Strategies for Functionalizing Lipoprotein-Based Nanoparticles - ACS

Chapter 6

Strategies for Functionalizing Lipoprotein-Based Nanoparticles Downloaded by UNIV OF FLORIDA on December 11, 2017 | http://pubs.acs.org Publication Date (Web): November 15, 2017 | doi: 10.1021/bk-2017-1271.ch006

Sean F. Gilmore, Wei He, Amy Rasley, and Nicholas O. Fischer* Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94551, United States *E-mail: [email protected].

In the design and development of nanoparticle platforms for biotechnology and biomedical applications, biocompatibility and functionality are important considerations. In this chapter, we discuss the key characteristics of lipoprotein-based nanoparticles, and how the versatility of their self-assembly accommodates a wide range of starting components to provide tunable control of size, stability, and functionality. Orthogonal strategies are described to facilitate the incorporation of bioactive cargo molecules (including drugs, nucleic acids, and proteins) for a wide range of applications, ranging from multifunctional vaccine formulations to targeted drug delivery.

Introduction Over the past decade, nanotechnologies have significantly improved diagnosis and therapy for a variety of human diseases (1). By the Food and Drug Adminstration’s (FDA) definition, nanomaterials used for biomedical applications have at least one dimension that is less than 100 nm (2), but there is substantial diversity in the chemical composition of nanomaterials currently under investigation. Due to their vastly increased surface area and unique mechanical, electronic, photonic, and magnetic properties, nanoparticles hold immense potential for various biomedical applications such as therapeutics and vaccine delivery (3, 4), imaging (5), and more recently, precision medicine (6). Currently, there are numerous examples of the effective use of nanoparticle-based

© 2017 American Chemical Society Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

Downloaded by UNIV OF FLORIDA on December 11, 2017 | http://pubs.acs.org Publication Date (Web): November 15, 2017 | doi: 10.1021/bk-2017-1271.ch006

diagnostic and therapeutic agents, a few of which are in advanced clinical trials or have already received FDA approval (7, 8). Since nanoparticles emerged as a tool for drug delivery in the early 1990s, these materials have shown several substantial advantages over conventional drugs. First, drugs formulated as nanoparticles demonstrated enhanced in vivo circulation time and bioavailability, largely due to reduced clearance from the body through excretion by the kidneys (9–12). As many drug active ingredients are hydrophobic, use of nanoparticles can help to overcome drug solubility issues (13, 14). Further, it has also been reported that nanoparticles can be internalized into cells through endocytosis, therefore bypassing the efflux pump and circumventing P-glycoprotein mediated resistance (15, 16). In addition, by encapsulating drugs, nanoparticles can also significantly reduce the systemic toxicity associated with the active drug components, a critical requirement for many drugs exhibiting extreme or acute toxicity profiles (17–22). One of the most important advantages of nanoparticles is the potential for targeted delivery. For anticancer agents formulated in nanoparticles, passive targeting of tumors can be achieved by exploiting a unique characteristic of many tumors referred to as the enhanced permeation and retention (EPR) effect (23), which allows nanoscale particles to leak into the tumor environment from blood vessels within the tumor at quantities 1-2 times the dose delivered to other tissues (24). To achieve finer control over particle localization, they can be functionalized using ligands that are recognized by various cognate receptors expressed on the surface of target cells, thus providing specificity for active targeting (25–28). Despite the numerous advantages that are imparted by synthetic nanoparticles, limitations remain, particularly with respect to safety concerns. Nanoparticle toxicity and inadvertent immunogenicity can arise due to a number of factors, ranging from surface charge to component incompatibility (29–35). To mitigate these safety concerns, the utilization of biomimetic nanoparticles has recently gained traction. Biomimetic nanoparticles can be derived from, or be inspired by, endogenous biological particles. As such, biomimetic nanoparticles are considered to have preferable biocompatible traits compared to synthetic nanoparticles. A prime example is the FDA-approved drug Abraxane, a formulation consisting of paclitaxel adsorbed onto serum albumin. Abraxane possesses excellent efficacy while exhibiting much lower toxicity than the traditional formulation of paclitaxel prepared with Cremophor EL (36). Alternative approaches include coating of gold or polymeric nanoparticles with intact membranes from cells such as erythrocytes or leukocytes, which confers longer circulation times, while simultaneously enabling nanoparticles to interact with cellular membranes (37–39). In addition, conjugation of peptides that are derived from cell-surface proteins that function as ‘self’ markers, such as CD47, can also substantially increase circulation time of particles by inhibiting macrophage-mediated degradation (40). High-density lipoproteins (HDLs) constitute a notable biological nanoparticle with important functions in mammalian cholesterol transportation and lipid metabolism. Synthetic versions of these particles, which have since been referred to as synthetic HDLs (sHDLs), reconstituted HDLs (rHDLs), nanodiscs, or nanolipoprotein particles (NLPs), were first developed by the Scanu laboratory 132 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


in the late 1960s (41). In recent years, nanolipoprotein particles have garnered significant attention due to their monodisperse size distribution, facile preparation, ease of functionalization, and low toxicity and immunogenicity profiles. NLPs, typically discoidal in shape (Figure 1) with diameters of 6–30 nm, are spontaneous assemblies of 2–6 apolipoproteins encircling a lipid bilayer. NLPs are water soluble, but feature a hydrophobic core that is capable of accepting hydrophobic or amphiphilic molecules. These particles are rapidly taken up by a variety of cell types in vitro and in vivo via endocytosis, and typically accumulate in endosomes where they are further processed (42, 43).

