Drug-Specific Design of Telodendrimer Architecture for Effective

Article pubs.acs.org/JPCB

Drug-Specific Design of Telodendrimer Architecture for Effective Doxorubicin Encapsulation Wenjuan Jiang,‡ Xiaoyi Wang,‡ Dandan Guo,† Juntao Luo,† and Shikha Nangia*,‡ †

Department of Pharmacology, Upstate Cancer Center, SUNY Upstate Medical University, Syracuse, New York 13210, United States Department of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York 13244, United States

‡

S Supporting Information *

ABSTRACT: Designing a versatile nanocarrier platform that can be tailored to deliver specific drug payloads is challenging. In general, effective drug encapsulation, high drug-loading capacity, uniform shape and size distribution, and enhanced stability are among the fundamental attributes of a successful nanocarrier design. These physiochemical features of the nanocarriers are intimately tied to the specific drug payload that they are tasked to deliver. The molecular architecture of the nanocarrier’s scaffold often needs to be tuned for each drug, especially if the target drugs are structurally and chemically distinct as in the case of doxorubicin (DOX) and paclitaxel (PTX). Starting from our previously reported telodendrimeric block copolymer platform optimized for PTX, we analyze three generations of telodendrimer architectures to arrive at the design that is capable of encapsulating another important chemotherapeutic drug, DOX. Multiple long-time-scale self-assembly simulations were performed both in atomistic and coarse-grained resolutions to generate equilibrated DOX-encapsulated nanocarriers. The results show how subtle changes in the molecular architecture of the telodendrimer head groups have profound effects on the nanocarrier size, morphology, and asphericity. The simulation results are in agreement with the experimental data for DOX-encapsulated nanocarriers. This work emphasizes the increasing role of molecular simulations in the rational design of nanocarriers, thereby eliminating the trial and error method that has been prevalent in experimental synthesis. The molecular-level insights gained from the simulations will be used to design the next generation of drug-specific nanocarriers.

1. INTRODUCTION The last two decades have witnessed the emergence of medicinal nanotechnology, where therapeutic drugs are encapsulated as cargo in nanosized carriers for targeted delivery to the tumor sites.1−9 Designing nanocarriers for anticancer treatment requires special attention because drug delivery to solid tumors encounters numerous physiological and transport barriers, for example, the phagocytic clearance and the heterogeneity of cancers.10−15 The impaired vasculature in a solid tumor provides the opportunity for small nanoparticles with sizes of 20−100 nm to selectively accumulate at tumor sites, which is well known as the enhanced permeability and retention (EPR) effect.16−18 Tumor-targeting ligands can be conjugated on the surface of nanocarriers to further improve the tumor-targeting properties of nanocarriers. However, aside from the active targeting, the physiochemical properties of nanocarriers are important to determine the in vitro drug encapsulation efficiency and the in vivo fate of drug-loaded nanoparticles, such as drug-loading capacity, particle size distribution, stability, blood circulation time, systemic clearance, biodistribution, and tumor targeting. Although drug-delivery nanocarriers are based upon the simple premise of the guest−host partnership, the specific © XXXX American Chemical Society

encapsulation of therapeutics in a nanoparticle core with controlled particle sizes, stability, and navigation to target sites remains a challenge. It is desired that nanocarriers encapsulate the guest drug in their core via structural relationships and nonbonded interactions.19,20 Therefore, the core-forming domain of the nanocarrier should be tailored to include drugbinding moieties that can stabilize drug molecules and enhance the nanocarrier’s drug-loading properties. A number of promising platform technologies have emerged to date; however, the flexibility and tunability of nanocarrier scaffolds have been shown to be critical for the successful encapsulation of various therapeutic cargoes by incorporation of a variety of drug-binding moieties into the nanocarrier. Luo and co-workers developed a versatile telodendrimerbased nanoplatform that forms core−shell micellar nanocarriers capable of efficient anticancer drug delivery. 21−26 The telodendrimer is a linear dendritic copolymer (Figure 1), composed of a polyethylene glycol (PEG)-tethered dendritic polylysine scaffold linked to the core-forming peripheral Received: June 16, 2016 Revised: August 10, 2016

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DOI: 10.1021/acs.jpcb.6b06070 J. Phys. Chem. B XXXX, XXX, XXX−XXX

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The Journal of Physical Chemistry B

Figure 1. Atomistic structure of (a) G1 PEG5KCA8, (b) G2 PEG5KRH4CA4, and (c) G3 PEG5KCA4-L-RH4 telodendrimers composed of building blocks PEG (gray), LYS (red), CA (blue), RH (green), and L (yellow).

line with the commercially available DOX nanoformulation, Doxil.28 In vivo anticancer studies of G2 and G3 nanocarriers with encapsulated DOX exhibited significantly improved anticancer effects than those of G1 telodendrimers, the free DOX and Doxil. Although G2 and G3 nanocarriers have the same building blocks, the G2 nanocarrier had a slower DOX release profile and better tolerance in animals than those of the G3 nanoformulation. This is mainly due to the different architectures of telodendrimers, which result in different molecular packings and morphologies of nanoparticles after drug loading, for example, more elongated micelles were observed for G3 than for G2 nanocarrier using transmission electron microscopy (TEM).28 Therefore, it is important to study the impact of the chemical structure and molecular architecture of the telodendrimer on the drug−nanocarrier interactions and the size and morphology of nanoparticles formed. Computational approaches are powerful in simulating the self-assembly process and offer insight into molecular packing and interactions. Given these experimental details and results, we established a computational methodology to study the features of the telodendrimer platform that could benefit the nanocarrier design for the encapsulation of other therapeutics. We conducted a number of computational studies that allowed us to simulate the self-assembly process for DOX encapsulation in molecular detail and characterize the physical interactions between DOX and RH. This enabled us to determine the role of CA in the drug-binding core domains and determine the cause of the slower DOX release observed in G2 versus G3 nanocarriers. These studies required that we bridge multiple lengths, from Angstroms to nanometers, and time scales from femtosecond to microsecond, for a large set of particles (∼1.3 million atoms) that undergo macromolecular self-assembly in the formation of the drug-encapsulated nanocarriers. Simulating such large systems in molecular detail is arduous and computationally expensive even with the state-of-the-art resources. To reduce computational cost, we used a multiscale approach and first mapped the system to a coarse-grained (CG) representation.27 This allowed us to take advantage of the reduced number of particles in the system and larger integration time steps in dynamics. Next, we reverted to the atomistic level to analyze the assembled nanocarrier by using an

