Development of SLA 3D printed drug eluting medical implants for local cancer treatment

The current dogma of drug formulation technology places heavy focus upon the use of systemic oral or intravenous routes, for the delivery of a medicine to a target tissue. An inherent problem with this approach is the requirement of a high dosing regimen to ensure that the drug reaches the site of interest for optimal therapeutic effect. However, this can lead to the prevalence of ‘off-target’ effects and poor compliance. In the case of cancer, the ‘off-target’ effects of cytotoxic chemotherapeutics can cause greater harm than benefit to the patient. The aim of this project is to develop a medical implant that obviates the requirement of systemic dosing by providing a method of local drug release to the target area. Through utilisation of SLA 3D printing, we aim to develop and produce a drug eluting device that provides unidirectional release of patient-specific payloads at pre-determined pharmacokinetic rates. However, before a specific focus could be placed on cancer, three major problems associated with SLA 3D printing pharmaceutics had to be solved. Firstly, SLA 3D printed materials have unsuitable physical properties for medical device applications. Secondly, photopolymer systems based on (meth)acrylate photopolymer systems are associate with toxicity and hence have limited use as pharmaceutics. Finally, commercial SLA 3D printers do not support the use of custom photopolymer systems. Solving each of these problems would provide solid groundwork for the development of SLA 3D printed drug eluting implants for local chemotherapy. To solve the issue of poor mechanical properties, a range of current and novel photopolymers were synthesised, characterised and compared against one another and reference materials. To solve the issue of material toxicity, different post-processing procedures were explored and utilised in attempt to render SLA 3D printed materials as biocompatible. Finally, an RT-FTIR spectroscopy tool was developed to bridge the gap between unprintable and printable photopolymer systems. Furthermore, extensive drug release studies were conducted with aim to characterise effect of different SLA 3D printed materials on drug release kinetics

Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health

In analogy to the periodic system that groups elements by their similarity in structure and chemical properties, the hazard of chemicals can be assessed in groups of similar structures and similar toxicological properties. Here we review case studies of grouping strategies that supported the assessment of hazard, exposure, and risk to human health. By the EU-REACh and the US-TSCA New Chemicals Program, structural similarity is commonly used as the basis for grouping, but that criterion is not always adequate and sufficient. Based on the lessons learned, we derive ten principles for grouping, including: transparency of the purpose, criteria and boundaries of the group; adequacy of methods used to justify the group; inclusion or exclusion of substances in the group by toxicological properties. These principles apply to initial grouping to prioritize further actions as well as to definitive grouping to generate data for risk assessment. Both can expedite effective risk management

Multi-tier framework for the inferential measurement and data-driven modeling

A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat així com la integració de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'anàlisi exploratòria de dades ha esta avaluat a partir de la caracterització de l'espai químic corresponent a la biodegradació de certs compostos orgànics. Fruit d'aquest anàlisi s'han establert relacions entre diverses variables físico-químiques que han estat emprades posteriorment per al desenvolupament de models de biodegradació. A nivell del preprocés de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecció de variables basada en l'ús del Mapes Autoorganitzats (SOM). Tot i que el mètode proposat selecciona, en general, un major nombre de variables que altres mètodes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat també tot un conjunt de tècniques d'imputació de dades basades en el SOM amb un conjunt de dades estàndard corresponent als paràmetres d'operació d'una planta de tractament d'aigües residuals. Es proposa i avalua en un problema de predicció de qualitat en aigua un nou model dinàmic per a ajustar el centre i la dispersió en xarxes de funcions de base radial. El mètode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat també al desenvolupament de models predictius i de classificació de les velocitats de biodegradació de compostos orgànics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximació per a desenvolupar models basats en dades en aquells casos en els que la complexitat de dinàmica del procés impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'ús de algorismes de generació de regles i de grafs de dependència bayesiana per a introduir una nova capa que faciliti la interpretació dels models. Els resultats preliminars obtinguts a partir de la classificació dels Modes d'acció Tòxica (MOA) apunten a que l'ús dels MOA com a indicadors intermediaris dels efectes dels compostos químics en la salut és una aproximació factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capaç d'inferir índexs de qualitat a partir de variables primàries de procés. El sensor resultant ha estat implementat en una planta química real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcinògens d'un grup de compostos aromàtics a partir de la seva estructura molecular. Els resultats obtinguts desprès d'aplicar el mètode de selecció de variables basat en el SOM milloren els resultats prèviament publicats. Aquest marc de treball s'ha usat també per a proporcionar una nova aproximació al modelat ambiental i l'anàlisi de risc amb sistemes d'informació geogràfica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposició i per a desenvolupar un nou mètode d'interpolació geogràfica. La combinació del SOM amb els models de mescla de gaussianes dona una nova formulació al problema de l'anàlisi de risc des d'un punt de vista probabilístic