Figure 1. Multifunctional NLPs are prepared by self-assembly (not to scale). A. Traditionally, purified components are solubilized with surfactant and mixed in aqueous solution. B. Surfactant removal initiates self-assembly of the multifunctional NLPs. C. Conjugation of additional cargo molecules is achieved by reacting with cognate NLP surface functionalities or through anchoring of lipidic moieties on amphiphilic cargo molecules. D. Final dimensions of functional NLP are dicated by the choice of starting components and ratios (e.g. lipids and apolipoproteins). To date, NLPs have been prepared using a plethora of components, spanning both natural and synthetic molecules. Many research groups are exploring the utility of these particles for a wide range of applications, including in vivo imaging, drug delivery, and vaccine formulation. Indeed, a number of excellent review articles have highlighted key characteristics and applications of synthetic lipoprotein particles (44, 45). In particular, Bricarello et al. have reviewed the formulations most frequently used to prepare these nanoparticles, and compiled a table detailing general physical characteristics such as size and shape (46). Huang et al. describe formulations and therapeutic targets of synthetic HDLs and LDLs loaded with therapeutic molecules (47). Simonsen has also compiled information on HDLs (particularly apoA1 formulations) used for drug delivery and gives information describing in vivo administration and a synopsis of the response to the particles (48). Finally, Darabi et al. outlined formulations of synthetic HDLs and the results of the clinical trials evaluating the intrinsic therapeutic capabilities of these particles, such as to treat atherosclerotic plaques (49). In this chapter, we focus on the latest advances in engineering and functionalizing NLPs that can be achieved by tailoring the nanoparticle constituents. The overarching goal is to highlight the versatility of the NLP, and provide strategies that can be applied to a given application. 133 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Exploring Structure and Composition Nanolipoprotein particles and other synthetic lipoproteins closely resemble their counterparts found in vivo in both structure and shape. Particle self-assembly is initiated using a variety of methods such as thermal cycling or gentle sonication of lipid vesicles in the presence of apolipoproteins, or by solubilizing the lipids with a detergent that is subsequently removed (by dialysis or incubation with detergent-adsorbing beads) after apolipoprotein addition (50, 51). The resultant particles are comprised of a lipid bilayer structure that is stabilized by amphipathic lipoproteins at the perimeter interface between the hydrophobic acyl chains and the aqueous environment. Particles that mimic high density lipoproteins are often discoidal in shape, with heights consistent with the thickness of a lipid bilayer (ca. five nanometers) (52). While apoAI and its derivatives are the most widely used scaffold proteins in the preparation of synthetic HDLs, particles have also been prepared with full length or truncated apoE (e.g. the N-terminal 22 kDa portion, termed apoE.22k) or apoB derivatives (46, 47). Additionally, linear or branched peptides derived from apolipoprotein helices can also be used to prepare lipoprotein mimetics that are functionally comparable to apoA1 particles with regard to their atheroprotectivity (53–55). Particles may also be produced using similar procedures with apolipoproteins from non-mammalian taxa, such as apolipophorin-III from the silk moth Bombyx mori (56–58). Discoidal lipid discs or bicelles resembling lipoprotein particles have also been prepared using distearoylphosphatidylcholine (DSPC) with polyethyleneglycol (PEG)-modified stearic acid (59). Collectively, these examples illustrate the versatility in the self-assembly strategies to produce nanoscale biomimetic particles featuring amphipathic characteristics that can be exploited for myriad in vivo and ex vivo applications. While reconstituted HDLs typically feature the discoidal shape characteristic of nascent HDLs, reconstituted spherical particles have also been reported in the literature (47). While these particles also possess a scaffold protein “belt” at the particle equator, their interiors are comprised of hydrophobic molecules, such as cholesteryl esters, encapsulated by a lipid monolayer. While these types of particles are typically prepared using apoB-derived peptides and are comparable in size to LDLs in vivo (60–63), spherical HDLs containing apoAI can also be formed by enzymatically converting cholesterol present in the lipid bilayer to cholesteryl ester (64). Since this configuration is the result of a post-assembly process, it is unclear whether discoidal particles produced with other scaffold proteins could also be transformed into spheroids, allowing the particles to encapsulate a larger amount of hydrophobic cargo, such as chemotherapeutic drugs (61). Given that this class of particles can be produced with a variety of scaffold protein types, the LDL moniker for synthetic particles may only be useful in describing specific characteristics or features of the particle, such as the LDL receptor binding domain on the scaffold protein, rather than the overall shape. The diameter of discoidal rHDLs can be tuned by the identity of the protein and lipid components, as well as their molar ratios. The number of scaffold proteins incorporated into a single rHDL is a function of both apolipoprotein identity and protein:lipid ratio. For example, apoAI rHDLs typically contain two 134 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


apolipoproteins per particle (65), compared to 5–6 for apoE rHDLs (66). As such, apoAI and its derivatives produce smaller particles relative to those prepared with apoE derivatives (Figure 2A) (56). In most reports, the size for reconstituted HDL particles can range from 6 to 30 nanometers (46, 47, 57), although LDL-like constructs can be larger (63). Some of these particles can be large enough to prevent rapid elimination from circulation via the kidneys, which makes them potentially useful for use in vivo applications in which the particles would be administered intravenously (9–12).

Figure 2. NLP size and homogeneity are dependent on scaffold protein, lipid composition, and lipid:protein ratios. A. Analytical SEC chromatrogram of two homogeneous NLPs prepared with apoE.22k or apoAI with a mixture of lipids containing primarily DOPC. B. Semi-preparative SEC chromatogram of NLPs prepared with apoE.22k and four different phospholipids. Adapted with permission from (86). C. Semi-preparative SEC chromatogram of NLPs assembled at decreasing lipid:apolipoprotein (apoLP-III) ratios, demonstrating the effect of lipid ratios on the formation of distinct NLP sizes. Column void volume (black arrow) and unincorporated scaffold protein (grey arrow) are indicated. Adapted with permission from (57). While the lipid-to-protein ratios reported for native HDLs can be used in the preparation of these reconstituted particles, optimal constituent ratios are often determined empirically through a careful screening process. In general, lipid-to-protein ratios ranging from 40:1 to 200:1 can generate particles, although this is highly dependent on the type of scaffold protein and the type of lipid. For example, apoAI NLPs prepared with a mixture of lipids containing primarily dioleoylphosphatidylcholine (DOPC) at a 40:1 ratio produces homogeneous particles. Using apoE.22k, an 80:1 ratio is required (Figure 2A). The lipid identity is also very important in determining NLP characteristics. While many different types of lipids can be used to form NLPs (Figure 2B), size and homogeneticy will be dictated by lipid ratios and are ultimately driven by individual lipid characteristics, such as head group polarity and acyl chain packing. For example, apoE.22k NLPs prepared with DOPC are optimally formed at an 80:1 ratio, while a 180:1 ratio is required when assembling with dimyristoylphosphatidylcholine (DMPC). In this example, the differential packing between the unsaturated DOPC and saturated DMPC plays a key role. Further, increasing the lipid-to-protein ratios will yield larger particles (Figure 2C). A low ratio can result in excess 135 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


unincorporated scaffold protein (gray arrow in 2C), whereas excess lipid at higher ratios yields larger lipidic structures that elute in the SEC void volume (black arrow in 2C). In addition to particle size, the molar ratios and types of apolipoproteins can impact the stability of the resultant self-assembly products. We have observed a dynamic rearrangement of particles over time using apolipophorin-based NLPs, resulting in a preferred NLP size (57). Similar stability observations have been reported for apoAI rHDLs as well (65). One outstanding question with synthetic lipoproteins is the extent to which they interact with nascent HDLs in vivo. Studies report that radiolabeled particles, when injected intravenously (i.v.), can be detected several hours later (67, 68). However, these studies track the apolipoprotein, not intact particles. Indeed, when experiments have been performed to track integrity of these particles in blood serum, it has been shown that these particles remodel over time and apolipoproteins can dissociate from the particles (42). Further, experiments on particles prepared with branched peptide scaffold have demonstrated that both lipid and scaffold protein can incorporate into endogenous serum HDLs (54). For applications in which atherosclerotic therapy is the goal, this is a potential benefit of synthetic particles. However, for formulation or targeted delivery of other drugs, this dynamic remodeling can abrogate any potential targeting or give rise to undesirable, off-target effects. For instance, if there is a partial exchange of contents with native HDLs, it is possible that the functional components of a formulation, such as a drug and a targeting ligand, could become separated in the process. If a native HDL accepts a therapeutic molecule from a synthetic HDL, then this drug would be delivered to any number of tissues that interact with lipoproteins, which would reduce the specificity of the nanoparticle formulation. To date, it has been demonstrated that HDL or LDL mimetics may be produced with many different types of scaffold proteins, but an even wider array of lipids can be used to prepare particles. Particles can readily be prepared to possess a lipid matrix that resembles the reported composition of the lipid mixture of lipoproteins obtained from serum using either lipid mixtures, components of these mixtures such as PC and DOPC lipids, sphingomyelin, or low-cost lipid extract mixtures such as EggPC or SoyPC (46). For the purpose of atherosclerotic therapy through the transport of cholesterol, while lipid composition has been shown to affect the rate of cholesterol efflux, there are mixed reports as to how lipid composition modulates this rate. This process may be dependent on cell type examined and its expression of receptors that interact with HDLs (69, 70). Alternatively, particles can also be prepared with a variety of pure lipid species, or lipid mixtures that would not be abundant in nascent HDL particles, including phosphatidylglycerol (PG) lipids, phosphatidylethanolamine (PE) lipids, phosphatidylserine (PS) lipids, and the ganglioside GM1 (71–74). Indeed, there are benefits associated with including some of these lipids into the particle’s lipid matrix. For example, DMPG causes apoAI particles to be more thermally stable than particles prepared with DMPC lipids (71, 75). While some lipid headgroups enhance stability of the particles, others such as PE may promote water penetration into the lipid bilayer causing destabilization of the particles (72). While PE may not be beneficial with regard to stability, it can serve as a substrate for conjugation through NHS ester chemistry. 136 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


