Antibiotic Molecular Design Using Artificial Bee Colony Algorithm

Research is acutely needed to develop novel therapies to treat resistant infections. This project aims to design a drug molecule via a computer aided molecular design approach to provide lead candidates for the treatment of bacterial infections caused by Staphylococcus aureus. In a recently published WHO report, a list of bacteria which pose the greatest threat to human health was given. The purpose of this report was to identify the most important resistant bacteria at global level for which immediate treatment is required. Staphylococcus aureus, which is on this list, is a pathogen causing infections such as pneumonia and bone disorders. A methodology which determines the structures of candidate antibiotic molecules is described. The Artificial Bee Colony algorithm has been used for the first time for molecular design in this work. It is necessary to predict physical and/or biological properties of compounds in order to design them. The prediction of properties is performed using Quantitative Structure Property Relationships (QSPRs). QSPRs are equations, which are developed using reported data for properties of interest by the method of regression analysis. This work applies connectivity indices and 3D MoRSE descriptors to develop QSPRs. The properties used in this work are minimum inhibitory concentration and Log P values. 3D MoRSE descriptors have been used for the first time for molecular design in this work. The QSPRs are combined with structural feasibility and connectivity constraints to formulate an optimization problem, which is a mixed integer nonlinear program (MINLP). Because of the large number of potential chemical structures and the uncertainty in the structure-property correlations, stochastic algorithms are preferred to solve the resulting MINLP. One stochastic algorithm which has shown promise to solve these problems is the Artificial Bee Colony algorithm, which relies on principles of swarm intelligence to find near-optimal solutions efficiently. The Artificial Bee Colony algorithm described in this work is used to derive solutions which serve as lead compounds for a narrowed search for novel antibiotics. Results show that the ABC algorithm is very effective in finding near optimal solutions to the MINLP, which is a combinatorial optimization problem. Molecular structures were obtained by optimizing objective function for individual property values and simultaneously for both the properties

Integrating Safety Issues in Optimizing Solvent Selection and Process Design

Incorporating consideration for safety issues while designing solvent processes has become crucial in light of the chemical process incidents involving solvents that have taken place in recent years. The implementation of inherently safer design concepts is considered beneficial to avoid hazards during early stages of design. The application of existing process design and modeling techniques that aid the concepts of ‘substitution’, ‘intensification’ and ‘attenuation’ has been shown in this work. For ‘substitution’, computer aided molecular design (CAMD) technique has been applied to select inherently safer solvents for a solvent operation. For ‘intensification’ and ‘attenuation’, consequence models and regulatory guidance from EPA RMP have been integrated into process simulation. Combining existing techniques provides a design team with a higher level of information to make decisions based on process safety. CAMD is a methodology used for designing compounds with desired target properties. An important aspect of this methodology concerns the prediction of properties given the structure of the molecule. This work also investigates the applicability of Quantitative Structure Property Relationship (QSPR) and topological indices to CAMD. The evaluation was based on models developed to predict flash point properties of different classes of solvents. Multiple linear regression and neural network analysis were used to develop QSPR models, but there are certain limitations associated with using QSPR in CAMD which have been discussed and need further work. Practical application of molecular design and process design techniques have been demonstrated in a case study on liquid-liquid extraction of acetic acid-water mixture. Suitable inherently safer solvents were identified using ICAS-ProCAMD, and consequence models were integrated into Aspen Plus simulator using a calculator sheet. Upon integrating flammable and toxic hazard modeling, solvents such as 5-nonanone, 2-nonanone and 5-methyl-2-hexanone provide inherently safer options, while conventionally-used solvent, ethyl acetate, provides higher degree of separation capability. A conclusive decision regarding feasible solvents and operating conditions would depend on design requirements, regulatory guidance, and safety criteria specified for the process. Inherent safety has always been an important consideration to be implemented during early design steps, and this research presents a methodology to incorporate the principles and obtain inherently safer alternatives

Computational Molecular Design of Cellulose-based Delivery Vehicles for Vaginal Microbicide Gels

