Kyle V. Camarda

Adhesive/Dentin Interface: The Weak Link in the Composite Restoration

Results from clinical studies suggest that more than half of the 166 million dental restorations that were placed in the United States in 2005 were replacements for failed restorations. This emphasis on replacement therapy is expected to grow as dentists use composite as opposed to dental amalgam to restore moderate to large posterior lesions. Composite restorations have higher failure rates, more recurrent caries, and increased frequency of replacement as compared to amalgam. Penetration of bacterial enzymes, oral fluids, and bacteria into the crevices between the tooth and composite undermines the restoration and leads to recurrent decay and premature failure. Under in vivo conditions the bond formed at the adhesive/dentin interface can be the first defense against these noxious, damaging substances. The intent of this article is to review structural aspects of the clinical substrate that impact bond formation at the adhesive/dentin interface; to examine physico-chemical factors that affect the integrity and durability of the adhesive/dentin interfacial bond; and to explore how these factors act synergistically with mechanical forces to undermine the composite restoration. The article will examine the various avenues that have been pursued to address these problems and it will explore how alterations in material chemistry could address the detrimental impact of physico-chemical stresses on the bond formed at the adhesive/dentin interface.

Simultaneous design of ionic liquid entrainers and energy efficient azeotropic separation processes

Abstract A methodology and tool set for the simultaneous design of ionic liquid entrainers and azeotropic separation processes is presented. By adjusting the cation, anion, and alkyl chain length on the cation, the properties of the ionic liquid can be adjusted to design an entrainer for a given azeotropic mixture. Several group contribution property models available in literature have been used along with a newly developed group contribution solubility parameter model and UNIFAC model for ionic liquids (UNIFAC-IL). For a given azeotropic mixture, an ionic liquid is designed using a computer-aided molecular design (CAMD) method and the UNIFAC-IL model is used to screen design candidates based on minimum ionic liquid concentration needed to break the azeotrope. Once the ionic liquid has been designed, the extractive distillation column for the azeotropic mixture is designed using the driving force method with a new proposed feed stage scaling to minimize energy inputs. Along with the distillation column, an ionic liquid recovery stage is designed and simulations are used to determine the overall heat duty for the entire process for the best ionic liquid candidates. Use of a designed ionic liquid reduces material and energy requirements when compared to an ionic liquid known to experimentally break a given azeotrope but not designed using CAMD methods. The acetone–methanol and ethanol–water azeotropes are provided as examples.

Design of ionic liquids via computational molecular design

Abstract Computational molecular design (CMD) is a methodology which applies optimization techniques to develop novel lead compounds for a variety of applications. In this work, a CMD method is applied to the design of ionic liquids (ILs), which are being considered for use as environmentally benign solvents. The molecularly tunable nature of ILs yields an extraordinary number of possible cation and anion combinations, the majority of which have never been synthesized. The product design framework developed in this work seeks to accelerate the commonly used experimental trial-and-error approach by searching through this large molecular space and providing a set of chemical structures likely to match a set of desired property targets. To predict the physical and chemical properties of an ionic liquid in a specific system, quantitative structure–property relations (QSPRs) have been developed. In this work, correlations were created for solubility, diffusivity, and melting temperature. The electronic structure of ionic liquids is quantified using molecular connectivity indices, which describe bonding environments, charge distribution, orbital hybridization and other interactions within and between ions. The resulting property prediction model is then integrated within a computational molecular design framework, which combines the QSPRs with structural feasibility constraints in a combinatorial optimization problem. The problem is reformulated as an MILP after exact linearization of structural constraints. An example is provided to test the formulation for the design of ionic liquids for use within a hydrofluorocarbon (refrigerant) gas separation system. A second example compares a stochastic optimization algorithm, Tabu Search, to a standard deterministic solver for the solution of a larger-scale refrigeration design problem. The computational efficiency and practical implementation of this product design methodology is also discussed.