In addition to headgroup structure, the lipid acyl chain structure is equally responsible for differences in particle stability, especially in biological fluids such as serum (42, 76). Using SEC coupled to a fluorescence detector, fluorescently-labeled scaffold protein can be tracked as either complexed as an NLP or unincorporated, even in a complex aqueous solution such as serum (Figure 3A). Particles prepared with apoE.22k exhibited variable stability in serum (reflected as the time at which half the particles have dissociated; t1/2), depending on whether DOPC or DMPC was used to form particles (Figure 3B) (76). We have recently extended these observations by studying serum stability of NLPs prepared using lipids with varying acyl chain lengths. We observed that an increase in stability of NLPs formed with monounsaturated lipids correlates to the increase in the length of the acyl chain (unpublished data).

Figure 3. NLP composition has implications on serum stability. A. The integrity of NLPs in biological fluids (e.g. serum) is facilitated by fluorescently labeling the NLP scaffold protein. Confounding absorbance of serum components can thereby be avoided. B. Stability of NLPs prepared with DMPC and DOPC illustrates particle stability dependence on lipid composition. Adapted with permission from (76).

Strategies for Functionalization and Cargo Loading The versatility in the NLP self-assembly process is a key feature that readily enables tailoring these particles for specific in vivo applications, ranging from delivery of biological cargo molecules to diagnostic imaging. The amphipathic nature of the NLP provides three chemically distinct regions for controllable modification: 1) the hydrophobic interior of the lipid bilayer, 2) the hydrophilic surface presented by the polar lipid heads groups, and 3) the scaffold protein. While recombinant techniques have been successfully used to engineer functionality into NLPs by fusing proteins to the scaffold protein (77), we will focus primarily on how to take advantage of the lipid bilayer itself. In particular, we will discuss how the discrete hydrophilic and hydrophobic regions provide the means to controllably incorporate functional moieties, cargo molecules, or structural elements into the particle (Figure 4). 137 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Figure 4. NLP functionalization and cargo loading can be achieved by various orthogonal strategies, depending on the physicochemical characteristics of the molecules to be incorporated (not to scale). A. Hydrophobic molecules are readily incorporated into the NLP bilayer during self-assembly. B. Polar molecules can be conjugated to NLPs bearing functional moieties at the polar headgroups of the lipids. C. Alternatively, polar molecules modified with a lipidic “anchor” can be added to the NLP after self-assembly. As shown schematically in Figure 4, there are three orthogonal approaches to incorporate molecules of interest into the NLPs: 1) incorporation during NLP self-assembly (Figure 4A), 2) conjugation to the bilayer surface (Figure 4B), or 3) incorporation of amphiphiles post-assembly (Figure 4C; termed add-back). Incorporating molecules into the NLP bilayer during self-assembly is the most traditional approach to loading cargo molecules or imparting functionality to the NLP. This approach is optimal for inherently hydrophobic molecules that are lipidic in nature. In the most straightforward sense, this can be easily achieved using lipids that are bioactive or have added functionality, such as fluorescent lipids. Commercially-available fluorescent lipids, such as NBD and DiD-related dyes, can be utilized in addition to individual labeling of lipids with the PE headgroup using a variety of NHS-activated fluorophores to provide additional flexibility in spectral range. Metal-chelating lipids charged with imaging contrast agents are also readily accommodated during the self-assembly process and have been used with great success in imaging atherosclerotic plaques (78, 79). Other functional lipids can be incorporated, including those featuring folate (for cancer targeting) (42), PEG end-groups (to minimize non-specific interactions in vivo), and lipids that have reactive groups for conjugation of a wide-variety of molecules of interest (e.g. maleimide-functionalized lipids). Hydrophobic drugs can also be loaded into NLPs during the self-assembly process, primarily as a means of solubilizing these highly insoluble drugs and enhancing their pharmacokinetic profiles. Typically, the hydrophobic molecules are mixed with lipids in a solvent such as chloroform and subsequently dried into 138 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


a film which is then reconstituted into aqueous solution using either sonication or surfactants. This approach was very successful in incorporating amphotericin B, a potent antifungal, into apoAI particles (80–82). Paclitaxel is another drug compound that has been incorporated into NLPs, either in its native form (83), or conjugated to an oleic acid to improve interactions with lipid membranes (61). In both cases, the drug is mixed in with the dissolved lipids used to form the particles. The amount of drug by weight incorporated into these hydrophobic formulations has been less than 3% for paclitaxel (83), ~6% paclitaxel oleate (61) and ~15% for amphotericin B (80). We expect that the choice of scaffold protein and lipid can be further optimized to enhance the overall loading. Lipid A, the acylated disaccharide in lipopolysaccharide, is another example of a highly hydrophobic compound readily incorporated into the NLP bilayer during self-assembly. Interestingly, monophosphoryl Lipid A (MPLA) formulated in this way is more immunostimulatory than DMPC-MPLA liposomes (43), and has been used in stimulating both innate and adaptive immune responses in vivo (43, 84). In contrast to hydrophobic molecules, hydrophilic compounds can be tethered to the NLP lipid bilayer surface. This is accomplished by first tailoring the bilayer surface to feature functional moieties that enable interactions with the biomolecule of interest. This can be achieved using both non-covalent and covalent chemistries. Charge complementarity provides an easy means of immobilizing highly charged molecules. For example, negatively-charged, short siRNA molecules are readily immobilized on cationic functionalized particles, which ultimately demonstrated enhanced cellular uptake relative to unformulated siRNA (85). An additional non-covalent conjugation scheme for biomolecules relies on incorporating lipids with nickel-chelating headgroups into the NLP bilayer (to form nickel-NLPs, or NiNLPs), which enables conjugation of poly-histidine-tagged proteins (84, 86–88). This approach has several key advantages as it utilizes a common peptide tag widely used in the purification of recombinant proteins, exhibits high conjugation efficiency, and enables control over protein orientation and site of conjugation (i.e. single point of conjugation, either N- or C-terminal). Figure 5A and 5B illustrate the conjugation of two His-tagged protein antigens of different molecular weights to NiNLPs for vaccine applications. Covalent conjugation of proteins and other molecules can be achieved by incorporating lipids bearing functional groups such as a maleimide or click-chemistry moieties (42, 89, 90). While extremely useful in other circumstances, using lipids featuring amine-reactive moieties is not advised, as these can react with free amines found on the scaffold protein, possibly leading to extensive crosslinking of the particles. The choice between covalent and non-covalent conjugation strategies will depend on the particle application (42). Non-covalent strategies are usually characterized by high efficiency in loading, as with the NiNLPs immobilizing His-tagged proteins (Figure 5C). His-tagged protein conjugation (filled circles) is efficient, allowing nearly 100% conjugation at protein:NLP ratios below 20:1 (although exact loading ratios are protein- and NLP-dependent). Covalent conjugation (open squares) is often less efficient, and under these experimental 139 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


conditions required 2-fold more protein to achieve similar loading as the non-covalent approach (42). It is possible to tune the ratio of cargo molecule to NLP to ensure complete immobilization of the cargo molecule (88). Using covalent strategies, this is often hard to accomplish, due to limitations in reaction rates, kinetics, and steric hinderances. Typically, an excess of cargo molecules is required to maximize loading on the NLP (42). However, non-covalent strategies are less amenable to applications such as targeting, as the off-rates of the non-covalent reactions can lead to rapid dissociation and displacement of the targeting element. For those applications, covalent conjugation would be recommended. Side-by-side comparisons between covalent and non-covalent conjugation strategies have been explored in liposomal systems, highlighting the enhanced stability of covalently conjugated moieties (91).