HIV/AIDS is a global pandemic that has claimed the lives of 39 million people and currently afflicts 36.9 million more. These effects are disproportionately felt in sub-Saharan Africa which has almost 70% of those current cases and where women account for 59% of current infections. These numbers are even more disproportionate for young women and complications associated with HIV/AIDS are the leading cause of death in women aged 15-44. This creates a need for female-controlled tools to prevent infection. Microbicides are one such tool. Microbicides are vaginally-delivered topical products that combine a therapeutic agent with activity against HIV and a delivery vehicle. The delivery vehicle can consist of many different formulations, depending on the therapeutic agent used and the delivery mechanism desired. Polymer solutions (known as “gels” in the microbicide field) have received the most attention and have been incorporated into the most formulations for use in clinical trial evaluations. There have been several high profile microbicide clinical trial failures, including nonoxynol-9 and cellulose sulfate, as well as one success, CAPRISA 004. Nonoxynol-9 failed in the direction of harm by producing host cell toxicity. The reasons for the failure of cellulose sulfate are not fully understood, but one hypothesis suggests unmeasured benefits provided by the delivery vehicle placebo caused the treatment arm to fail in the direction of harm. Conversely, CAPRISA 004 displayed moderate protection against HIV, which was highly correlated with adherence. The VOICE and FACTS 001 follow-up studies failed to prove the same efficacy, specifically citing low adherence as the reason for failure. These previous studies illustrate the need for delivery vehicles with fully understood physical properties that are safe, allow for effective microbicide delivery, and are acceptable to patients. This study focused on rationally designing next generation microbicide delivery vehicles that are safe, effective, and acceptable. This rational design was accomplished using the methodology of computational molecular design (CMD). This process involves measuring relevant physical properties for a set of known compounds, creating structure-property correlations that relate these data to molecular structure, formulating an optimization problem that incorporates these correlations, and solving this problem to generate novel structures that possess target physical properties. Novel delivery vehicles were designed to have ideal safety, efficacy, and adherence by creating a predictive model that considered rheology, biocompatibility, and drug compatibility. Rheology describes the viscous and elastic character of a fluid. These properties influence the flow behavior of the fluid. The efficacy of liquid microbicide formulations depends upon the delivery vehicle flowing from the point of delivery to cover the entire epithelium to release the active therapeutic over this whole surface area as well as provide a lubricating barrier to pathogens. Additionally, patient acceptability and thus adherence is greatly influenced by how the fluid feels during insertion and use, what the consistency and color looks like, and whether the fluid leaks out prematurely. This study measured the rheology of a set of cellulose ethers, common liquid microbicide delivery vehicles, and related these data with molecular weight, molecular structure, and concentration to form the first part of the overall predictive model. Biocompatibility characterizes the safety and incidence of harm to host cells caused by compounds. This study defined biocompatibility as in vitro vaginal cell cytotoxicity, inflammation represented as the upregulation of two cytokines, and inhibition of C. trachomatis infection. These properties were measured for the same set of cellulose ethers and correlated with molecular structure and molecular weight to form the second part of the overall predictive model. Drug compatibility considers behavior of the active therapeutic in the delivery vehicle. This study defined drug compatibility as solubility of the drug in the delivery vehicle and release of the drug out of the delivery vehicle. Tenofovir, used in the successful CAPRISA 004 trial, was chosen as the model active therapeutic. The properties were again measured for the same set of compounds and correlated with molecular structure and molecular weight to form the final part of the predictive model. This predictive model was finally incorporated into an optimization problem formulation along with structural feasibility constraints. Target properties for all the measured physical properties were selected and the optimization problem was solved to minimize the difference between the properties predicted by the model and these targets. This optimization process resulted in novel structures for candidate delivery vehicles with improved properties compared to the measured set. These candidate delivery vehicles can be synthesized and incorporated into future microbicide formulations

Systematic approaches for design of ionic liquids and their mixtures for enhanced carbon capture purpose