The Distribution and Fate of Microplastic Pollution in Polar Environments

The distribution of microplastics in polar regions is relatively unknown, but it is key to understanding the fate and potential impact of this pervasive and complex pollutant in these remote and threatened environments. This thesis focuses on the most accessible and arguably vital matrix in which microplastics may exist in the Arctic and Antarctic; the near-surface environments such as seawater and snow. Although likely to be transient, microplastics in these matrices present a direct interface between humans (the polluter) and the environment (the polluted). Determining distribution at the surface is vital to understanding the impact of an increasing human presence in these regions and an increasing human footprint via long-range transport. Three distinct environments have been explored in this thesis to provide data on the characteristics and concentrations of microplastics and facilitate the development of methods that enable perceived “pristine” environments to be effectively and rigorously investigated. In both the Canadian Arctic and the Southern Ocean, this thesis shows that microplastic concentrations are low compared with global concentrations. In the Arctic, it is shown that a 300 µm mesh, which has typically been used in marine microplastic research, retains only 6% of the particulate, which can be potentially captured on a 50 µm mesh, therefore significantly underestimating microplastic abundance and overlooking the characterisation of the most bioavailable size fraction to polar ecosystems. In the Southern Ocean, although concentrations are low, it is demonstrated that these are significantly high enough for microplastics to be encountered and therefore potentially ingested by pelagic amphipods. With little known about the subsidiary impacts of microplastics on the biogeochemistry of other pollutants in the Southern Ocean, an experiment exploring the impact of microplastics on mercury uptake by Antarctic krill has been carried out. Results from this ship-based laboratory experiment indicate that virgin microplastics, compared to particulate organic matter, play an insignificant role in mercury uptake by Antarctic krill. As methods developed, the final environmental dataset collected in Antarctic snow was analysed using automated analysis, revealing remarkably high concentrations of the smallest microplastics, heterogeneously distributed in continental Antarctica. These findings provide valuable insight into the distribution and potential fate of microplastics in polar environments whilst also providing vital information on the methods of carrying out polar plastics research. In combination, this is key to providing an evidence base for needs and ways to monitor and understand the impact of microplastics in remote polar regions

QSAR models for the (eco-)toxicological characterization and prioritization of emerging pollutants: case studies and potential applications within REACH.