Figure 5. Proteins can be conjugated to NLPs using both non-covalent and covalent strategies. A, B. SEC demonstrates the non-covalent conjugation of His-tagged proteins (blue traces) with nickel-chelating NLPs (black traces) to form protein:NLP complexes (red traces). This is readily achieved with relatively small proteins (A, 24 kDa) and larger proteins (B, 81 kDa) at a 4:1 protein:NLP ratio. C. Incorporation of a 22 kDa protein was assessed by SEC and gel electrophoresis to compared the efficiency of conjugation between non-covalent (His:nickel interaction, filled circles) or covalent (copper-free click chemistry, open squares) strategies. Adapted with permission from (42). Amphiphilic molecules that feature distinct hydrophobic and hydrophilic domains are often amenable to incorporation into NLPs during self-assembly or through an add-back technique (90). In this approach, the amphiphilic molecules are incubated with pre-formed NLPs. The lipidic portion of the molecule is readily anchored into the NLP bilayer, leaving the polar region 140 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


exposed to solution. This approach has been successfully used with naturally amphipathic molecules (e.g. the vaccine adjuvant FSL-1, unpublished data) and engineered molecules modified with a lipidic anchor. A prime example of this latter strategy is cholesterol-modified CpG oligodeoxynucleotide (chol-CpG), whereby the cholesterol anchor readily tethers the CpG to the pre-formed NLP. Cholesterol-modified siRNAs tethered onto rHDLs during assembly have been used for tumor gene therapy applications (92). In most cases, the incorporation rate is very high, obviating the need to undergo additional purification steps prior to use. Taken together, the three orthogonal strategies for biomolecule conjugation (depicted schematically in Figure 4) provide a powerful and versatile means of functionalizing NLPs for specific applications. Multifunctional NLPs for vaccine applications are readily prepared using a combination of all three strategies (illustrated in Figure 1). Such an approach facilitates co-localized delivery of vaccine antigens and adjuvants on a single nanoparticulate platform, thereby enhancing immunogenicity (93, 94). Despite many of the adjuvants and antigens having disparate physicochemical characteristics, we have optimized the outlined strategies to easily fabricate multifunctional NLPs to incorporate MPLA (very hydrophobic adjuvant), a protein antigen (soluble, bearing a poly-His tag), and chol-CpG (modified for add-back incorporation). NLPs are assembled to incorporate MPLA and Ni-bearing lipids. Once purified, these NLPs are simultaneously incubated with both His-tagged protein and chol-CpG. Due to the efficiency in conjugation, subsequent purification is not required. A key feature of NLPs is the ability to incorporate membrane (or membrane-associated) proteins within the lipid bilayer. This approach was pioneered by Sligar in the late 1990s and has been successfully used to solubilize and study discrete membrane proteins in a native membrane environment (95, 96). Two other approaches to incorporate membrane proteins into NLPs that bear mentioning in this context are cell-free expression approach and protein add-back. In the former approach, in vitro transcription/translation is used as a means of de novo membrane protein production directly into NLPs. This can be accomplished by translating membrane proteins into pre-formed NLPs (97) or during simultaneous co-translation of both membrane protein and scaffold protein, whereby NLP formation is concomitant with membrane protein expression and insertion (98). The cell-free co-expression method is particularly useful in producing membrane proteins in a soluble and functional state that are otherwise difficult to express or purify. This technique has been used to study membrane proteins of interest including rhodopsin, G-protein coupled receptors, receptor tyrosine kinases and pore forming channel proteins (99–102). In the case of membrane-associated proteins that feature a lipidic anchoring moiety, simple add-back achieves anchoring to the NLP bilayer. This approach has been successfully adapted to study the GTPase KRAS, as native KRAS is farnesylated at Cys-185. This post-translational modification allows KRAS to attach to lipid membranes and is critical to KRAS trafficking, subcellular localization, and protein-protein interactions. Incorporating farnesylated KRAS4b protein onto PS-containing NLPs has provided a means of studying functional KRAS in its native lipid environment (103). It is important to keep in mind that NLPs 141 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

bearing membrane proteins (or membrane-associated proteins) are amenable to the majority of conjugation schemes outlined above (Figure 4), providing a rich pool of additional biological cargo molecules for in vitro and in vivo applications.


Enhancing Structural Stability NLPs possess an array of features that make them desirable tools for use in vivo. However, one intrinsic property of HDLs that must be considered is that nascent HDLs found in vivo can remodel and exchange molecules with other lipoproteins. As synthetic HDLs also have the potential to exchange molecules with endogenous HDLs in vivo, concern exists for applications where off-target effects could be problematic. Additionally, some NLP formulations have even been demonstrated to remodel over time in PBS (57), or completely dissociate when placed in solutions containing serum (42). For some applications, it is then necessary to consider the need for preserving the NLP structure and preventing any changes in composition to individual particles that could occur in vivo or otherwise. Since NLPs are structures that are maintained by non-covalent interactions, one option is to enhance stability through intraparticle crosslinking of constituent molecules. For example, intraparticle crosslinking of scaffold proteins is achieved using short, homo-bifunctional crosslinkers reacting with lysines on the protein surfaces. Using this approach, remodeling of the particles, which can occur over time even in PBS at 4°C, was successfully inhibited (57). Alternatively, lipids that possess crosslinking groups can be incorporated into the lipid component of the particles. Following particle assembly, these groups can then be activated using the appropriate catalyst.

Figure 6. NLP stability can be enhanced by incorporating crosslinkable lipids into the lipid bilayer. A. The presence of UV-crosslinkable DiynePC monomers incorporated into the NLP bilayer decreases as a function of UV exposure time. B. Fluorescently-labeled, crosslinked and non-crosslinked NLPs (incorporating 0, 10, or 20% crosslinked Diyne PC) were incubated in 100% serum at 37°C for 10 minutes, and quantified by SEC for the presence of intact NLPs. Adapted with permission from (76). As shown in Figure 6, NLPs were prepared using a lipid mixture containing 20% 1,2-bis(10,12-tricosadiynoyl)-sn-glycero-3-phosphocholine (DiynePC). Upon UV irradiation, diyne groups in adjacent lipids in the bilayer polymerize, 142 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


covalently linking the lipids. The lipid polymerization can be indirectly monitored by quantifying the amount of DiynePC monomer using reverse phase HPLC as a function of irradiation time (Figure 6A). Importantly, intraparticle crosslinking greatly enhances particle stability in serum relative to non-crosslinked particles (Figure 6B), and crosslinked particles can remain stable in serum for up to 48 hours (76). This characteristic enables the possibility that these particles may be used for drug formulation and targeted delivery. Since the DiynePC component of the formulation is only a minority of the lipid mixture (typically less than 25% of total lipid), other functional lipids or hydrophobic molecules may also be incorporated into the particle, such as lipids that can serve as anchors for soluble proteins, peptides, or other molecules of interest. Collectively, these features demonstrate that NLPs have the potential to function as effective tools for therapeutic formulation and delivery.

