END_REF], without interfering with the CXCL12-CXCR4 signaling axis [START_REF] Hara | CXCL14 antagonizes the CXCL12-CXCR4 signaling axis[END_REF]. Conversely, a recent study showed that the high-affinity interaction of CXCL14 with CXCR4 acts synergistically with CXCL12 in chemotaxis [START_REF] Collins | Epithelial chemokine CXCL14 synergizes with CXCL12 via allosteric modulation of CXCR4[END_REF]. Very little concerning CXCL14 in liver pathogenesis has been reported, although it has been shown to have antitumor activity in hepatocellular carcinoma (HCC) [START_REF] Wang | Antitumor efficacy of C-X-C motif chemokine ligand 14 in hepatocellular carcinoma in vitro and in vivo[END_REF].

Little is known concerning the role of CXCL10, CXCL11, CXCL12, and CXCL14 in human ALI, although their expression has been reported in many in vivo animal models of ALI and they have been shown to play important roles in human chronic liver diseases [START_REF] Basset | Interleukin-27 and IFNgamma regulate the expression of CXCL9, CXCL10, and CXCL11 in hepatitis[END_REF][START_REF] Erhardt | CXCR3 deficiency exacerbates liver disease and abrogates tolerance in a mouse model of immune-mediated hepatitis[END_REF][START_REF] Yoneyama | Neutralization of CXCL10 accelerates liver regeneration in carbon tetrachloride-induced acute liver injury[END_REF][START_REF] Deleve | VEGF-sdf1 recruitment of CXCR7+ bone marrow progenitors of liver sinusoidal endothelial cells promotes rat liver regeneration[END_REF][START_REF] Saiman | Inhibition of the CXCL12/CXCR4 chemokine axis with AMD3100, a CXCR4 small molecule inhibitor, worsens murine hepatic injury[END_REF][START_REF] Wilson | CXC chemokine receptor-4 signaling limits hepatocyte proliferation after hepatic ischemia-reperfusion in mice[END_REF][START_REF] Li | Neutralization of chemokine CXCL14 (BRAK) expression reduces CCl4 induced liver injury and steatosis in mice[END_REF]. Here we aimed to provide evidence for the presence of these circulating chemokines during human ALI and to identify distinct chemokine expression patterns in ALI patients with various etiologies to propose new biomarkers.

Materials and methods

ALI patients and healthy donors

The study protocol was approved by the ethics committee of the Pontchaillou University Hospital and written informed consent was obtained from participants. Inclusion criteria were any ALI with ALT activity > 129 UI/L (equivalent to three times the upper normal limit, considered in this study to be 43 UI/L). Patients with acute-on-chronic liver injury were excluded or if another source of ALT activity was suspected (rhabdomyolysis, myocardial infarction, etc.). None of the patients had HCC. A control group consisting of 36 healthy volunteers from the French Blood Institute, who were negative for HBV, HCV, and Immunodeficiency Virus (HIV), and had normal ALT activity (< 43UI/L), were also studied.

The principal patient characteristics and those of the healthy controls (age, sex, and transaminase activity) are given in Table 1. ALI patients were divided into eight groups, depending on the cause of ALI (Table 2). In this cohort, ALI was caused by direct [acute viral infection, autoimmune disease, toxic injury (including drugs, mushrooms, or alcohol intoxication), and ischemia-reperfusion injury groups] or indirect mechanisms. Indirect injuries are collateral effects caused by vascular problems leading to blood stasis and/or liver ischemia (congestive hepatopathy and vascular and obstetrical groups), as well as bile fluid accumulation, due to obstruction of the bile duct (biliary group). Blood stasis, liver ischemia, and bile fluid accumulation generate tissue damage leading to ALI.

Chemokine detection

Serum was obtained at the time of entry into the study in separator tubes, centrifuged at 2000g at 4°C for 10 min, aliquoted, and stored at -80C° until further analysis. Serum concentrations of chemokines were measured using enzyme-linked immunosorbent assay (ELISA) kits (R&D Systems, Wiesbaden, Germany for CXCL10 and CXCL14 and Peprotech, Rocky Hill, USA for CXCL11 and CXCL12), according to the manufacturers' instructions. The lower detection limits were 31.2 pg/ml for CXCL10, 32 pg/ml for CXCL11, 63 pg/ml for CXCL12 and 62.5 for CXCL14.

Statistical analysis

Data are shown graphically as box-and-whisker plots. The box-and-whisker plots display a statistical summary of the median, quartiles, inter-quartile range and extreme values. The degree of association between two variables was assessed by Spearman's parametric test rank correlation. Comparisons of parameters between two groups were analyzed by the Mann-Whitney u-test with α = 0.05. All statistical analyses were performed using GRAPHPAD PRISM (GraphPad software Inc., La Jolla, CA, USA).

Results

Serum concentrations of CXCL10, CXCL11, and CXCL12, but not CXCL14, are higher in ALI patients than healthy donors.

We first compared the serum concentrations of the four chemokines in ALI patients to those of the healthy donors. The mean age and gender ratio between healthy donors and ALI patients were not statistically different (Table 1). Serum CXCL14 level was not significantly higher in ALI patients than healthy controls (Figure 1). On the contrary, the serum concentrations of CXCL10, CXCL11, and CXCL12 were significantly higher in the ALI cohort than healthy donors (p < 0.0001 for CXCL10, CXCL11 and CXCL12) (Figure 1).

CXCL10, CXCL11, CXCL12, and CXCL14 serum concentrations show distinct profiles among the various groups of ALI patients.

We next compared the serum concentration of the four chemokines of the various ALI patient groups with those of the healthy donor subjects (Table 3). The level of CXCL10 was highly significantly elevated (p < 0.001) in all groups of ALI patients compared to healthy donors except for auto-immune group which showed lower significance (p = 0.0168). The level of CXCL11 was highly significantly elevated for ischemia-reperfusion post liver graft, toxic, congestive hepatopathy, acute viral infection and obstetrical groups. Other etiologies showed either moderate (p = 0.0013 for the auto-immune group), slight (p = 0.0101 for the vascular group), or non-significant (biliary group) elevation of CXCL11 levels. Regarding CXCL12, the level was highly significantly elevated for all groups except for auto-immune, obstetrical and biliary groups which showed a moderate significance (p = 0.0018, p = 0.0018 and p = 0.0060 respectively). The level of CXCL14 was highly significantly elevated for the acute viral infection and vascular groups only. Other groups didn't show any significantly levels compared to healthy donors. Data from auto-immune, vascular and obstetrical groups should be taken cautiously due to the low number of patients in each group. Furthermore, we found no correlation between the extent of necrosis (measured by ALT and AST activity) and circulating chemokine levels.

CXCL10, CXCL11 and CXCL12 chemokines correlate each other's in the different groups of patients.

We first evaluated all correlations among the upregulated chemokines in all patients (Table 4). The best correlation was found for CXCL10 and CXCL11, which was moderate (r = 0.4852), but highly significant (p < 0.0001). However, CXCL10 and CXCL12 (r = 0.2213; p = 0.0045), but also CXCL11 and CXCL12 (r = 0.3369; p < 0.0001) were also correlated.

Then we looked for any correlation of these 3 chemokines among each group of ALI patients (Table 4). The results showed that the highest and most significant correlation was found for CXCL10 and CXCL11 in acute viral infection group (r = 0.6905; p < 0.0006). Other groups showed lower degree of correlation or even no significant correlation (Table 4).

