Keywords: molecular modeling, quantum chemistry, molecular dynamics, conformational analysis, neuraminidase inhibitors

Conformational study of glycal-type neuraminidase inhibitors

The conformational flexibility of two glycal-type neuraminidase inhibitors has been studied, using several molecular modeling techniques. In agreement with the experimental data available, an intramolecular hydrogen bond, representing a key structural feature that controls the conformer distribution in solution, has been identified. The contribution of each substituent on the overall equilibrium was evaluated using simplified derivatives. Additionally, four methods for estimating NMR coupling constants from dihedral angles were evaluated and the Haasnoot method was found to be appropriate for this class of sugars. These results should allow a better understanding of the structural parameters governing physico-chemical properties of glycal-like compounds.

Introduction

The Influenza neuraminidase (NA) enzyme is one of the validated targets against influenza viruses. [START_REF] Moscona | Neuraminidase inhibitors for influenza[END_REF][START_REF] Von Itzstein | The war against influenza: discovery and development of sialidase inhibitors[END_REF] Two NA inhibitors are currently present on the market, oseltamivir phosphate (Tamiflu ® ) [START_REF] Kim | Influenza neuraminidase inhibitors possessing a novel hydrophobic interaction in the enzyme active site: design, synthesis, and structural analysis of carbocyclic sialic acid analogues with potent anti-influenza activity[END_REF] and zanamivir (Relenza ® ). [START_REF] Von Itzstein | Rational design of potent sialidase-based inhibitors of influenza virus replication[END_REF] However, resistance phenomena observed during the last few years, especially for Tamiflu ® , [START_REF] De Jong | Oseltamivir resistance during treatment of influenza A (H5N1) infection[END_REF][START_REF] Moscona | Oseltamivir resistance -disabling our influenza defenses[END_REF][START_REF] Ferraris | Mutations of neuraminidase implicated in neuraminidase inhibitors resistance[END_REF][START_REF] Van Der Vries | Fatal oseltamivir-resistant influenza virus infection[END_REF][START_REF] Moscona | Global transmission of oseltamivir-resistant influenza[END_REF] and the poor biodisponibility of Relenza ® make research for novel anti-viral molecules a priority.

In this context, we have been interested in the development and the synthesis of Relenza-type compounds modified at the C4 or C6 position. [START_REF] Soulé | Coupling the Petasis condensation to an iron(III) chloride-promoted cascade provides a short synthesis of Relenza congeners[END_REF][START_REF] Chang | Regio-and stereocontrolled palladium-catalyzed allylic substitution on N-acetylneuraminic acid derivatives[END_REF][START_REF] Chang | Useful sialic acid modifications catalyzed by palladium[END_REF] In this work, a combination of several molecular modeling techniques [START_REF] Roldós | Carbohydrate-protein interactions: a 3D view by NMR[END_REF] (conformational analysis, molecular dynamics and quantum chemistry calculations) has been used to obtain insight into the flexibility of two influenza neuraminidase inhibitors (Figure 1) previously described, [START_REF] Soulé | Coupling the Petasis condensation to an iron(III) chloride-promoted cascade provides a short synthesis of Relenza congeners[END_REF][START_REF] Beau | Chemical behaviour of cytidine 5′-monophospho-N-acetylβ-D-neuraminic acid under neutral and alkaline conditions[END_REF][START_REF] Zbiral | Structural variations of N-acetylneuraminic acid, 10. Synthesis of 2,7-, 2,8-, and 2,9dideoxy-and 2,4,7-trideoxy-2,3-didehydro-N-acetylneuraminic acids and their behavior towards sialidase from Vibrio cholerae[END_REF] presenting a scaffold similar to zanamivir. Glycal-type compounds are known to be conformationally flexible. [START_REF] Chalmers | Conformational studies of D-glycals by 1 H nuclear magnetic resonance spectroscopy[END_REF][START_REF] Rico | Complete analysis of the 1 H-NMR spectra of acetylated glycals -a conformational study[END_REF][START_REF] Lessard | Conformational analysis of 2-substituted methylenecyclohexanes and 3-substituted cyclohexenes and the anomeric effect[END_REF][START_REF] Burfitt | A 13 C nuclear magnetic resonance study of glycals (1,5-Anhydro-hex-1-enitols)[END_REF][START_REF] Lessard | Conformational analysis of 7substituted 2-methoxy-methylenecyclohexanes: Evidence against no --> π* orbital stabilization of the axial conformer[END_REF][START_REF] Ishiyama | n-π Interaction between an oxygen lone pair and π-electrons in cyclic allylic alcohols and ethers; carbon-13 nuclear magnetic resonance spectra, catalytic hydrogenation, and CNDO/2 calculations[END_REF][START_REF] Korytnyk | Application of molecular-mechanics calculations to D-glucal and its acetate; a comparison of X-ray and n.m.r. results[END_REF][START_REF] Thiem | Studies of hexuronic acid ester glycals and the synthesis of 2-deoxy-β-glycoside precursors[END_REF][START_REF] Maier | De-novo-Synthese von Kohlenhydraten und verwandten Naturstoffen, 18. Funktionell substituierte 3,4-Dihydro-2H-pyrane durch Hetero-Diels-Alder-Reaktionen[END_REF][START_REF] Jäkel | Organotransition metal modified sugars: Part 22. Direct metalation of glycals: short and efficient routes to diversely protected stannylated glycals[END_REF][START_REF] Bonnet | Molecular dynamics and free energy analysis of neuraminidase-ligand interactions[END_REF] With these systems the 3 J values of the coupling constants in the 1 H-NMR spectra are often diagnostic of the conformational equilibrium between the two 6 H5 ("all-equatorial") and 5 H6 ("all-axial") half-chair conformations. In our synthesis of zanamivir congeners, [START_REF] Soulé | Coupling the Petasis condensation to an iron(III) chloride-promoted cascade provides a short synthesis of Relenza congeners[END_REF] we were surprised by the value of the J4,5 coupling constant at room temperature in 1 (4.5 Hz) [START_REF] Soulé | Coupling the Petasis condensation to an iron(III) chloride-promoted cascade provides a short synthesis of Relenza congeners[END_REF] compared to the same J value in Neu5Ac2en (DANA) 2 (8.5 Hz), [START_REF] Beau | Chemical behaviour of cytidine 5′-monophospho-N-acetylβ-D-neuraminic acid under neutral and alkaline conditions[END_REF][START_REF] Zbiral | Structural variations of N-acetylneuraminic acid, 10. Synthesis of 2,7-, 2,8-, and 2,9dideoxy-and 2,4,7-trideoxy-2,3-didehydro-N-acetylneuraminic acids and their behavior towards sialidase from Vibrio cholerae[END_REF] since the two compounds only differ by the nature of the side chain at C6. Our aim of this study was to explain how the substituent change on C6 might be responsible for the differences observed experimentally in the NMR coupling constants.

Insert Figure 1 here

Materials and Methods

Conformational Analysis

Three-dimensional structures of the compounds used in this study were generated using CORINA software. [START_REF]version 3.44[END_REF] The conformational analysis was carried out using MacroModel with the OPLS_2005 force field and the "Mixed torsional/Large-scale low-mode conformational sampling" procedure. [START_REF]MacroModel, version 9.8[END_REF] This is a hybrid technique that combines broad Monte Carlo sampling of torsional space [START_REF] Chang | An internal-coordinate Monte Carlo method for searching conformational space[END_REF] with local low frequency eigenvector sampling in the vicinity of the current conformation. [START_REF] Kolossvary | Low mode search. An efficient, automated computational method for conformational analysis: Application to cyclic and acyclic alkanes and cyclic peptides[END_REF] After 500,000 steps using an energy window of 42 kJ/mol, a number of 1043 and 1176 unique conformers were obtained for compounds 1 and 2, respectively.

Molecular Dynamics

Molecular dynamics simulations were performed using GROMACS 4.0.7 software package [START_REF] Hess | GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation[END_REF] with the OPLS-AA force field. [START_REF] Jorgensen | Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids[END_REF][START_REF] Kaminski | Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides[END_REF] Force field topologies of compounds 1 and 2 were generated using an in-house developed script. The system was constructed by positioning the molecule in a cubic periodic box of TIP4P waters, with a minimum distance of 1.0 nm on each side, then energy-minimized until convergence using a steepest descent algorithm to remove close contacts of water molecules with the solute.

