<p>Activated carbon is an adsorbent that is commonly used for removing organic contaminants from air due to its abundant pores and large internal surface area. This thesis is concerned with the static adsorption capacity and adsorption kinetics for single and binary organic compounds on different types of activated carbon. These are important parameters for the design of filters and for the estimation of filter service life. Existing predictive models for adsorption capacity and kinetics are based on fundamental “hard” knowledge of adsorption mechanisms. These models have several drawbacks, especially in complex situations, and extensive experimental data are often needed as inputs. In this work we present a systematic approach that can contribute to the further development of predictive models, especially for complex situations. The approach is based on Multivariate Data Analysis (MVDA), which is ideally suited for the development of soft models without incorporating any assumptions about the mathematical form or fundamental physical principles involved. </p><p>Adsorption capacity and adsorption kinetics depend on the properties of the carbon and the adsorbate as well as experimental conditions. Therefore, to make general statements regarding adsorption capacity and kinetics it is important for the resulting models to be representative of the conditions they will simulate. Accordingly, the first step in the investigations underlying this thesis was to select a minimum number of representative and chemically diverse organic compounds. The next steps were to study the dependence of the derived affinity coefficient, β, in the Dubinin-Radushkevich equation on properties of organic compounds and to establish a new, improved model. This new model demonstrates the importance of adding descriptors for the specific interaction with the carbon surface to the size and shape descriptors. The adsorption capacities of the same eight organic compounds at low relative pressures were correlated with compound properties. It was found that different compound properties are important in the various stages of adsorption, reflecting the fact that different mechanisms are involved. Ideal adsorbed solution theory (IAST) in combination with the Freundlich equation was developed to predict the adsorption capacities of binary organic compound mixtures. A new model was proposed for predicting the rate coefficient of the Wheeler-Jonas equation which is valid for breakthrough ratios up to 20%. Finally, it was shown that the Wheeler-Jonas equation can be adapted to describe the breakthrough curves of binary mixtures. New models were proposed for predicting its parameters, the adsorption rate coefficients, and the adsorption capacities for both components of the binary mixture. Thus, multivariate data analysis can not only be used to assist in the understanding of adsorption mechanisms, but also contribute to the development of predictive models of adsorption capacity and breakthrough time for single and binary organic compounds.</p>

<p>Rheumatoid arthritis (RA) is an autoimmune disease affecting approximately 1% of the population in the western world. It is characterised by a tissue specific attack of cartilage in peripheral joints. Collagen induced arthritis (CIA) is one of the most commonly used animal models for (RA), with similar symptoms and histopathology. CIA is induced by immunisation of mice with type II collagen (CII), and the immunodominant part was previously found to be located between residues 256-270. This thesis describes the interaction between the MHC molecule, glycopeptide antigens from CII and the T cells that is essential in development of CIA. The glycopeptide properties for binding to the mouse MHC molecule Aq have been studied, as well as interaction points in the glycopeptide that are critical for stimulation of a T-cell response.</p><p>The thesis is based on five studies. In the first paper the minimal glycopeptide core, that is required for binding to the Aq molecule while still giving a full T cell response was determined. The second paper studied the roles of amino acid side-chains and a backbone amide bond as T-cell contact points. In the third paper the hydrogen bond donor-acceptor characteristics of the 4-OH galactose hydroxyl group of the glycopeptide was studied in detail. In the fourth paper we established a structure activity relationship (QSAR model) for (glyco)peptide binding to the Aq molecule. Finally, the stereochemical requirements for glycopeptide binding to the Aq molecule and for T-cell recognition was studied in the fifth paper.</p><p>The study was performed using collagen glycopeptide analogues, which were synthesised on solid phase. Amide bond and hydroxyl group isosteres were introduced for study of hydrogen bond donor-acceptor characteristics. Statistical methods were used to design a representative peptide test set and in establishing a QSAR model.</p><p>The results give a deeper understanding of the interactions involved in the ternary MHC-glycopeptide-T cell complex. This information contributes to research directed towards finding new treatments for RA.</p>