Computer-aided molecular design using Tabu search

Abstract A detailed implementation of the Tabu search (TS) algorithm for computer-aided molecular design (CAMD) of transition metal catalysts is presented in this paper. Previous CAMD research has applied deterministic methods or genetic algorithms to the solution of the optimization problems which arise from the search for a molecule satisfying a set of property targets. In this work, properties are estimated using correlations based on connectivity indices, which allows the TS algorithm to use several novel operators to generate neighbors, such as swap and move, which would have no effect with a traditional group contribution-based approach. In addition, the formulation of the neighbor generation process guarantees that molecular valency and connectivity constraints are met, resulting in a complete molecular structure. Results on two case studies using TS are compared with a deterministic approach and show that TS is able to provide a list of good candidate molecules while using a much smaller amount of computation time.

Design of novel pharmaceutical products via combinatorial optimization

Abstract This paper describes a new methodology for the application of computer-aided molecular design to the design of pharmaceutical products with prespecified physical properties. For a pharmaceutical product to be effective, it must not only cause a desirable therapeutic effect, but it must also have proper values of physical properties such as solubility and density. In order to apply computer-aided molecular design to the discovery of new pharmaceuticals, it is therefore necessary to be able to first predict the physical and biological properties of a given molecule, and then optimize over an entire set of molecules to find one which matches target values on those properties. This work employs topological descriptors to predict the physical properties of pharmaceutical molecules. The descriptors used here, called connectivity indices, are easy to compute, yet contain valuable information about the internal molecular structure of a molecule. Property prediction, via connectivity indices, can be viewed as an improvement over group contribution methods, since these indices take into account molecular connectivity and internal electronic structure in addition to the identity of each group in the molecule. Thus these indices correlate well with physical properties which are important in pharmaceutical design. Furthermore, these indices are fairly simple to compute, and a proper choice of variables to describe the molecule allows the equations for these indices to be written in a linear form. The optimization problem used here combines a set of basic groups, which are defined as a non-hydrogen atom at a given valency state bonded to a given number of hydrogens, to form a candidate molecule. Each candidate molecule can then be tested by computing estimated property values and comparing those to prespecified target values. Structural constraints are also added to the problem to ensure a connected, stable molecule is generated. The set of constraints and the correlation equations for property prediction are then combined and reformulated, resulting in a mixed integer linear program (MILP). If a certain functional group is known to be required in the molecule, this requirement can also be added to the constraint set. For problems consisting of a smaller number of basic groups or property targets, commercial solvers can be employed to solve the resulting MILP. The effectiveness of this method is presented through the solution of a small example problem.

Pharmaceutical product design using combinatorial optimization

Abstract A two-step computational method for designing new molecules in medicinal chemistry is described. In the first step, topological indices are used to develop structure-based correlations for properties of interest. Zeroth and first order connectivity indices are employed to develop linear correlations for three physical properties of interest in pharmaceutical chemistry: octanol–water partition coefficient (OWPC), melting point and water solubility. These correlations are then used within an optimization framework to design molecules having the desired properties. This step involves formulating a mixed integer linear program (MILP) which includes the property correlations, structural constraints which ensure that a stable, connected molecule is formed, and an objective function which minimizes the deviation from a set of property targets. A new data structure, known as a partitioned adjacency matrix, is employed to allow the connectivity index definitions to be written linearly, such that they can be included in an MILP and solved using a standard branch-and-bound method. The connectivity of the molecule is ensured by the inclusion of network flow constraints within the formulation. Three examples show the efficacy of this approach.

Environmentally-benign transition metal catalyst design using optimization techniques