Post-combustion capture using amine-based solvents has been considered as the most viable technology for carbon capture, to mitigate industrial carbon dioxide (CO2) emissions; but the solvents show a number of shortcomings. Recently, ionic liquids (ILs) are suggested as possible alternative to amine-based solvents, for they can be molecularly engineered to match various target thermophysical properties. This work focused on the development of systematic approaches to design IL-based solvents for carbon capture purpose. The first focus of this work is to develop an insight-based based visual approach to determine potential IL solvents as substitute to conventional carbon capture solvents. This approach allows visualisation of high-dimensional problem to be visualised in two or three dimensions, and assist designers without mathematical programming background in IL design. Following that, a mathematical optimisation approach to design optimal IL solvent for CO2 capture purpose was developed as second focus of this thesis. This has been done by formulating the IL solvent design problem as mixed integer non-linear programming (MINLP) optimisation problem. The abovementioned approaches were developed to design task-specific ILs with high CO2 absorption capacity as substitute to common carbon capture solvents. However, studies show that such ILs are relatively more expensive and have higher viscosities. To reduce the cost and viscosity of solvent, task-specific IL can be mixed with conventional IL, ensuring CO2 solubility remains high, while viscosity and cost are acceptable. Hence, the previously developed visual approach was extended to design pure ILs and IL mixtures, specifically to capture CO2. In order to ensure the designed IL is performing at its optimum (highest CO2 solubility in this case), the operating conditions of the carbon capture process shall be considered because they will affect the thermophysical properties and CO2 solubility of ILs. Therefore, the forth focus of this work will be incorporation of operating temperature and pressure into design of IL solvents. Similarly, the design problem was formulated as MINLP problem and solved using mathematical optimisation approach, where operating temperature and pressure were defined as variables through disjunctive programming. Replacing solvent for carbon capture system to IL-based solvent or installing carbon capture system will affect the overall process, as this will affect the utilities consumption of carbon capture system. Therefore, process design has been integrated with IL design in this thesis, to study how the solvent substitution affects the entire process, and followed by retrofitting of the entire process including carbon capture system accordingly. The design problem was formulated and solved as MINLP problem. Finally, this thesis concludes with possible extensions and future works in this area of research work

CHARACTERIZATION OF HYDROPHILIC-RICH PHASE MIMIC IN DENTIN ADHESIVE AND COMPUTER-AIDED MOLECULAR DESIGN OF WATER COMPATIBLE VISIBLE LIGHT INITIATORS

The clinical lifetime of moderate-to-large dental composite restorations is lower than dental amalgam restorations. With the imminent and significant reduction in the use and availability of dental amalgam, the application of composite for the restoration of teeth will increase. Since composite has a higher failure rate, the increased use of composite will translate to an increase in the frequency of dental restoration replacement, overall cost for dental health and discomfort for patients. The composite is too viscous to bond directly to the tooth and thus, a low viscosity adhesive is used to form the bond between the composite and tooth. The bond at the adhesive/tooth is intended to form an impervious seal that protects the restored tooth from acids, oral fluids and bacteria that will undermine the composite restoration. The integrity of the adhesive/tooth bond (the exposed tooth structure is largely composed of enamel and dentin) plays an important role in preventing secondary caries which undermine the composite restoration. This study focuses on the durability of etch-and-rinse dental adhesives. As the adhesive infiltrates the demineralized dentin matrix, it undergoes phase separation into hydrophobic- and hydrophilic-rich phases. The hydrophilic-rich phase contains the conventional hydrophobic photo-initiator system (camphorquinone/ethyl 4-(dimethylamino)benzoate) and cross-linker both in inadequate concentrations. This may compromise the polymerization reaction and the cross-linking density of this phase, making it vulnerable to failure. The goal of this study is to characterize the hydrophilic-rich phase of the dental adhesive by monitoring its polymerization kinetics and glass transition temperature under the presence of an iodonium salt (reaction accelerator), and varying water concentration, photo-initiator concentration and light intensity. The final goal is to develop a computational framework for designing water compatible visible light photosensitizers specifically for the hydrophilic-rich phase of dental adhesives. It was observed that the degree of conversion of the hydrophilic-rich mimics is dominated by the photo-initiator concentration and not the cross-linker. A secondary rate maxima was observed in the case of hydrophilic-rich phase mimics which was associated with the formation of microgels during polymerization. A polymerization mechanism involving polymerization- and solvent-induced phase separation was proposed for the hydrophilic-rich mimics. The hydrophilic dental resins were sensitive to light intensity, i.e. at low light intensities the degree of conversion of the hydrophilic resin was reduced substantially in the presence of camphorquinone/ethyl 4-(dimethylamino)benzoate as photo-initiators, whereas a substantial degree of conversion was observed for the hydrophobic resin even at these lower light intensities. The addition of iodonium salt in the hydrophilic resin significantly improved the degree of conversion of the hydrophilic resin at low light intensities. These studies also showed that the iodonium salt could lead to enhanced cyclization and shorter polymer chain lengths within the hydrophilic-rich phase. For the physically separated hydrophilic-rich phase specimens, it was observed that in the presence of the conventional photo-initiator system (camphorquinone/ethyl 4-(dimethylamino)benzoate), there was no polymerization, mostly due to the insufficient partition concentrations of the photo-initiator components within this phase. The addition of iodoinum salt in this case significantly improved the degree of conversion but it was still significantly lower. These studies indicated that the overall polymerization efficiency of the hydrophilic-rich phase was lower than the hydrophobic-rich phase. The lower polymerization efficiency of the hydrophilic-rich phase led to a phase that lacks integrity; the hydrophilic-rich phase could be infiltrated by oral fluids and cariogenic bacteria. The infiltration of these noxious agents at the interface between the material and tooth could pave the way for enhanced degradation of the tooth structure (collagen and mineral) as well as the adhesive polymer. Novel photosensitizer molecules were proposed to improve the polymerization efficiency of this phase. Computer-aided molecular design (CAMD) was employed to obtain the new photosensitizers. These photosensitizers were capable of improving the degree of conversion of the hydrophilic-rich phase. An enhanced degree of conversion of the hydrophilic-rich phase would lead to a better seal at the adhesive/dentin interface and higher bond strength. Computer-aided molecular design (CAMD) is a fast and inexpensive technique compared to the conventional trial-and-error method to rationally design products. For this case, hydrophilic molecules with photosensitizing capability in the visible range were selected and several target properties of these molecules were determined. The target properties for this design were: octanol/water partition coefficient, relative normalized photon absorption efficiency, molar extinction coefficient at 480 nm, degree of conversion and polymerization rate of the hydrophilic-rich phase. These data for the target properties were used to develop quantitative structure property relationships (QSPRs). These correlations and structural constraints were used to develop a mixed integer non-linear program, which was solved via an optimization algorithm, minimizing the difference between the properties of the solutions and the target values. Four candidate novel molecular structures for the photosensitizer were proposed, which were predicted to be hydrophilic in nature and exhibit a substantial degree of conversion within the hydrophilic-rich phase. All these molecules contained iminium ions, which suggested that this specific feature could play a vital role in the formation of efficient radicals. This investigation clearly indicates that the hydrophilic-rich phase forms a weak region and provides several directions towards fortifying this phase against failure