Discussion

Our results show that CXCL10, CXCL11, CXCL12, and CXCL14 levels are elevated in the blood of ALI patients, consistent with results obtained in animal models. Indeed, in vivo studies have shown that CXCR3 ligands are upregulated in mouse models of ALI using Concanavalin A [START_REF] Basset | Interleukin-27 and IFNgamma regulate the expression of CXCL9, CXCL10, and CXCL11 in hepatitis[END_REF][START_REF] Erhardt | CXCR3 deficiency exacerbates liver disease and abrogates tolerance in a mouse model of immune-mediated hepatitis[END_REF]. Furthermore, the serum CXCL10 concentration rises after a single administration of CCl4, reaching a peak at day 1, and neutralization of CXCL10 using a monoclonal anti-CXCL10 antibody promotes hepatocyte proliferation from an early phase [START_REF] Yoneyama | Neutralization of CXCL10 accelerates liver regeneration in carbon tetrachloride-induced acute liver injury[END_REF]. In our cohort, circulating CXCL11 was elevated for all etiologies except for biliary group. CXCL10 was highly or moderately elevated in all groups, and to a less extent in the auto-immune group. The serum levels of CXCL10 and CXCL11, which are interferon-ϒ inducible chemokines correlated with each other in the ALI cohort and with the highest degree in the acute viral infection group compared to the other upregulated chemokines. The level of CXCL10 was recently shown to be elevated in the blood of ALI patients infected by hepatitis A virus, consistent with our results [START_REF] Sung | CXCL10 is produced in hepatitis A virus-infected cells in an IRF3dependent but IFN-independent manner[END_REF].

The CXCL12/CXCR4 axis was shown to be indispensable for liver regeneration in a partial hepatectomy mouse model [START_REF] Deleve | VEGF-sdf1 recruitment of CXCR7+ bone marrow progenitors of liver sinusoidal endothelial cells promotes rat liver regeneration[END_REF]. Injection of AMD3100, a specific inhibitor of CXCR4, worsened ALI after CCl4 injection, providing additional clues to the hepatoprotective role of this axis through the recruitment of MSC from bone marrow to the injured liver [START_REF] Saiman | Inhibition of the CXCL12/CXCR4 chemokine axis with AMD3100, a CXCR4 small molecule inhibitor, worsens murine hepatic injury[END_REF]. In contrast, injection of AMD3100 resulted in increased hepatocyte proliferation and reduced necrosis after reperfusion in an ischemia-reperfusion injury mouse model, suggesting a specific mechanism following oxygen deprivation [START_REF] Wilson | CXC chemokine receptor-4 signaling limits hepatocyte proliferation after hepatic ischemia-reperfusion in mice[END_REF]. Serum CXCL12 levels were higher for every etiology in our cohort, consolidating the idea that CXCL12 is a major liver homeostatic chemokine. In addition, ALI patients of the obstetrical group, of whom six of seven suffered from preeclampsia, had elevated levels of circulating CXCL12, consistent with recent findings [START_REF] Karakus | SDF-1/CXCL12 and CXCR4 gene variants, and elevated serum SDF-1 levels are associated with preeclampsia[END_REF].

Circulating CXCL14 levels have been shown to be higher in a CCl4 ALI mouse model. Neutralization of CXCL14 using monoclonal antibodies reduced the severity of liver injury and steatosis with reduced mouse mortality [START_REF] Li | Neutralization of chemokine CXCL14 (BRAK) expression reduces CCl4 induced liver injury and steatosis in mice[END_REF]. Surprisingly, CXCL14 was the least represented chemokine in our ALI cohort. However, we detected elevated levels in the acute viral infection and vascular groups, suggesting a specific mechanism for its release following these injuries. ALI patients belonging to the congestive hepatopathy and obstetrical groups suffer from indirect ALI, caused by heart failure in one and thrombosis in the other. Other organs may also be affected, and we cannot exclude that this may have been a source of the studied chemokines. Patients of the ischemia-reperfusion injury group were taking anti-rejection medication when samples were collected. It is likely that these drugs reduced the concentration of these circulating chemokines, as they are used to reduce the immune response and inflammation to avoid graft rejection. Besides, results obtained from obstetrical, vascular and auto-immune groups should be interpreted cautiously due to the low number of patients in each group.

In addition, blood was collected at different moments of the ALI for each patient. Indeed, some samples may have been collected at the most acute stage of the liver injury, whereas others may have been collected at a more distant time. This element must be considered in the interpretation of the results, because the release dynamics of these chemokines is unknown for the various etiologies studied, and the levels of the studied chemokines did not correlate with transaminase activity.

This study provides new data on circulating inflammation mediators during ALI, given the important roles of CXCL10, CXCL11, and CXCL12 mediated by their receptors (CXCR3 for CXCL10 and CXCL11, CXCR4 for CXCL12, and CXCR7 for CXCL12) in chronic liver injury shown by in vitro and animal model studies. It also adds new knowledge on CXCL14, as its role following interaction with CXCR4 is not yet well established. Additional data on lymphocyte or monocyte phenotypes during ALI could provide new complementary evidence concerning the inflammation processes that occur during these disorders.

In conclusion, these results provide the first evidence that circulating CXCL10, CXCL11, CXCL12, and CXCL14 are found in ALI patients in a large cohort of patients reflecting many types of disorders. We observed distinct expression patterns depending on the etiologies, suggesting the potential use of these chemokines as ALI biomarkers.

Figure 1 .

 1 Figure 1. Box-and-whisker plots illustrating serum chemokine levels in patients (n=163) and healthy donors (n=36). The box extends from the 25 th to 75 th percentile. The line in the middle

Figure 1 .

 1 Figure 1. Box-and-whisker plots illustrating serum chemokine levels in patients (n=163) and healthy donors (n=36).The box extends from the 25 th to 75 th percentile. The line in the middle denote the median value, and the lines extending from either end of each box denote the extent of the data beyond the 25 th and 75 th percentiles and outliers, if any. (A-C) ELISA for CXCL10, CXCL11, CXCL12 and CXCL14 respectively. Concentrations are given in pg/ml. (*** p<0,001 ; statistical significance was determined by Mann-Whitney u-test) A B

Table 1 .

 1 ALI patients/healthy donors main characteristics and ALT/AST activity.

		Healthy donors	ALI patients
	Case (n)	36	163
	Sex (male/female) (n)	21/15	113/50
	Age (year)	48	53
		(33-65)	(13-99)
	ALT (IU/L)	22,2	1205,2
		(12-42)	(134,0-8587,0)
	AST (IU/L)	25,3	1215,4
		(16,0-40,0)	(32,0-11369,0)

Table 2 .

 2 Description of the etiologies. ALI patients were divided into 8 groups

	of different injuries : auto-immune (n=6), vascular (n=7), obstetrical (n=7),
	Biliary (n=19), acute viral infection (n=22), Congestive hepatopathy (n=23),
	Toxic (n=27) and ischemia reperfusion post liver graft (n=52).
	Auto-immune (n=6)
	Auto-immune hepatitis (n=5)
	Auto-immune cholangitis (n=1)
	Vascular (n=7)
	Liver ischemia (n=4)
	Portal veins thrombosis (n=2)
	Liver veno-oclusive disease (n=1)
	Obstetrical (n=7)
	Preeclampsia (n=6)
	Cholestasis of pregnancy (n=1)
	Biliary (n=19)
	Malignant biliary obstruction (n=6)
	Angiocholitis (n=4)
	Acute biliary pancreatitis (n=4)
	Biliary lithiasis migration (n=3)
	Cholecystitis (n=2)
	Acute viral infection (n=22)
	Hepatitis A (n=8)
	Hepatitis E (n=7)
	Hepatitis B (n=5)
	Herpes simplex virus 2 (n=2)
	Congestive hepatopathy (n=23)
	Heart failure (n=11)
	Cardiogenic shock (n=4)
	Pericardial tamponade (n=4)
	Dilated cardiomyopathy (n=3)
	Valvular cardiomyopathy (n=1)
	Toxic (n=27)
	Drug intoxication (other than acetaminophen) (n=9)
	AAH (Acute Alcoholic Hepatitis) (n=7)
	Acetaminophen intoxication (n=5)
	Mushrooms intoxication (n=3)
	AAH + acetaminophen intoxication (n=2)
	Drugs intoxication + AAH (n=1)
	Ischemia reperfusion post liver graft (n=52)

Table 3 .