groups. The chemical functionality of the core domain is designed to interact with the desired drug via nonbonded interactions, resulting in self-assembled micelles with the drugencapsulated core. The PEG domain provides the hydrated exterior that is important for nanocarrier stability and prolonged blood circulation times. In addition to the versatility of telodendrimer design, the precise control of its synthesis using efficient peptide chemistry lends itself to easy chemical manipulation. In our previous work, we showed that first-generation (G1) telodendrimers, with a core-forming domain of amphiphilic cholic acid (CA) groups, self-assemble into a highly efficient nanocarrier for paclitaxel (PTX) encapsulation up to 37% w/ w.27 The CA residues are key to the high PTX drug loading in this G1 telodendrimer by virtue of their ability to interact with the hydrophobic PTX and simultaneously interface with the hydrophilic shell to form a stable nanocarrier. Using a systematic approach, each copolymer block of G1 telodendrimer PEG5KCA8 (5 kDa PEG chain; 8 CA residues) was optimized to yield stable monodispersed nanocarriers with 19 ± 4 nm diameter size, optimal for enhanced EPR effects.27 However, this G1 telodendrimer is not optimal for the structurally and chemically different drugs, for example, doxorubicin (DOX).28 Unlike PTX, DOX is a polycyclic aromatic molecule that forms self-aggregated stacks in solution via nonbonded intermolecular π−π interactions. The π−π stacking motif is also responsible for the chemotherapeutic action of DOX and allows it to intercalate between DNA base pairs and inhibit both DNA replication and RNA transcription, resulting in cell death. To achieve optimal DOX loading using the telodendrimer platform, a physiochemically compatible molecule needs to be included in the core-forming domain of the telodendrimer. Therefore, second- and third-generation (G2, G3) nanocarriers were designed (Figure 1) with CA and Rhein (RH) in the core drug-binding domain. Compared to G1 telodendrimers (PEG5KCA8) that contain a homotypic core of eight CAs, G2 telodendrimers contain a hybrid core of CA and RH (PEG5KRH4CA4), whereas G3 telodendrimers contain these two groups (PEG5K-CA4‑L-RH4) spatially segregated by a short PEG linker (L). The G2 and G3 nanocarriers improved the stability with sustained DOX release and prolonged circulation, which are in B


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The Journal of Physical Chemistry B Table 1. Summary of Intermolecular Distances (nm) for DOX and RH Molecule Pairs quantum

atomistic

CG

molecule pairs

planar

T-shaped

planar

T-shaped

planar

T-shaped

DOX−DOX DOX−RH RH−RH

0.445 ± 0.002 0.446 ± 0.002 0.451 ± 0.001

0.692 ± 0.009 0.702 ± 0.007 0.522 ± 0.010

0.508 ± 0.10 0.516 ± 0.11 0.468 ± 0.09

0.724 ± 0.13 0.740 ± 0.10 0.578 ± 0.11

0.512 ± 0.12 0.522 ± 0.11 0.470 ± 0.09

0.730 ± 0.04 0.746 ± 0.10 0.580 ± 0.08

Figure 2. Snapshots of atomistic (a) DOX−RH and (b) DOX−CA along with their coarse-grained simulations (c) and (d), respectively. Color scheme: DOX (purple), CA (blue), and RH (green). Water is not shown for clarity.

parameterization of any system requires careful benchmarking to ensure that the high-fidelity representation of the underlying system faithfully captures overall system dynamics.27,29 Once the dynamical process is complete, it is critical that the CG system is mapped back to the atomistic detail to reveal the molecular level structural and chemical properties. In developing the multiscale model for this work, we performed (1) high-level density functional calculations for DOX−DOX, RH−RH, and DOX−RH small-molecule pairs to determine nonbonded π−π stacking interactions and (2) AA molecular dynamics (MD) simulations with explicit solvent to obtain structural and conformational properties important for parameterizing CG models. (3) Then, we extended our G1 CG model to include DOX and RH in G2 and G3 telodendrimers and benchmark the developed CG force-field parameters by a direct comparison to those in AA simulations. Finally, we (4) conducted dynamical self-assembly CG simulations to form DOX-loaded G2 and G3 nanocarriers until equilibrium was

in-house mapping scheme developed for the telodendrimer system. Our approach of multiscale forward and reverse mapping consolidates the strengths of the CG and all-atom (AA) resolutions for the complex chemical system at hand. The results indicate a strong codependence of drug−drug and drug−host interactions on the overall morphology (size, shape and anisotropy) and stability (drug loading, efficacy, and circulation lifetimes) of the nanocarrier.

2. MULTISCALE APPROACH A multiscale modeling approach was adopted to span multiple length and time scales to provide a CG description of the system and capture its ensemble-averaged properties at the atomistic level. By coarse-graining, the prohibitive cost of computing thousands of degrees of freedom at the atomistic level is eliminated, resulting in at least 2 orders of magnitude enhancement in simulation time scales. However, CG C


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Figure 3. CG mapping schemes for (a) RH and (b) DOX from their respective atomistic structures, along with CG representations of (c) G1, (d) G2, and (e) G3 telodendrimers. Color scheme of the CG beads: PEG (gray), LYS (red), CA (blue), L (pink), RH (green), and DOX (purple); atomistic system: C (cyan), O (red), N (blue), and H (white).