<p>Aqueous solubility is a key parameter influencing the bioavailability of drugs and drug candidates. In this thesis computational models for the prediction of aqueous drug solubility were explored. High quality experimental solubility data for drugs were generated using a standardised protocol and models were developed using multivariate data analysis tools and calculated molecular descriptors. In addition, structural features associated with either solid-state limited or solvation limited solubility of drugs were identified.</p><p>Solvation, as represented by the octanol-water partition coefficient (log<i>P</i>), was found to be the dominant factor limiting the solubility of drugs, with solid-state properties being the second most important limiting factor.</p><p>The relationship between the chemical structure of drugs and the strength of their crystal lattice was studied for a dataset displaying log<i>P</i>-independent solubility. Large, rigid and flat molecules with an extended ring-structure and a large number of conjugated π-bonds were found to be more likely to have their solubility limited by a strong crystal lattice than were small, spherically shaped molecules with flexible side-chains.</p><p>Finally, the relationship between chemical structure and drug solvation was studied using computer simulated values of the free energy of hydration. Drugs exhibiting poor hydration were found to be large and flexible, to have low polarisability and few hydrogen bond acceptors and donors.</p><p>The relationship between the structural features of drugs and their aqueous solubility discussed in this thesis provide new rules-of-thumb that could guide decision-making in early drug discovery.</p>

The polychlorinated biphenyls (PCBs) comprise a group of 209 congeners varying in the number of chlorine atoms and substitution patterns. These compounds tend to be biomagnified in foodwebs and have been shown to induce an array of effects in exposed organisms. The structural characteristics of the PCBs influence their potency as well as mechanism of action. In order to assess the biological potency of these compounds a multi-step quantitative structure-activity relationship (QSAR) procedure was used in the project described in this thesis. The ultraviolet absorption (UV) spectra were measured for all 209 PCBs, and digitised for use as physico-chemical descriptors. Interpretations of the spectra using principal component analysis (PCA) showed the number of ortho chlorine atoms and para-para substitution patterns to be significant. Additional physico-chemical descriptors were derived from semi-empirical calculations. These included various molecular energies, the ionisation potential, electron affinity, dipole moments, and the internal barrier of rotation. The internal barrier of rotation was especially useful for describing the conformation of the PCBs on a continuous scale. In total 52 physico-chemical descriptors were compiled and analysed by PCA for the tetra- to hepta-chlorinated congeners. The structural variation within these compounds was condensed into four principal properties derived from a PCA for use as design variables in a statistical design to select congeners representative for these homologue-groups. The 20 selected PCBs have been applied to study structure-specific biochemical responses in a number of bioassays, and to study the biomagnification of the PCBs in various fish species. QSARs were established using partial least squares projections to latent structures (PLS) for the PCBs potency to inhibit intercellular communication, activate respiratory burst, inhibit dopamine uptake in synaptic vesicles, compete with estradiol for binding to estrogen receptors, and induce cytochrome P4501A (CYP1A) related activities. By the systematic use of the designed set of PCBs the biological potency was screened over the chemical domain of the class of compounds. Further, sub-regions of highly potent PCBs were identified for each response measured. For risk assessment of the PCBs potency to induce dioxin-like activities the predicted induction potencies (PIPs) were calculated. In addition, two sets of PCBs were presented that specifically represent congeners of environmental relevance in combination with predicted potency to induce estrogenic and CYP1A related activities.

Aqueous solubility is a key parameter influencing the bioavailability of drugs and drug candidates. In this thesis computational models for the prediction of aqueous drug solubility were explored. High quality experimental solubility data for drugs were generated using a standardised protocol and models were developed using multivariate data analysis tools and calculated molecular descriptors. In addition, structural features associated with either solid-state limited or solvation limited solubility of drugs were identified. Solvation, as represented by the octanol-water partition coefficient (logP), was found to be the dominant factor limiting the solubility of drugs, with solid-state properties being the second most important limiting factor. The relationship between the chemical structure of drugs and the strength of their crystal lattice was studied for a dataset displaying logP-independent solubility. Large, rigid and flat molecules with an extended ring-structure and a large number of conjugated π-bonds were found to be more likely to have their solubility limited by a strong crystal lattice than were small, spherically shaped molecules with flexible side-chains. Finally, the relationship between chemical structure and drug solvation was studied using computer simulated values of the free energy of hydration. Drugs exhibiting poor hydration were found to be large and flexible, to have low polarisability and few hydrogen bond acceptors and donors. The relationship between the structural features of drugs and their aqueous solubility discussed in this thesis provide new rules-of-thumb that could guide decision-making in early drug discovery.