 3 Serum chemokine levels among the different groups of patients and healthy donors. Median and range (in parenthesis) for CXCL10, CXCL11, CXCL12 and CXCL14 are given. Statistical analysis of each group and for each chemokine compared to healthy donors (p-value of u-test are given).

			Concentrations (pg/ml)
		n	CXCL10 P-value CXCL11 P-value CXCL12 P-value CXCL14 P-value
	Healthy donors	36	32,6	-	71,6	-	351,5		0,0	-
			(0,0-		(0,0-		(0,0-	-	(0,0-
			630,9)		411,6)		1207,0)		651,0)
	Auto-immune	6	200,0	0,0168 559,3	0,0013 1523,0		0,0	NS
			(20,9-		(75,2-		(550,6-	0,0018	(0,0-
			1598,0)		1327,0)		3426,0)		186,3)
	Vascular	7	220,2 (50,8-	0,0006 403,1 (0,0-	0,0101 1352,0 (606,2-	<0,0001	(0,0-650,5	0,0009
			2316)		627,3)		6416,0)		902,5)
	Obstetrical	7	194,4	<0,0001 617,9	<0,0001 1000,0		0,0	NS
			(98,8-		(365,0-		(603,8-	0,0018	(0,0-
			297,6)		1096,0)		4122,0)		259,9)
	Biliary	19	76,5	0,0002 147,5	NS	760,4		0,0	NS
			(21,7-		(0,0-		(0,0-	0,0060	(0,0-
			342,1)		696,5)		2022,0)		460,3)
	Acute viral infection 22	162,9 (42,0-	<0,0001 675,4 (0,0-	<0,0001 1257,0 (164,0-	<0,0001	(0,0-386,8	0,0005
			1211,0)		1856,0)		3234,0)		1030,0)
	Congestive hepatopathy	23	66,1 (35,5-	<0,0001 210,8 (32,5-	0,0001 1524,0 (463,9-	<0,0001	(0,0-0,0	NS
			638,1)		829,4)		7034,0)		2584,0)
	Toxic	27	103,6 (0,0-	0,0002 226,9 (0,0-	0,0004 1832,0 (384,0-	<0,0001	(0,0-0,0	NS
			1552,0)		1549,0)		7679,0)		2647,0)
	Ischemia reperfusion post liver graft	52	121,3 (0,0-	<0,0001 324,3 (0,0-	<0,0001 1260,0 (0,0-	<0,0001	(0,0-0,0	NS
			1844,0)		1604,0)		9018,0)		183,2)

Table 4 .

 4 Correlation of CXCL10, CXCL11 and CXCL12 chemokines among the different groups of patients. Spearman's rank correlation test. Only significant results are shown. (r, correlation coefficient, p, p-value)

Acknowledgements

We thank Inserm, The "Ministère de l'Education Nationale de la Recherche et de la Technologie", the University of Rennes 1, the "Région Bretagne" and ANRT for their financial support.

Statement of financial support:

This work was supported by INSERM, The "Ministère de l'Education Nationale de la Recherche et de la Technologie", the University of Rennes 1 and the "Région Bretagne". AC was supported by a PhD fellowship from ANRT-CIFRE fellowship with NG Biotech. CD was supported by a PhD fellowship from the "Région Bretagne" and Inserm.


Giovanni Bocci

Amélie Moreau

Philippe Vayer

Claire Denizot

Olivier Fardel

Yannick Parmentier

New insights in the in vitro characterisation and molecular modelling of the Pglycoprotein inhibitory promiscuity

Keywords: ADMET: adsorption, distribution, metabolism, excretion, toxicity, DDIs: drug-drug interactions, NME: new molecular entity, Rh123: rhodamine 123, H33342: Hoechst P-glycoprotein, drug-drug interactions, docking, in vitro, binding site, structure-based

come

INTRODUCTION

The assessment of potential drug-drug interactions (DDIs) is a crucial step in the development of any new molecular entity (NME) in current drug discovery [START_REF] Rodrigues | Drug-drug interactions[END_REF]. Clinically significant DDIs are mainly mediated by pharmacokinetic (PK) mechanisms with alterations in the adsorption, distribution, metabolism and excretion (ADME)

A C C E P T E D

M A N U S C R I P T 2 profile of a drug caused by a co-administered drug. DDIs risk is monitored at the very beginning of the NMEs development process because it could reduce both the efficacy and the safety of drugs. In the most unfortunate cases, emerging of adverse drug reactions, due to DDIs, ends up with the NME failure or its withdrawal from the market [START_REF] Onakpoya | Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: a systematic review of the world literature[END_REF][START_REF] Schuster | Why drugs fail-a study on side effects in new chemical entities[END_REF]Zhang et al., 2009). Thus, considering the cost for a NME just to come up with its failure, early detection of DDIs is a major concern.

The safety impairment of a drug often involves the inhibition of several anti-target proteins such as metabolism enzymes and transporters (Varma et al., 2015). Among these, two of the most studied are the cytochrome P450 3A4 (CYP3A4) and the P-glycoprotein (P-gp). CYP3A4 is the main enzyme involved in the metabolism of drugs and presents several sub-sites for both metabolism and inhibition [START_REF] Kapelyukh | Multiple substrate binding by cytochrome P450 3A4: estimation of the number of bound substrate molecules[END_REF][START_REF] Kenworthy | CYP3A4 drug interactions: correlation of 10 in vitro probe substrates[END_REF][START_REF] Obach | The utility of in vitro cytochrome P450 inhibition data in the prediction of drug-drug interactions[END_REF].

P-gp, also known as ATP-binding cassette sub-family B member 1 (ABCB1) and encoded by the multidrug resistance gene 1 (MDR1), is a membrane efflux transporter belonging to the ATP binding cassette (ABC) transporter subfamily, which is notably expressed at the intestine, liver, kidney and blood-brain barrier level. P-gp drives the elimination of several structurally unrelated xenobiotics from cells through an ATP dependent mechanism and its inhibition is directly linked to DDIs [START_REF] Lin | Drug-drug interaction mediated by inhibition and induction of P-glycoprotein[END_REF]. The importance of ABC proteins such as P-gp in regulating drug adsorption, distribution, metabolism, excretion and toxicity (ADMET) has been extensively reviewed [START_REF] Giacomini | Transporters in drug development and clinical pharmacology[END_REF]Szakacs et al., 2008), as well as the efforts done to predict P-gp-ligand interactions [START_REF] Chen | Computational models for predicting substrates or inhibitors of Pglycoprotein[END_REF][START_REF] Montanari | Prediction of drug-ABC-transporter interaction-Recent advances and future challenges[END_REF].

While P-gp role and function is well known, its behaviour is yet only little understood. P-gp promiscuity in recognizing molecules is due to its large and flexible transport site where its amino acids define a broad and dynamic range of weak interactions. In particular, the high volume of the pocket facilitates the binding of different ligands in different regions (sub-pockets) which may differ for both spatial location and network of interactions. [START_REF] Martin | Communication between multiple drug binding sites on P-glycoprotein[END_REF] An exact mapping of interacting regions is not available because the human P-gp crystallographic structure has not been solved yet. Consequently, elucidating structure-activity relationships and predicting ligands interaction with P-gp are not trivial tasks.