Molecular dynamics with position restraints on the solute atoms were then performed for 200 ps to relax the water molecules around the solute, followed by the production run of 250 ns. During the position restraints and production runs, the Parinello-Rahman method [START_REF] Parrinello | Polymorphic transitions in single crystals: A new molecular dynamics method[END_REF] was used for pressure coupling, and the temperature was coupled using the Nosé-Hoover method [START_REF] Nosé | A molecular dynamics method for simulations in the canonical ensemble[END_REF][START_REF] Hoover | Canonical dynamics: Equilibrium phase-space distributions[END_REF] at 300 K. Electrostatics were calculated with the particle mesh Ewald method. [START_REF] Darden | Particle mesh Ewald: An N-log(N) method for Ewald sums in large systems[END_REF][START_REF] Essmann | A smooth particle mesh Ewald method[END_REF] The P-LINCS algorithm [START_REF] Hess | LINCS: A parallel linear constraint solver for molecular simulation[END_REF] was used to constrain bond lengths, and a time step of 2 fs was used throughout. All simulations were performed using constant temperature, pressure, and number of particles (NPT) and periodic boundary conditions. The cut-off distance for Lennard-Jones interactions and electrostatic interactions were both set at 1.0 nm. Root-mean-square deviation (RMSD) conformational clustering was performed using the g_cluster module from GROMACS and a 0.05 Å cut-off value.

Graphics were plotted using the XmGrace package [40] and images for compounds 1-7 were generated using Chimera. [START_REF] Pettersen | UCSF Chimera -a visualization system for exploratory research and analysis[END_REF]

Quantum chemistry

The geometries of the compounds 1-7 ("all-equatorial" and "all-axial" conformations), as well as those of the corresponding intermediates and transition states for the conformer interconversion reaction, were optimized in gas-phase using the Gaussian 09 package [START_REF] Frisch | Revision A.2[END_REF] with the Becke's three-parameter hybrid exchange functional (B3LYP) [START_REF] Lee | Development of the Colle-Salvetti correlationenergy formula into a functional of the electron density[END_REF][START_REF] Becke | Density-functional thermochemistry. III. The role of exact exchange[END_REF] and the 6-31+G(d,p) basis set. Subsequent vibrational frequency calculations confirmed that these conformations were local minima and maxima, respectively. IRC calculations were carried out to confirm that the transition states are directly connected to the neighboring minima on the reaction path. Unfortunately, these calculations were unsuccessful for all compounds using the default SCF and integration grid values.

Increasing the accuracy to irc=(vtight,stepsize=2) and int(grid=ultrafine) led to successful results only for the simplest derivatives 4, 6 and 7. Under these conditions, it is worth noting that these latter compounds are characterized by relatively high negative frequency values (ranging from -99 to -115, see Supplementary Information), whereas all other compounds present low negative frequency values (ranging from -23 to -74), which might explain why the IRC calculations were not successful in these cases.

Finally, we validated all the transition states by visual inspection in GaussView5 and by geometry optimization of the structures with amplitudes +1 and -1 extracted from Display Vibrations/Manual Displacement menu.

Results and discussion

A combined approach of conformational analysis, molecular dynamics and quantum chemistry calculations allowed the assessment of the conformational flexibility for compounds 1 and 2 (Figure 2). The most stable structures present half-chair conformations with the substituents on C4, C5 and C6 in either an equatorial (conformations A) or an axial position (conformations B).

Insert Figure 2 here Extended molecular dynamics (MD) simulations (5 µs overall) of compounds 1 and 2 in explicit water were performed to explore their conformational flexibility, in complement to the conformational analysis implemented in MacroModel, which uses an implicit solvent. The distribution of ring dihedral angles and the evolution of these angles over time were plotted and show characteristic values for "all-axial" and "allequatorial" conformations (see Supporting Information). The conformational change from "all-axial" to "all-equatorial" could be observed in some cases, for both compounds 1 and 2, but the reverse process never occurred (Table 1). This behavior, in contradiction with the quantum calculations which predict that these conformational changes are equally probable (see below), might be explained by the fact that the force field parameters for sugars were mainly fitted on saturated systems and are probably biased towards the "all-equatorial" conformation. In any case, these MD simulations should be considered only qualitatively, for the assessment of ligand conformations in water, and not quantitatively, for the evaluation of energy barrier for the conformer interconversion process, the quantum calculations being more appropriate for this latter task.

Reduced structural ensembles of representative conformations present during the simulations were generated using root-mean-square deviation (RMSD) conformational clustering and the number of clusters obtained in each case is shown in Table 1. The clustering analysis indicates that compound 1 is represented by a relatively important number of clusters, which implies a high degree of flexibility in solution. In contrast, compound 2 shows fewer clusters, suggesting a good conformational stability in water. This behavior might be explained by the presence of additional stabilizing intramolecular hydrogen bonds compared to compound 1. These intramolecular hydrogen bonds have been evidenced in vacuo (quantum chemistry calculations) and in solution (molecular dynamics simulations), and they can coexist with the intermolecular solute-solvent interactions. [START_REF] Mammino | Interplay of intramolecular hydrogen bonds, OH orientations, and symmetry factors in the stabilization of polyhydroxybenzenes[END_REF] Insert Table 1 here

The geometries of compounds 1 and 2 (A and B conformations) were then optimized and transition states and intermediates for the conformer interconversion process identified (Figures 2 and3). An interesting structural feature of compounds 1 and 2 revealed by this study is the presence of intramolecular hydrogen bonds. Both equatorial conformers show strong hydrogen bonds between the OH substituent on C4 and the carbonyl oxygen of the acetyl substituent on C5, with an additional interaction between the nitrogen atom of the acetyl substituent on C5 and the OH group on C8 in the case of 2 (Figure 2, Table 2). The situation is different for the axial conformers, where compound 1 presents no hydrogen bonds and compound 2 shows a strong hydrogen bond between the OH substituent on C4 and the OH group on C7 (Figure 2, Table 2).

Insert Table 2 here

The transition states 1TS and 2TS' correspond to the rotation of the N-acetyl group and breaking of the hydrogen bond between this group and the OH substituent in position 4. The transition states 1TS' and 2TS" correspond to the dihydropyran ring conformational interconversion, which flips the C5 and C6 atoms up and down, whereas all other ring atoms are rigid. In the case of compound 2, there is an additional transition state (2TS), involving the rotation of the substituent on C6 and breaking of the hydrogen bond between this group and the N-acetyl moiety. The overall activation barriers differ by about 2 kcal/mol and are in the range 10-12 kcal/mol, which makes in both cases the conformer interexchange possible at room temperature.

Insert Figure 3 here In order to better understand the details of this conformer interconversion process, which is relatively complex, we have studied the behavior of simpler members of the glycal family (Figure 4). Compound 3 represents the common core of 1 and 2, compound 7 is completely unsubstituted in the positions 4, 5 and 6, whereas compounds 4, 5 and 6 bear only one substituent, in the positions 4, 5 or 6, respectively.

Insert Figure 4 here Similarly to 1 and 2, the geometries of "all-equatorial" and "all-axial" conformers were optimized, as well as the transition states and (in the case of 3) the intermediates in the conformer interconversion process (Figures 5 and6). The conformer 3A shows a hydrogen bond between the 4-OH and 5-NAc substituents, which is one common feature of compounds 1 and 2, whereas all other conformers are devoided of any hydrogen bond (Figure 5).

Insert Figure 5 here

The reaction path for compound 3 is very similar to the calculated path for 1, with two transition states, one for the N-acetyl group rotation (3TS) and one for the ring conformational change (3TS'), although the overall energy barrier is somewhat lower.

In the absence of internal hydrogen bonds, compounds 4-7 present only one transition state, which corresponds to the ring conformational change (Figure 6).

Insert Figure 6 here Without substituents on C4, C5 and C6, the two half-chair conformations of compound 7 have the same energy and an equal amount of these conformations is expected in solution. The situation is different for compounds 4-7: the 4-OH and 5-NAc substituents stabilize the "all-axial" conformation, whereas the 6-Me substituent stabilizes the "all-equatorial" conformation. Compound 3, which combines the influences of all the three substituents, has the "all-equatorial" conformer stabilized, although to a less extent than 6.

