Tracking chemical processing pathways in combinatorial polymer libraries via data mining

Changes in the molecular structure and composition of interpenetrating polymer networks (IPNs) can be used to tailor their properties. While the properties of IPNs are typically different than polymer blends, a clear understanding of the impact of changing polymerization sequence on the physical properties and the corresponding molecular bonding is needed. To address this issue, a data mining approach is used to identify the change with polymerization sequence of tensile and rheological properties of acrylate-epoxy IPNs. The experimental approach used to study the molecular structure is high throughput Fourier transform infrared (FTIR) spectroscopy. Analysis of the FTIR spectra of IPNs synthesized with different polymerization sequences leads to an understanding of the molecular bonding responsible for the tensile and rheological properties. From the interpretation of the wavenumber bands and associated molecular bonds, we find that the polymerization sequence most affects hydrogen bonding and aromatic ring bond energies. This work defines the relationships between chemistry, structure, processing, and properties of the IPN samples

Analyzing Drug Release Kinetics from Water-Soluble Polymers

The ability to develop predictive mathematical models of therapeutic release from pharmaceutical formulations has enormous potential to enhance our understanding of such systems and improve the controlled release of the payload. The current work describes the development and testing of a one-dimensional model of drug transport from amorphous, swelling/dissolving polymers. Model parameters such as the diffusivities of water and drug, the initial loading of the drug, the polymer dissolution rate, drug-polymer interactions, and the tablet thickness were varied, demonstrating the ability to tune the release to be controlled by either drug diffusion or polymer chain disentanglement. In addition, predictions of the concentration profiles of water and drug within the gel layer, the locations of the erosion and swelling boundaries, and gel layer thickness were obtained for diffusion- and disentanglement-controlled release. To highlight the generalizability of this model, multiple parameters were varied, and it was shown that increasing the diffusivities of water and drug and the initial drug loading and decreasing the polymer dissolution rate sufficiently resulted in diffusion-controlled release. The model was fit to experimental data for a model tablet system comprising of sodium diclofenac entrapped in a poly(vinyl pyrrolidone) matrix and yielded physically meaningful values of the model parameters. The work presented here demonstrates the predictive power of the model for rapid and rational design of future pharmaceutical formulations for controlled drug delivery

Identifying factors controlling protein release from combinatorial biomaterial libraries via hybrid data mining methods

Polyanhydrides are a class of degradable biomaterials that have shown much promise for applications in drug and vaccine delivery. Their properties can be tailored for controlled drug release, drug/protein stability, and immune regulation (adjuvant effect). Identifying the relationship between the molecular structures of the polymers and the drug release kinetics profiles would help understand the release mechanism and aid in the accurate prediction of drug release and the rational design of polymer-based drug carrier systems. The molecular structure descriptors that had the most impact on the release kinetics were identified using a prediction/optimization data mining approach. Using this new approach for modeling nonlinear release kinetics behavior, we determined that the descriptors which had the greatest effect on the release kinetics were the number of backbone -COO- nonconjugated bonds, the number of aromatic rings, and the number of -CH 2- bonds

Amphiphilic polyanhydride nanoparticles stabilize bacillus anthracis protective antigen

Advancements toward an improved vaccine against Bacillus anthracis, the causative agent of anthrax, have focused on formulations composed of the protective antigen (PA) adsorbed to aluminum hydroxide. However, due to the labile nature of PA, antigen stability is a primary concern for vaccine development. Thus, there is a need for a delivery system capable of preserving the immunogenicity of PA through all the steps of vaccine fabrication, storage, and administration. In this work, we demonstrate that biodegradable amphiphilic polyanhydride nanoparticles, which have previously been shown to provide controlled antigen delivery, antigen stability, immune modulation, and protection in a single dose against a pathogenic challenge, can stabilize and release functional PA. These nanoparticles demonstrated polymer hydrophobicity-dependent preservation of the biological function of PA upon encapsulation, storage (over extended times and elevated temperatures), and release. Specifically, fabrication of amphiphilic polyanhydride nanoparticles composed of 1,6-bis(p-carboxyphenoxy)hexane and 1,8-bis(p-carboxyphenoxy)-3,6- dioxaoctane best preserved PA functionality. These studies demonstrate the versatility and superiority of amphiphilic nanoparticles as vaccine delivery vehicles suitable for long-term storage

Pentaerythritol-based lipid A bolsters the antitumor efficacy of a polyanhydride particle-based cancer vaccine

The primary objective of this study was to enhance the antitumor efficacy of a model cancer vaccine through co-delivery of pentaerythritol lipid A (PELA), an immunological adjuvant, and a model tumor antigen, ovalbumin (OVA), separately loaded into polyanhydride particles (PA). In vitro experiments showed that encapsulation of PELA into PA (PA-PELA) significantly enhanced its stimulatory capacity on dendritic cells as evidenced by increased levels of the cell surface costimulatory molecules, CD80/CD86. In vivo experiments showed that PA-PELA, in combination with OVA-loaded PA (PA-OVA), significantly expanded the OVA-specific CD8+ T lymphocyte population compared to PA-OVA alone. Furthermore, serum OVA-specific antibody titers of mice vaccinated with PA-OVA/PA-PELA displayed a significantly stronger shift toward a Th1-biased immune response compared to PA-OVA alone, as evidenced by the substantially higher IgG2C:IgG1 ratios achieved by the former. Analysis of E.G7-OVA tumor growth curves showed that mice vaccinated with PA-OVA/PA-PELA had the slowest average tumor growth rate