Given the need to anticipate as soon as possible the DDIs risk, in vitro P-gp inhibition experiments are routinely carried out for new pharmacologically interesting drugs. However, these tests are extremely sensitive to the experimental conditions (e.g. the nature of test system, consisting in cell line or vesicles, the nature of the probe substrate used, etc.) (https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM581965.pdf; [START_REF] Bentz | Variability in P-glycoprotein inhibitory potency (IC50) using various in vitro experimental systems: implications for universal digoxin DDI risk assessment decision criteria[END_REF][START_REF] Rautio | In vitro p-glycoprotein inhibition assays for assessment of clinical drug interaction potential of new drug candidates: a recommendation for probe substrates[END_REF]Zolnerciks et al., 2011). Thus, close experimental protocols may lead to inconsistent results. The importance of the probe used was emphasised by the observation of different inhibitory behaviour emerging from in vitro P-gp inhibition experiments when changing the probe substrate used. In a recent work, an alternative assay for the P-gp IC50 determination has been proposed [START_REF] Jouan | Evaluation of P-glycoprotein inhibitory potential using a rhodamine 123 accumulation assay[END_REF]. The authors evaluated rhodamine 123 (Rh123) to

A C C E P T E D M A N U S C R I P T

3 replace the more problematic digoxin and compared the results obtained with both the probes. For some molecules, while the inhibition was detectable with digoxin, no inhibition was observed with Rh123. These molecules were presumed to not interact with the Rh123 binding site (R-site) of P-gp.

In order to go deeper in understanding this P-gp promiscuity, we tested a third probe substrate: Hoechst 33342 (H33342). This choice was made for two main reasons. First, because it has quite different molecular features such as lipophilicity, size and aromaticity (i.e. different experimental conditions are required for the assay) compared with digoxin and Rh123; hence, it could be used as an alternative probe substrate in P-gp inhibition experiments. Second, because it has been reported to bind on a different site (H-site) [START_REF] Martin | Communication between multiple drug binding sites on P-glycoprotein[END_REF][START_REF] Shapiro | Positively cooperative sites for drug transport by P-glycoprotein with distinct drug specificities[END_REF]. Thus, we suspected it could give rise to a third different inhibitory behaviour respect to Rh123 and digoxin.

Therefore, the safety of these "substrate-dependent" inhibitors may not be fully unravelled even at late stages of drug development. Literature is quite ambiguous when dealing with P-gp substrate-dependent inhibition. Some efforts have been done to get new insights, in particular by agencies such as FDA not to propose standard protocols, but encourage to calibrate our own model (https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM581965.pd f). To face this issue, one can either increase the number of tests including all the probes (exhaustive approach) or use a specific probe, selected from strong evidences and knowledge, to answer a specific question (adaptive knowledge based approach). In this context, molecular modelling tools can be applied and integrated with well-designed experimental data.

This could also help to complete missing or ambiguous literature data.

Starting from our experimental data obtained with digoxin, Rh123 and H33342 as probe substrates, we developed a new structure-based modelling strategy to predict the substrate-dependent inhibition of P-gp. We used the knowledge concerning structural features of human P-gp to develop a structure-based model defined by three transport sub-sites within the main binding pocket. Successively, we rationalized the P-gp ligands behaviour by assuming a competitive inhibition mechanism within the main pocket itself. The pharmacophoric methodology fingerprints for ligands and proteins (FLAP) [START_REF] Baroni | A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application[END_REF] was employed to conduct all the modelling strategy. The model has been validated with a separate set of experiments and can be further used in routine to improve experimental efficiency by anticipating P-gp inhibition and DDIs depending on the substrate used. We can assert that our study is the first computational approach which, in synergy with experimental testing, gives an exhaustive picture of substrate-dependent P-gp inhibition issue.

MATERIALS AND METHODS

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Dataset. The starting set comprises 30 compounds and 69 experimental inhibition records (30 for digoxin, 24 for H33342 and 15 for Rh123). The model validation set comprises 14 compounds of which eight were already tested with two substrates (starting set) and six for which no record was available for any substrate yet. The complete set of molecules, with related SMILES, can be found in Supporting Information 02 (as an excel file). For both starting set and validation set, experimental data about P-gp inhibition with respect to transport of digoxin, H33342 and Rh123 were retrieved from the scientific literature or were generated in the present study using appropriate functional assays (See below) when required, i.e., when no experimental data has been previously reported.

P-gp inhibition assays. Effects of compounds on P-gp-mediated transport was assessed using accumulation (digoxin or Rh123) or efflux (H33342) assays, in P-gp overexpressing MCF7R cells [START_REF] Jouan | Evaluation of P-glycoprotein inhibitory potential using a rhodamine 123 accumulation assay[END_REF]. These transport assays were performed in a well-defined transport medium consisting of 136 mM NaCl, 5.3 mM KCl, 1.1 mM KH2PO4, 0.8 mM MgSO4, 1.8 mM CaCl2, 11 mM D-glucose and 10 mM Hepes and adjusted to pH 7.4, as previously described [START_REF] Chedik | Inhibition of human drug transporter activities by the pyrethroid pesticides allethrin and tetramethrin[END_REF]. For accumulation assays, MCF7R cells were incubated with 5.25 µM rhodamine 123 (Sigma-Aldrich, Saint-Quentin Fallavier, France) or 12.5 nM [ 3 H]-digoxin (specific activity = 39.8 Ci/mmol) (PerkinElmer, Villebon sur Yvette, France) for 30 min at 37°C, in the absence (control) or presence of chemicals to be tested (a 100 µM concentration was used for each compound, excepted for terfenadine for which 10 µM was retained for solubility consideration) or of 100 µM verapamil, used here as a reference P-gp inhibitor. After washing with cold phosphate-buffered saline (PBS), intracellular accumulations of Rh123 and digoxin were measured by spectrofluorimetry and scintillation counting, respectively. Data were finally expressed as percentages of P-gp substrate accumulation found in control cells. For efflux assays, MCF7R cells were first loaded with 16.2 µM H33342 (Sigma-Aldrich) for 30 min at 37°C. After washing with cold PBS, MCF7R cells were re-incubated in H33342-free medium, in the absence (control) or presence of compounds to be tested or of verapamil for 90 min at 37°C. Intracellular retention of H33342 was finally determined by spectrofluorimetry (excitation and emission wavelengths were 355 and 460 nm, respectively). Data were expressed as percentages of initial loading of the dye. Accumulation and efflux data were statistically analysed through analysis of variance (ANOVA) followed by the Dunnett's post hoc test. The level of significance was p<0.05. Compounds which significantly modulate accumulation (digoxin, Rh123) or retention (H33342) of a P-gp substrate were considered as inhibitors of P-gp-mediated efflux of the substrate.

Modelling. The structure-based modelling strategy applied in this work is based on the pharmacophoric method fingerprints for ligands and proteins (FLAP) (http://www.moldiscovery.com/; [START_REF] Baroni | A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application[END_REF]. FLAP is a computational tool, which has been applied in virtual screening [START_REF] Brincat | Discovery of Novel Inhibitors of the NorA Multidrug Transporter of Staphylococcus aureus[END_REF][START_REF] Cross | FLAP: GRID Molecular Interaction Fields in Virtual Screening. Validation using the DUD Data Set[END_REF][START_REF] Goracci | A Rational Approach for the Identification of Non-Hydroxamate HDAC6-Selective Inhibitors[END_REF],

A C C E P T E D M A N U S C R I P T 5
2016), but that can also be used for developing pharmacophore-based models. In this study, FLAP was used to prepare ligands structures, to define the target pocket, to perform docking and to classify ligands after the docking. Fig. 1 shows the flowchart of these main steps. The docking algorithm implemented in FLAP allows to explore the potential binding of ligands with a target protein. This is done by an accurate comparison between molecular interaction fields (MIFs) [START_REF] Goodford | A computational procedure for determining energetically favorable binding sites on biologically important macromolecules[END_REF] of ligands with the MIFs of a pocket previously identified.

