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In this chapter, tablet designs (oral drug dosage forms), which could lead to desired drug release profiles, are discussed. The targeted drug release profiles include constant (i.e., zero-order) release and pulsatile release. The proposed designs are supported through mathematical modeling and simulations. Since the excipients affect the performance of the tablet, a computer-aided molecular design approach is proposed for generating the polymer (an excipient). Finally, a desirability function has been used to rank the generated polymers based on a desired property value. The proposed new polymers need further investigation to be practical for oral drug delivery.

This chapter presents some multiscale approaches for process design. To illustrate the methodology, the authors employ zinc sulfide (ZnS) deposition in a chemical vapor deposition (CVD) reactor from two points of view. The first point of view employs a deterministic and multiphysics modeling approach that uses detailed physics and the associated modeling equations. This approach allows for mechanistic understanding of the process. The second point of view uses agent-based simulation in which the physics is inherently captured through pseudo evolutionary random events involving molecules or particles. Here, the traditional mechanistic understanding is sacrificed. However, a different kind of mechanistic understanding is introduced in which one observes the emergence of patterns of behavior that are consistent with an expectation of what happens in a CVD reactor. The challenge is to consider the synergistic effects of marrying both types of modeling when studying systems of this nature. The approach is also a jump-off point for studying the bridging of the scales, namely length and time.

Polymerase chain reaction (PCR) is routinely used for analysis of genes from targeted cells leading to an understanding of the fundamental molecular biology involved in cellular events and for detecting abnormal pathways involved in disease development. In a previous paper we argued that the three commonly known strategies to alleviate the impact of cell pretreatment on PCR had serious shortcomings. Furthermore microfluidic strategies that permit simple operation, simple device design and are compatible with Taq polymerase are highly in demand. In this paper, we discuss optimization of microfluidic platform designs, which may be more efficient than the current one we have developed. The microfluidic reactor modelled in this paper relies on the diffusion of molecules from the loading chambers into a small reaction chamber. This results show that diffusion into the reaction chamber can be modified using different loading chamber geometries. Based on the results we have began to develop a mixed integer optimization model to automatically design the geometry to meet the desired transport rates of species in PCR.

Achieving calcium homeostasis in the human body, whereby extracellular fluid calcium is maintained within a narrow range (2.4 ± 0.25 mM) necessary for normal physiological function, is the task of a biological control system that is still not completely understood. Although general information is available on several of the component processes involved in calcium homeostasis, there is currently no mechanistic understanding of the emergent characteristics of the entire system. To this end, we present an engineering control framework for understanding calcium homeostasis, which is predicated on a configuration of the physiological system in terms of appropriately connected control system components (sensor, controller and actuator). Using information from the literature, we develop mathematical models for each component and integrate them into a holistic, comprehensive dynamic model of the complete system. The framework enables a more systematic study of observable physiological responses such as hypo- and hypercalcemia; it also allows us to interpret pathologies in terms of control system component defects.

Cyclohexane is generally used as an intermediate chemical where about 54 percent of its production is used in the production of adipic acid for nylon-6/6, 39 percent for caprolactam for nylon-6, and 7 percent for miscellaneous including solvents, insecticides and plasticizers. The demand for nylon (and hence cyclohexane) in engineering thermoplastics in resins and films is growing at about 6% annually. The production of cyclohexane from hydrogenation of benzene and hydrogen is a highly exothermic reaction where the reactor temperature control is very critical. Consequently, in most conventional commercial cyclohexane processes, multi-stage reactors involving recycling of cyclohexane, inter-reactor cooling to allow efficient removal of the heat of hydrogenation, and staged addition of the benzene feed are employed. To address the temperature control problem more efficiently, we have taken advantage of the efficient and rapid heat removal characteristics of a microreactor system; our design offers more than two orders of magnitude better temperature removal and control than conventional reactors.. Our design also achieved minimal benzene, methylcyclopentane and other impurities (by-products) in the product thus having less impact on the environment compared with the conventional process. This paper suggests a general microreactor technology framework for the production of cyclohexane and other similar commodity chemicals. The framework is economical and leads to a safer route than the conventional technology.

A drug release mechanism is combined with the Compartmental Absorption and Transit (CAT) model to develop a complete pharmaco-kinetic model for predicting orally administered drug release profile. Within an optimization framework, the developed model has been used for evaluating optimal values for key parameters including the size of the tablet and pharmacokinetic parameters like peak plasma concentration (Cmax), area under the curve (AUC) and bioavailability needed to obtain plasma concentration profiles within the therapeutically required range.