Nanoscale morphology of polyanhydride copolymers

The microphase separation in polyanhydride random copolymers composed of 1,6-bis(p-carboxyphenoxy)hexane and sebacic acid is described. Though the copolymers are random, the monomers are sufficiently long and the segment-segment interaction parameter is sufficiently high to promote microphase separation when the copolymer is rich in one component. Solid-state NMR spin diffusion experiments and synchrotron small-angle X-ray scattering are used to discern the length scales of the microphase separation. Both techniques reveal a nanostructure with domain sizes less than 25 Ã…. This nanostructure is compared to approximate calculations of chain dimensions based on a random coil model and discussed in the context of the rational design of these materials for drug delivery applications

Development of indices for agricultural drought monitoring using a spatially distributed hydrologic model

Farming communities in the United States and around the world lose billions of dollars every year due to drought. Drought Indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are widely used by the government agencies to assess and respond to drought. These drought indices are currently monitored at a large spatial resolution (several thousand km2). Further, these drought indices are primarily based on precipitation deficits and are thus good indicators for monitoring large scale meteorological drought. However, agricultural drought depends on soil moisture and evapotranspiration deficits. Hence, two drought indices, the Evapotranspiration Deficit Index (ETDI) and Soil Moisture Deficit Index (SMDI), were developed in this study based on evapotranspiration and soil moisture deficits, respectively. A Geographical Information System (GIS) based approach was used to simulate the hydrology using soil and land use properties at a much finer spatial resolution (16km2) than the existing drought indices. The Soil and Water Assessment Tool (SWAT) was used to simulate the long-term hydrology of six watersheds located in various climatic zones of Texas. The simulated soil water was well-correlated with the Normalized Difference Vegetation Index NDVI (r ~ 0.6) for agriculture and pasture land use types, indicating that the model performed well in simulating the soil water. Using historical weather data from 1901-2002, long-term weekly normal soil moisture and evapotranspiration were estimated. This long-term weekly normal soil moisture and evapotranspiration data was used to calculate ETDI and SMDI at a spatial resolution of 4km ?? 4km. Analysis of the data showed that ETDI and SMDI compared well with wheat and sorghum yields (r > 0.75) suggesting that they are good indicators of agricultural drought. Rainfall is a highly variable input both spatially and temporally. Hence, the use of NEXRAD rainfall data was studied for simulating soil moisture and drought. Analysis of the data showed that raingages often miss small rainfall events that introduce considerable spatial variability among soil moisture simulated using raingage and NEXRAD rainfall data, especially during drought conditions. The study showed that the use of NEXRAD data could improve drought monitoring at a much better spatial resolution

Sustained antigen release polyanhydride-based vaccine platform for immunization against bovine brucellosis

Brucellosis is a bacterial zoonosis and a significant source of economic loss and a major public health concern, worldwide. Bovine brucellosis, as caused primarily by Brucella abortus, is an important cause of reproductive loss in cattle. Vaccination has been the most effective way to reduce disease prevalence contributing to the success of control and eradication programs. Currently, there are no human vaccines available, and despite the success of commercial vaccines for livestock, such as B. abortus strain RB51 (RB51), there is need for development of novel and safer vaccines against brucellosis. In the current study, we report the fabrication of and immune responses to an implantable single dose polyanhydride-based, methanol-killed RB51 antigen containing delivery platform (VPEAR) in cattle. In contrast to animals vaccinated with RB51, we did not observe measurable RB51-specific IFN-γ or IgG responses in the peripheral blood, following initial vaccination with VPEAR. However, following a subsequent booster vaccination with RB51, we observed an anamnestic response in both vaccination treatments (VPEAR and live RB51). The magnitude and kinetics of CD4+ IFN-γ-mediated responses and circulating memory T cell subpopulations were comparable between the two vaccination treatments. Additionally, IgG titers were significantly increased in animals vaccinated with VPEAR as compared to live RB51- vaccinated animals. These data demonstrate that killed antigen may be utilized to generate and sustain memory, IFN-γ-mediated, CD4+ T cell and humoral responses against Brucella in a natural host. To our knowledge, this novel approach to vaccination against intracellular bacteria, such as Brucella, has not been reported before

A data analytics approach for rational design of nanomedicines with programmable drug release

Drug delivery vehicles can improve the functional efficacy of existing antimicrobial therapies by improving biodistribution and targeting. A critical property of such nanomedicine formulations is their ability to control the release kinetics of their payloads. The combination of (and interactions between) polymer, drug, and nanoparticle properties gives rise to nonlinear behavioral relationships and a large data space. These factors complicate both first-principles modeling and screening of nanomedicine formulations. Predictive analytics may offer a more efficient approach toward rational design of nanomedicines by identifying key descriptors and correlating them to nanoparticle release behavior. In this work, antibiotic release kinetics data were generated from polyanhydride nanoparticle formulations with varying copolymer compositions, encapsulated drug type, and drug loading. Four antibiotics, doxycycline, rifampicin, chloramphenicol, and pyrazinamide, were used. Linear manifold learning methods were used to relate drug release properties with polymer, drug, and nanoparticle properties, and key descriptors were identified that are highly correlated with release properties. However, these linear methods could not predict release behavior. Non-linear multivariate modeling based on graph theory was then used to deconvolute the governing relationships between these properties, and predictive models were generated to rapidly screen lead nanomedicine formulations with desirable release properties with minimal nanoparticle characterization. Release kinetics predictions of two drugs containing atoms not included in the model showed good agreement with experimental results, validating the model and indicating its potential to virtually explore new polymer and drug pairs not included in training data set. The models were shown to be robust after inclusion of these new formulations in that the new inclusions did not significantly change model regression. This approach provides the first steps towards development of a framework that can be used to rationally design nanomedicine formulations by selecting the appropriate carrier for a drug payload to program desirable release kinetics