Evaluating Compatibility and Distribution Due in large part to their biomimetic nature, NLPs exhibit favorable compatibility profiles in vitro and in vivo (42). Quantification of cellular cytotoxicity (e.g. lactose dehydrogenase (LDH) release assays) demonstrated no cellular cytotoxicity in vitro using a variety of cell lines and primary immune cells, including the human liver cell line, Hep G2, which has been used as a model cell line to assess potential cytotoxicity of drugs in the liver. Upon incubation with varying concentrations of apoE.22k-based NiNLPs (ca. 15 nm in diameter), all cell lines tested exhibited viability profiles similar to PBS controls. Furthermore, acute toxicity in vivo was not observed. Acute toxicity experiments were performed according to the National Toxicology Program protocol published by the Department of Health and Human Services (104). Male and female mice were injected with NiNLPs daily by both intraperitoneal (i.p.) and intranasal (i.n.) routes for 14 consecutive days. During the 14-day study period, animal weights were recorded daily. At the end of the 14-day study period, liver, lung, kidney and spleen were collected, weighed, and visually inspected for lesions and tissue damage. All organs harvested from mice injected with NiNLPs appeared similar to animals receiving PBS with no obvious abnormalities noted. Furthermore, no statistically significant differences were observed in normalized weights of the organs between the mice injected with NiNLPS compared with the animals that received PBS. Importantly, histological analyses of individual organs did not reveal any differences in tissue microstructure between the NiNLP groups and PBS controls. While this particular formulation of NiNLPs was well-tolerated, other NiNLP formulations would need to undergo similar analyses to assess toxicity. The distribution of NiNLPs in vitro and in vivo has also been a topic of much investigation. Cellular uptake of NLPs has been demonstrated using both cell lines and primary cells (42). Fluorescent labeling of the NiNLP scaffold protein allowed us to determine uptake in both J774 macrophage cell lines and primary murine macrophages. After 2 hours of incubation with NiNLPs, greater than 96% of cells were positive for fluorescence indicating NiNLP uptake. Interestingly, this 143 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


rapid internalization of NiNLPs was not cargo dependent as NiNLPs conjugated with cargo molecules exhibited similar levels of internalization as NiNLPs alone (43). Furthermore, subcellular localization of NiNLPs was assessed in J774 cells preloaded with LysoTracker dye. AF488-labeled NiNLPs co-localized with the LysoTracker dye, indicating that the particles were trafficked to lysosomes. Taken together, these results demonstrate that NLP constructs are rapidly internalized by mouse macrophages and trafficked to lysosomes for degradation. However, it remains to be seen whether NLPs are trafficked in the same way universally among mammalian cells. The fate of NiNLPs in vivo was also determined using NiNLPs labeled with a near IR fluorophore. Normal, healthy mice were injected by a variety of routes and at various times post-administration, organs were harvested for fluorescence imaging analysis (Figure 7A) (42). In broad terms, the distribution of fluorescent NiNLPs in vivo depends in large part on how they are administered. NiNLPs that were injected systemically showed enhanced fluorescent signal in the kidney and liver indicating clearance through those organs (Figure 7B). In contrast, animals given fluorescent NiNLPs intranasally exhibited enhanced fluorescent signals in the lung with diminished signal in both kidney and liver indicating that the NiNLPs are retained in the lung for longer periods of time before being cleared by the kidney and liver. Additional studies using tumor bearing mice coupled with other imaging modalities such as positron emission tomography (PET)/computed tomography (CT), demonstrate nanodiscs injected intravenously (i.v.) tend to exhibit clearance via the kidney and liver (105), consistent with previous reports (42). Of course, the composition and physicochemical properties are also expected to significantly influence NLP tropism.

Figure 7. In vivo characterization of NLPs elucidate distribution and tailorable immunogenicity. A. NLPs labeled with a far red fluorophore (AlexaFluor 750) can be tracked in vivo and ex vivo. Adapted with permission from (42). B. Quantification of fluorescence signal in organs (normalized to total organ weight) demonstrates differential distribution dependant on route of administration (intranasal, i.n.; subcutaneous, s.c.; intramuscular, i.m.; intraperitoneal, i.p.; intravenous, i.v.). Mice received equivalent doses, and distribution was monitored 4 hours post-administration. Adapted with permission from (42). C. Species-matching the scaffold protein with the animal model is imperative. Anti-apoE.22k IgG antibody titers in mice were assessed with adjuvanted NLPs prepared with either human or mouse scaffold proteins. 144 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


NLPs exhibit low immunogenicity profiles in vivo as evidenced by the lack of detectable antibody titers against the apoE-derived scaffold protein, provided that the NLP formulation administered is prepared with species-matched scaffold proteins. NLPs adjuvanted with either MPLA (MPLA:NLPs) or cholesterol-modified CpG ODN 1826 (CpG:NLP) were prepared with recombinantly expressed human or mouse apoE.22k. Animals were vaccinated twice, and sera was assessed four weeks later for the presence of anti-apoE.22k IgG antibodies. Human-based NLPs elicited very high anti-apoE.22k titers, while mouse-based NLPs has no discernable titer above background (Figure 7C). These results demonstrate that the NiNLP platform, in the absence of a cargo molecule, does not elicit overt immunogenicity provided that the formulation is species-matched. This finding underscores its versatility as a minimally-immunogenic particle for in vivo applications.

Conclusions NLPs possess a number of intrinsic features that underscore their utility for a variety of in vivo applications. NLPs can be prepared using a wide range of methods and constituents which contribute to their overall shape and physicochemical properties. Furthermore, a variety of strategies may be applied to both functionalize and stabilize NLPs for specific applications. In addition, the NLPs biodistribution profile is dependent on the route of administration. NLPs are biocompatible, exhibiting no overt toxicity or immunogenicity when the scaffold protein is species matched to the model system being studied. These are key characteristics for any nanoparticle to be used for in vivo applications. In summary, the NLP is a desirable platform for various in vivo applications due to their facile assembly and ability to be readily functionalized and stabilized while exhibiting biocamptible profiles in vivo.

Acknowledgments The authors thank Craig Blanchette for his contributions in screening speciesmatched scaffold proteins in vivo. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-JRNL-727197.

References 1. 2.

3. 4.

Farokhzad, O. C.; Langer, R. ACS Nano 2009, 3 (1), 16–20. Considering Whether an FDA-Regulated Product Involves the Application of Nanotechnology https://www.fda.gov/RegulatoryInformation/Guidances/ ucm257698.htm (accessed April 1, 2017). Gregory, A. E.; Titball, R.; Williamson, D. Front. Cell. Infect. Microbiol. 2013, 3, 13. Zhao, L.; Seth, A.; Wibowo, N.; Zhao, C.-X.; Mitter, N.; Yu, C.; Middelberg, A. P. J. Vaccine 2014, 32 (3), 327–337. 145

Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

5. 6. 7. 8. 9. 10.