The increasing mass and complexity of chemicals being produced and transported has resulted in more rigorous demands on both authorities as well as chemical-handling industries to assess the risks involved. The Environment-Accident Index (EAI), has been proposed as a planning tool created as an equation in which chemical properties (variables describing the chemical involved) are combined with site-specific properties (variables describing the accident site). The EAI is intended to facilitate assessment of the environmental effects related to chemical accident scenarios and hence assist the organisation of preventative programs. The main objective of the work described in this thesis was to evaluate, develop and improve the proposed EAI. The steps involved in the development process included I) evaluation of the feasibility of the EAI approach, II) selection of a representative and diverse set of chemical accidents to be used in the development III) the use of questionnaires and expert judgements to develop response values for environmental effects of a chemical accident, and IV) to create a new EAI model using multivariate modelling (PLS). The EAI approach proved to be useful in the work to protect the environment in case of a chemical accident. A representative set of accidents was selected by means of statistical multivariate design (PCA) based on assembled data related to a set of 55 chemical accidents. The selection generated a set of accidents representing a diverse spectrum of chemical accident scenarios. To develop a measure of environmental effects of the chemical accidents i.e. responses, an expert panel was asked to judge their environmental effects (such as effects on animal life in the aquatic or terrestrial environment). The results showed that the judgements give a rough estimate of environmental effects that could be used as responses in the development of the EAI. The developed responses were then related to the chemical and site-specific properties to create a new EAI model. This resulted in a PLS-based EAI connected to a new classification scale. The advantages of the new EAI are that it can be calculated without the use of tables; it can estimate the effects for all included responses, and make a rough classification of chemical accidents according to the new classification scale. Finally, the new EAI is a more stable model than the previously proposed EAI, and it is founded on a valid base of accident scenarios, making its use for a variety of chemicals and situations more reliable since it covers a broader spectrum of accident scenarios. The new EAI can be expressed as a regression model to facilitate calculation of the index for people that do not have access to PLS. The highest priorities for further refining the new EAI in the future are: external validation of the EAI; further refinement of the formula’s structure; adjustment of the new classification scale; and real-life evaluation of the EAI.

The risks associated with a soil contaminated by polycyclic aromatic hydrocarbons (PAHs) are generally assessed by measuring individual PAHs in the soil and correlating the obtained amounts to known adverse biological effects of the PAHs. The validity of such a risk estimation is dependent on the presence of additional compounds, the availability of the compounds (including the PAHs), and the methods used to correlate the measured chemical data and biological effects. In the work underlying this thesis the availability, chemical composition and biological effects of PAHs in samples of soils from PAH-contaminated environments were examined. It can be concluded from the results presented in the included papers that the PAHs in the studied soils from industrial sites were not generally physically trapped in soil material, indicating that the availability of the PAHs was not restricted in this sense. However, the bioavailable fraction of the PAHs, as assessed by bioassays with the earthworm Eisenia Fetida, could not be assessed by a number of abiotic techniques (including: solid phase micro extraction, SPME; use of semi-permeable membrane devices, SPMDs; leaching with various solvent mixtures, leaching using additives, and sequential leaching) and it seems to be difficult to find a chemical method that can accurately assess the bioavailability of PAHs. Furthermore, it was shown that PAH-polluted samples may be extensively chemically characterized by GC-TOFMS using peak deconvolution, and over 900 components can be resolved in a single run. The chemical characterization also revealed that samples that appeared to be similar in terms of their PAH composition were heterogeneous in terms of their overall composition. Finally, single compounds from this large set of compounds, which correlated with different biological effects, could be identified using the multivariate technique partial least squares projections to latent structures (PLS). This indicates that PLS may provide a valid alternative to Effect Directed Analysis (EDA), an established method for finding single compounds that correlate to the toxicity of environmental samples. Thus, the instrumentation and data evaluation tools used in this thesis are clearly capable of providing a broad chemical characterization as well as linking the obtained chemical data to results from bioassays. However, the link between the chemical analyses and the biological tests could be improved as as an organic solvent that solubilised virtually all of the contaminants was used during the chemical analysis while the biological tests were performed in an aqueous solution with limited solubility for a number of compounds. Consequently the compounds probably have a different impact in the biological tests than their relative abundance in profiles obtained by standard chemical analyses suggests. The availability and bioavailability of contaminants in soil also has to be studied further, and such future studies should focus on the molecular interactions between the contaminants and different compartments of the soil. By doing so, detailed knowledge could be obtained which could be applied to a number of different contaminants and soil types. Such studies would generate the data needed for molecular-based modelling of availability and bioavailability, which would be a big step forward compared to current risk assessment practices.