The study of simplified derivatives 3-7 showed that substituents in different positions can have opposite influences in stabilizing the ring conformers. Moreover, the influences of the dihydropyran ring substituents seem not to be additive. The nature and the geometry of substituents on C4, C5 and C6 seem to be the key features that govern the conformer distribution in solution and therefore, a careful choice of these substituents might allow the fine-tuning of physico-chemical properties for these compounds.

The conformer ratio for compounds 1 and 2 was evaluated using the Boltzmann distribution function from the minimized energies of the "all-equatorial" and "all-axial" conformers and compared with the experimental data (Tables 345). Although the energy differences are relatively small (less than 1 kcal/mol), the corresponding conformational distributions are significantly different, in agreement with the distinct behavior experimentally observed.

Insert Tables 345here Four different methods were employed to estimate the NMR vicinal coupling constants ( 3 JH-C-C-H) starting from the equatorial and axial conformations of compounds 1 and 2. The relationship between the dihedral angle and the coupling constant observed from 1 H NMR spectra can be calculated using the Karplus equation [START_REF] Karplus | Vicinal proton coupling in nuclear magnetic resonance[END_REF] , where J0 has a value of 14 (90° < θ < 180°) or 10 (0° < θ < 90°), and K = 0:

3 JHH = J0*cos 2 θ -K
An alternative method for calculating vicinal coupling constants is given by the Durette-Horton equation: [START_REF] Durette | Correlations of observed proton -proton coupling constants with the generalized Karplus equation[END_REF] 3 JHH = 7.8 -cosθ + 5.6*cos2θ However, these two methods do not seem to be adapted for the evaluation of glycal-like systems, as values of 0.9-9.9 Hz and 2.5-10.6 Hz for 3 JH6-H5 are calculated respectively, whereas the experimentally measured value is outside this range (10.9 Hz).

A third method used in this study is given by the Bothner-By equation [START_REF] Pople | Nuclear spin coupling between geminal hydrogen atoms[END_REF] , which represents a slight modification of the previous equation, but which provides more realistic values for 3 JH6-H5 (2.6-11.0 Hz):

3 JHH = 7 -cosθ + 5*cos2θ
The last method makes use of the Haasnoot equation [START_REF] Haasnoot | The relationship between proton-proton NMR coupling constants and substituent electronegativities -I : An empirical generalization of the Karplus equation[END_REF] , which takes into account the Huggins electronegativities [START_REF] Huggins | Bond energies and polarities[END_REF] of the neighboring atoms and for which two cases can be distinguished. When four substituents are present on the central atoms of the dihedral angle ( 3 JH6-H5 and 3 JH5-H4), the following equation can be used: The coupling constants calculated for the equatorial and axial conformers using the four methods presented above were subsequently used to evaluate the conformer distribution using the linear regression fit against the experimentally measured values of the coupling constants. The first two methods provided unrealistic results and the Bothner-By method gave relatively poor correlation, whereas the Haasnoot method provided very good results for both compounds included in this study (Table 5). From these data, it seems that the inclusion of neighboring substituents' electronegativities is critical for the reliable prediction of the 3 JHH NMR coupling constants, and therefore the Haasnoot method appears to be the most appropriate for evaluating glycal-like compounds.

A fifth method used in this study, developed by Roush et al., [START_REF] Roush | Stereoselective synthesis of 2-deoxyβ-glycosides from glycal precursors. 1. Stereochemistry of the reactions of Dglucal derivatives with phenylsulfenyl chloride and phenylselenenyl chloride[END_REF] correlates directly the 3 JH6-H5 coupling constants with the conformer distribution in solution.

However, in our hands this method provided conformer distribution values that are quite different compared to the Boltzmann distributions (Table 5).

Conclusion

In this work, the conformational flexibility of two glycal-type neuraminidase inhibitors has been evaluated, using different molecular modeling techniques (conformational analysis, molecular dynamics and quantum chemistry calculations). We have shown that, in agreement with the experimental data available, compound 2 behaves very differently in solution compared with compound 1, and this behavior is mainly due to the presence of an additional strong intramolecular hydrogen bond between the nitrogen atom of the acetyl group and the OH group on C8. This hydrogen bond seems to represent the key structural feature that allows the fine-tuning of conformer stabilization, thus controlling the conformer distribution in solution. Several simplified derivatives of 1 and 2 were also studied and the contribution of each substituent on the overall conformational equilibrium evaluated. Additionally, four different methods for the estimation of NMR coupling constants from dihedral angles were evaluated and only one, the method described by Haasnoot et al., [START_REF] Haasnoot | The relationship between proton-proton NMR coupling constants and substituent electronegativities -I : An empirical generalization of the Karplus equation[END_REF] gave very good results and therefore seems to be the most appropriate for the analysis of this class of sugars. The results presented here should allow a better understanding of the structural parameters governing the physico-chemical properties of glycal-like compounds. 2.76 153 Table 3. Dihedral angles measured on representative "all-equatorial" (A) and "all-axial" (B) conformations for compounds 1 and 2, after geometry optimization using Gaussian [START_REF] Frisch | Revision A.2[END_REF] .

Calculated values

Experimental values [START_REF] Soulé | Coupling the Petasis condensation to an iron(III) chloride-promoted cascade provides a short synthesis of Relenza congeners[END_REF] Calculated values Experimental values [START_REF] Beau | Chemical behaviour of cytidine 5′-monophospho-N-acetylβ-D-neuraminic acid under neutral and alkaline conditions[END_REF][START_REF] Zbiral | Structural variations of N-acetylneuraminic acid, 10. Synthesis of 2,7-, 2,8-, and 2,9dideoxy-and 2,4,7-trideoxy-2,3-didehydro-N-acetylneuraminic acids and their behavior towards sialidase from Vibrio cholerae[END_REF]

A/B ratio R 2 coefficient A/B ratio R 2 coefficient
Linear regression of calculated against experimental NMR coupling constants Karplus [START_REF] Karplus | Vicinal proton coupling in nuclear magnetic resonance[END_REF] Durette-Horton [START_REF] Durette | Correlations of observed proton -proton coupling constants with the generalized Karplus equation[END_REF] Bothner-By [START_REF] Pople | Nuclear spin coupling between geminal hydrogen atoms[END_REF] Haasnoot [START_REF] Haasnoot | The relationship between proton-proton NMR coupling constants and substituent electronegativities -I : An empirical generalization of the Karplus equation[END_REF] 62 Theoretical data for compound 1A :

Final Energy in Hartrees: HF=-978.3002685 (B3LYP/6-31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------- Final Energy in Hartrees: HF = ---978.2943184 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------- Theoretical data for compound 1C :

Final Energy in Hartrees: HF = ---978.2970991 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------- Theoretical data for compound 1TS' :

Final Energy in Hartrees: HF = ---978.2843704 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------- Final Energy in Hartrees: HF = -978.3001577 (B3LYP/6-31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------- -------------------------------------------------------------------- ------------------------------------------------------------------ -------------------------------------------------------------------- Theoretical data for compound 2TS :

Final Energy in Hartrees: HF= ---1086.0161907 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ -------------------------------------------------------------------- Final Energy in Hartrees: HF=---1086.016375 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ -------------------------------------------------------------------- Theoretical data for compound 2TS' :

Final Energy in Hartrees: HF= ---1086.0106485 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ -------------------------------------------------------------------- Theoretical data for compound 2D :

Final Energy in Hartrees: HF= ---1086.0118575 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ -------------------------------------------------------------------- Theoretical data for compound 2TS" :

Final Energy in Hartrees: HF= ---1085.9986171 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------- -------------------------------------------------------------------- -------------------------------------------------------------------- ------------------------------------------------------------------- -------------------------------------------------------------------- Final Energy in Hartrees: HF = ---781.7194156 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------- Final Energy in Hartrees: HF = ---781.7160383 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------- ------------------------------------------------------------------- Final Energy in Hartrees: HF = ---781.7167761 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------- Final Energy in Hartrees: HF = ---781.7084934 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------- ------------------------------------------------------------------- Final Energy in Hartrees: HF = ---781.7186536 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------- Final Energy in Hartrees: HF = ---534.3670502 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------ Final Energy in Hartrees: HF = ---534.3589057 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------ Theoretical data for compound 4B :