Chemical structures, for both P-gp ligands and substrates, were retrieved as SMILES from PubChem (https://pubchem.ncbi.nlm.nih.gov/). The FLAPdb module was used to store molecules into a database by generating their canonical 3D structures. MoKa [START_REF] Milletti | New and Original pKa Prediction Method Using Grid Molecular Interaction Fields[END_REF] was used to generate tautomeric and charged forms whose abundance was of at least 20% at pH 7.4. If the stereochemistry was not explicit in the SMILES (e.g. verapamil, disopyramide, etc.), all the possible stereoisomers were generated. Thus, for the same molecule, different structural forms could be generated. In those cases, all of them were considered, because no information was available to distinguish which form or stereoisomer was more likely to interact with P-gp. Hence, they are assumed to have the same chance to do it. On the other hand, not considering all the possible structural forms is a hazard that could significantly decrease the accuracy of pharmacophoric models.

The mouse P-gp crystallographic 3D structure was downloaded from Protein Data Bank (PDB code: 4M1M) [START_REF] Li | Refined structures of mouse P-glycoprotein[END_REF] and imported into FLAP. The FLAPsite module was used to compute the main binding pocket by setting the "search for pockets" parameter "sensitivity" equal to 9; all the other parameters were kept by default.

Successively, the FLAPdock module was used to perform the docking. Except for "pocket radius" (which was set to 1.5) and "scale van der Walls external" (which was set to 0.80), the docking parameters were kept by default. In order to identify their binding sub-pockets, probe substrates were docked first. Next, P-gp ligands were docked. Only the best poses were selected (those having the highest S-Score approximated to the first decimal place) for the final classification. If the same molecule was present in more structural forms, best poses were selected for each form. This applies to both substrates and ligands. Concerning substrates, three poses were selected for digoxin, only one for H33342

and four for Rh123. Their location allowed to identify the relative binding sites: D-site, H-site and R-site.

In order to retrieve the classification, the FLAPvs module was used to perform a static cross screening (without alignment) between the selected poses of the three substrates and the selected poses of ligands. This operation was designed to translate the relative substrate-ligand binding position (generated by the docking) into a number that depicts the competition between ligands and substrates for specific sub-pockets. This number equals to the MIFs similarity score Glob-Sum (GS). Hence, for the same ligand, three GS scores are computed: one for digoxin (DGS), one for H33342

(HGS) and one for Rh123 (RGS). Finally, a global GS threshold was set to distinguish substrate-specific inhibitors and non-inhibitors. Ligands having a GS equal to zero are predicted non-inhibitors. Whereas, ligands having a GS major of

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zero are predicted inhibitors. In most of the cases, more than one ligand pose was selected for the final classification. If that was happening, only the highest GS among the poses was used. It is true that, since all selected poses have the same importance (i.e. the same probability to exist), it would be more correct to average all GS scores. However, in that way we obtained worst results when reproducing the experimental data. The same principle was also used to condense the prediction over probe substrates poses; and it was applied to condense classifications of different structural forms (stereoisomers, tautomers and protomers) for the same ligand. The classification principle just explained was applied to all the P-gp ligands studied in this work.

The pocket dimensions define the applicability domain of our model. Consequently, all the molecules that could fit in the pocket can be predicted. On the other hand, those molecules that are too big to fit in cannot be predicted.

RESULTS

Determination of P-gp-mediated transport inhibition. Inhibitory potential of various compounds towards P-gp mediated transport of digoxin, H33342 and/or Rh123, were collected from the scientific literature when data are available (Supporting information 02). For compounds for which data about P-gp inhibition are lacking, their effects on P-gpmediated transport of the P-gp substrates were experimentally studied in P-gp-overexpressing human MCF7R cells. As indicated in Fig. 2, prazosin, reserpine and loperamide, like the reference P-gp inhibitor verapamil, significantly enhanced digoxin accumulation in MCF7R cells, thus providing evidence that they inhibit P-gp-mediated transport of digoxin. By contrast, terfenadine did not alter cellular level of digoxin (Fig. 2), thus indicating it has no inhibitory effect towards Pgp-related digoxin efflux. With respect to H33342, carvedilol, mibefradil, loperamide, reserpine, prazosin and terfenadine, unlike itroconazole, enhanced its retention in MCF7R cells (Fig. 2), indicating that they block P-gp-mediated efflux of H33342. For Rh123 accumulation, cisapride zafirkulast, amitriptyline, loperamide, reserpine and terfenadine significantly increased it (Fig. 2), indicating that these compounds inhibit P-gp-mediated transport of Rh123. By contrast, disopyramide, midazolam and prazosin failed to significantly enhance Rh123 accumulation (Fig. 2), thus ruling out any inhibitory effect toward P-gp-related efflux of the dye.

Transport sub-sites detection. In order to identify the location of substrates binding sites, we computed the main binding pocket from the original 3D structure (PDB code: 4M1M) [START_REF] Li | Refined structures of mouse P-glycoprotein[END_REF]. Successively, we docked the three probe substrates into the main pocket. We used a cut-off on the ligand-pocket similarity ranking score (S-Score) to define which and how many poses to consider. Hence, substrates binding sites were defined on their top-ranked (higher probability) binding poses (see Fig. 3A). Three top-ranked binding poses of digoxin define its binding site (D-site, Fig. 3B), one top-

A C C E P T E D M A N U S C R I P T 7
ranked binding pose of H33342 defines alone its binding site (H-site, Fig. 3D) and four top-ranked binding poses of Rh123 define its binding site (R-site, Fig. 3C).

Docking of P-gp substrate-dependent inhibitors. From starting set of inhibition experiments, some compounds were found to inhibit the transport of some substrates but not of others. Such behaviour was detected for zafirlukast, disopyramide, lansoprazole, itraconazole and sertraline. These compounds were further inspected with docking.

P-gp experimental inhibition by zafirlukast, a compound known to interact with P-gp [START_REF] Sun | Zafirlukast antagonizes ATP-binding cassette subfamily G member 2-mediated multidrug resistance[END_REF], was measured with digoxin and H33342. It was found to inhibit digoxin transport, whereas no substantial inhibition was seen for H33342 up to the highest concentration tested. No information concerning Rh123 inhibition was available at the time in the literature. In Fig. 4A, the top-ranked docking poses for zafirlukast are reported. As shown, since its binding takes place in the digoxin binding region and far from the H33342 binding region, it is predicted to inhibit digoxin and to not inhibit H33342. Similarly, it is also predicted to inhibit Rh123 efflux.

Lansoprazole, another P-gp inhibitor [START_REF] Pauli-Magnus | Interaction of omeprazole, lansoprazole and pantoprazole with P-glycoprotein[END_REF], was tested in vitro with a different combination of substrates: digoxin and Rh123; it was found to inhibit the P-gp mediated transport of the first one and not of the second one (Table 1). In Fig. 4B, the top-ranked poses for lansoprazole are reported. In accordance with experiments, it is predicted to inhibit both digoxin and H33342, whereas no inhibition should be expected with Rh123.

Itraconazole was tested with digoxin and Rh123 and, as for lansoprazole, it resulted to inhibit digoxin, but not Rh123 [START_REF] Jouan | Evaluation of P-glycoprotein inhibitory potential using a rhodamine 123 accumulation assay[END_REF] (Table 1). In Fig. 4C, the top-ranked poses for itraconazole are reported. In this case, there is only a partial agreement between experiments and docking: correctly, itraconazole is predicted to inhibit digoxin transport, but it is also wrongly predicted to inhibit Rh123 transport. Concerning disopyramide and sertraline, we can draw similar conclusions as for zafirlukast and lansoprazole (see Supporting Information 01).

Docking of P-gp substrate-independent inhibitors.