Under the European REACH regulation (Registration, Evaluation, Authorisation and Restriction of Chemical substances - (EC) No 1907/2006), there is an urgent need to acquire a large amount of information necessary to assess and manage the potential risk of thousands of industrial chemicals. Meanwhile, REACH aims at reducing animal testing by promoting the intelligent and integrated use of alternative methods, such as in vitro testing and in silico techniques. Among these methods, models based on quantitative structure-activity relationships (QSAR) are useful tools to fill data gaps and to support the hazard and risk assessment of chemicals. The present thesis was performed in the context of the CADASTER Project (CAse studies on the Development and Application of in-Silico Techniques for Environmental hazard and Risk assessment), which aims to integrate in-silico models (e.g. QSARs) in risk assessment procedures, by showing how to increase the use of non-testing information for regulatory decision-making under REACH. The aim of this thesis was the development of QSAR/QSPR models for the characterization of the (eco-)toxicological profile and environmental behaviour of chemical substances of emerging concern. The attention was focused on four classes of compounds studied within the CADASTER project, i.e. brominated flame retardants (BFRs), fragrances, prefluorinated compounds (PFCs) and (benzo)-triazoles (B-TAZs), for which limited amount of experimental data is currently available, especially for the basic endpoints required in regulation for the hazard and risk assessment. Through several case-studies, the present thesis showed how QSAR models can be applied for the optimization of experimental testing as well as to provide useful information for the safety assessment of chemicals and support decision-making. In the first case-study, simple multiple linear regression (MLR) and classification models were developed ad hoc for BFRs and PFCs to predict specific endpoints related to endocrine disrupting (ED) potential (e.g. dioxin-like activity, estrogenic and androgenic receptor binding, interference with thyroxin transport and estradiol metabolism). The analysis of modelling molecular descriptors allowed to highlight some structural features and important structural alerts responsible for increasing specific ED activities. The developed models were applied to screen over 200 BFRs and 33 PFCs without experimental data, and to prioritize the most hazardous chemicals (on the basis of ED potency profile), which have been then suggested to other CADASTER partners in order to focus the experimental testing. In the second case-study, MLR models have been developed, specifically for B-TAZs, for the prediction of three key endpoints required in regulation to assess aquatic toxicity, i.e. acute toxicity in algae (EC50 72h Pseudokirchneriella subcapitata), daphnids (EC50 48h Daphnia magna) and fish (LC50 96h Onchorynchus mykiss). Also in this case, the developed QSARs were applied for screening purposes. Among over 350 B-TAZs lacking experimental data, 20 compounds, which were predicted as toxic (EC(LC)50 64 10 mg/L) or very toxic (EC(LC)50 64 1 mg/L) to the three aquatic species, were prioritized for further experimental testing. Finally, in the third case-study, classification QSPR models were developed for the prediction of ready biodegradability of fragrance materials. Ready biodegradation is among the basic endpoints required for the assessment of environmental persistence of chemicals. When compared with some existing models commonly used for predicting biodegradation, the here proposed QSPRs showed higher classification accuracy toward fragrance materials. This comparison highlighted the importance of using local models when dealing with specific classes of chemicals. All the proposed QSARs have been developed on the basis of the OECD principles for QSAR acceptability for regulatory purposes, paying particular attention to the external validation procedure and to the statistical definition of the applicability domain of the models. QSAR models based on molecular descriptors generated by both commercial (DRAGON) and freely-available (PaDELDescriptor, QSPR-Thesaurus) software have been proposed. The use of free tool allows for a wider applicability of the here proposed QSAR models. Concluding, the QSAR models developed within this thesis are useful tools to support hazard and risk assessment of specific classes of emerging pollutants, and show how non-testing information can be used for regulatory decisions, thus minimizing costs, time and saving animal lives. Beyond their use for regulatory purposes, the here proposed QSARs can find application in the rational design of new safer compounds that are potentially less hazardous for human health and environment

New Analytical Methodologies based on Chromatography-Atmospheric Pressure Ionization-Mass Spectrometry for the Determination of Halogenated Organic Contaminants