Systematic approaches for design of ionic liquids and their mixtures for enhanced carbon capture purpose

Post-combustion capture using amine-based solvents has been considered as the most viable technology for carbon capture, to mitigate industrial carbon dioxide (CO2) emissions; but the solvents show a number of shortcomings. Recently, ionic liquids (ILs) are suggested as possible alternative to amine-based solvents, for they can be molecularly engineered to match various target thermophysical properties. This work focused on the development of systematic approaches to design IL-based solvents for carbon capture purpose. The first focus of this work is to develop an insight-based based visual approach to determine potential IL solvents as substitute to conventional carbon capture solvents. This approach allows visualisation of high-dimensional problem to be visualised in two or three dimensions, and assist designers without mathematical programming background in IL design. Following that, a mathematical optimisation approach to design optimal IL solvent for CO2 capture purpose was developed as second focus of this thesis. This has been done by formulating the IL solvent design problem as mixed integer non-linear programming (MINLP) optimisation problem. The abovementioned approaches were developed to design task-specific ILs with high CO2 absorption capacity as substitute to common carbon capture solvents. However, studies show that such ILs are relatively more expensive and have higher viscosities. To reduce the cost and viscosity of solvent, task-specific IL can be mixed with conventional IL, ensuring CO2 solubility remains high, while viscosity and cost are acceptable. Hence, the previously developed visual approach was extended to design pure ILs and IL mixtures, specifically to capture CO2. In order to ensure the designed IL is performing at its optimum (highest CO2 solubility in this case), the operating conditions of the carbon capture process shall be considered because they will affect the thermophysical properties and CO2 solubility of ILs. Therefore, the forth focus of this work will be incorporation of operating temperature and pressure into design of IL solvents. Similarly, the design problem was formulated as MINLP problem and solved using mathematical optimisation approach, where operating temperature and pressure were defined as variables through disjunctive programming. Replacing solvent for carbon capture system to IL-based solvent or installing carbon capture system will affect the overall process, as this will affect the utilities consumption of carbon capture system. Therefore, process design has been integrated with IL design in this thesis, to study how the solvent substitution affects the entire process, and followed by retrofitting of the entire process including carbon capture system accordingly. The design problem was formulated and solved as MINLP problem. Finally, this thesis concludes with possible extensions and future works in this area of research work