achieved and (v) compared these results with experimental data. 2.1. Quantum Calculations. Stable geometries of noncovalent complexes of DOX−DOX, DOX−RH, and RH−RH (in the absence of water) were computed using the M06-2X density functional suite30 with the 6-311+G(d,p) basis set. Because of the aromatic rings, both DOX and RH self-aggregate via π−π interactions.31,32 In the case of the DOX−DOX complex, two stable dimers were observed, one with displacedantiparallel orientation of the DOX molecules and the other with displaced-parallel orientation, with an interplanar DOX− DOX distance of 0.445 nm in both cases (Table 1). A third, less stable nonplanar T-shaped dimeric structure was also observed with a plane-to-center distance of 0.692 nm. The DOX−RH complex revealed a planar minimum-energy structure with an interplanar distance of 0.446 nm (similar to DOX−DOX) and a T-shaped dimer with a plane-to-center distance of 0.702 nm. The minimum-energy structure of the RH−RH homodimer was also planar with an interplanar distance of 0.451 nm. 2.2. Atomistic Simulations. The energy-minimized DOX and RH structures from electronic structure calculations were used to generate the atomistic force-field parameter using the PRODRG online server.33 PRODRG is a useful tool for small molecular parameterization, and it is capable of creating molecular topologies compatible with a variety of MD packages, including GROMACS. On the basis of the geometry of the small molecule (text drawing or three-dimensional (3D) coordinate), PRODRG identifies the hybridization state and chirality of the heavy (nonhydrogen) atoms and matches them to the GROMOS atom types. Using a bond distance cutoff parameter, a connection table is generated between heavy atoms, which is used to assign parameters for bond lengths, bond angles, and dihedral angles from the GROMOS forcefield parameter set. The generality of the method has led to diverse applications of the PRODRG tool. In this work,

PRODRG DOX and RH topologies were used for structure equilibration of these molecules in water solvent and to assess the π−π stacking between DOX−DOX, RH−RH, and DOX− RH. Table 1 summarizes the distances observed in the planar and T-shaped conformations for DOX and RH pairs. 2.3. CG Simulations. CG mapping of RH and DOX was developed individually from their equilibrated atomistic structures. On average, four heavy atoms were assigned to one CG bead, following the protocol used in our previous work. The CG beads were assigned MARTINI bead types based on their polarity and hydrogen-bonding tendencies.34,35 To validate the RH and DOX CG models and benchmark the interactions in the DOX-encapsulated nanocarrier core, we performed three sets of simulations in both atomistic and CG representations that included DOX−RH, DOX−CA, and DOX−RH−CA in explicit solvent. The DOX−RH simulations show propensity of DOX−DOX and RH−RH self-stacking as well as DOX−RH co-stacking via nonbonded π−π interactions (Figure 2). A comparison of the average interplanar distances of the π−π stacked molecules showed good agreement between the AA and CG simulation results (±0.16 nm) as well as with the quantum calculations (Table 1). Similarly, nonplanar T-shaped interfaces also showed good agreement. Because of the larger size of the individual CG bead, the interplanar distances are slightly larger than those in the atomistic system; however, they are within the margin of error. Furthermore, the interaction of DOX and CA in both DOX−CA simulation and in the presence of RH in DOX− RH−CA simulation shows that CA binds to the DOX π−π stacks in a peripheral manner because of nonplanarity and inability to form intercalated stacks (Figure 2) in both AA and CG resolutions. Unlike DOX−CA, π−π stacking is observed between DOX and RH molecules, which helps build the stable nanocarrier core. D


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performed with the GROMACS 4.5.5/5.0 software package.38 Each system was energy-minimized using the steepest-decent algorithm with a 20 fs time step. The systems were then equilibrated for 10 ns in an isothermal−isochoric NVT ensemble (fixed number of particles N, volume V, and temperature T) at T = 300 K, followed by 10 ns in an isothermal−isobaric NPT (fixed number of particles N, pressure P, and temperature T) ensemble at P = 1 atm and T = 300 K. The production runs were performed for 3 μs using 20−40 fs time steps in the NPT ensemble using the Nosè−Hoover thermostat at 300 K. Isotropic pressure coupling was used to maintain P = 1 bar using the Parrinello−Rahman barostat.39−41 The nonbonded interaction terms were computed with the standard cutoff of 1.2 nm. Using the standard shift function, the Lennard-Jones potential was shifted from r = 0.9 nm to the cutoff distance so that both the energy and force smoothly vanish at the cutoff value. Simulations for each of the G1−G3 systems were repeated three times with a new initial configuration and a new initial random seed for velocity distribution. The polarized water model was tested as a solvent but due to the nonionic nature of the system, standard MARTINI water (with 10% antifreeze) was sufficient for all of the simulations presented here. All CG simulations were run for 3 μs; trajectory data (i.e., coordinates, velocities, and forces) were saved with 1 ns frequency, and energy data were saved at 0.1 ns time intervals. For atomistic simulations, energy minimization was performed until the maximum force on any atom was below 100 kJ mol−1 nm−1. For the production runs’ NPT simulations, temperature was maintained at 300 K using velocity-rescale(rescaling) and the pressure was maintained at 1 bar using the Parrinello−Rahman barostat42 with a pressure time constant of 0.1 ps. The nonbonded interactions were simulated with a cutoff scheme with the van der Waals and real-space Coulomb interaction cutoffs of 1.4 and 0.9 nm, respectively. The particle mesh Ewald method was used for long-range electrostatic interactions with 0.12 nm grid spacing. Bond lengths were constrained with LINCS. To analyze the stacking motifs and to classify them as planar and T-shaped, we developed an in-house Fortran 90 code to compute the angle between the adjoining RH−RH, DOX− DOX, and RH−DOX. Gromacs utility g_sas was employed to compute the solvent-accessible surface area (SASA) of the drug molecules with a solvent probe size of 0.56 nm following the method used previously for CG systems. To quantify the morphologies of the nanocarriers, the asphericity factor (A) was computed using the gyration tensor for each micelle.43 The value of the asphericity factor varies as 0 ≤ A ≤ 1, where A = 0 for spherical micelles and A = 1 for perfect cylinders. Furthermore, a systematic protocol was employed to compute the average size of the nanocarrier micelles because at any given time during the simulation, there were multiple micelles in the system. First, individual micelles in the system were isolated for asphericity and size analysis. For spherical micelles with A ≤ 0.4, the COM was computed for the encapsulated drug molecules and core domain groups (without the hydrophilic PEG beads). For rest of the micelles with A ≥ 0.4, the COM was computed along the contour length of micelles in segments of 2 nm persistence length. The hydrophilic shell size was determined by computing the distance of each bead in the telodendrimer from the COM of the micelle or segment and using the distance of the farthest bead as the radius.