Final Energy in Hartrees: HF = ---534.3688084 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------ Final Energy in Hartrees: HF = ---667.1778869 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------ Final Energy in Hartrees: HF = ---667.169153 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------ Final Energy in Hartrees: HF = ---667.1794514 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------ Final Energy in Hartrees: HF = ---498.4738783 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ ------------------------------------------------------------------ Final Energy in Hartrees: HF = ---498.4616404 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ 2.417054 0.630174 --------------------------------------------------------------------- Final Energy in Hartrees: HF = ---498.4711274 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ .807889 --------------------------------------------------------------------- Final Energy in Hartrees: HF = ---459.1503584 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ -0.638153 --------------------------------------------------------------------- Final Energy in Hartrees: HF = ---459.1407397 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ 116146 --------------------------------------------------------------------- Final Energy in Hartrees: HF = ---459.1503584 (B3LYP/6---31+G(D,P))

Standard orientation: -------------------------------------------------------------------- ------------------------------------------------------------------ 041043 ---------------------------------------------------------------------

3

  JHH = 13.24*cos 2 θ -0.91*cosθ + ∑Δχ{0.53 -2.41*cos 2 (θ + 15.5*|Δχ|)}, where Δχ = Δχ α-substituent -0.19Δχ β-substituent If only three substituents are present ( 3 JH4-H3), the equation to be used is slightly different: 3 JHH = 13.22*cos 2 θ -0.99*cosθ + ∑Δχ{0.57 -2.46*cos 2 (θ + 19.9*|Δχ|)}, where Δχ = Δχ α-substituent

Figure 1 .

 1 Figure1. Chemical structures of neuraminidase inhibitors 1 and 2 used in this study, and the "all-equatorial" (A, 6 H5) and "all-axial" (B, 5 H6) half-chair conformations that these compounds can adopt.

Figure 2 .

 2 Figure2. Three-dimensional representation of compounds 1 and 2 as "all-equatorial" (A) and "all-axial" (B) conformers, generated by conformational analysis and molecular dynamics simulations, followed by geometry optimization. Only hydrogen atoms involved in hydrogen bonds are shown.

Figure 3 .

 3 Figure 3. Energy diagram for the "all-equatorial" (A) and "all-axial" (B) conformer interconversion of compounds 1 and 2.

Figure 4 .

 4 Figure 4. Chemical structures of simplified derivatives 3-7.

Figure 5 .

 5 Figure 5. Three-dimensional representation of simplified derivatives 3-7 as "allequatorial" (A) and "all-axial" (B) conformers, after geometry optimization. Only hydrogen atoms involved in hydrogen bonds are shown.

Figure 6 .

 6 Figure 6. Energy diagram for the "all-equatorial" (A) and "all-axial" (B) conformer interconversion of simplified derivatives 3-7.

Table 1 .

 1 Overview of molecular dynamics simulations performed in this study.

	Compound Conformer	Simulation	Number of	Remarks
			time (ns)	clusters
	1	1A	5*250	34	Conformationally stable
		1B	5*250	44	Conformational change 1B → 1A
					observed in 2 simulations
	2	2A	5*250	7	Conformationally stable
		2B	5*250	6	Conformational change 2B → 2A
					observed in 1 simulation

Table 2 .

 2 Hydrogen bonds (X-H …. Y) identified in the structures of compounds 1 and 2.

	Compound Conformation X-Y distance (Å) X-H …. Y angle (deg.)
	1	1A	2.78	157
		1B	-	-
	2	2A	2.73 (O-H …. O)	158 (O-H …. O)
			2.87 (N-H …. O)	155 (N-H …. O)
		2B

Table 4 .

 4 NMR coupling constants for representative "all-equatorial" (A) and "all-axial" (B) conformations of compounds 1 and 2 calculated using four different methods compared with experimental values.

		Compound 1	Compound 2
		1A	1B	2A	2B
	θH6-H5 (deg.)	0.4	70.6	3.9	72.7
	θH5-H4 (deg.)	24.0	96.3	20.9	102.5
	θH4-H3 (deg.)	71.6	53.7	73.0	46.3

3

J calculation

Table 5

 5

	Compound or conformer		1A	1B	1	2A	2B	2
		Karplus	10.0	1.1		9.9	0.9
	3 JH6-H5 (Hz)	Durette-Horton Bothner-By	9.8 11.0	2.5 2.8	8	10.6 11.0	2.5 2.6	10.9
		Haasnoot	11.7	1.2		12.1	0.9
		Karplus	8.3	0.2		8.7	0.6
	3 JH5-H4 (Hz)	Durette-Horton Bothner-By	7.9 9.4	1.8 2.2	4.5	8.2 9.8	2.2 2.7	8.5
		Haasnoot	9.4	0.3		9.8	0.8
		Karplus	1.0	3.5		0.8	4.8
	3 JH4-H3 (Hz)	Durette-Horton Bothner-By	2.0 2.7	3.6 4.9	4.5	1.9 2.6	4.5 6.1	2.3
		Haasnoot	2.0	4.1		1.8	5.6
						Compound 1		Compound 2
			Method used for
	Method used for	NMR coupling
	A/B ratio calculation	constants
			calculation

. Comparison of conformer distribution for compounds 1 and 2 estimated from NMR coupling constants (using five different methods) and from quantum chemistry energy calculations.

Acknowledgements. This work was funded in part by the French Agency for Research (grant No. ANR-2010-BLAN-708-1). We thank Professor Jean-Yves Lallemand and the Institut de Chimie des Substances Naturelles for a research followship (to G.S.).

Supporting Information

Conformational study of glycal-type neuraminidase inhibitors

Georgiana Surpateanu, Jean-François Soulé, Jean-Marie Beau, Stéphanie Norsikian, Bogdan I. Iorga * Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Centre de Recherche de Gif-sur-Yvette, F-91198 Gif-sur-Yvette, France

Table of contents :

Theoretical data for compounds 1-7 and the corresponding transition states S2

Dihedral angles plots and distributions for compounds 1 and 2 from molecular dynamics simulations S31

Dihedral angles plots and distributions for compounds 1 and 2 from molecular dynamics simulations Legend :


Benoît Chauvin
email: benoit.chauvin@u-psud.fr

Athena Kasselouri
email: athena.kasselouri@u-psud.fr

Bogdan Iorga
email: bogdan.iorga@icsn.cnrs-gif.fr

Pierre Chaminade
email: pierre.chaminade@u-psud.fr

Jean-Louis Paul
email: jean-louis.paul@u-psud.fr

Philippe Maillard
email: philippe.maillard@curie.fr

Patrice Prognon
email: patrice.prognon@u-psud.fr

Plasma distribution of tetraphenylporphyrin derivatives relevant for Photodynamic Therapy: importance and limits of hydrophobicity

Keywords: meso-tetraphenylporphyrin, photodynamic therapy, plasma, l i p o p r o t e i n, albumin, hydrophobicity TPP : 5, 10, 15, 20-tetraphenylporphyrin, meso-tetraphenylporphyrin MCR-ALS : Multivariate Curve Resolution -Alternating Least Squares PDT

In the course of a Photodynamic Therapy (PDT) p r o t o c o l , desagregation o f t h e sensitizer upon binding to plasma proteins and lipoproteins is one of the first step following intraveinous administration. This step governs its subsequent biodistribution, and has even been evoked as possibly orientating mechanism of tumor destruction. It is currently admitted as being mainly dependent on sensitizer's hydrophobicity. In this context, as far as glycoconjugation, a promising strategy to improve targeting of retinoblastoma cells, confers to the sensitizer an amphiphilic character, we have studied the effect of this strategy on binding to plasma proteins and lipoproteins. With the exception of the majoritary protein-binding (more than 80%) of more hydrophilic para-tetraglycoconjugated derivatives, high-density lipoproteins (HDL) appear as main plasma carriers of the other amphiphilic glycoconjugated photosensitizers. This HDL-binding is a combined result of binding affinities (log Ka ranging from 4.90 to 8.77 depending on the carrier and the TPP derivative considered) and relative plasma concentrations of the different carriers. Evaluation of binding affinities shows that if hydrophobicity can account for LDL-and HDL-affinities, it is not the case for albumin-affinity. Molecular docking simulations show that, if interactions are mainly of hydrophobic nature, polar interactions such as hydrogen bonds are also involved. Those combination of interaction modalities should account for the absence of correlation between albumin-affinity and hydrophobicity. T a k e n together, our findings clarify the importance, but also the limits, of hydrophobicity's role i n structure -plasma distribution relationship.