In this thesis technical nonionic surfactants are studied using multivariate techniques. The surfactants studied were alkyl ethoxylates (AEOs) and alkyl polyglucosides (APGs). The aquatic toxicity of the surfactants towards two organisms, a shrimp and a rotifer, was examined. The specified effect was lethality, LC50, as indicated by immobilisation. In a comparative study, the LC50 values obtained were used to develop two different types of model. In the log P model the toxicity was correlated to log P alone, while in the multivariate model several physicochemical variables, including log P, were correlated to the toxicity. The multivariate model gave smaller prediction errors than the log P model. Further, the change in reactivity when a surfactant mixture was added to dissolving pulp under alkaline conditions was studied, using the amount of residual cellulose as a measure of the reactivity. Ten AEO/APG mixtures were tested, and the mixture with greatest potential was studied in more detail. An optimum in the amount of added surfactant was found that seems to coincide, according to surface tension measurements, with the CMC.

Fluidized bed dryers are used in the pharmaceutical industry for the batch drying of pharmaceutical granulate. Maintaining optimal hydrodynamic conditions throughout the drying process is essential to product quality. Due to the complex interactions inherent in the fluidized bed drying process, mechanistic models capable of identifying these optimal modes of operation are either unavailable or limited in their capabilities. Therefore, empirical models based on experimentally generated data are relied upon to study these systems.<p> Principal Component Analysis (PCA) and Partial Least Squares (PLS) are multivariate statistical techniques that project data onto linear subspaces that are the most descriptive of variance in a dataset. By modeling data in terms of these subspaces, a more parsimonious representation of the system is possible. In this study, PCA and PLS are applied to data collected from a fluidized bed dryer containing pharmaceutical granulate. <p>System hydrodynamics were quantified in the models using high frequency pressure fluctuation measurements. These pressure fluctuations have previously been identified as a characteristic variable of hydrodynamics in fluidized bed systems. As such, contributions from the macroscale, mesoscale, and microscales of motion are encoded into the signals. A multiresolutional decomposition using a discrete wavelet transformation was used to resolve these signals into components more representative of these individual scales before modeling the data. <p>The combination of multiresolutional analysis with PCA and PLS was shown to be an effective approach for modeling the conditions in the fluidized bed dryer. In this study, datasets from both steady state and transient operation of the dryer were analyzed. The steady state dataset contained measurements made on a bed of dry granulate and the transient dataset consisted of measurements taken during the batch drying of granulate from approximately 33 wt.% moisture to 5 wt.%. Correlations involving several scales of motion were identified in both studies.<p> In the steady state study, deterministic behavior related to superficial velocity, pressure sensor position, and granulate particle size distribution was observed in PCA model parameters. It was determined that these properties could be characterized solely with the use of the high frequency pressure fluctuation data. Macroscopic hydrodynamic characteristics such as bubbling frequency and fluidization regime were identified in the low frequency components of the pressure signals and the particle scale interactions of the microscale were shown to be correlated to the highest frequency signal components. PLS models were able to characterize the effects of superficial velocity, pressure sensor position, and granulate particle size distribution in terms of the pressure signal components. Additionally, it was determined that statistical process control charts capable of monitoring the fluid bed hydrodynamics could be constructed using PCA<p>In the transient drying experiments, deterministic behaviors related to inlet air temperature, pressure sensor position, and initial bed mass were observed in PCA and PLS model parameters. The lowest frequency component of the pressure signal was found to be correlated to the overall temperature effects during the drying cycle. As in the steady state study, bubbling behavior was also observed in the low frequency components of the pressure signal. PLS was used to construct an inferential model of granulate moisture content. The model was found to be capable of predicting the moisture throughout the drying cycle. Preliminary statistical process control models were constructed to monitor the fluid bed hydrodynamics throughout the drying process. These models show promise but will require further investigation to better determine sensitivity to process upsets.<p> In addition to PCA and PLS analyses, Multiway Principal Component Analysis (MPCA) was used to model the drying process. Several key states related to the mass transfer of moisture and changes in temperature throughout the drying cycle were identified in the MPCA model parameters. It was determined that the mass transfer of moisture throughout the drying process affects all scales of motion and overshadows other hydrodynamic behaviors found in the pressure signals.

No description available.