The benchmarked small molecules were then incorporated into the G2 and G3 telodendrimers by catenation of individual CG building blocks; for example, for the G2 telodendrimer, 112 PEG beads, 7 LYS units, 4 RH, and 4 CA were combined (Figure 3). The CG topologies of G2 and G3 telodendrimers (including the CG bead types and the bonded and nonbonded parameters) are provided in the Supporting Information. 2.4. Reverse Mapping (CG to AA). We developed a reverse-mapping scheme for structural analysis of DOXencapsulated G2- and G3-based nanocarriers. The two-stage scheme starts with reverse mapping of DOX and five telodendrimer building blocks (PEG, LYS, CA, RH, and linker L). For each bead, geometric projection of the atomistic sites was computed in the 3D Cartesian space. At the end of each 3 μs simulation, the entire CG system was mapped onto its atomistic representation using an in-house MATLAB code. For example, the CG PEG chains were mapped back to three atomic centers of ethylene glycol repeat unit (C−C−O); only heavy atoms were considered and hydrogen atoms were ignored in this mapping scheme. The CG lysine was reversed from 3 beads to 9 atoms based on its chemical structure; CG CA was reverse-mapped from 7 beads to 28 atoms; CG RH was reverse-mapped from 9 beads to 20 atoms; and DOX was reverse-mapped from 13 beads to 39 atoms. All of these backward mapping assignments follow their original atomistic structures. Standard MARTINI water was reverse-mapped back to four explicit water molecules. The resulting local structure is then optimized to match the center of mass (COM) of the atomistic configuration to the corresponding CG site by using energy minimization using steepest descent or conjugate gradient methods. In the second stage, reverse-mapping algorithms are used to map micelles and relax the structure through energy minimizations. The resulting systems were relaxed with several short cycles of energy minimization using the CHARMM36 force field.36

3. METHODS The MD simulations were performed in the CG representation for G1−G3 (PEG5kCA8, PEG5KRH4CA4, PEG5K-CA4‑L-RH4) systems with 0−30% DOX loading. A total of 12 systems were studied (Table 2), in which the telodendrimers, drug molecules, and CG water molecules were randomly packed in a cubic simulation box using Packmol37 scripts. The simulations were Table 2. Simulation Systems number of units in simulation box system

DOX/telo (% w/w)

generation

box size (nm)

water

DOX

telo

I II III IV V VI VII VIII IX X XI XII

0 10 22 30 0 10 23 30 0 10 23 30

G1 G1 G1 G1 G2 G2 G2 G2 G3 G3 G3 G3

25.0 25.0 35.0 35.0 25.0 25.0 35.0 35.0 27.0 35.0 35.0 35.0

125 377 127 024 279 330 361 062 125 357 126 783 267 714 360 727 127 126 362 724 320 570 362 072

0 140 614 614 0 140 614 614 0 140 614 490

112 79 135 89 112 81 135 93 112 84 135 76 E


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Figure 4. Empty telodendrimer micelles. Snapshots of self-assembled micelles in simulations and the corresponding TEM image for (a,b) G2 (system V) and (c,d) G3 (system IX). Color scheme: RH (green) and CA (blue); PEG and water are not shown for clarity. (e) Population analysis of RH−RH stacking in G2 (blue) and G3 (orange) micelles in systems V and IX, respectively.

PEG5KRH8 prevents the availability of the RH head groups for intermolecular π−π interactions essential for micellization. For this same reason, PEG5KRH8 is also not a suitable candidate for DOX encapsulation. We observed that the presence of four CA and four RH groups in the core domain of G2 PEG5KRH4CA4 prevents intramolecular π−π stacking and enables the telodendrimer to self-assemble into a micelle, albeit wormlike (system V) with A = 0.41, average contour length of 12.1 ± 0.8, and diameter of 8.5 ± 0.3 nm. The micelles were in dynamic equilibrium at 3 μs and were well correlated morphologically to the experimental observation. TEM (Figure 4a) shows monodispersed micelles reported in the absence of DOX. Remarkably, the spatial segregation of CA and RH domains in the G3 PEG5K-CA4-LRH4 telodendrimer has a profound effect on micellization (system IX). The micelles were longer with A = 0.68, average length of 16.6 ± 0.7, and diameter of 7.9 ± 0.4 nm and are in good agreement with the TEM images (Figure 4b). To determine the origin of the difference in the morphology of the resulting micelles, molecular analysis of individual RH− RH pair interactions was performed and classified into three motifs: planar, T-shaped, and intermediate. In the case of G2 PEG5KRH4CA4, the population distribution of each binding model is planar (34%), T-shaped (33%), and intermediate (33%) motifs. Similar analysis in G3 PEG5K-CA4-L-RH4 micelles revealed a very different population distribution with planar (62%), T-shaped (16%), and intermediate (22%). It is apparent from this analysis that G2 and G3 telodendrimers have π−π motifs with planar/T-shaped ratios of 1:1 and 4:1, respectively. The shift in the motifs from G2 to G3 shows the sensitivity of the telodendrimer platform for nanoformulations. It also demonstrates tunability of the telodendrimer platform that was achieved by spatially separating CA and RH groups. This anisotropic change in the micelle morphology is a favorable attribute if the drug to be encapsulated also selfaggregates anisotropically as stacks. 4.2. Drug-Encapsulated Nanocarriers. Unlike empty micelles, G1 telodendrimers formed anisotropic micelles in the presence of DOX, irrespective of the drug loading in systems (II−IV). For comparison with experiments, the time-lapsed snapshots of system III (22% w/w DOX/G1) show the formation of aggregates within 0.6 μs from the start of the