Introduction

Photodynamic Therapy (PDT) is an emerging technique which combines administration of a drug, called photosensitizer, and exposure of targeted t i s s u e t o l i g h t o f a p p r o p r i a t e wavelength. Treatment effect results from the potency of the photosensitizer once activated by light to generate singlet oxygen and radical species responsible for cellular death. PDT has already proven its efficacy in the field of oncology for the treatment of lung, gastrointestinal or cutaneous tumours. It has also be applied to non-malignant diseases such as age-related macular degeneration [1]. In that case, transparency of ocular tissues to light makes PDT of particular interest. This property should also been exploited for the treatment of malignant ocular pathologies, such as retinoblastoma, the most frequent intraocular tumor conservative treatments expose patients to a risk of developing secondary tumors [START_REF] Doz | Retinoblastoma in: Paediatric Oncology[END_REF]. PDT appears as promising, combining a physical selectivity (tissular volume illuminated) and a chemical one (tissular v o l u m e containing t h e photosensitizer). When applied to retinoblastoma tumors, photosensitizers developed for other pathologies have shown poor efficiencies and selectivities, leading to side-effects such as long lasting photosensitization of normal tissues. Design of new photosensitizers adapted to retinoblastoma appears necessary [START_REF] Winther | [END_REF].

Our group is involved in the evaluation of glycoconjugation of tetrapyrrolic macrocycles. This strategy combines targeting of cellular sugar receptors and improvement of photosensitizer solubility. The former promotes selective destruction of malignant cells, the latter favors rapid elimination from healthy tissues. In vitro photocytotoxicity and in vivo pharmacokinetics studies have confirmed the p o t e n t i a l i n t e r e s t o f t h i s a p p r o a c h [4,5]. Efficacity of a glycoconjugated TPP, TPP(pODEGO ManOH) 3 , has been attested in vivo, especially with a particular administration protocol (double drug dose with a 3 hour interval), which combines targeting of cancer cells and of blood vessels. Indeed, at the time of illumination, drug administered 10 min before is still present in the vicinity of blood vessels whereas drug administered 3 hour before has reached tumor cells [6]. Destruction of blood vessels indirectly kills tumor tissue, through deprivation of oxygen and nutriments [7].

Photo-induced destruction of blood vessels i s o f p a r t i c u l a r i n t e r e s t i n t h e c a s e o f a n application of PDT to retinoblastoma as far as this tumor is considered as extremely sensitive to vascular insufficiency [8]. However, this possible mechanism of action rises the question of selectivity. This concept, defined as the ratio of sensitizer concentrations in tumor relative to healthy adjacent tissue, must not be considered as the exclusive result of tumor cells specificities. Tumor vasculature particularities c o u l d also be involved. Indeed, tumor angiogenesis leads to the formation of permeable neo-vessels [9]. However, Roberts has shown that this particular permeability is insufficient to account for selective retention of photosensitizers. Excluding a possible difference in lymphatic drainage, he formulated the hypothesis that selectivity results from a particular affinity of photosensitizers for endothelium of neo-vessels, presuming an implication of drug carriers, such as albumin and lipoproteins [10]. Binding to the latter has retained particular attention since the observation by Jori of a strong correlation between fraction of photosensitizer bound to LDL and selectivity [11].

Overexpression of LDL-receptors by tumor cells and also by endothelial cells reinforces this hypothesis [12]. If LDL-binding is associated to tumour cell delivery, binding of sensitizer to high density lipoprotein (HDL) or albumin has been associated with vascular sequestration of photosensitizer, leading to vascular damages upon photoactivation [START_REF] Hasan | Cancer medicine[END_REF]. A strict correlation between binding to a carrier and localization remains difficult to establish, localization being time-dependent. Thus, biodistribution studies of BPD-MA conjugated to lipoproteins has shown the role of plasma carriers in modulation of pharmacokinetics: conjugation to LDL increases selectivity whereas conjugation to HDL delays tumor accumulation [START_REF] Allison | [END_REF].

Plasma distribution studies have evidenced the major role of lipoproteins in photosensitizer transport, compared with the albumin binding of most drugs [START_REF] Hasan | Cancer medicine[END_REF]15]. This particularity is attributed to the high hydrophobic character of sensitizers. This property seems to govern plasma distribution, as it is frequently considered that hydrophilic compounds bind to proteins (especially albumin) and lipophilic ones to LDL. Amphiphilic derivatives present a tendency to bind mainly to HDL [16]. In this point of view, glycoconjugation, which increases the solubility of the sensitizer and decreases its hydrophobicity, should affect interactions with plasma proteins and lipoproteins. Thus it appears essential to focus on the impact of the glycoconjugation on drug distribution between plasma components. This study covers ten

Materials and Methods

Chemicals

TPP(pOH) 4 was purchased from Sigma-Aldrich ® (Germany) and TPP(mOH) 4 from Frontier Scientific ® (USA). All other porphyrins were synthesized according to previously published protocols [17][18][19][20]. Stock solutions were prepared in DMSO and kept in the dark at + 4°C.

Theophylline, 5-phenyl-1H-tetrazole, indole, propiophenone and valerophenone w e r e provided by Acros Organics (USA), benzimidazole, butyrophenone, colchicine, potassium bromide and ammonium acetate by Merck (Germany), acetophenone by Carlo Erba (Italia), 0.9 % sodium chloride solution by Aguettant (France). HPLC grade acetonitrile, methanol and dimethylsulfoxyde came from VWR (Germany), pH 7.4 PBS and human serum albumin from Sigma-Aldrich (Germany). Two different references of the latter (corresponding to different purification levels) were used, one is essentially fatty acid free (HSA), the other is not fatty acid free (HSAlip). Ultrapure water was provided by an Alpha-Q device (Millipore ® , France). Human plasma was taken from normolipemic hemochromatosis patients.

Determination of Chromatographic Hydrophobicity Index (CHI)

The procedure proposed by Valko has been applied to the TPP derivatives [21]

Distribution in human plasma

After 24-hour incubation with one percent of a porphyrin solution in dimethylsulfoxide, plasma samples were brought to the density of 1.21 g.mL An extraction was performed on the samples according to the method proposed by Wang [22]. 1900 µL of a mixture dimethylsulfoxidemethanol 1:4 (v/v) was added to 100 µL of each fraction collected. After centrifugation (10 min, 4000 rpm), fluorescence intensity was read on the supernatant with a Perkin-Elmer LS-50B spectrofluorimeter, with an excitation wavelength set at 420 nm. Plasma distribution between the different fractions was calculated on the basis of those fluorescence intensities.

Spectroscopic study of interactions with plama proteins and lipoproteins

Preparation of LDL and HDL fractions

Human plasma density is adjusted to 1.019 g.mL -1 with KBr. After 24 h centrifuging (45 000 rpm, 4°C), supernatant is removed and density of the remaining is further increased to 1.063 g.mL -1 with KBr. After 48 h centrifuging (45 000 rpm, 4°C), two fractions are obtained, the upper one corresponding to LDL, the lower one to HDL. Molar concentrations of LDL and HDL particles were determined on the basis of apoprotein quantitation according to the method proposed by Ohnishi [23]. Fluorescence emission spectra were recorded with a Perkin-Elmer LS-50B spectrofluorimeter, with an excitation wavelength set at 420 nm (excitation and emission slits equal to 7 nm).