From the experiments, several ligands considered in this study were P-gp inhibitors independently from the probe substrate used. In order to confirm their experimental behaviour, but also to better assess the model reliability of predictions, we docked these compounds as well. In Fig. 5, the examples of elacridar and quinidine, two well-known P-gp inhibitors [START_REF] Rautio | In vitro p-glycoprotein inhibition assays for assessment of clinical drug interaction potential of new drug candidates: a recommendation for probe substrates[END_REF]Weiss et al., 2003), are reported. Although with much different orders of potency, both of them in vitro inhibited all the probe substrates (Table 1). The docking results are in agreement with the experiments. The complete list of compounds docked poses is provided in Supporting Information 01; whereas, the complete starting set of experiments and predictions for both substrate-dependent and substrate-independent inhibitors is reported in Table 1.

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Model validation. To validate the three binding sites model hypothesis, we docked six additional compounds that were known to interact with P-gp: amitriptyline, loperamide, nifedipine, prazosin, reserpine and terfenadine [START_REF] Doan | Passive permeability and P-glycoprotein-mediated efflux differentiate central nervous system (CNS) and non-CNS marketed drugs[END_REF][START_REF] Polli | Rational use of in vitro P-glycoprotein assays in drug discovery[END_REF]. Successively, each of these was tested in vitro with the three substrates. Moreover, we also tested in vitro eight additional compounds for which the information concerning one substrate was missing (e.g., zafirlukast was tested with Rh123; lansoprazole was tested with H33342; etc.). The results are shown in Table 2. These are in accordance with 88% of model predictions.

Integration of different modelling strategies.

It should be noted that our model is a pharmacophoric model, thus based on the modelling of specific interactions, that was not meant to predict globally P-gp inhibition/non-inhibition, but it could be used to refine predictions of other more general strategies. The binding of a molecule to P-gp could not be purely related to specific interactions: in fact, it could be also related to its physiochemical properties which determine if the molecule is reaching or not the binding site. Consequently, we wanted to test the possibility to use our model along with a different methodology to better predict P-gp inhibition. As proposed by Broccatelli and co-workers [START_REF] Broccatelli | A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields[END_REF], physiochemical and pharmacophoric based models could be used in a composite strategy for the study of P-gp inhibition. Their original model was based on a sequence of different "blocks", composed by the molecular description obtained with the software VolSurf+ and FLAP, as well as PLS and LDA as regression/classification methods. In a recent work, we reviewed the PLS part of the model and we derived a new QSAR pharmacokinetic model for P-gp [START_REF] Bocci | ADME-Space: a new tool for medicinal chemists to explore ADME properties[END_REF]. Here we tried to substitute the LDA part with our model. To test the effect of such substitution we used the dataset proposed by Keogh et al. which is composed of 40 marketed compounds whose inhibitory power toward P-gp was tested in vitro with digoxin as substrate [START_REF] Keogh | Development, validation and utility of an in vitro technique for assessment of potential clinical drug-drug interactions involving P-glycoprotein[END_REF]. Based on this strategy, molecules resulting uncertain from the QSAR model are passed to our structure-based model to retrieve predictions. The result of this operation is shown in Fig. 6. What happens is that our model succeeds in classifying most of the compounds that QSAR was not able to classify.

DISCUSSION

P-gp plays a major role in ADMET processes and its implication in determining DDIs still is a main concern.

Consequently, researchers are encouraged to study more effective in vitro protocols and to develop more reliable in silico models. Owing to the lack of standard protocols for in vitro P-gp inhibition experiments, determining high intra/interlaboratory variability of results still remains a problem [START_REF] Hayeshi | Comparison of drug transporter gene expression and functionality in Caco-2 cells from 10 different laboratories[END_REF], and a lot of efforts have been done to optimize these assays [START_REF] Polli | Rational use of in vitro P-glycoprotein assays in drug discovery[END_REF]Szakacs et al., 2008). In particular, the choice of the proper probe substrate is a drastic parameter [START_REF] Rautio | In vitro p-glycoprotein inhibition assays for assessment of clinical drug interaction potential of new drug candidates: a recommendation for probe substrates[END_REF]. Indeed, not only their molecular features, such as permeability, may have a deep impact on the results, but also the binding of the test molecule and the substrate to different sites may produce misleading

A C C E P T E D M A N U S C R I P T 9

results. Assuming only a competitive inhibition mechanism, a molecule binding to a different region respect to the probe substrate do not block the efflux of the latter, but interacts with the protein anyway. Hence, since several substrates binding sites are present for P-gp, using only one probe substrate it could be possible to fully reveal the potential inhibition only if it interacts strongly the same with all the different sub-sites. If a probe is not or less efficient for the inhibition of at least one sub-site, P-gp related DDI would be under estimated.

To illustrate this point, the experimental data reported in the present study (Fig. 2) indicate that terfenadine was able to inhibit P-gp-mediated transport of H33342 and Rh123, but not that of digoxin. In the same way, prazosin significantly blocked P-gp-related efflux of digoxin and H33342, but not that of Rh123. By contrast, other compounds such as reserpine and loperamide counteracted transport of the three P-gp substrates. Overall, the nature of the substrate is therefore crucial for assessing P-gp inhibition potential, as previously suggested [START_REF] Rautio | In vitro p-glycoprotein inhibition assays for assessment of clinical drug interaction potential of new drug candidates: a recommendation for probe substrates[END_REF].

To understand at a molecular level the possible mechanisms beyond these observations, we developed a structure-based model based on the experimental inhibition data obtained. In terms of in silico modelling, the high flexibility and the large size of its active sites make the study of this protein not trivial at all. Furthermore, the absence of a human P-gp structure crystallized, even if it would not solve all the issues, is a big limitation in understanding the behaviour of this protein. Nevertheless, the use of the recent mouse P-gp crystal structure could provide reliable information [START_REF] Li | Refined structures of mouse P-glycoprotein[END_REF]. Hence, we decided to use it and try to give an explanation to the substrate-dependent inhibition detected.

First, we identified three well-distinguished binding regions for digoxin, Rh123 and H33342 and we named them respectively D, R and H sites. In terms of respective location, our results are in accordance with some previous findings.

R and H sites are located in opposite trans membrane alpha-helices domains [START_REF] Lugo | Interaction of LDS-751 with P-glycoprotein and mapping of the location of the R drug binding site[END_REF][START_REF] Pajeva | Protein Contacts and Ligand Binding in the Inward-Facing Model of Human P-Glycoprotein[END_REF][START_REF] Shapiro | Positively cooperative sites for drug transport by P-glycoprotein with distinct drug specificities[END_REF]. Furthermore, the large D-site partially overlaps with the smaller R-site as reported by Rautio et al. [START_REF] Rautio | In vitro p-glycoprotein inhibition assays for assessment of clinical drug interaction potential of new drug candidates: a recommendation for probe substrates[END_REF] However, if we compare the R and H sites with those recently characterized by Ferreira and coworkers [START_REF] Ferreira | Molecular docking characterizes substrate-binding sites and efflux modulation mechanisms within P-glycoprotein[END_REF], substantial differences are emerging. In fact, our H and R sites are located much deeper in the main pocket and their respective positions are inverted from those described by Ferreira et al. These findings may suggest a wrong assignment for R and H sites in the previous study. Nevertheless, the authors postulated that these sites might interchange in one another during the efflux cycle and we could speculate that the crystallographic "snapshot" has registered this event. Furthermore, Li et al. highlighted the residues involved in what they call "drug translocation pathway" deep within the pocket which corroborates our finding [START_REF] Li | Refined structures of mouse P-glycoprotein[END_REF]. Anyway, since none of the tests we did substantially changed the R and H sites locations, we decided to proceed without modifying our pocket assignment. The model validation proved that our decision was correct. Concerning the D-site, although its existence has been explored

previously [START_REF] Ledwitch | Unravelling the complex drug-drug interactions of the cardiovascular drugs, verapamil and digoxin, with P-glycoprotein[END_REF], no molecular description has been provided so far. Our study provides a first

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10 characterization of the digoxin binding site in terms of location respect to the other sites and affinities with several P-gp ligands.