Abstract Transition metal catalysts play a crucial role in many industrial applications, including the manufacture of lubricants, smoke suppressants, corrosion inhibitors and pigments. The development of novel catalysts is commonly performed using a trial-and-error approach which is costly and time-consuming. The application of computer-aided molecular design (CAMD) to this problem has the potential to greatly decrease the time and effort required to improve current catalytic materials in terms of their efficacy and biological effects. This work applies an optimization approach to redesign environmentally-benign homogeneous catalysts, specifically those which contain transition metal centers, to improve certain physical properties. Two main tasks must be achieved in order to perform the molecular design of a novel catalyst: biological and chemical properties must be estimated directly from the molecular structure, and the resulting optimization problem must be solved in a reasonable time. In this work, connectivity indices are used for the first time to predict the physical properties of a homogeneous catalyst. The existence of multiple oxidation states for transition metals requires a reformulation of the original equations for these indices. Once connectivity index descriptors have been defined for transition metal catalysts, structure–property correlations are then developed based on regression analysis using literature data for various properties of interest, including toxicity and electronegativity. These structure–property correlations are then used within an optimization framework to design novel homogeneous catalyst structures for use in a given application. The use of connectivity indices which define the topology of the molecule within the formulation guarantees that a complete molecular structure is obtained when the global optimum is found. In this work, second-order connectivity indices are used to obtain more information about steric features of the catalyst molecules, and non-linear correlations are employed to improve the accuracy of the property prediction equations. The structure–property correlations are then combined with linear structural feasibility constraints to form a mixed-integer non-linear program (MINLP), which when solved to optimality results in a catalyst molecule which most closely matches given property targets. To solve the resulting optimization problem, two methods are applied: Tabu search (a stochastic method), and outer approximation, a deterministic approach. For the outer approximation solution, a data structure is used which permits all equations except for the property prediction expressions to be written in linear forms. The computational efficiency of Tabu search is not strongly dependent on the existence of non-linear constraints, so for solution using this method, a non-linear form for the second-order connectivity index was chosen, which decreases the number of binary variables required. The solution methods are compared using three examples involving the design of environmentally-benign homogeneous catalysts containing molybdenum centers. Results show the efficacy of the formulation, and provide evidence that the Tabu search algorithm is more suitable for this type of molecular design algorithm than the commercially available deterministic approach.

Use of glass transitions in carbohydrate excipient design for lyophilized protein formulations

This work describes an effort to apply methods from process systems engineering to a pharmaceutical product design problem, with a novel application of statistical approaches to comparing solutions. A computational molecular design framework was employed to design carbohydrate molecules with high glass transition temperatures and low water content in the maximally freeze-concentrated matrix, with the objective of stabilizing lyophilized protein formulations. Quantitative structure-property relationships were developed for glass transition temperature of the anhydrous solute, glass transition temperature of the maximally concentrated solute, melting point of ice and Gordon-Taylor constant for carbohydrates. An optimization problem was formulated to design an excipient with optimal property values. Use of a stochastic optimization algorithm, Tabu search, provided several carbohydrate excipient candidates with statistically similar property values, as indicated by prediction intervals calculated for each property.

Sustainable process design & analysis of hybrid separations

Abstract Distillation is an energy intensive operation in chemical process industries. There are around 40,000 distillation columns in operation in the US, requiring approximately 40% of the total energy consumption in US chemical process industries. However, analysis of separations by distillation has shown that more than 50% of energy is spent in purifying the last 5–10% of the distillate product. Membrane modules on the other hand can achieve high purity separations at lower energy costs, but if the flux is high, it requires large membrane area. A hybrid scheme where distillation and membrane modules are combined such that each operates at its highest efficiency, has the potential for significant energy reduction without significant increase of capital costs. This paper presents a method for sustainable design of hybrid distillation-membrane schemes with guaranteed reduction of energy consumption together with two illustrative examples.

Optimizing Protein-Excipient Interactions for the Development of Aggregation-Reducing Lyophilized Formulations

Abstract As protein drugs become increasing popular therapeutic approaches, formulations must be developed to ensure stability of the final drug product. Lyophilization, or freeze - drying, is an approach commonly used to produce stable protein drugs, yet protein aggregation may still occur in lyophilized formulations. Excipients in the formulation may be selected to reduce the propensity of a protein to aggregate through interaction with aggregation prone regions. In the following work, molecular docking simulations were used to predict the regions on a protein where different excipients were most likely to interact. Simulation results compared variably with experimental hydrogen/deuterium exchange experiments used to determine regions of protein-excipient interactions. The results were used to design formulations for lyophilized calmodulin (PID #1CLL). Sugar and surfactant pairs were selected that would maximize protection of different aggregation prone regions through direct interactions.