Sample preparation and conditions of spectra recording

Determination of binding constants

When compared with absorption spectroscopy, determination of binding constants by fluorimetry presents two advantages: the possibility of working with lower TPP concentrations (~10 -7 M) than with absorption spectroscopy (~5.10 -7 M), and the lower diffusion due to plasma carriers. Combined together, those two advantages widen the TPP -carrier ratio range possible to study. Classical binding of drugs to plasma proteins and lipoproteins is described by an equilibrium involving the free drug, the free carrier on the one side and the drug-carrier complex on the other side. Thus, if binding involves a change in drug fluorescence intensity at one wavelength, affinity constants can be determined through monitoring of fluorescence at this wavelength:

(

) 1
where F free and F bound are fluorescence emission intensities respectively of the free and of the bound drug, [Carrier] the concentration of the drug carrier and K a the affinity constant defined by the following relationship:

(2)

where [Drug] and [Drug -Carrier] are the respective concentrations of the free drug and of the drug-carrier complex. This method relies on the proportionnality of F free and F bound to the respective concentrations of these two forms, [Drug] and [Drug -Carrier]. However, in the particular case of TPP derivatives, this is not the case. Indeed, free drug is not an homogeneous form and covers in fact two different forms: an aggregated one (poorly fluorescent) and a solubilized one (moderately fluorescent).Then, fluorescence intensity of the free drug is no more directly proportional to its concentration, because it will depend on its agregation rate, which is probably inversely related with its concentration.

To overcome limitations of monowavelength monitoring in this particular case, multivariate curve resolution -alternating least squares (MCR-ALS) has been applied on fluorescence emission spectra recorded with different carrier concentrations [24]. MCR-ALS consists in the decomposition of this data matrix (D) into the product of two matrices: 1) a C matrix containing concentration profiles of the different species, 2) a S matrix with their fluorescence spectra.

D = C . S T + E (3)

E matrix represents difference between experimental values and data predicted by the model, that is residuals. Data analysis method proposed by Diewok for MatLab [25] has been adapted here to R software [START_REF]R: A Language and Environment for Statistical Computing[END_REF]. Optimization is based on als algorithm contained in the ALS package [START_REF] Van Stokkum | [END_REF]. High agregation of certain TPP derivatives combined with a strong affinity for some of the studied plasma carriers reduces contribution of the solubilized drug. In as far as fluorescence emission spectra of this particular species are the same whatever the carrier considered, a column-wise extended approach has been used to improve results. D matrix is constituted by spectra recorded on one TPP derivative with the four carriers studied : HSA, HSAlip, LDL, HDL. C and S matrices respectively contain concentration and spectra profiles of five species : the free solubilized drug and the four complexes formed by the TPP with each of the four carriers studied. Because of its poor fluorescence, the aggregated free drug is not included directly. Its presence is taken into account by applying no concentration closure constraint (sums of concentrations of the other species at each carrier concentration are not forced to be equal to one). For each carrier, concentration profile of the bound drug is adjusted to follow relationship (2), before subsequent spectra optimization. When further optimizations no more reduce residues' amount, the four binding constants are determined by non-linear regression of the concentration profile with equation (2).

Molecular docking simulations

Blind docking of TPP d e r i v a t i v e s i n t o h u m a n s e r u m a l b umin (PDB code 1AO6) was performed with AutoDock Vina 1.0 (exhaustiveness value of 100 and maximum output of 20 structures) [28]. Unsubstituted TPP crystal structure has been downloaded from the Cambridge Structural Database (MOLFEZ). After substituents' addition with UCSF Chimera, ligands were prepared for docking using AutoDock Tools to calculate Gasteiger charges and set active torsions (the four bonds between porphyrin core and phenyls, all rotatable bonds between the phenyl and the sugar residue). UCSF Chimera was used to visualize dockings, calculate contact surfaces and monitor hydrogen bonds. The selection of the main binding depended on the frequence of the different sites among the twenty output structures.

Results

Hydrophobicity of TPPs

As expected, glycoconjugation induces a decrease of hydrophobicity relative to the hydroxylated parent compound. Moreover, hydrophobicity is further reduced with increasing number of sugar residues. If these conclusions apply both to para and meta series, it is to note that para-derivatives are less hydrophobic than their meta isomers. Thus, CHI of TPP(pO GluOH) 4 (28.3) is lower than that of the TPP(mO GluOH) 4 (39.3). This also holds true for hydroxylated compounds, when comparing TPP(pOH) 4 (CHI=100.2) and TPP(mOH) 4

(117.2). Because of minor differences of hydrophobicity between mannose and galactose residues, the large CHI increase between T P P ( pO GalOH) 3 (CHI=40.

Distribution in human plasma

For eight of the ten studied compounds, more than 75 % of the sensitizer is found in lipoproteic fraction. Exceptions to this rule are constituted by the two paratetraglycoconjugated derivatives, TPP(pO GalOH) 4 and TPP(pO GluOH) 4 , lone compounds to be mainly bound -about 80% -to the proteic fraction. This behavior is particular striking when compared with t h e q u i t e e x c l u s i v e l i p o p r o t e i c t r a n s p o r t o f t h e metatetraglycoconjugated derivative. Among compounds majoritary bound to lipoproteins, the para-triglycoconjugated T P P ( pO GalOH) 3 p r e s e n t s a s i g n i f i c a n t l y h i g h e r p r o t e i n -bound fraction than other compounds, including TPP(pODEGO ManOH) 3 . Drug binding to proteic fraction concerns one quarter of the former but is negligeable in the case of the latter (less than 6%). This comparison shows that inclusion of a spacer between the sugar and the phenyle has a dramatic effect on plasma distribution.

HDL are main lipoproteic carriers o f photosensitizers. Indeed, with the exception of TPP(pO GalOH) 4 and TPP(pO GluOH) 4 , those structures bind more than half of sensitizer present in plasma. Binding to LDL is always minoritary, the highest proportion being reached with the TPP(mO GluOH) 4 .

Binding constants toward plasma proteins and lipoproteins

Binding of TPPs to plasma carriers induces spectral modifications, accounting for the disruption of TPPs aggregates upon formation of a complex between the TPP and the carrier. Those equilibria can be followed by absorption or fluorescence spectroscopies. In the absence of plasma carrier, absorption spectrum of TPP(pO GalOH) 3 presents a large Soret band at 417 nm, with a distinct shoulder at 437 nm, the latter resulting from the formation of J-aggregates. HSA addition leads to the disappearance of the 437-nm shoulder characteristic of aggregates, and to the appearance of a new intense band at 422 nm, which attests for the formation of the complex. Concerning fluorescence spectroscopy, binding of TPP to HSA induces a slight modification of spectral shape but a significant increase in fluorescence intensity.

If all TPP are likely to bind to LDL, HDL and HSA, affinities dramatically vary according to carrier and substitution of the TPP core. However, it is remarkable to observe that, whatever the TPP considered, affinities towards the different plasma carriers decrease when passing from LDL to HDL and finally to HSA (whether fatty acid free or not). Even compounds mainly bound to proteins in plasma (TPP(pO GalOH) 4 and TPP(pO GluOH) 4 ) present a higher affinity for LDL than for other studied plasma components. Those para-derivatives present higher affinity constants towards HSA and HSAlip t h a n t h e i r meta-homologous, an observation that applies whatever the substitution considered.

An other noteworthy result is the large difference in binding affinities for compounds with similar plasma distribution. That is the case of TPP(pOH) 4 and TPP(pODEGO ManOH) 3 , two compounds bound at ~85 % to HDL. Binding affinity to LDL and HSA is ten-fold higher for the former than for the latter. W h e n c o m p a r e d w i t h TPP(pO GalOH) 3 , TPP(pODEGO ManOH) 3 presents the same order of magnitude in their binding constants towards LDL and HDL. Spacer mainly affects binding to HSA, decreasing ten fold binding affinities, which could account for the lower protein binding of this compound when compared with TPP(pO GalOH) 3 .

Molecular docking simulations

Depending on their substitution, TPPs interact at different locations on the HSA molecule.

The most noticeable result is the impossibility for those bulky structures to insert into the two hydrophobic pockets that constitute Sudlow binding sites common to most drugs. It is difficult to privilegiate one binding site for non-glycoconjugated TPPs. Those structures are spread at different locations d e p e n d i n g o n their substitution. On the opposite, glycoconjugated porphyrins present preferential clusters.

If considering glycoconjugated porphyrins, the most noticeable result is the drastic effect of sugar position. Sugar nature and number don't seem to affect binding location. The two meta derivatives, TPP(mO GluOH) 3 and TPP(mO GluOH) 4 , bind on the same location in the inter-domain crevice whereas the three para derivatives without spacer share the same binding site. For the latter three compounds, TPP(pO GalOH) 3 , TPP(pO GalOH) 4 and TPP(pO GluOH) 4 , the tetrapyrrole is located between residues Q104 and K466, with two phenyles of both sides of residue K106.