11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.

29.

Nune, S. K.; Gunda, P.; Thallapally, P. K.; Lin, Y.-Y.; Forrest, M. L.; Berkland, C. J. Expert Opin. Drug Deliv. 2009, 6 (11), 1175–1194. Schwartz, S. Nanomedicine 2017, 12 (4), 271–274. Pillai, G. Pharm. Pharm. Sci. 2014, 1, 1–13. Cho, K.; Wang, X.; Nie, S.; Chen, Z. (G.); Shin, D. M. Clin. Cancer Res. 2008, 14 (5), 1310. van Vlerken, L. E.; Duan, Z.; Little, S. R.; Seiden, M. V.; Amiji, M. M. Mol. Pharm. 2008, 5 (4), 516–526. Swenson, C. E.; Bolcsak, L. E.; Batist, G.; Guthrie, T. H. J.; Tkaczuk, K. H.; Boxenbaum, H.; Welles, L.; Chow, S.-C.; Bhamra, R.; Chaikin, P. Anticancer Drugs 2003, 14 (3), 239–246. Kadam, R. S.; Bourne, D. W. A.; Kompella, U. B. Drug Metab. Dispos. 2012, 40 (7), 1380–1388. Blanco, E.; Shen, H.; Ferrari, M. Nat. Biotechnol. 2015, 33 (9), 941–951. Kim, C. K.; Ghosh, P.; Pagliuca, C.; Zhu, Z.-J.; Menichetti, S.; Rotello, V. M. J. Am. Chem. Soc. 2009, 131 (4), 1360–1361. Burt, H. M.; Zhang, X.; Toleikis, P.; Embree, L.; Hunter, W. L. Colloids Surf., B 1999, 16 (1–4), 161–171. Venne, A.; Li, S.; Mandeville, R.; Kabanov, A.; Alakhov, V. Cancer Res. 1996, 56 (16), 3626. Alakhov, V. Y.; Moskaleva, E. Y.; Batrakova, E. V.; Kabanov, A. V. Bioconjug. Chem. 1996, 7 (2), 209–216. Safra, T.; Muggia, F.; Jeffers, S.; Tsao-Wei, D. D.; Groshen, S.; Lyass, O.; Henderson, R.; Berry, G.; Gabizon, A. Ann. Oncol. 2000, 11 (8), 1029–1033. Leonard, R. C. F.; Williams, S.; Tulpule, A.; Levine, A. M.; Oliveros, S. The Breast 2009, 18 (4), 218–224. Fossa, S. D.; Aass, N.; Paro, G. Eur. J. Cancer 1998, 34 (7), 1131–1132. O’Byrne, K. J.; Thomas, A. L.; Sharma, R. A.; DeCatris, M.; Shields, F.; Beare, S.; Steward, W. P. Br. J. Cancer 2002, 87 (1), 15–20. Richardson, D. S.; Kelsey, S. M.; Johnson, S. A.; Tighe, M.; Cavenagh, J. D.; Newland, A. C. Invest. New Drugs 1997, 15 (3), 247–253. Rahman, A.; Fumagalli, A.; Goodman, A.; Schein, P. S. Semin. Oncol. 1984, 11 (4), 45–55. Matsumura, Y.; Maeda, H. Cancer Res. 1986, 46 (12) (Part 1), 6387. Nakamura, Y.; Mochida, A.; Choyke, P. L.; Kobayashi, H. Bioconjug. Chem. 2016, 27 (10), 2225–2238. Xu, L.; Pirollo, K. F.; Tang, W. H.; Rait, A.; Chang, E. H. Hum. Gene Ther. 1999, 10 (18), 2941–2952. Cho, K.; Wang, X.; Kim, G.; Gjyrezi, A.; Giannakakou, P.; Nie, S.; Chen, Z.; Shin, D. Cancer Res. 2014, 67 (9) (Supplement), 2311. Lin, T.; Li, Y.-P.; Zhang, H.; Luo, J.; Goodwin, N.; Gao, T.; de Vere White, R.; Lam, K. S.; Pan, C.-X. Nanomedicine 2013, 8 (8), 1239–1251. Cho, Y. W.; Park, S. A.; Han, T. H.; Son, D. H.; Park, J. S.; Oh, S. J.; Moon, D. H.; Cho, K.-J.; Ahn, C.-H.; Byun, Y.; Kim, I.-S.; Kwon, I. C.; Kim, S. Y. Biomaterials 2007, 28 (6), 1236–1247. Goodman, C. M.; McCusker, C. D.; Yilmaz, T.; Rotello, V. M. Bioconjug. Chem. 2004, 15 (4), 897–900. 146



30. Barshtein, G.; Arbell, D.; Yedgar, S. IEEE Trans. NanoBioscience 2011, 10 (4), 259–261. 31. Gojova, A.; Guo, B.; Kota, R. S.; Rutledge, J. C.; Kennedy, I. M.; Barakat, A. I. Environ. Health Perspect. 2007, 115 (3), 403–409. 32. Sun, J.; Wang, S.; Zhao, D.; Hun, F. H.; Weng, L.; Liu, H. Cell Biol. Toxicol. 2011, 27 (5), 333–342. 33. Johnston, S. R.; Gore, M. Eur. J. Cancer 2001, 37 (Supplement 9), 8–14. 34. Zolnik, B. S.; González-Fernández, Á.; Sadrieh, N.; Dobrovolskaia, M. A. Endocrinology 2010, 151 (2), 458–465. 35. Lee, S. C.; Parthasarathy, R.; Botwin, K.; Kunneman, D.; Rowold, E.; Lange, G.; Klover, J.; Abegg, A.; Zobel, J.; Beck, T.; Miller, T.; Hood, W.; Monahan, J.; McKearn, J. P.; Jansson, R.; Voliva, C. F. Biomed. Microdevices 2004, 6 (3), 191–202. 36. Green, M. R.; Manikhas, G. M.; Orlov, S.; Afanasyev, B.; Makhson, A. M.; Bhar, P.; Hawkins, M. J. Ann. Oncol. 2006, 17 (8), 1263–1268. 37. Parodi, A.; Quattrocchi, N.; van de Ven, A. L.; Chiappini, C.; Evangelopoulos, M.; Martinez, J. O.; Brown, B. S.; Khaled, S. Z.; Yazdi, I. K.; Enzo, M. V.; Isenhart, L.; Ferrari, M.; Tasciotti, E. Nat. Nano 2013, 8 (1), 61–68. 38. Piao, J.-G.; Wang, L.; Gao, F.; You, Y.-Z.; Xiong, Y.; Yang, L. ACS Nano 2014, 8 (10), 10414–10425. 39. Gao, W.; Zhang, L. AIChE J. 2015, 61 (3), 738–746. 40. Rodriguez, P. L.; Harada, T.; Christian, D. A.; Pantano, D. A.; Tsai, R. K.; Discher, D. E. Science 2013, 339 (6122), 971. 41. Scanu, A. J. Biol. Chem. 1967, 242 (4), 711–719. 42. Fischer, N. O.; Weilhammer, D. R.; Dunkle, A.; Thomas, C.; Hwang, M.; Corzett, M.; Lychak, C.; Mayer, W.; Urbin, S.; Collette, N.; Chiun Chang, J.; Loots, G. G.; Rasley, A.; Blanchette, C. D. PLoS ONE 2014, 9 (3), e93342. 43. Weilhammer, D. R.; Blanchette, C. D.; Fischer, N. O.; Alam, S.; Loots, G. G.; Corzett, M.; Thomas, C.; Lychak, C.; Dunkle, A. D.; Ruitenberg, J. J.; Ghanekar, S. A.; Sant, A. J.; Rasley, A. Biomaterials 2013, 34 (38), 10305–10318. 44. Lacko, A. G.; Sabnis, N. A.; Nagarajan, B.; McConathy, W. J. Front. Pharmacol. 2015, 6, 247. 45. Mutharasan, R. K.; Foit, L.; Thaxton, C. S. J. Mater. Chem. B 2016, 4 (2), 188–197. 46. Bricarello, D. A.; Smilowitz, J. T.; Zivkovic, A. M.; German, J. B.; Parikh, A. N. ACS Nano 2011, 5 (1), 42–57. 47. Huang, H.; Cruz, W.; Chen, J.; Zheng, G. Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 2015, 7 (3), 298–314. 48. Simonsen, J. B. Nanomedicine 2016, 12 (7), 2161–2179. 49. Darabi, M.; Guillas-Baudouin, I.; Le Goff, W.; Chapman, M. J.; Kontush, A. Pharmacol. Ther. 2016, 157, 28–42. 50. Matz, C. E.; Jonas, A. J. Biol. Chem. 1982, 257 (8), 4535–4540. 51. Jonas, A. Methods Enzym. 1986, 128, 553–582. 52. Chromy, B. A.; Arroyo, E.; Blanchette, C. D.; Bench, G.; Benner, H.; Cappuccio, J. A.; Coleman, M. A.; Henderson, P. T.; Hinz, A. K.; 147 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