Encapsulation efficiency was determined by counting the number of drug beads encapsulated by the micelle in relation to the total number of drug beads in the system. As we reported previously,27 cone algorithm44 was employed to identify surface particles of the micelles according to their geometric positions. The encapsulation efficiency was calculated by taking the ratio of the drug molecules identified as the core of the micelle to the total number of drug beads in the system (core plus surface). The TEM images of blank and DOX-loaded PEG5KRH4CA4 and PEG5K-CA4‑L-RH4 nanoparticles were taken on a JEOL JEM-2100 HR instrument with operation at 200 kV. The TEM images of blank and DOX-loaded PEG5KCA8 nanoparticles were taken on a JEOL JEM-1400 instrument with operation at 80 kV. All of the samples were stained by 1% uranyl acetate.

4. RESULTS AND DISCUSSION 4.1. Empty Micelles. In the absence of DOX, selfassembled micelles of G1−G3 telodendrimers show remarkable differences in their structural properties. G1 telodendrimers with only CA groups form stable spherical micelles, whereas CA- and RH-containing G2 and G3 both form wormlike micelles. The anisotropic change in the micelle morphology from G1 to G2−G3 was a consequence of two competing π−π interactions. First is the intramolecular RH π−π stacking that has a higher propensity of occurrence in G3 compared to that in G2 because of spatially separated RH groups. Second is the intermolecular RH π−π stacking, which is sensitive to steric factors and the availability of RH groups. To analyze these competing π−π interactions, G2 and G3 telodendrimers were allowed to self-assemble in the solvent without DOX. Furthermore, to prevent any biasing or artifacts of the simulation setup, both sets of simulations were performed with identical number of telodendrimers, box dimensions, and simulation parameters (Table 2). In the presence of only CA as the core-forming domain, G1 PEG5KCA8 telodendrimers form spherical micelles with an average diameter of 18.7 nm (system I). The time-lapsed images show that smaller clusters are formed initially, which later coalesce to form larger micelles. However, if the eight CA groups were replaced by RH to form an analogous G1 PEG5KRH8 telodendrimer, micellization is not achieved. The intramolecular π−π stacking among the RH groups in F


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Figure 5. Snapshots of DOX (purple) and G1 PEG5KCA8 telodendrimers’ (gray, blue, red) self-assembly process taken at (a) 0, (b) 0.6, (c) 1.2, (d) 1.8, (e) 2.4, and (f) 3.0 μs. Water has been removed for clarity.

optimal as a lower payload (system VI), which resulted in the formation of empty or almost empty micelles in some telodendrimers, whereas a higher payload (system VIII) caused free DOX stacks. The dynamic process of G2 micelle formation in system VII with 23% DOX loading is shown in the timelapsed snapshots (Figure 6). In the 2−2.5 μs of the simulation, DOX is almost fully encapsulated in the micelles. The SASA data confirms full encapsulation of the DOX molecules. The dynamic process of G3 micelle formation is shown in the timelapsed snapshots of system XI (Figure 7). Starting from the initial random pack, telodendrimers and DOX form small asymmetric clusters that continue to grow until 2−2.5 μs. A comparison of G3 to G2 micelles in the 3 μs snapshot suggests that segregated CA and RH domains favor DOX−RH costacking. The SASA data shows complete encapsulation of DOX. A higher (30%) DOX payload (system XII) resulted in free DOX or partial encapsulation of the DOX in multiple simulations. Conversely, system X with 10% DOX loading also shows wormlike micelles with full DOX encapsulation. 4.3. π−π Stacking Analysis. The π−π stacking analysis of the RH−RH and RH−DOX pairs in G2 PEG5KRH4CA4 nanocarriers shows lack of a preferential stacking motif (Figure 8a). For example, 67% of the RH−RH interacting pairs were intermediate with only 13 and 20% planar and T-shaped, respectively. Similarly, the RH−DOX pairs showed a population of 37% intermediate motifs and planar and Tshaped stacking of 21 and 42%, respectively. The low planar co-

simulation from randomly packed initial system (Figure 5). The equilibrated nonuniform structures at 3 μs compare well in size and distribution with the asymmetric aggregates observed in the experiment. A detailed analysis of the layer-by-layer assembly of the groups involved in micelle formation shows a stacked DOX core with a discontinuous peripheral CA shell. The reversemapped micelle confirmed π−π stacking on DOX molecules, similar to that shown in Figure 2. The CA groups being amphiphilic, nonplanar, are unable to co-stack with DOX and are unable to provide a stealth layer to the DOX core. The SASA calculations show that about 29% DOX surface is exposed to the solvent. Considering that DOX forms stacks mediated by the π−π interactions in the aqueous medium, the wormlike micelle is expected; however, the peripheral CA groups are able to prevent the long stacks in areas of high local density wherever possible, leading to a nonuniform distribution of size and shape. Increasing the drug loading enhances the SASA of the drug, which is undesirable. Lowering the drug loading below 10% threshold is also undesirable for the purpose of drug delivery. Overall, the simulations and experimental results consistently show that G1 DOX-PEG 5k CA 8 is inadequate to encapsulate DOX with desired drug loading. In the presence of DOX, G2 telodendrimers form wormlike micelles over the drug-loading range of 10−30% w/w (systems VI−VIII). The goal of adopting this range was to determine the optimal drug-loading capability of the G2 telodendrimers. We observed, like that in the experiments, 22−23% w/w to be G


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Figure 6. Snapshots of DOX (purple) encapsulation by G2 PEG5KRH4CA4 telodendrimers (gray, blue, red, green) at (a) 0, (b) 0.6, (c) 1.2, (d) 1.8, (e) 2.4, and (f) 3.0 μs. Water is not shown for clarity.

