Chang Sun Kong

Activation of innate immune responses in a pathogen-mimicking manner by amphiphilic polyanhydride nanoparticle adjuvants

Techniques in materials design, immunophenotyping, and informatics can be valuable tools for using a molecular based approach to design vaccine adjuvants capable of inducing protective immunity that mimics a natural infection but without the toxic side effects. This work describes the molecular design of amphiphilic polyanhydride nanoparticles that activate antigen presenting cells in a pathogen-mimicking manner. Biodegradable polyanhydrides are well suited as vaccine delivery vehicles due to their adjuvant-like ability to: 1) enhance the immune response, 2) preserve protein structure, and 3) control protein release. The results of these studies indicate that amphiphilic nanoparticles possess pathogen-mimicking properties as evidenced by their ability to activate dendritic cells similarly to LPS. Specific molecular descriptors responsible for this behavior were identified using informatics analyses, including the number of backbone oxygen moieties, percent of hydroxyl end groups, polymer hydrophobicity, and number of alkyl ethers. Additional findings from this work suggest that the molecular characteristics mediating APC activation are not limited to hydrophobicity but vary in complexity (e.g., presentation of oxygen-rich molecular patterns to cells) and elicit unique patterns of cellular activation. The approach outlined herein demonstrates the ability to rationally design pathogen-mimicking nanoparticle adjuvants for use in next-generation vaccines against emerging and re-emerging diseases.

Rational Design of Pathogen-Mimicking Amphiphilic Materials as Nanoadjuvants

An opportunity exists today for cross-cutting research utilizing advances in materials science, immunology, microbial pathogenesis, and computational analysis to effectively design the next generation of adjuvants and vaccines. This study integrates these advances into a bottom-up approach for the molecular design of nanoadjuvants capable of mimicking the immune response induced by a natural infection but without the toxic side effects. Biodegradable amphiphilic polyanhydrides possess the unique ability to mimic pathogens and pathogen associated molecular patterns with respect to persisting within and activating immune cells, respectively. The molecular properties responsible for the pathogen-mimicking abilities of these materials have been identified. The value of using polyanhydride nanovaccines was demonstrated by the induction of long-lived protection against a lethal challenge of Yersinia pestis following a single administration ten months earlier. This approach has the tantalizing potential to catalyze the development of next generation vaccines against diseases caused by emerging and re-emerging pathogens.

Information-Theoretic Approach for the Discovery of Design Rules for Crystal Chemistry

In this work, it is shown that for the first time that, using information-entropy-based methods, one can quantitatively explore the relative impact of a wide multidimensional array of electronic and chemical bonding parameters on the structural stability of intermetallic compounds. Using an inorganic AB2 compound database as a template data platform, the evolution of design rules for crystal chemistry based on an information-theoretic partitioning classifier for a high-dimensional manifold of crystal chemistry descriptors is monitored. An application of this data-mining approach to establish chemical and structural design rules for crystal chemistry is demonstrated by showing that, when coupled with first-principles calculations, statistical inference methods can serve as a tool for significantly accelerating the prediction of unknown crystal structures.

Identification and Optimization of AB2 Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms

Available data for a large number of AB2 compounds were subjected to a rigorous study using a combination of Principal Component Analysis (PCA) technique, multiobjective genetic algorithms, and neural networks that evolved through genetic algorithms. The identification of various phases and phase-groups were very successfully done using a decision tree approach. Since the variable hyperspaces for the different phases were highly intersecting in nature, a cumulative probability index was defined for the formation of individual compounds, which was maximized along with Paulings electronegativity difference. The resulting Pareto-frontiers provided further insight into the nature of bonding prevailing in these compounds.

A systems approach to designing next generation vaccines: combining α-galactose modified antigens with nanoparticle platforms.

Innovative vaccine platforms are needed to develop effective countermeasures against emerging and re-emerging diseases. These platforms should direct antigen internalization by antigen presenting cells and promote immunogenic responses. This work describes an innovative systems approach combining two novel platforms, αGalactose (αGal)-modification of antigens and amphiphilic polyanhydride nanoparticles as vaccine delivery vehicles, to rationally design vaccine formulations. Regimens comprising soluble αGal-modified antigen and nanoparticle-encapsulated unmodified antigen induced a high titer, high avidity antibody response with broader epitope recognition of antigenic peptides than other regimen. Proliferation of antigen-specific CD4+ T cells was also enhanced compared to a traditional adjuvant. Combining the technology platforms and augmenting immune response studies with peptide arrays and informatics analysis provides a new paradigm for rational, systems-based design of next generation vaccine platforms against emerging and re-emerging pathogens.

Data-driven model for estimation of friction coefficient via informatics methods

As technologies progress, the development of new mechanical systems demands the rapid determination of friction coefficients of materials. Data mining and materials informatics methods are used here to generate a predictive model that enables efficient high-throughput screening of ceramic materials, some of which are candidate high-temperature, solid-state lubricants. Through the combination of principal component analysis and recursive partitioning using a small dataset comprised of intrinsic material properties, we develop a decision tree-based model comprised of if-then rules which estimates the friction coefficients of a wide range of materials. This data-driven model has a high degree of accuracy with an R2 value of 0.8904 and provides a range of possible friction coefficients that accounts for the possible variability of a material’s actual friction coefficient.

Informatics-Based Uncertainty Quantification in the Design of Inorganic Scintillators

A soft computing platform, integrating rough sets, fuzzy inferences, and genetic algorithms, is used to develop a series of design rules as a guideline for optimizing inorganic scintillator materials in terms of light yield. The range of values for electrochemical factor, density, Stokes shift, valence electron factor, and size factor which lead to the highest light yield values are identified, with the range corresponding to the uncertainty in the data. The results presented in this article demonstrate how our approach can address the issues of approximation, vagueness, and uncertainty inherent in a relatively small database. We discuss how the results from this work can be used to enhance previously reported models for predicting light yield.