53. 54. 55.


56.

57. 58. 59. 60. 61. 62. 63. 64. 65. 66.

67. 68. 69.

70.

Kuhn, E. A.; Pesavento, J. B.; Segelke, B. W.; Sulchek, T. A.; Tarasow, T.; Walsworth, V. L.; Hoeprich, P. D. J. Am. Chem. Soc. 2007, 129 (46), 14348–14354. Zhao, Y.; Black, A. S.; Bonnet, D. J.; Maryanoff, B. E.; Curtiss, L. K.; Leman, L. J.; Ghadiri, M. R. J. Lipid Res. 2014, 55 (10), 2053–2063. Zhao, Y.; Imura, T.; Leman, L. J.; Curtiss, L. K.; Maryanoff, B. E.; Ghadiri, M. R. J. Am. Chem. Soc. 2013, 135 (36), 13414–13424. Van Lenten, B. J.; Wagner, A. C.; Anantharamaiah, G. M.; Navab, M.; Reddy, S. T.; Buga, G. M.; Fogelman, A. M. Curr. Atheroscler. Rep. 2009, 11 (1), 52–57. Chromy, B. A.; Arroyo, E.; Blanchette, C. D.; Bench, G.; Benner, H.; Cappuccio, J. A.; Coleman, M. A.; Henderson, P. T.; Hinz, A. K.; Kuhn, E. A.; Pesavento, J. B.; Segelke, B. W.; Sulchek, T. A.; Tarasow, T.; Walsworth, V. L.; Hoeprich, P. D. J. Am. Chem. Soc. 2007, 129 (46), 14348–14354. Fischer, N. O.; Blanchette, C. D.; Segelke, B. W.; Corzett, M.; Chromy, B. A.; Kuhn, E. A.; Bench, G.; Hoeprich, P. D. PLoS ONE 2010, 5 (7), e11643. Weers, P. M. M.; Ryan, R. O. Insect Biochem. Mol. Biol. 2006, 36 (4), 231–240. Zhang, W.; Sun, J.; Liu, Y.; Tao, M.; Ai, X.; Su, X.; Cai, C.; Tang, Y.; Feng, Z.; Yan, X.; Chen, G.; He, Z. Mol. Pharm. 2014, 11 (10), 3279–3290. Baillie, G.; Owens, M. D.; Halbert, G. W. J. Lipid Res. 2002, 43 (1), 69–73. Nikanjam, M.; Gibbs, A. R.; Hunt, C. A.; Budinger, T. F.; Forte, T. M. J. Controlled Release 2007, 124 (3), 163–171. Hayavi, S.; Halbert, G. W. Biotechnol. Prog. 2005, 21 (4), 1262–1268. Owens, M. D.; Baillie, G.; Halbert, G. W. Int. J. Pharm. 2001, 228 (1–2), 109–117. Jonas, A.; Wald, J. H.; Toohill, K. L.; Krul, E. S.; Kézdy, K. E. J. Biol. Chem. 1990, 265 (36), 22123–22129. Cavigiolio, G.; Shao, B.; Geier, E. G.; Ren, G.; Heinecke, J. W.; Oda, M. N. Biochemistry 2008, 47 (16), 4770–4779. Blanchette, C. D.; Law, R.; Benner, W. H.; Pesavento, J. B.; Cappuccio, J. A.; Walsworth, V.; Kuhn, E. A.; Corzett, M.; Chromy, B. A.; Segelke, B. W.; Coleman, M. A.; Bench, G.; Hoeprich, P. D.; Sulchek, T. A. J. Lipid Res. 2008, 49 (7), 1420–1430. Eisenberg, S.; Windmueller, H. G.; Levy, R. I. J. Lipid Res. 1973, 14 (4), 446–458. Daerr, W. H.; Pethke, W.; Windler, E. T. E.; Greten, H. Biochim. Biophys. Acta, Lipids Lipid Metab. 1990, 1043 (3), 311–317. Yancey, P. G.; de la Llera-Moya, M.; Swarnakar, S.; Monzo, P.; Klein, S. M.; Connelly, M. A.; Johnson, W. J.; Williams, D. L.; Rothblat, G. H. J. Biol. Chem. 2000, 275 (47), 36596–36604. Schwendeman, A.; Sviridov, D. O.; Yuan, W.; Guo, Y.; Morin, E. E.; Yuan, Y.; Stonik, J.; Freeman, L.; Ossoli, A.; Thacker, S.; Killion, S.; Pryor, M.; Chen, Y. E.; Turner, S.; Remaley, A. T. J. Lipid Res. 2015, 56 (9), 1727–1737. 148