system III shows spherical micelles that have entangled PEG shells, which correlate well with the micelles observed in the TEM images. The morphology shifts toward wormlike micelles with the introduction of RH groups, as evident from both the simulation and TEM images of the G2 telodendrimeric system (Figure 9b) and even longer micelles in G3 systems (Figure 9c). Further quantification of the nanocarrier asphericities clearly shows the difference in the three telodendrimer systems (Figure 10). The asphericity factor distributions show a welldefined peak corresponding to spherical micelles for G1 and wormlike cylindrical nanocarriers for G3, whereas the G2 system has a bimodal distribution corresponding to highly asymmetric nanocarriers that are elongated spheroids and wormlike. 4.5. Drug Encapsulation. The DOX encapsulation process in G1−G3 telodendrimeric systems was observed to occur in three stages: rapid initial clustering, micelle ripening, and steady state. In the first 0.1 μs of the simulation, randomly packed DOX and telodendrimers coalesce rapidly into small clusters with 2−3 molecules. The telodendrimers participated in cluster formation to varying degrees depending on the generation. The second-stage clustering was more gradual as in this stage the bulkier clusters diffused slower and grew larger at the expense of smaller ones over 1−2 μs. Once the optimal size was achieved, the third stage was dynamically steady with a reduced number of merging and disruption events.

stacking population among the RH and DOX groups impacts the morphology of the nanocarrier, prevents DOX−DOX selfstacking, and causes the micelles to be wormlike. However, with about 40% planar and 40% intermediate DOX−DOX pairs, the nanocarriers are not extended wormlike micelles. In addition, the CA groups lie peripheral to DOX−RH and RH−RH and assist in disrupting extended wormlike assemblies. Thus, although the CA groups do not directly participate in the wormlike assembly of the nanocarrier, they help maintain the solubility of the micelle in water and act as a buffer layer between the core components (i.e., RH and DOX) and the hydrophilic shell (i.e., PEG chains), resulting in an efficient drug-delivery nanocarrier. In contrast, the π−π stacking analysis of G3-DOX nanocarriers shows a predominately planar population among RH− RH (62%), RH−DOX (57%), and DOX−DOX (68%) pairs (Figure 8b). This is remarkably different from the G2 nanocarriers. The population of T-shaped motifs in all pairs ranges between 10 and 20% and that of intermediate motifs ranges between 20 and 24%. Both these motifs are secondary to the dominant planar stacking, leading to long wormlike micelles. 4.4. Nanocarrier Morphologies. The equilibrated micelle structures in systems III, VII, and XI show morphological features similar to those in the corresponding experimental TEM images (Figure 9). The 3 μs simulation snapshot for H


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Figure 7. Snapshots of DOX (purple) encapsulation by G3 PEG5KCA4-L-RH4 telodendrimers (gray, blue, red, green) at (a) 0, (b) 0.6, (c) 1.2, (d) 1.8, (e) 2.4, and (f) 3.0 μs. Water is not shown for clarity.

Figure 8. Percent population of π−π stacking motifs in (a) G2-DOX and (b) G3-DOX nanocarriers. Color scheme: planar (blue), T-shaped (orange), and intermediate (gray). The inset image shows the planar DOX−DOX, RH−RH, and RH−DOX interactions in various motifs. Inset colors: DOX (purple, spheres); RH (green spheres).

the same time span, the telodendrimers are only 30% clustered. At 3 μs, all DOX is clustered whereas 13% of telodendrimers are in an unbound state, indicating that telodendrimers are only partially participating in micelle formation. In system VII, initially in 0.2 μs, DOX stacking is similar to that in the G1 system, but then it slows down as DOX co-stacks with the RH groups of G2 telodendrimers. At 0.5 μs, 75% of telodendrimers

Interestingly, over the three stages, the number of unbound DOX molecules and telodendrimers in representative G1−G3 systems (III, VII, and XI) shows different features (Figure 11). DOX clustering occurs rapidly in all three systems relative to that in the larger telodendrimers. In system III, in which G1 telodendrimers do not participate in π−π co-stacking, the DOX molecules undergo self-stacking of up to 90% within 0.2 μs. In I


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Figure 9. Comparison of (a) G1, (b) G2, and (c) G3 nanocarriers observed in simulations (left) and experimental TEM images (right). As a guide to the eye, individual micelle cores are highlighted (circles and ovals) in the simulation snapshots (orange) and in TEM images (blue).

Figure 11. Evolution of monomers of DOX (red) and telodendrimers (blue) for systems III (G1, circles), VII (G2, triangle), and XI (G3, squares).

Figure 10. Asphericity factor distributions for G1 (blue), G2 (orange), and G3 (green) nanocarriers.