An ab initio density functional method is used to calculate activation energies of permeation for various gases (He, H 2 , Ne, CO, CO 2 , CH 4 ) through silica-based membranes. Previous studies have shown that the permeation through these silica-based membranes is based on a mechanism that involves the hopping of gas species between solubility sites. Permeation through these membranes is modeled as the passage of the permeating species through six-membered siloxane rings that act as critical openings between the solubility sites. A hybrid functional of the density functional theory method (Becke3lyp) with accurate basis sets (6-311G (2d, p), LANL2DZ) is applied to optimize these ring structures and the permeating species. The activation energies are estimated as the interaction energy of the permeating species approaching and passing through H 12 Si 5 O 6 X cyclosiloxane-based rings. These siloxane rings contain aluminum, boron, silicon, titanium, yttrium, and zirconium, which can be introduced into the structure to potentially improve the permeation properties of the silica membranes. The optimized siloxane-based rings have a buckled conformation due to the asymmetry created by the inorganic oxides. The calculated activation energies of the buckled silica–yttria and silica–zirconia rings (20–40 kJ mol − 1 ) are found to be similar to those reported for planar silica rings (10–30 kJ mol − 1 ). The calculated activation energies for the buckled silica–alumina, silica–boria, silica–titania rings (60–100 kJ mol − 1 ) are found to be higher than those reported for planar silica rings and suggest a much denser structure for these membranes.

This chapter presents case studies relevant to the pharmaceutical industry. The design of crystallization solvents for pharmaceuticals is discussed. In the manufacture of many pharmaceuticals, the high-molecular weight chemicals are recovered as pure solids from impure solutions via crystallization. One of the key decisions in designing solution-crystallization processes is the selection of solvents. This chapter describes a systematic approach involving computer aided molecular design (CAMD) technique, for solvent design/selection for crystallization of pharmaceuticals. Two case studies relating to the design of solvents for ibuprofen, an important pharmaceutical compound, are presented: (1) the first case study involves cooling crystallization solvent design while the (2) second case study involves design of solvent/antisolvent mixture for drowning out crystallization. An experimental verification step is also presented for the ibuprofen cooling crystallization solvent-design problem. Within the chemical process industry, crystallization is one of the key unit operations in the production of high-value chemicals. Crystallization from solution is widely used for the purification of drugs during the final stages of manufacture.

The objective of this paper is to report a mathematical improvement of the self-organizing map (SOM) algorithm implemented using real georeferenced biomedical and disease informatics data. The SOM algorithm is a very powerful unsupervised neural network with both competitive and cooperative learning abilities. It provides a foundation for knowledge discovery in large spatial databases and has successfully been applied to recognize patterns in several problem domains. Although significant progress has been achieved in using SOM to visualize multidimensional data or utilizing SOM for data mining purposes, certain limitations related to its performance still exist. In this paper, we propose a mathematical improvement as a result of discovering these limitations while using SOM-trained data for biomedical applications. The paper also introduces a new SOM-based model, mathematically improved learning-SOM (MIL-SOM*).

Computer Aided Molecular/Mixture design (CAMD) is one of the most promising techniques for solvent design and selection. A decomposition based CAMD methodology has been formulated where the mixture design problem is solved as a series of molecular and mixture design sub-problems. This approach is able to overcome most of the difficulties associated with the solution of mixture design problems. The new methodology has been illustrated with the help of a case study involving the design of solvent-anti solvent binary mixtures for crystallization of Ibuprofen.

This chapter highlights some of the challenges and opportunities for computer aided molecular design (CAMD). The expanded problem of computer-aided product design (CAPD) can be stated as the determination of the optimum material, structured product, and/or formulation to meet a given set of design objectives. The chapter discusses hybrid modeling approach, knowledge extraction, selection of reaction schemes, and others. Material design differs significantly from the more traditional design problem. The existing approaches to the identification of reaction paths can be divided in two categories— that is, information-based systems with their roots in chemistry and logic-based systems with their roots in mathematics. A CAMD approach is formulated to identify the potential leads that are selective—that is, simultaneously active against pc DHFR and tg DHFR and relatively inactive against human dihydrofolate reductase (DHFR). As chemical product design problems are multidisciplinary in nature, development of a systematic framework based on identified workflow and data-flow for the various inter-related activities would make a significant contribution.

The computer-aided molecular design (CAMD) problem can often be posed as a mathematical program, in which a number of binary and continuous variables define the search space. A binary variable is an integer variable that can have one of the two possible values, for example 0 and 1. This chapter discusses a branch and bound approach to solving the resulting mathematical program. A typical molecular design problem may be modeled as a single objective minimization or maximization subject to the structural and performance constraints. The group contribution model is a structure–property correlation that has found wide use in the chemical process industry. The constraints involve the following: (1) structural feasibility, (2) physical property targets, and (3) process constraints. The nonlinear mathematical programming model for the CAMD problem (P MD ) has the following features: (1) it is a nonconvex mixed integer nonlinear problem (MINLP), involving a large number of binary variables, (2) the number of linear constrains is larger than the number of nonlinear constraints, and (3) most of the components of the design vector ( u ) participate in the nonlinear terms. The chapter presents the CAMD design procedure, using the branch and bound (BB) method, and presents a simple example.

