Charge shielding effects of PEG bound to NH2-terminated PAMAM dendrimers - an experimental approach.

Cationic poly(amido amine) (PAMAM) dendrimers exhibit great potential for use in drug delivery, but their high charge density leads to an inherent cytotoxicity. To increase biocompatibility, many studies have attached poly(ethylene glycol) (PEG) chains to the dendrimer surface. It is unclear how these tethered PEG chains influence the physicochemical properties of the dendrimer. Here, we develop a fluorescence-based assay utilizing anionic biological tissue to quantify the electrostatic binding affinity of a library of PEG-PAMAM conjugates with various PEG chain lengths and grafting densities. We find that covalently bound PEG chains reduce the electrostatic binding affinity more significantly than what can be achieved through covalent bonds only. Contrary to previous thought, this reduction is not explained by the steric hindrance effects of PEG chains, suggesting that other, non-covalent interactions between PEG and PAMAM are present. Using acetylated PAMAM conjugates, we convert electrostatic binding affinity to the number of charged amines accessible to the physiological environment. These data, coupled with 1H-NMR, allows us to study more closely the non-covalent interactions between PEG and PAMAM. We find that increasing PEG chain length increases the number of non-covalent interactions. Additionally, at low grafting densities, increasing the number of PEG chains on the PAMAM surface also increases the non-covalent interactions. At higher grafting densities, however, PEG chains sterically repel one another, forcing chains to elongate away from the surface and reducing the number of interactions between PAMAM and individual PEG chains. The data presented here provides a framework for a more precise mechanistic understanding of how the length and density of tethered PEG chains on PAMAM dendrimers influence drug delivery properties.

Synthesis of Zwitterionic Pluronic Analogs.

Novel polymer amphiphiles with chemical structures designed as zwitterionic analogs of Pluronic block copolymers were prepared by controlled free radical polymerization of phosphorylcholine (PC) or choline phosphate (CP) methacrylate monomers from a difunctional poly(propylene oxide) (PPO) macroinitiator. Well-defined, water-dispersible zwitterionic triblock copolymers, or "zwitteronics", were prepared with PC content ranging from 5 to 47 mol percent and composition-independent surfactant characteristics in water, which deviate from the properties of conventional Pluronic amphiphiles. These PC-zwitteronics assembled into nanoparticles in water, with tunable sizes and critical aggregation concentrations (CACs) based on their hydrophilic-lipophilic balance (HLB). Owing to the lower critical solution temperature (LCST) miscibility of the hydrophobic PPO block in water, PC-zwitteronics exhibited thermoreversible aqueous solubility tuned by block copolymer composition. The chemical versatility of this approach was demonstrated by embedding functionality, in the form of alkyne groups, directly into the zwitterion moieties. These alkynes proved ideal for cross-linking the zwitteronic nanoparticles and for generating nanoparticle-cross-linked hydrogels using UV-initiated thiol-yne "click" chemistry.

The effect of comb architecture on complex coacervation.

Complex coacervation is a widely utilized technique for effecting phase separation, though predictive understanding of molecular-level details remains underdeveloped. Here, we couple coarse-grained Monte Carlo simulations with experimental efforts using a polypeptide-based model system to investigate how a comb-like architecture affects complex coacervation and coacervate stability. Specifically, the phase separation behavior of linear polycation-linear polyanion pairs was compared to that of comb polycation-linear polyanion and comb polycation-comb polyanion pairs. The comb architecture was found to mitigate cooperative interactions between oppositely charged polymers, as no discernible phase separation was observed for comb-comb pairs and complex coacervation of linear-linear pairs yielded stable coacervates at higher salt concentration than linear-comb pairs. This behavior was attributed to differences in counterion release by linear vs. comb polymers during polyeletrolyte complexation. Additionally, the comb polycation formed coacervates with both stereoregular poly(l-glutamate) and racemic poly(d,l-glutamate), whereas the linear polycation formed coacervates only with the racemic polyanion. In contrast, solid precipitates were obtained from mixtures of stereoregular poly(l-lysine) and poly(l-glutamate). Moreover, the formation of coacervates from cationic comb polymers incorporating up to ∼90% pendant zwitterionic groups demonstrated the potential for inclusion of comonomers to modulate the hydrophilicity and/or other properties of a coacervate-forming polymer. These results provide the first detailed investigation into the role of polymer architecture on complex coacervation using a chemically and architecturally well-defined model system, and highlight the need for additional research on this topic.

Automated optimization and construction of chemometric models based on highly variable raw chromatographic data.

Direct chemometric interpretation of raw chromatographic data (as opposed to integrated peak tables) has been shown to be advantageous in many circumstances. However, this approach presents two significant challenges: data alignment and feature selection. In order to interpret the data, the time axes must be precisely aligned so that the signal from each analyte is recorded at the same coordinates in the data matrix for each and every analyzed sample. Several alignment approaches exist in the literature and they work well when the samples being aligned are reasonably similar. In cases where the background matrix for a series of samples to be modeled is highly variable, the performance of these approaches suffers. Considering the challenge of feature selection, when the raw data are used each signal at each time is viewed as an individual, independent variable; with the data rates of modern chromatographic systems, this generates hundreds of thousands of candidate variables, or tens of millions of candidate variables if multivariate detectors such as mass spectrometers are utilized. Consequently, an automated approach to identify and select appropriate variables for inclusion in a model is desirable. In this research we present an alignment approach that relies on a series of deuterated alkanes which act as retention anchors for an alignment signal, and couple this with an automated feature selection routine based on our novel cluster resolution metric for the construction of a chemometric model. The model system that we use to demonstrate these approaches is a series of simulated arson debris samples analyzed by passive headspace extraction, GC-MS, and interpreted using partial least squares discriminant analysis (PLS-DA).