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(5) Jiwpanich, S.; Ryu, J. H.; Bickerton, S.; Thayumanavan, S. Noncovalent Encapsulation Stabilities in Supramolecular Nanoassemblies. J. Am. Chem. Soc. 2010, 132, 10683−10685. (6) Hood, E.; Simone, E.; Wattamwar, P.; Dziubla, T.; Muzykantov, V. Nanocarriers for Vascular Delivery of Antioxidants. Nanomedicine 2011, 6, 1257−1272. (7) Sahu, A.; Kasoju, N.; Goswami, P.; Bora, U. Encapsulation of Curcumin in Pluronic Block Copolymer Micelles for Drug Delivery Applications. J. Biomater. Appl. 2011, 25, 619−639. (8) Zhang, W.; Shi, Y.; Chen, Y. Z.; Hao, J. G.; Sha, X. Y.; Fang, X. L. The Potential of Pluronic Polymeric Micelles Encapsulated with Paclitaxel for the Treatment of Melanoma Using Subcutaneous and Pulmonary Metastatic Mice Models. Biomaterials 2011, 32, 5934− 5944. (9) Qu, Q. Q.; Wang, Y.; Zhang, L.; Zhang, X. B.; Zhou, S. B. A Nanoplatform with Precise Control over Release of Cargo for Enhanced Cancer Therapy. Small 2016, 12, 1378−1390. (10) Jain, R. K. Barriers to Drug-Delivery in Solid Tumors. Sci. Am. 1994, 271, 58−65. (11) Au, J. L. S.; Jang, S. H.; Zheng, J.; Chen, C. T.; Song, S.; Hu, L.; Wientjes, M. G. Determinants of Drug Delivery and Transport to Solid Tumors. J. Controlled Release 2001, 74, 31−46. (12) Jang, S. H.; Wientjes, M. G.; Lu, D.; Au, J. L. S. Drug Delivery and Transport to Solid Tumors. Pharm. Res. 2003, 20, 1337−1350. (13) Corti, A.; Pastorino, F.; Curnis, F.; Arap, W.; Ponzoni, M.; Pasqualini, R. Targeted Drug Delivery and Penetration into Solid Tumors. Med. Res. Rev. 2012, 32, 1078−1091. (14) Sefidgar, M.; Soltani, M.; Raahemifar, K.; Bazmara, H.; Nayinian, S. M. M.; Bazargan, M. Effect of Tumor Shape, Size, and Tissue Transport Properties on Drug Delivery to Solid Tumors. J. Biol. Eng. 2014, 8, 12. (15) Khawar, I. A.; Kim, J. H.; Kuh, H. J. Improving Drug Delivery to Solid Tumors: Priming the Tumor Microenvironment. J. Controlled Release 2015, 201, 78−89. (16) Maeda, H.; Wu, J.; Sawa, T.; Matsumura, Y.; Hori, K. Tumor Vascular Permeability and the EPR Effect in Macromolecular Therapeutics: A Review. J. Controlled Release 2000, 65, 271−284. (17) Maeda, H. Tumor-Selective Delivery of Macromolecular Drugs Via the EPR Effect: Background and Future Prospects. Bioconjugate Chem. 2010, 21, 797−802. (18) Torchilin, V. Tumor Delivery of Macromolecular Drugs Based on the EPR Effect. Adv. Drug Delivery Rev. 2011, 63, 131−135. (19) Kita, K.; Dittrich, C. Drug Delivery Vehicles with Improved Encapsulation Efficiency: Taking Advantage of Specific Drug-Carrier Interactions. Expert Opin. Drug Delivery 2011, 8, 329−342. (20) Bickerton, S.; Jiwpanich, S.; Thayumanavan, S. Interconnected Roles of Scaffold Hydrophobicity, Drug Loading, and Encapsulation Stability in Polymeric Nanocarriers. Mol. Pharmaceutics 2012, 9, 3569−3578. (21) Xiao, K.; Luo, J.; Fowler, W. L.; Li, Y.; Lee, J. S.; Xing, L.; Cheng, R. H.; Wang, L.; Lam, K. S. A Self-Assembling Nanoparticle for Paclitaxel Delivery in Ovarian Cancer. Biomaterials 2009, 30, 6006− 6016. (22) Luo, J.; Xiao, K.; Li, Y.; Lee, J. S.; Shi, L.; Tan, Y.-H.; Xing, L.; Cheng, R. H.; Liu, G.-Y.; Lam, K. S. Well-Defined, Size-Tunable, Multifunctional Micelles for Efficient Paclitaxel Delivery for Cancer Treatment. Bioconjugate Chem. 2010, 21, 1216−1224. (23) Xiao, K.; Luo, J.; Li, Y.; Lee, J. S.; Fung, G.; Lam, K. S. PegOligocholic Acid Telodendrimer Micelles for the Targeted Delivery of Doxorubicin to B-Cell Lymphoma. J. Controlled Release 2011, 155, 272−281. (24) Xiao, W.; Luo, J.; Jain, T.; Riggs, J. W.; Tseng, H. P.; Henderson, P. T.; Cherry, S. R.; Rowland, D.; Lam, K. S. Biodistribution and Pharmacokinetics of a Telodendrimer Micellar Paclitaxel Nanoformulation in a Mouse Xenograft Model of Ovarian Cancer. Int. J. Nanomed. 2012, 7, 1587−1597. (25) He, W.; Luo, J.; Bourguet, F.; Xing, L.; Yi, S. K.; Gao, T.; Blanchette, C.; Henderson, P. T.; Kuhn, E.; Malfatti, M.; et al. Controlling the Diameter, Monodispersity, and Solubility of Apoa1

are clustered, which is much higher than that in the G1 system. The G3 micellization is most rapid and complete. In system XI, there is a rapid co-stacking of DOX and telodendrimers with 90 and 75% bound, respectively, at 0.5 μs. Tracking the drug encapsulation process has been very informative, as it reflects the molecular subtleties that make the three generations different.

5. CONCLUSIONS This work examines the structure−property relationships of the telodendrimer drug-delivery platform for specific drug payloads using computational methods. Multiple long-time-scale selfassembly simulations were performed to generate equilibrated DOX-encapsulated nanocarriers. The results show how subtle changes in the molecular architecture of the telodendrimer core-forming domain have profound effect on the properties of the nanocarriers. Although G2 and G3 nanocarriers have the same building blocks, different architectures of telodendrimers result in different molecular packings and morphologies of nanoparticles after drug loading. This work emphasizes the increasing role of molecular simulations in the rational design of nanocarriers, thereby eliminating the trial and error method that has been prevalent in experimental synthesis. The molecular-level insights gained from the simulations will be used to design the next generation of drug-specific nanocarriers.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.6b06070. GROMACS topology for CG PEG5KRH4CA4 and PEG5K-CA4-L-RH4 telodendrimers (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 315-443-0571. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank XSEDE supercomputing facility for providing computational resources for some of the calculations. They also thank the Nappi Family Research Fund and Syracuse University for the part of the financial support of this project. The financial supports from NIH/NCI R01CA140449 and NIH/NIBIB 1R21EB019607 are greatly acknowledged.