This chapter presents an introduction to computer-aided molecular design (CAMD). The design process for a chemical product involves a number of steps, through which scientific principles may be applied for the solution of the specified design problem. Four principal steps were suggested in their design process: (1) define needs; (2) generate ideas to meet needs; (3) select among ideas; and (4) manufacture product. The molecular and mixture/blend design problems can be solved independent of the process design problem or as an integrated product-process design problem. For the solution of the molecular and mixture/blend design problems, various approaches, ranging from empirical trial, and error approaches to mathematical programming to hybrid methods can be applied as the solution technique. Computer aided molecular design problems are defined as “given a set of building blocks and a specified set of target properties, determine the molecule or molecular structure that matches these properties.” The formulation of the property constraints is a prerequisite for solving any CAMD problem. Most property estimation methods used in CAMD methodologies are based on the group contribution approach (GCA). The minimization of uncertainty is important when performing complex calculations.

This chapter presents a case study for the purpose of designing globally optimal solvents to be used for cleaning in lithographic. These solvents should have the following qualities: (1) have a minimal drying time, (2) dissolve residue ink, (3) not swell the blanket, and (4) be environmentally benign. Drying time is correlated with the heat of vaporization of the solvent. The ink residue is assumed to consist of phenolic resins. Solvents are extensively used as a major component of ink in the printing industry. Blanket wash solvents are mostly solvent mixtures as opposed to single component solvents. The chapter presents a case study, where structural feasibility constraints are employed to ensure feasible molecular structures. The chapter also presents the Churi–Achenie model for solving a formulation. The chapter presents another case study, where solvents are designed, with entirely different criteria, and the most desirable attribute of the solvent is recoverability. After the blanket wash operation is performed the solvents compounds that evaporate are recovered by a solvent recovery system. This case study finds a solvent compound that will be least expensive to recover. The branching functions are determined from the special tree function representation of both the objective function and constraints. In order to construct the corresponding linear underestimators, the sweep method was employed.

This paper presents a hybrid global optimization approach for solving product design (specifically solvent design) problems modeled by mixed integer nonlinear programming (MINLP). The strategy incorporates a variant of the Outer Approximation mathematical programming algorithm and a soft computing global optimization approach (namely simulated annealing). The suggested approach is not provably globally optimal. However, computational experience with benchmark examples and solvent design MINLP models indicate strongly that the approach gives near globally optimal solutions.

Several data analysis algorithms exist for the analysis of gene expression data resulting from cDNA microarray experiments. From a biological perspective, all of these have a number of disadvantages, some of which are addressed in this study. Dimensionality reduction, a priori specification of the number of classes and the need for a training set are a few of these disadvantages. To address these issues, we propose two novel approaches based on systems engineering principles. The proposed algorithms do not (1) require a training set, (2) require the a priori specification of the number of classes and (3) perform any dimensionality reduction. The first algorithm has been used on three gene expression data sets (yeast cell cycle data, human fibroblast response to serum data and the cutaneous melanoma data) from the open literature, while the second has been used on the fibroblast data set. The results found in general are at least in excellent agreement with studies in the open literature or they reveal further knowledge, which was not available previously. The present study, thus, establishes the viability and strength of the proposed algorithms for gene expression data analysis.

Publisher Summary This chapter discusses the computer-aided product design (CAPD) approach for reaction solvent design and a systematic framework for designing optimal reaction solvents while simultaneously considering process constraints, environmental constraints, safety, and material compatibility issues. Solvent attributes for extractive fermentation processes, such as biocompatibility, inertness, and phase splitting, are investigated. The mathematical programming based solvent design framework is successfully solved for ethanol extractive fermentation using an outer approximation approach. For the case studies, the proposed design approach not only identifies a solvent for the new application but also finds a new solvent for ethanol extractive fermentation. The example is presented to discuss the objective function for solvent design to maximize the extraction efficiency––that is, the ratio of the volumetric productivity of the ethanol recovered in the solvent phase and the total volumetric ethanol productivity––while achieving a conversion greater than 0.75. The chosen solvent should be liquid under the reaction conditions. This is achieved by putting limits on the boiling and melting points.

Publisher Summary Recent advances in complementary DNA (cDNA) microarray technology have resulted in a surge of gene expression data. Whole-genome hybridization results have been reported for many organisms (yeast and worm) under different experimental conditions. Studies have been reported on various methods for assigning functionality to previously unknown genes. Some of these methods are clustering techniques, self-organizing maps, and knowledge-based support vector machines (SVMs). These techniques use a similarity measure to associate genes with unknown functionality with genes of known functionality. This chapter discusses the genome-wide functional annotation, for yeast data set, based on an SVM strategy. In SVM model, each gene can potentially be assigned to more than one class. A gene is referred to as a positive sample of a given class if it belongs to that class; otherwise the gene is referred to as a negative sample. The viability of the SVM model using yeast data with several missing experimental measurements is established. The SVM model correctly annotated the positive samples in all classes. The negative genes were not so well annotated. This is attributed to the missing data for the negative gene set.