In a second step the docking of P-gp inhibitors was performed. The whole binding pocket previously computed to dock them was used. As previously done for substrates, the same cut-off principle on the S-Score was used to select the best poses for each inhibitor. Successively, by inspecting their location respect to the three substrates binding sites, we expected to unravel their eventual substrate-dependent inhibition. Assuming our model was purely competitive, it did not consider other types of inhibition mechanism such as the non-competitive or acting outside of the active site. In addition, the binding affinity was not taken into account because we saw that none of the docking scores reproduced or had a trend similar to the experimental findings. Hence, the model predictions were based only on the respective location of substrates and inhibitors: if the ligand is located on the same region of the substrate, the inhibition takes place, whereas, if it is located away from the substrate, the inhibition does not occur. In this way, we were able to explain why zafirlukast, lansoprazole, sertraline and disopyramide were not blocking the efflux of all the probe substrates. From the experiments, zafirlukast and disopyramide were inhibiting digoxin efflux but not H33342 efflux. This suggested that the two compounds could compete with digoxin on the D-site, but not with H33342 on the H-site. The docking agreed with this hypothesis (see Fig. 4A). This happens when all the top-ranked binding poses, for both the compounds, are located on the D-site and not on the H-site. Concerning lansoprazole and sertraline, these compounds were inhibiting digoxin but not Rh123 (sertraline inhibited H33342 as well) suggesting a similar interaction mechanism to the previous two compounds. Hence, lansoprazole and sertraline competitively inhibit digoxin, whereas Rh123 inhibition does not occur because none of the compounds binds to the R-site (see Fig. 4B). Fig. 4C depicts the case of itraconazole, which is one of the few cases where the model disagrees with the experiments. Indeed, while the docking was able to reproduce the digoxin inhibition, it fails to reproduce the Rh123 non-inhibition by locating itraconazole poses also on the R-site. It is difficult to conclude if this was an isolate case or a more spread limitation of the model. Probably a different protein conformation is required to locate itraconazole correctly respect to all the substrates binding sites. The model may wrongly predict the binding site of big and quite rigid molecules (also having elongated shape), such as itraconazole.

To confirm the model robustness, we docked several compounds for which no substrate-dependent inhibition was observed. We expected that, for these compounds, the docking would be less selective; hence, it is likely to locate them on more substrates binding sites. In Fig. 5, the docking result for elacridar and quinidine are reported. These compounds differ of several orders of potency for inhibiting P-gp and this could be due to a different binding mechanism.

Indeed, there is a substantial difference in terms of binding. We can see that elacridar poses are filling all the bottom of the active sites, impairing in one shot all the substrates binding sites. On the other hand, quinidine could bind with equal

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probability to all sites. Hence, the results are the same, but substantial differences in the magnitude of inhibition could emerge.

These results are in agreement with the 4-sites model proposed by Martin et al. where three specific sites and one nonspecific site were postulated for P-gp ligands [START_REF] Martin | Communication between multiple drug binding sites on P-glycoprotein[END_REF]. Our docking model illustrates at the molecular level the proposal of Martin et al based on experimental inhibition patterns. For both elacridar (Fig. 5A) and nifedipine (Supporting Information 01 -Fig. S14) the assignment to the nonspecific site seems to be confirmed by our findings. This is the site where "pure" P-gp inhibitors (i.e. not transported by P-gp) are supposed to bind [START_REF] Ferreira | Molecular docking characterizes substrate-binding sites and efflux modulation mechanisms within P-glycoprotein[END_REF][START_REF] Martin | Communication between multiple drug binding sites on P-glycoprotein[END_REF]. However, since the aim of our study is not to differentiate transported inhibitors from non-transported inhibitors, we did not needed to define a separate fourth site for P-gp.

Recent site-directed mutagenesis experiments on P-gp showed that when residues of the primary drug-binding site are mutated, substrates and inhibitors bind to secondary sites on the transporter [START_REF] Chufan | Multiple transportactive binding sites are available for a single substrate on human P-glycoprotein (ABCB1)[END_REF]. In addition, the transport of several substrates by multiple-residues mutant P-gp is also not significantly altered, showing that substrates bound at secondary sites are still transported. The effect of mutagenesis on P-gp binding was not studied here as our goal was not to characterise precisely the binding sub-sites in the P-gp structure. Our goal was to point first the existence at a molecular level of P-gp sub-sites according to Martin and co-workers and, second, the ability for ligand to be classified according to a molecular based classification of inhibitors.

Our model performance was verified in predicting the substrate-dependent inhibition, by using a different set of experiments. A first group of experiments was meant to fill in the blanks for all the probes. Thus, four experiments were targeted with H33342 (carvedilol, itraconazole, lansoprazole and mibefradil) and four with Rh123 (cisapride, disopyramide, midazolam and zafirlukast). A second group of experiments included six additional molecules (for which interaction with P-gp was already detected in the literature or was functionally in vitro measured in the present study) which were tested with all probes. These compounds were amitriptyline, loperamide, nifedipine, prazosin, reserpine and terfenadine. First, compounds were docked to predict their inhibitory behaviour and, successively, data from docking were confronted to experimental P-gp inhibition data.

The results are reported in Table 2. The model correctly anticipates most of the experimental results. There is a complete agreement between predictions and experiments for the probe H33342. The agreement for Rh123 is 9 over 10 correctly predicted and for digoxin is 4 over 6 correctly predicted. The model performances in prediction are reported in Table 3. As already mentioned, there can be several explanations on the origin of wrong predictions and these are related to some interaction aspects not considered by the model such as the non-competitive mechanism, the binding affinity and the protein flexibility. A future aim could be the incorporation of such information in the model through mechanism data and molecular dynamics studies.

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The case of terfenadine is quite odd because it is the only compound, in our set, that inhibited H33342 and Rh123, but not digoxin. In the future, it could be quite interesting to further inspect its mechanism of interaction and enrich the model with other compounds bearing similar information.

Both our experimental and computational findings suggest that the type of inhibition (whether it is dependent or independent from the substrate used) would depend only on the number of transport sub-sites that can be blocked by the test compound. Consequently, in presence of a molecule binding to only one of them, the other sites may continue to work independently from each other.

We used our model to improve the predictability of the ligand-based P-gp inhibition model developed by [START_REF] Broccatelli | A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields[END_REF] and recently reviewed by us [START_REF] Bocci | ADME-Space: a new tool for medicinal chemists to explore ADME properties[END_REF]. Here we substitute the LDA part with our model resulting in a more efficient combined model. Both physiochemical and pharmacophoric (ligandbased or structure-based) models are proposed to predict P-gp inhibition. The first considers the holistic physiochemical molecular features, such as lipophilicity and molecular size, that are expected to be the main determinants of the interaction (or not) of a molecule with a protein. Differently, pharmacophoric models dig more into the reasons of interaction by inspecting in detail H-bonding interactions, hydrophobic interactions and size/shape fit between protein and ligand. Hence, pharmacophoric models should be a successive step to a first physiochemical passage. Indeed, the physiochemical model provides a filter for molecules which reach and interact with the P-gp active site. From this prospective, the model presented in this study is not intended as an alternative way to separate P-gp inhibitors from P-gp non-inhibitors, but it can provide a supplement were other strategies do not return predictions. By using the dataset proposed by Keogh and co-workers [START_REF] Keogh | Development, validation and utility of an in vitro technique for assessment of potential clinical drug-drug interactions involving P-glycoprotein[END_REF], we predicted the inhibition with the physiochemical model.

Then, we docked those molecules resulting not predicted by the previous model. As it can be seen in Fig. 6, the coverage of correct predictions increases consistently, although the model accuracy does not change much (data not shown).