TPP(mO GluOH) 3 binds between subdomains Ib and IIIa, with the TPP core located below residue R114. The three sugar residues insert into three polar pockets: i) the first formed by residues R114, R117, R186 and K519, ii) the second constituted by residues N109, S419, T422, K466 and T467, iii) the third composed by amino acids D108, H146, K190, R197 and Q459. In the case of the tetraglycoconjugated TPP(mO GluOH) 4 , three sugars insert in the same pockets, the fourth interacting with K524.

Of particular interest is the modulation of distribution pattern induced by the presence of the spacer. If this particularity doesn't prevent TPP(pODEGO ManOH) 3 from interacting at the same location than TPP(pO GalOH) 3 ), it favors binding on a site next t o that of TPP(mO GluOH) 3 , on a site inacessible to the tri-paraglycoconjugated derivative without spacer (TPP(pO GalOH) 3 ). In this particular conformation, the tetrapyrrole is close to residue P421, one sugar is located between residues Q33 and E86, one other between residues K419 and K500. The last mannose residue inserts into the third polar pocket described for TPP(mO GluOH) 3 .

The fact that sugar residues are suceptible to insert into polar pockets in the case of TPP(pODEGO ManOH) 3 o r meta-derivatives results in an higher contribution of the substituent in the interaction surface for those derivatives (table 3). For those particular structures, TPP ring is less accessible to solvent than in the case of para derivatives without spacer. This latter fact is confirmed by the percentage of the TPP nucleus involved in the interaction (table 3). Interaction surfaces i n c r e a s e w i t h i n c r e a s i n g s u r f a c e s o f t h e T P P derivatives. The main exception to this rule is para-tetraglycoconjugated derivatives, their interface surfaces being lower than that of TPP(pO GalOH) 3 . This fact probably results from the rigidity of para-conformation, which induces a reduced possibility to insert into favorable pockets upon increasing molecular volume. Indeed, flexibility of meta-derivatives confers to those derivatives the ability to form higher interface surfaces with the protein than para derivatives. Analysis of interaction modalities shows that TPPs interact with HSA mainly through hydrophobic interactions but also through hydrogen bonds. The latter, which are stronger interactions, mainly concern glycoconjugated compounds, due to their increased number of hydroxyle groups.

Discussion

Plasma distribution of photosensitizers

Plasma distributions of glycoconjugated TPPs are consistent with common considerations on the relationships between plasma distribution and hydrophobicity. Differences in hydrophobicity mainly result from differences in exposure of the TPP ring due to the presence of polar substituents. This principle accounts for the effect of substituent's nature and number but also position. Indeed, para-substitution confers to the molecule a planar conformation different from the globular conformation resulting from meta-substitution. The latter allows an easier access to the hydrophobic TPP core.

Binding to the proteic fraction of para-tetraglycoconjugated derivatives can be explained by the more pronounced hydrophilic character of those compounds. TPP(pO GalOH The effect of para-glycoconjugation appears similar to that of para-sulfonation as described by Kongshaug [START_REF] Hasan | Cancer medicine[END_REF]: only the tetrasubstituted compound binds mainly to proteins, other derivatives (whether mono-, di-or tri-sulfonated) bind mainly to lipoproteins, majoritarily HDL.

Binding to LDL is commonly associated with the hydrophobic character of TPPs. However, in our series, there is no correlation between proportion bound to LDL and CHI. This finding is similar to that described in the case of the sulfonated TPPs : a disulfonated TPP presents a higher proportion bound to LDL than the more hydrophobic monosulfonated derivative [START_REF] Hasan | Cancer medicine[END_REF].

Moreover, in our series, similar hydrophobicities do not imply similar distribution patterns, as can be evinced by comparing TPP(pO GalOH) 3 and TPP(mO GluOH) 4 .

From plasma distribution to binding constants

The most striking conclusion of the comparison between plasma distribution and binding constants is that even compounds predominantly bound to proteins in plasma have a higher affinity towards lipoproteins, especially LDL. This striking result recalls that relative affinities towards separated plasma carriers is just a part of its plasma distribution, the latter being also the result of relative concentrations of plasma carriers. Involvement of plasma protein and lipoprotein concentrations h a s b e e n u n d e r l i n e d b y K o n g s h a u g i n t h e c a s e o f hematoporphyrin [29]. This compound presents a majoritary binding to HDL in plasma, despite a higher affinity towards LDL than towards HDL. Thus, plasma distributions of TPP(pO GalOH) 4 and TPP(pO GluOH) 4 are not the consequence of a particular affinity towards albumin, but the result of a ratio of affinities towards lipoproteins and albumin not high enough to overcome the difference in the concentrations of those carriers. Indeed, albumin is the most abundant p l a s m a p r o t e i n ( 0 . 5 -0.8 mM) whereas lipoprotein concentration is much lower (~1 µM for LDL and 13µM for HDL).

Despite presumed protein-affinity of hydrophilic compounds, there is no correlation between affinity towards HSA and CHI. Hydrophilic compounds, such as TPP(mO GluOH) 4 , present low binding constants but it is also the case of most hydrophobic structures such as TPP(mOH) 3 . Highest binding constants are characteristic of compounds (TPP(pOH) 4 , TPP(mOH) 4 or TPP(pO GalOH) 3 ) with intermediate hydrophobicities. On the contrary, TPPs' affinity towards lipoproteins can be globally accounted for by their hydrophobicity. Affinity increase with CHI applies both to HDL and LDL but is more pronounced in the case of the latter. This observation can be linked to the classical idea of a preferential binding of more hydrophobic structures to LDL. However, this rule knows exceptions and in the studied series, despite correlation of affinity with CHI, proportion of LDL-binding is not correlated with hydrophobicity. The latter fact is the consequence of the absence of correlation between affinity towards HSA and CHI.

Similar considerations should explain an exception to the classical rule reported by Hasan.

Protoporphyrin and hematoporphyrin bind in the same proportions to plasma proteins despite the higher hydrophobicity of the former. This result must be viewed as the consequence of the difference in substitution which confers a much higher affinity towards albumin for protoporphyrin (280.10 6 M -1 ) than for hematoporphyrin (1,4.10 6 M -1 ). This albumin affinity increase counterbalances the probable hydrophobicity-induced increase in affinity towards lipoproteins, resulting in a similar plasma distribution.

Interactions with Human Serum Albumin

Contrary to HDL-and LDL-affinities, an increase in hydrophobicity doesn't result in an increased affinity towards albumin. Confronted with similar observations, some authors have underlined the importance of the amphiphilic character of the photosensitizer in its interactions with proteins [30]. Those conclusions strengthen the interest of docking simulations to better understand phenomena governing interactions between TPPs and HSA.

Docking results have shown that substitution affects location of the TPP derivative on the protein. Moreover, they have led to exclude interactions at classical drug binding sites I and II, unlike what has been described for some sensitizers: chlorin p6, purpurin 18 [32] or bacteriochlorin derivatives [33]. This difference probably results from steric difference between those tetrapyrroles not bearing phenyles at meso positions and the bulky TPP core.

Results obtained with other tetra-parasubstituted TPPs conclude to a binding at the surface of the albumin molecule, a result consistent with our findings. Fluorescence lifetime studies performed on a series of sulfonated phthalocyanines have shown that degree of sulfonation influences insertion in hydrophobic pockets. Tetrasulfonated derivative bind at the surface of the protein whereas lower sulfonation degree allows insertion into hydrophobic cavities [34].

However, effect of substituent is o n l y p a r t l y s t e r i c . It also plays a r o l e i n i n t e r a c t i o n s modalities between sensitizer and HSA. Sulfone groups could form ionic interactions with basic amino acids ( h i s t i d i n e a n d l y s i n e ) , an hypothesis strengthened by sensitivity of interactions to ionic strength [35].