Programa de Doctorat en Química Analítica i Medi Ambient[eng] The environment sustainability is being threatened by the continuous release of pollutants that can negatively affect not only environmental compartments but also wildlife and human beings. Among these pollutants, halogenated persistent organic pollutants and new emerging contaminants have cause great concern due to their toxicity, persistence, bioaccumulation and biomagnification capacity and/or their high mobility in the environment. Moreover, their hazardous effects are manifested even at trace levels, thus requiring very selective and sensitive analytical methodologies to face their detection in environmental samples. In this sense, atmospheric pressure ionization (API) sources such as atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI) for both liquid-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) could offer great advantages to overcome the limitations observed in the determination of these group of substances. In the present Thesis, the feasibility of these ionization sources, especially APPI, has been evaluated to develop sensitive and selective LC-API-MS and GC-API-MS methodologies to monitor relevant halogenated contaminants in environmental samples. API sources have been thoroughly tested to achieve an efficient ionization of neutral per- and polyfluoroalkyl substances (nPFAS). The ionization behavior of these compounds was assessed through optimization of the mobile phase composition, the addition of additives or dopants as well as critical ion source working parameters. These studies have led to highly selective and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-high- resolution mass spectrometry (GC-HRMS) methodologies (up to fg injected on column). Furthermore, an efficient solid-phase extraction and a fast and in-situ preconcentration headspace solid-phase microextraction procedures have been developed to analyze nPFAS in river water samples avoiding analyte losses observed during evaporation steps. Additionally, the fragmentation pathways of ions generated for these compounds in API sources has been tentatively proposed using the combined information of in-source fragmentation, tandem mass spectrometry and high-resolution mass spectrometry. These fragmentation pathways aim to provide useful information for the development of target, suspect and non-targeted analysis strategies for the identification of known and new families of nPFAS in complex environmental samples. Furthermore, in this Thesis, the novel GC-APPI-HRMS (Orbitrap) system is proposed to face the main limitations that have been observed in the currently used analytical determinations of relevant chlorinated contaminants such as dechlorane plus (DP) and analogs, polychlorinated naphthalenes (PCNs), polychlorinated dibenzo-ρ-dioxins and dibenzofurans (PCDD/Fs), dioxin-like polychlorinated biphenyls (dl- PCBs), and short-chain chlorinated paraffins (SCCPs). The soft ionization of GC-APPI has been used to promote molecular or quasi-molecular ions as well as characteristic cluster ions such as [M‒Cl+O]‒, allowing the development of sensitive and selective methods. The use of dopant vapors has been thoroughly investigated to detect the critical parameters that allow maximizing the ionization efficiency of the analytes. Additionally, anion-attachment ionization strategies have been also studied to reduce the in-source fragmentation and to improve sensitivity and selectivity for SCCPs. Furthermore, multidimensional separation strategies (using novel stationary phases and/or ion mobility separation) have been also evaluated to improve the separation of those compounds that often coelute (PCNs or SCCPs).The GC- APPI-HRMS methods developed in this Thesis have shown a great detection capability (up to low fg injected on column) and a high selectivity due to both the exact mass measurements (Orbitrap) and the soft ionization provided by the GC-APPI source. Moreover, they have demonstrated a good performance to determine these compounds in marine sediments, fly ashes, gull eggs, or fishes among other complex environmental samples.[spa] La incesante emisión de contaminantes supone una amenaza tanto para el medio ambiente como para los seres vivos. Entre los contaminantes que han despertado un mayor interés ambiental destacan los compuestos orgánicos halogenados debido a su alta toxicidad a bajas concentraciones, persistencia, capacidad de bioacumulación y para ser transportados a largas distancias. La monitorización de estos contaminantes a bajos niveles de concentración en muestras ambientales requiere de metodologías analíticas selectivas y con una alta capacidad de detección. Así, en esta Tesis se ha evaluado la capacidad de las fuentes de ionización química (APCI) y la fotoionización a presión atmosférica (APPI) para ionizar eficientemente estas familias de contaminantes y así proponer métodos basados en cromatografía de líquidos y de gases acopladas a la espectrometría de masas en tándem y/o de alta resolución (LC-MS/MS y GC-HRMS). Los estudios desarrollados durante esta Tesis han permitido establecer condiciones de trabajo que reducen la fragmentación en la fuente y aumentan la respuesta de los iones generados. Para ello, se han evaluado la composición de la fase móvil, la adición de aditivos que influyen en la ionización de los analitos y diversas condiciones de trabajo, permitiendo establecer tendencias en la ionización de estos contaminantes, haciendo especial énfasis en la novedosa fuente de ionización del sistema GC-APPI-HRMS. Además, se ha estudiado la fragmentación MS/MS de estos compuestos, establecido rutas de fragmentación e identificado/caracterizado los iones observados. Estos estudios han permitido desarrollar métodos que ofrecen una solución a algunas de las limitaciones observadas en las metodologías existentes, mejorando la selectividad y la capacidad de detección. Además, los estudios de ionización y el uso de la MS/MS y de la HRMS han permitido desarrollar estrategias de análisis no dirigido para facilitar la identificación en muestras complejas de compuestos similares a los estudiados en esta Tesis

New Analytical Methodologies based on Chromatography-Atmospheric Pressure Ionization-Mass Spectrometry for the Determination of Halogenated Organic Contaminants