The strategy applied in this study takes advantages in the complementarity between modelling and experimental teams, which is the key for successful models and can be used to improve experimental efficiency. Indeed, only molecules interacting with P-gp (QSAR predictions) are proposed for further in vitro testing with the most appropriate probe depending on the site identified by the structure-based model. This results in most efficient DDI experiment in vitro focused on the most appropriate probe.

Previous P-gp models were mainly meant to discriminate binders from non-binders or to differentiate ligands by their mechanism of interaction (e.g. inhibitors/non-substrates, substrate-inhibitors, etc) [START_REF] Ferreira | Molecular docking characterizes substrate-binding sites and efflux modulation mechanisms within P-glycoprotein[END_REF]Zeino et al., 2014). In contrast our approach was most focus on the need to classify compounds according to sub-site classification to provide a real help in the study of DDIs.

A C C E P T E D M A N U S C R I P T 13

The model presented provides an improvement in the classification of DDIs for P-gp because it is not based on the chemical similarity comparison between substrates and inhibitors structures, but on similarity scoring between ligands and sites of interaction. Our sub-site model is now available and validated, some advantage from its use could be in drug design, in the interpretation and analysing observations, in drug development for more efficient assays or in drug delivery.

In medicinal drug design our model can help to select the appropriate probe to be used in experimental phase. In the case of drugs potentially interacting with P-gp but with specific sub-sites interaction unknown, the model could access the molecular interpretation for DDI by demonstrating the interaction in a specific sub-site or when all sub-sites are involved.

It can also be used in virtual optimisation to get proposed optimized structures. The model can also be used to give the opportunity to visualise, at a molecular level the results obtained. The scientist can better understand the mechanism behind odd inhibition patterns. Moreover, it can help to design structures that shall avoid interacting with sub-sites used by another drug for a specific treatment; thus, reducing the global DDIs risk and improve access to the target organ. This could be used in cancer therapy for drug delivery using drug cocktail where drugs should not interact on the same P-gp sub-site but both inhibiting P-gp resulting in an improved absorption of drug in tumours.

CONCLUSIONS

Here we described a new computational strategy to predict the P-gp substrate-dependent inhibition in synergy with in vitro experiments. We used the P-gp crystal structure to detect the D-site, H-site and R-site, which are the hypothetical binding sites of P-gp substrates digoxin, H33342 and Rh123 respectively. Successively, the docking of Pgp experimental substrate-dependent inhibitors has unravelled the reasons for their behaviour.

The model is based on the simple competition of the ligand towards each sub-site detected. In this way, we were able to rationalize most of our stating experimental data (see Table 3). The model is consistent also for those compounds showing substrate-independent inhibition. The performances observed from the initial set of experiments are supported by a separate set of experiments whose results confirm the majority of the model predictions (Table 3). To our knowledge, this is the only P-gp structure-based model supported by experimental validation.

The modelling work and the results obtained provide a deeper knowledge of the complex P-gp functionalities and may substantially simplify the design of new drugs. The model described can be used prior in vitro testing to target the proper substrate in inhibition experiments for a more efficient detection of drug-drug interactions. In combination with QSAR physiochemical-based models, it can speed up experimental studies by focusing only on compounds interacting with P-gp. It could elucidate the sometimes fuzzy literature data concerning P-gp inhibition where the impact of the probe used could not always be well documented. If addressing P-gp as target (e.g. in cancer therapies), it could be used to design sub-site specific competitors to enhance the permeability of other co-administered drugs.

A C C E P T E D M A N U S C R I P T 14

In the future, the model may significantly benefit of additional data experiments (especially non-competitive) and molecular dynamics studies with better P-gp structures focused on a small subset of differently behaving P-gp ligands.

Furthermore, this approach could be extended in the near future to other P-gp substrates and to other membrane transport proteins, once their structures will become available.

Fig. 1 .

 1 Fig. 1. Flowchart of the model steps to generate predictions for new ligands. DGS (digoxin Glob-Sum), HGS (H33342 Glob-Sum), RGS (Rh123 Glob-Sum).

Fig. 2 .

 2 Fig. 2. Inhibitory effects of compounds on P-gp-mediated transport of digoxin, H33342 and Rh123. Accumulation of digoxin and Rh123 and retention of H33342 in P-gp-expressing MCF7R cells were determined in the absence (control)

Fig. 3 .Fig. 4 .

 34 Fig. 3. (A) Top-ranked poses for the three probe substrates used: digoxin (green), Rh123 (orange) and H33342 (purple). (B) D-site depiction (green). (C) R-site depiction (orange). (D) H-site depiction (purple).

Fig. 5 .Fig. 6 .

 56 Fig. 5. Top-ranked poses for elacridar (A) and quinidine (B). The substrates binding sites are depicted transparently: green (D-site), orange (R-site) and purple (H-site).

Graphical abstract

ACKNOWLEDGMENTS

We thank Mrs Elodie Jouan for expert technical assistance for in vitro assays. distribution, metabolism, excretion and toxicity (ADME-Tox). Drug discovery today 13, 379-393.

Varma, M.V., Pang, K.S., Isoherranen, N., Zhao, P., 2015. Dealing with the complex drug-drug interactions: towards mechanistic models. Biopharm. Drug Dispos. 36, 71-92. Weiss, J., Dormann, S.-M.G., Martin-Facklam, M., Kerpen, C.J., Ketabi-Kiyanvash, N., Haefeli, W.E., 2003. Inhibition of P-glycoprotein by newer antidepressants. J. Pharmacol. Exp. Ther. 305, 197-204. Zeino, M., Saeed, M.E., Kadioglu, O., Efferth, T., 2014. The ability of molecular docking to unravel the controversy and challenges related to P-glycoprotein-a well-known, yet poorly understood drug transporter. Invest. New Drugs 32, 618-625. Zhang, L., Zhang, Y.D., Zhao, P., Huang, S.-M., 2009. Predicting drug-drug interactions: an FDA perspective. The AAPS journal 11, 300-306. Zolnerciks, J.K., Booth-Genthe, C.L., Gupta, A., Harris, J., Unadkat, J.D., 2011. Substrate-and species-dependent inhibition of p-glycoprotein-mediated transport: Implications for predicting in vivo drug interactions. J. Pharm. Sci. 100, 3055-3061. MANUSCRIPT TABLES & FIG.S Compound digoxin H33342 Rh123 amiodarone INHIBITOR INHIBITOR INHIBITOR diltiazem INHIBITOR INHIBITOR INHIBITOR elacridar INHIBITOR INHIBITOR INHIBITOR felodipine INHIBITOR INHIBITOR INHIBITOR nicardipine INHIBITOR INHIBITOR INHIBITOR nitrendipine INHIBITOR INHIBITOR INHIBITOR quinidine INHIBITOR INHIBITOR INHIBITOR sertraline INHIBITOR INHIBITOR NONINHIBITOR verapamil INHIBITOR INHIBITOR INHIBITOR zosuquidar INHIBITOR INHIBITOR INHIBITOR alfentanil INHIBITOR INHIBITOR amlodipine INHIBITOR INHIBITOR astemizole INHIBITOR INHIBITOR cisapride INHIBITOR INHIBITOR disopyramide INHIBITOR NONINHIBITOR ketoconazole INHIBITOR INHIBITOR loratadine INHIBITOR INHIBITOR lovastatin INHIBITOR INHIBITOR nefazodone INHIBITOR INHIBITOR pimozide INHIBITOR INHIBITOR quinine INHIBITOR INHIBITOR sildenafil INHIBITOR INHIBITOR zafirlukast INHIBITOR NONINHIBITOR carvedilol INHIBITOR INHIBITOR isradipine INHIBITOR INHIBITOR itraconazole INHIBITOR NONINHIBITOR lansoprazole INHIBITOR NONINHIBITOR mibefradil INHIBITOR INHIBITOR troglitazone INHIBITOR INHIBITOR

 1).

digoxin H33342 Rh123

Starting set 97% 92% 88%