The double acting effect of the substituent, likely to form direct interactions with HSA but also to induce steric limitations, also applies to our series of hydroxylated and glycoconjugated porphyrins. Glycoconjugated derivatives form more hydrogen bonds than hydroxylated ones, and meta-derivatives more than para-derivatives. However, even when glycoconjugated, TPP derivatives interact with the protein mainly through hydrophobic interactions. The direct involvement of the substituent in the binding distinguishes TPP interactions with proteins from their interactions with the C18 surface in the HPLC experiments. Indeed, CHI values are highly correlated with ratios of TPP nucleus surface to the total TPP derivatives surface (r² = 0,94 when excluding the highly flexible TPP(pODEGO ManOH) 3 ), which illustrates the probable lack of direct interactions between the substituent and apolar surfaces. In the case of interactions with albumin, susbtituents interact directly with the protein, especially if the TPP derivative possesses some flexibility (case of meta-derivatives and TPP(pODEGO ManOH) 3 ). Rigidity of planar para-derivatives prevents them to form specific interactions with albumin, which could explain the absence of difference in distribution pattern between TPP(pO GalOH) 4 and TPP(pO GluOH) 4 despite modification of the nature of sugar residue. This observation also applies to the respective affinities of those particular derivatives.

When compared with the more widespread distribution pattern of para-derivatives, metaderivatives seem to present stronger and more specific interactions. This result, conflicting at the first sight with affinity constants (higher in the para series), should maybe be considered differently: globular conformation of meta-derivatives prevents them from interacting at the surface of albumin molecule, thus restraining their possible binding sites. In this perspective, higher overall binding constants measured on para-derivatives could result from a higher number of sites of almost equivalent affinities.

Considerations about the particular affinity for LDL

Photosensitizers are likely to interact with lipoproteins according to two modes, whether with the proteic portion and/or with the lipidic one [36]. Existence of high affinity sites on apoprotein coexisting with secondary solubilization in lipidic portion has been supposed in the case of interactions of chlorin e6 with LDL [37]. If global binding constant is of the same order of magnitude than that obtained for glycoconjugated TPPs, a preferential binding to apoprotein is unlikely for the latters. Good correlation between affinity towards lipoproteins and hydrophobicity tend to privilegiate the idea of an interaction with the lipidic portion. It seems probable that interactions of TPPs with the hydrophobic stationnary phase in HPLC are quite similar to their interactions with the hydrophobic lipidic portion. Moreover, lower binding affinity towards lipoproteins of glycoconjugated derivatives -likely to interact strongly with proteic portion through hydrogen bonding -reinforces the hypothesis of an interaction with the lipidic portion. At last, this hypothesis is confirmed by comparison with affinities of TPPs towards liposomes [38]. Ranking of binding affinities towards those phospholipidic vesicules is close to that obtained with HDL.

Difference in binding affinities towards HDL and LDL leads to consider a possible role of certain lipids in the preferential binding of TPPs to LDL than HDL. Interactions of hypericin with biological membranes have shown that this structure presents a particular affinity for cholesterol [39], a fact that could account for its location in LDL, between hydrophobic core and phospholipid shell [40]. Involving cholesterol is unlikely for our compounds, more amphiphilic than hypericin, and thus less able to insert deeply in the lipoprotein core. This hypothesis is supported by studies o f i n c l u s i o n o f d e n d r i m e r i c p o r p h y r i n s i n b i o l o g i c a l membranes, that show no impact of cholesterol proportion [41], contrary to what could have been described for others photosensitizers, such as deuteroporphyrin [42]. Preferential affinity for LDL than for HDL could result from differences in surface properties: LDL surface is less hydrophobic and its outer layer is more fluid [43]. More hydrophobic character of HDL surface results from the presence of more triglycerides and cholesterol esters in the outer layer [44]. Combined together, amphiphilic structures could better interact with LDL, insertion of hydrophobic pole being easier and interaction of hydrophilic part with the surface being favored.

Conclusion

Those observations give a new insight in plasma distribution. Increasing hydrophobicity should orientate distribution towards LDL, whereas lowering this parameter results in a majoritary protein binding. Exceptions to this rule should result from specific interactions between a photosensitizer and a carrier, interactions not directly related to its hydrophobicity.

Our s t u d y also shows that measuring the fraction bound to LDL is not sufficient to understand the behavior of TPPs in plasma. Binding constant determinations are essential. If it is commonly admitted that plasma distribution plays a decisive role in orientating biodistribution, binding affinities are likely to affect photosensitizer's ability to pass from the carrier to its final target, a fact that should not be underestimateed when reconsidering the link between plasma behavior and tumor localization.

6. 1. Defined as the ratio between the surface of the substituent in contact with the protein and the total surface of the TPP derivative interacting with the protein

  in childhood. I n d e e d , b e s i d e s p o o r e f f i c i e n c y f o r a d v a n c e d t u m o r s , c u r r e n t l y a v a i l a b l e

  meso-tetraphenylporphyrin derivatives, six o f w h i c h a r e g l y c o c o n j u g a t e d a c c o r d i n g t o different modalities, and thus different lipophilicities. The aim is, beyond a description of the relationship b e t w e e n s t r u c t u r e a n d p l a s m a d i s t r i b u t i o n , t o better understand factors governing interactions of TPP sensitizers with plasma proteins and lipoproteins.

  An intermediate dilution of TPP stock solutions in pH 7.4 phosphate buffer saline (PBS) was used to prepare mixtures of a TPP with the studied plasma carrier (HSA, HSA-LIP, HDL ou LDL). Dimethylsulfoxide final proportion in this solution was 0.5 %. TPP final concentration was 1.10 -7 M for fluorescence measurements and 5.10 -7 M for absorption study. Transporter concentration varied from 0 to 1.10 -4 M. The mixtures were kept in darkness at 37 ºC for 24 hours. UV -V i s i b l e a b s o r p t i o n s p e c t r a w e r e r e c o r d e d o n a V a r i a n ® C a r y B i o 1 0 0 spectrophotometer (Australia), with an optical path of 10 mm and a slit width of 2 nm.

  8) and TPP(pODEGO ManOH) 3 (CHI=62.4) s h o u l d b e a t t r i b u t e d t o t h e p r e s e n c e o f a s p a c e r between the sugar and the phenyle. The para-derivative with the spacer is even more hydrophobic than the meta-triglycoconjugated derivative, TPP(mO GluOH) 3 (CHI=55.7).

  ) 3 presents an intermediate CHI and an intermediate behavior between hydrophilic protein-bound derivatives and more hydrophobic compounds quite exclusively bound to lipoproteins. The latter compounds present the typical behavior of amphiphilic compounds, mainly bound to HDL. Binding to LDL concerns always a minoritary proportion of TPPs on the studied series.

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Structure of meso-tetraphenylporphyrin derivatives

Figure 4 .

 4 Figure 4. Binding sites of glycoconjugated TPPs according to blind docking results

Table 1 .

 1 Plasma distribution of meso-tetraphenylporphyrin derivatives

	Lipoproteins

Table 2 .

 2 Binding affinities of meso-tetraphenylporphyrin derivatives (expressed as log K a )

	Albumin	Lipoproteins

Table 3 .

 3 Properties of interface surfaces between HSA and the different TPP derivatives

		Interface surface	Percentage of the TPP	Contribution of
					surface	the substituent
		Polar	Apolar	Total	involved in the	in the interaction 1
					interaction
	TPP	129.6	315.1	444.7	35.1%	0.0%
	TPP(mO GluOH) 3	296.3	412.4	708.7	34.4%	64.4%
	TPP(mO GluOH) 4	391.1	530.3	921.4	32.9%	62.6%
	TPP(mOH) 3	121.9	297.1	419.0	37.7%	11.5%
	TPP(mOH) 4	134.7	271.4	406.1	42.9%	23.4%
	TPP(pO GalOH) 3	200.6	404.7	605.3	27.9%	55.5%
	TPP(pO GalOH) 4	276.2	305.2	581.4	25.9%	52.7%
	TPP(pO GluOH) 4	260.3	304.7	564.9	29.0%	54.3%
	TPP(pOH) 3	97.4	234.2	331.5	28.0%	11.4%
	TPP(pOH) 4	94.7	216.4	311.1	24.5%	11.5%
	TPP(pODEGO ManOH) 3	352.9	464.5	817.4	30.6%	69.5%

Acknowledgements

B. Chauvin has benefited from a "Postes d'accueil CNRS -CEA -APHP" grant. The authors thank technicians of HEGP Biochemistry service for their precious contribution to plasma distribution studies.

7.

-(para-O--D-glucopyranosyloxyphenyl)porphyrin TPP(pODEGO ManOH) 3 : 5,10,15-tri{para-O-[(2-(2-O--D-mannosyloxy)-ethoxy)-ethoxy