[eng] The environment sustainability is being threatened by the continuous release of pollutants that can negatively affect not only environmental compartments but also wildlife and human beings. Among these pollutants, halogenated persistent organic pollutants and new emerging contaminants have cause great concern due to their toxicity, persistence, bioaccumulation and biomagnification capacity and/or their high mobility in the environment. Moreover, their hazardous effects are manifested even at trace levels, thus requiring very selective and sensitive analytical methodologies to face their detection in environmental samples. In this sense, atmospheric pressure ionization (API) sources such as atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI) for both liquid-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) could offer great advantages to overcome the limitations observed in the determination of these group of substances. In the present Thesis, the feasibility of these ionization sources, especially APPI, has been evaluated to develop sensitive and selective LC-API-MS and GC-API-MS methodologies to monitor relevant halogenated contaminants in environmental samples. API sources have been thoroughly tested to achieve an efficient ionization of neutral per- and polyfluoroalkyl substances (nPFAS). The ionization behavior of these compounds was assessed through optimization of the mobile phase composition, the addition of additives or dopants as well as critical ion source working parameters. These studies have led to highly selective and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-high- resolution mass spectrometry (GC-HRMS) methodologies (up to fg injected on column). Furthermore, an efficient solid-phase extraction and a fast and in-situ preconcentration headspace solid-phase microextraction procedures have been developed to analyze nPFAS in river water samples avoiding analyte losses observed during evaporation steps. Additionally, the fragmentation pathways of ions generated for these compounds in API sources has been tentatively proposed using the combined information of in-source fragmentation, tandem mass spectrometry and high-resolution mass spectrometry. These fragmentation pathways aim to provide useful information for the development of target, suspect and non-targeted analysis strategies for the identification of known and new families of nPFAS in complex environmental samples. Furthermore, in this Thesis, the novel GC-APPI-HRMS (Orbitrap) system is proposed to face the main limitations that have been observed in the currently used analytical determinations of relevant chlorinated contaminants such as dechlorane plus (DP) and analogs, polychlorinated naphthalenes (PCNs), polychlorinated dibenzo-ρ-dioxins and dibenzofurans (PCDD/Fs), dioxin-like polychlorinated biphenyls (dl- PCBs), and short-chain chlorinated paraffins (SCCPs). The soft ionization of GC-APPI has been used to promote molecular or quasi-molecular ions as well as characteristic cluster ions such as [M‒Cl+O]‒, allowing the development of sensitive and selective methods. The use of dopant vapors has been thoroughly investigated to detect the critical parameters that allow maximizing the ionization efficiency of the analytes. Additionally, anion-attachment ionization strategies have been also studied to reduce the in-source fragmentation and to improve sensitivity and selectivity for SCCPs. Furthermore, multidimensional separation strategies (using novel stationary phases and/or ion mobility separation) have been also evaluated to improve the separation of those compounds that often coelute (PCNs or SCCPs).The GC- APPI-HRMS methods developed in this Thesis have shown a great detection capability (up to low fg injected on column) and a high selectivity due to both the exact mass measurements (Orbitrap) and the soft ionization provided by the GC-APPI source. Moreover, they have demonstrated a good performance to determine these compounds in marine sediments, fly ashes, gull eggs, or fishes among other complex environmental samples.[spa] La incesante emisión de contaminantes supone una amenaza tanto para el medio ambiente como para los seres vivos. Entre los contaminantes que han despertado un mayor interés ambiental destacan los compuestos orgánicos halogenados debido a su alta toxicidad a bajas concentraciones, persistencia, capacidad de bioacumulación y para ser transportados a largas distancias. La monitorización de estos contaminantes a bajos niveles de concentración en muestras ambientales requiere de metodologías analíticas selectivas y con una alta capacidad de detección. Así, en esta Tesis se ha evaluado la capacidad de las fuentes de ionización química (APCI) y la fotoionización a presión atmosférica (APPI) para ionizar eficientemente estas familias de contaminantes y así proponer métodos basados en cromatografía de líquidos y de gases acopladas a la espectrometría de masas en tándem y/o de alta resolución (LC-MS/MS y GC-HRMS). Los estudios desarrollados durante esta Tesis han permitido establecer condiciones de trabajo que reducen la fragmentación en la fuente y aumentan la respuesta de los iones generados. Para ello, se han evaluado la composición de la fase móvil, la adición de aditivos que influyen en la ionización de los analitos y diversas condiciones de trabajo, permitiendo establecer tendencias en la ionización de estos contaminantes, haciendo especial énfasis en la novedosa fuente de ionización del sistema GC-APPI-HRMS. Además, se ha estudiado la fragmentación MS/MS de estos compuestos, establecido rutas de fragmentación e identificado/caracterizado los iones observados. Estos estudios han permitido desarrollar métodos que ofrecen una solución a algunas de las limitaciones observadas en las metodologías existentes, mejorando la selectividad y la capacidad de detección. Además, los estudios de ionización y el uso de la MS/MS y de la HRMS han permitido desarrollar estrategias de análisis no dirigido para facilitar la identificación en muestras complejas de compuestos similares a los estudiados en esta Tesis