The integration of safety and health aspects in chemical product design framework

Computer aided molecular design (CAMD) is a powerful technique to design molecules or chemical mixtures that fulfil a set of desirable target properties as specified by users. Molecular physical and thermodynamic properties are selected as the target properties to ensure that the designed molecules can achieve the property functionalities. However, the aspects of safety and health are not strongly emphasised as design objectives in many CAMD problems. In order to ensure that the synthesised molecule does not cause much harm and health-related risks to the consumers, it is critical to integrate both safety and health aspects as design factors in the current CAMD approaches. The main focus of this research is to develop a novel chemical product design methodology that integrates the concept of inherent safety and occupational health aspects in a CAMD framework. The generated molecules that are optimised with respect to the target properties must be evaluated in terms of their safety and health performance. The assessment is conducted by safety and health-related parameters/sub-indexes that have significant adverse impact on both aspects. This proposed approach ensures that a product that possesses the desirable properties, and at the same time meets the safety and health criteria, is produced. The next focus of this research is to generate optimal molecules with the desired functionalities and favourable safety and health attributes in a single-stage CAMD framework. Besides target properties, the concept of inherent safety and health is also considered as design objective to ensure that the synthesised molecules are simultaneously optimised with regards to both criteria. Fuzzy optimisation approach is applied to optimise these two principal design criteria in this work. As molecular properties are utilised as the parameters to examine the safety and health features of the molecules, these properties are often estimated through property prediction models. This research also focuses on the management of uncertainty resulted from properties used in the sub-indexes. The quantification of uncertainty helps to revise the safety and health measurement so that it can better reflect the inherent hazard level of the molecules. The fourth focus of this research is to address the limitations present in the current method of molecular hazard quantification. The enhancement is carried out by adopting the ordered weighted averaging (OWA) operator method with the analytic hierarchy process (AHP) approach in the safety and health assessment. Two case studies on solvent design are considered to demonstrate the presented methodologies

The integration of safety and health aspects in chemical product design framework

Computer aided molecular design (CAMD) is a powerful technique to design molecules or chemical mixtures that fulfil a set of desirable target properties as specified by users. Molecular physical and thermodynamic properties are selected as the target properties to ensure that the designed molecules can achieve the property functionalities. However, the aspects of safety and health are not strongly emphasised as design objectives in many CAMD problems. In order to ensure that the synthesised molecule does not cause much harm and health-related risks to the consumers, it is critical to integrate both safety and health aspects as design factors in the current CAMD approaches. The main focus of this research is to develop a novel chemical product design methodology that integrates the concept of inherent safety and occupational health aspects in a CAMD framework. The generated molecules that are optimised with respect to the target properties must be evaluated in terms of their safety and health performance. The assessment is conducted by safety and health-related parameters/sub-indexes that have significant adverse impact on both aspects. This proposed approach ensures that a product that possesses the desirable properties, and at the same time meets the safety and health criteria, is produced. The next focus of this research is to generate optimal molecules with the desired functionalities and favourable safety and health attributes in a single-stage CAMD framework. Besides target properties, the concept of inherent safety and health is also considered as design objective to ensure that the synthesised molecules are simultaneously optimised with regards to both criteria. Fuzzy optimisation approach is applied to optimise these two principal design criteria in this work. As molecular properties are utilised as the parameters to examine the safety and health features of the molecules, these properties are often estimated through property prediction models. This research also focuses on the management of uncertainty resulted from properties used in the sub-indexes. The quantification of uncertainty helps to revise the safety and health measurement so that it can better reflect the inherent hazard level of the molecules. The fourth focus of this research is to address the limitations present in the current method of molecular hazard quantification. The enhancement is carried out by adopting the ordered weighted averaging (OWA) operator method with the analytic hierarchy process (AHP) approach in the safety and health assessment. Two case studies on solvent design are considered to demonstrate the presented methodologies