71. Wadsater, M.; Maric, S.; Simonsen, J. B.; Mortensen, K.; Cardenas, M. Soft Matter 2013, 9 (7), 2329–2337. 72. Miyazaki, M.; Tajima, Y.; Ishihama, Y.; Handa, T.; Nakano, M. Biochim. Biophys. Acta, Biomembr. 2013, 1828 (5), 1340–1346. 73. Bricarello, D. A.; Mills, E. J.; Petrlova, J.; Voss, J. C.; Parikh, A. N. J. Lipid Res. 2010, 51 (9), 2731–2738. 74. Huang, M.; Hu, M.; Song, Q.; Song, H.; Huang, J.; Gu, X.; Wang, X.; Chen, J.; Kang, T.; Feng, X.; Jiang, D.; Zheng, G.; Chen, H.; Gao, X. ACS Nano 2015, 9 (11), 10801–10816. 75. Surewicz, W. K.; Epand, R. M.; Pownall, H. J.; Hui, S. W. J. Biol. Chem. 1986, 261 (34), 16191–16197. 76. Gilmore, S. F.; Blanchette, C. D.; Scharadin, T. M.; Hura, G. L.; Rasley, A.; Corzett, M.; Pan, C.; Fischer, N. O.; Henderson, P. T. ACS Appl. Mater. Interfaces 2016, 8 (32), 20549–20557. 77. Iovannisci, D. M.; Beckstead, J. A.; Ryan, R. O. Biochem. Biophys. Res. Commun. 2009, 379. 78. Cormode, D. P.; Briley-Saebo, K. C.; Mulder, W. J.; Aguinaldo, J. G.; Barazza, A.; Ma, Y.; Fisher, E. A.; Fayad, Z. A. Small 2008, 4. 79. Cormode, D. P.; Frias, J. C.; Ma, Y.; Chen, W.; Skajaa, T.; Briley-Saebo, K.; Barazza, A.; Williams, K. J.; Mulder, W. J.; Fayad, Z. A.; Fisher, E. A. Clin. Lipidol. 2009, 4 (4), 493–500. 80. Oda, M. N.; Hargreaves, P. L.; Beckstead, J. A.; Redmond, K. A.; van Antwerpen, R.; Ryan, R. O. J. Lipid Res. 2006, 47 (2), 260–267. 81. Tufteland, M.; Ren, G.; Ryan, R. O. J. Pharm. Sci. 2008, 97 (10), 4425–4432. 82. Nguyen, T.-. S.; Weers, P. M. M.; Raussens, V.; Wang, Z.; Ren, G.; Sulchek, T.; Hoeprich, P. D.; Ryan, R. O. Biochim. Biophys. Acta 2008, 1778. 83. Wang, J.; Jia, J.; Liu, J.; He, H.; Zhang, W.; Li, Z. Drug Deliv. 2013, 20 (8), 356–363. 84. Fischer, N. O.; Rasley, A.; Corzett, M.; Hwang, M. H.; Hoeprich, P. D.; Blanchette, C. D. J. Am. Chem. Soc. 2013, 135 (6), 2044–2047. 85. Ghosh, M.; Ren, G.; Simonsen, J. B.; Ryan, R. O. Biochem. Cell Biol. Biochim. Biol. Cell. 2014, 92 (3), 200–205. 86. Fischer, N. O.; Blanchette, C. D.; Chromy, B. A.; Kuhn, E. A.; Segelke, B. W.; Corzett, M.; Bench, G.; Mason, P. W.; Hoeprich, P. D. Bioconjug. Chem. 2009, 20 (3), 460–465. 87. Fischer, N. O.; Infante, E.; Ishikawa, T.; Blanchette, C. D.; Bourne, N.; Hoeprich, P. D.; Mason, P. W. Bioconjug. Chem. 2010, 21. 88. Blanchette, C. D.; Fischer, N. O.; Corzett, M.; Bench, G.; Hoeprich, P. D. Bioconjug. Chem. 2010, 21 (7), 1321–1330. 89. Weilhammer, D.; Dunkle, A. D.; Blanchette, C. D.; Fischer, N. O.; Corzett, M.; Lehmann, D.; Boone, T.; Hoeprich, P.; Driks, A.; Rasley, A. Vaccine 2017, 35 (11), 1475–1481. 90. Kuai, R.; Ochyl, L. J.; Bahjat, K. S.; Schwendeman, A.; Moon, J. J. Nat. Mater. 2016, 16. 91. Watson, D. S.; Szoka, F. C. Vaccine 2009, 27 (34), 4672–4683. 149 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


92. Ding, Y.; Wang, W.; Feng, M.; Wang, Y.; Zhou, J.; Ding, X.; Zhou, X.; Liu, C.; Wang, R.; Zhang, Q. Biomaterials 2012, 33 (34), 8893–8905. 93. Li, Z.; Liu, Z.; Yin, M.; Yang, X.; Ren, J.; Qu, X. Adv. Healthcare Mater. 2013, 2 (10), 1309–1313. 94. Tao, Y.; Ju, E.; Li, Z.; Ren, J.; Qu, X. Adv. Funct. Mater. 2014, 24 (7), 1004–1010. 95. Denisov, I. G.; Sligar, S. G. Chem. Rev. 2017, 117 (6), 4669–4713. 96. Rouck, J. E.; Krapf, J. E.; Roy, J.; Huff, H. C.; Das, A. FEBS Lett. 2017, 591 (14), 2057–2088. 97. Katzen, F.; Fletcher, J. E.; Yang, J.-P.; Kang, D.; Peterson, T. C.; Cappuccio, J. A.; Blanchette, C. D.; Sulchek, T.; Chromy, B. A.; Hoeprich, P. D.; Coleman, M. A.; Kudlicki, W. J. Proteome Res. 2008, 7 (8), 3535–3542. 98. Cappuccio, J. A.; Blanchette, C. D.; Sulchek, T. A.; Arroyo, E. S.; Kralj, J. M.; Hinz, A. K.; Kuhn, E. A.; Chromy, B. A.; Segelke, B. W.; Rothschild, K. J.; Fletcher, J. E.; Katzen, F.; Peterson, T. C.; Kudlicki, W. A.; Bench, G.; Hoeprich, P. D.; Coleman, M. A. Mol. Cell. Proteomics 2008, 7 (11), 2246–2253. 99. He, W.; Scharadin, T. M.; Saldana, M.; Gellner, C.; Hoang-Phou, S.; Takanishi, C.; Hura, G. L.; Tainer, J. A.; Carraway, K. L., III; Henderson, P. T.; Coleman, M. A. Sci. Rep. 2015, 5, 12896. 100. Gao, T.; Blanchette, C. D.; He, W.; Bourguet, F.; Ly, S.; Katzen, F.; Kudlicki, W. A.; Henderson, P. T.; Laurence, T. A.; Huser, T.; Coleman, M. A. Protein Sci. Publ. Protein Soc. 2011, 20 (2), 437–447. 101. Gao, T.; Petrlova, J.; He, W.; Huser, T.; Kudlick, W.; Voss, J.; Coleman, M. A. PLoS ONE 2012, 7 (9), e44911. 102. Ly, S.; Bourguet, F.; Fischer, N. O.; Lau, E. Y.; Coleman, M. A.; Laurence, T. A. Biophys. J. 2014, 106 (2), L05–L08. 103. Gillette, W. K.; Esposito, D.; Abreu Blanco, M.; Alexander, P.; Bindu, L.; Bittner, C.; Chertov, O.; Frank, P. H.; Grose, C.; Jones, J. E.; Meng, Z.; Perkins, S.; Van, Q.; Ghirlando, R.; Fivash, M.; Nissley, D. V.; McCormick, F.; Holderfield, M.; Stephen, A. G. Sci. Rep. 2015, 5, 15916. 104. 14-Day Toxicity Protocol. https://ntp.niehs.nih.gov/testing/types/cartox/ protocols/14day/index.html (accessed April 1, 2017). 105. Huda, P.; Binderup, T.; Pedersen, M. C.; Midtgaard, S. R.; Elema, D. R.; Kjær, A.; Jensen, M.; Arleth, L. PLoS ONE 2015, 10 (7), e0129310.

150 Ilies; Control of Amphiphile Self-Assembling at the Molecular Level: Supra-Molecular Assemblies with Tuned ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

Strategies for Functionalizing Lipoprotein-Based Nanoparticles - ACS

Recommend Documents