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REFERENCES

(1) Matsumura, Y.; Hamaguchi, T.; Ura, T.; Muro, K.; Yamada, Y.; Shimada, Y.; Shirao, K.; Okusaka, T.; Ueno, H.; Ikeda, M.; et al.et al. Phase I Clinical Trial and Pharmacokinetic Evaluation of Nk911, a Micelle-Encapsulated Doxorubicin. Br. J. Cancer 2004, 91, 1775−1781. (2) Tang, N.; Du, G. J.; Wang, N.; Liu, C. C.; Hang, H. Y.; Liang, W. Improving Penetration in Tumors with Nanoassemblies of Phospholipids and Doxorubicin. J. Natl. Cancer Inst. 2007, 99, 1004−1015. (3) Aryal, S.; Hu, C. M. J.; Zhang, L. F. Combinatorial Drug Conjugation Enables Nanoparticle Dual-Drug Delivery. Small 2010, 6, 1442−1448. (4) Choi, S. J.; Choi, G. E.; Oh, J. M.; Oh, Y. J.; Park, M. C.; Choy, J. H. Anticancer Drug Encapsulated in Inorganic Lattice Can Overcome Drug Resistance. J. Mater. Chem. 2010, 20, 9463−9469. K


Article

The Journal of Physical Chemistry B Nanolipoprotein Particles Using Telodendrimer Chemistry. Protein Sci. 2013, 22, 1078−1086. (26) Shi, C.; Yuan, D.; Nangia, S.; Xu, G.; Lam, K. S.; Luo, J. A Structure−Property Relationship Study of the Well-Defined Telodendrimers to Improve Hemocompatibility of Nanocarriers for Anticancer Drug Delivery. Langmuir 2014, 30, 6878−6888. (27) Jiang, W.; Luo, J.; Nangia, S. Multiscale Approach to Investigate Self-Assembly of Telodendrimer Based Nanocarriers for Anticancer Drug Delivery. Langmuir 2015, 31, 4270−4280. (28) Shi, C.; Guo, D.; Xiao, K.; Wang, X.; Wang, L.; Luo, J. A DrugSpecific Nanocarrier Design for Efficient Anticancer Therapy. Nat Commun. 2015, 6, No. 7449. (29) Nangia, S.; Sureshkumar, R. Effects of Nanoparticle Charge and Shape Anisotropy on Translocation through Cell Membranes. Langmuir 2012, 28, 17666−17671. (30) Zhao, Y.; Truhlar, D. G. The M06 Suite of Density Functionals for Main Group Thermochemistry, Thermochemical Kinetics, Noncovalent Interactions, Excited States, and Transition Elements: Two New Functionals and Systematic Testing of Four M06-Class Functionals and 12 Other Functionals. Theor. Chem. Acc. 2008, 120, 215−241. (31) Agrawal, P.; Barthwal, S. K.; Barthwal, R. Studies on SelfAggregation of Anthracycline Drugs by Restrained Molecular Dynamics Approach Using Nuclear Magnetic Resonance Spectroscopy Supported by Absorption, Fluorescence, Diffusion Ordered Spectroscopy and Mass Spectrometry. Eur. J. Med. Chem. 2009, 44, 1437− 1451. (32) Tsoneva, Y.; Jonker, H. R. A.; Wagner, M.; Tadjer, A.; Lelle, M.; Peneva, K.; Ivanova, A. Molecular Structure and Pronounced Conformational Flexibility of Doxorubicin in Free and Conjugated State within a Drug−Peptide Compound. J. Phys. Chem. B 2015, 119, 3001−3013. (33) Schüttelkopf, A. W.; van Aalten, D. M. Prodrg: A Tool for HighThroughput Crystallography of Protein-Ligand Complexes. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2004, 60, 1355−1363. (34) Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; de Vries, A. H. The Martini Force Field: Coarse Grained Model for Biomolecular Simulations. J. Phys. Chem. B 2007, 111, 7812−7824. (35) de Jong, D. H.; Singh, G.; Bennett, W. F. D.; Arnarez, C.; Wassenaar, T. A.; Schafer, L. V.; Periole, X.; Tieleman, D. P.; Marrink, S. J. Improved Parameters for the Martini Coarse-Grained Protein Force Field. J. Chem. Theory Comput. 2013, 9, 687−697. (36) Brooks, B. R.; Brooks, C. L., III; Mackerell, A. D., Jr.; Nilsson, L.; Petrella, R. J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; et al. CHARMM: The Biomolecular Simulation Program. J. Comput. Chem. 2009, 30, 1545−1614. (37) Martínez, L.; Andrade, R.; Birgin, E. G.; Martinez, J. M. Packmol: A Package for Building Initial Configurations for Molecular Dynamics Simulations. J. Comput. Chem. 2009, 30, 2157−2164. (38) Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A. E.; Berendsen, H. J. C. Gromacs: Fast, Flexible, and Free. J. Comput. Chem. 2005, 26, 1701−1718. (39) Ackland, G. J.; D’Mellow, K.; Daraszewicz, S. L.; Hepburn, D. J.; Uhrin, M.; Stratford, K. The Moldy Short-Range Molecular Dynamics Package. Comput. Phys. Commun. 2011, 182, 2587−2604. (40) Okumura, H.; Itoh, S. G.; Ito, A. M.; Nakamura, H.; Fukushima, T. Manifold Correction Method for the Nose-Hoover and NosePoincare Molecular Dynamics Simulations. J. Phys. Soc. Jpn. 2014, 83, No. 024003. (41) Patra, P. K.; Bhattacharya, B. A Deterministic Thermostat for Controlling Temperature Using All Degrees of Freedom. J. Chem. Phys. 2014, 140, No. 064106. (42) Parrinello, M.; Rahman, A. Polymorphic Transitions in Single Crystals: A New Molecular Dynamics Method. J. Appl. Phys. 1981, 52, 7182−7190. (43) Rudnick, J.; Gaspari, G. The Aspherity of Random Walks. J. Phys. A: Math. Gen. 1986, 19, L191.

(44) Wang, Y.; Teitel, S.; Dellago, C. Melting of Icosahedral Gold Nanoclusters from Molecular Dynamics Simulations. J. Chem. Phys. 2005, 122, No. 214722.

L


Drug-Specific Design of Telodendrimer Architecture for Effective

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