Semi-synthetic bile acids as novel drug candidate in liver diseases: physico-chemical characterization and HPLC-ES-MS/MS methods for their quali-quantitative analysis in different experimental animal models

The physico-chemical characterization, structure-pharmacokinetic and metabolism studies of new semi synthetic analogues of natural bile acids (BAs) drug candidates have been performed. Recent studies discovered a role of BAs as agonists of FXR and TGR5 receptor, thus opening new therapeutic target for the treatment of liver diseases or metabolic disorders. Up to twenty new semisynthetic analogues have been synthesized and studied in order to find promising novel drugs candidates. In order to define the BAs structure-activity relationship, their main physico-chemical properties (solubility, detergency, lipophilicity and affinity with serum albumin) have been measured with validated analytical methodologies. Their metabolism and biodistribution has been studied in “bile fistula rat”, model where each BA is acutely administered through duodenal and femoral infusion and bile collected at different time interval allowing to define the relationship between structure and intestinal absorption and hepatic uptake ,metabolism and systemic spill-over. One of the studied analogues, 6α-ethyl-3α7α-dihydroxy-5β-cholanic acid, analogue of CDCA (INT 747, Obeticholic Acid (OCA)), recently under approval for the treatment of cholestatic liver diseases, requires additional studies to ensure its safety and lack of toxicity when administered to patients with a strong liver impairment. For this purpose, CCl4 inhalation to rat causing hepatic decompensation (cirrhosis) animal model has been developed and used to define the difference of OCA biodistribution in respect to control animals trying to define whether peripheral tissues might be also exposed as a result of toxic plasma levels of OCA, evaluating also the endogenous BAs biodistribution. An accurate and sensitive HPLC-ES-MS/MS method is developed to identify and quantify all BAs in biological matrices (bile, plasma, urine, liver, kidney, intestinal content and tissue) for which a sample pretreatment have been optimized.

Simulation of Smart Materials

The aim of this thesis is the elucidation of structure-properties relationship of molecular semiconductors for electronic devices. This involves the use of a comprehensive set of simulation techniques, ranging from quantum-mechanical to numerical stochastic methods, and also the development of ad-hoc computational tools. In more detail, the research activity regarded two main topics: the study of electronic properties and structural behaviour of liquid crystalline (LC) materials based on functionalised oligo(p-phenyleneethynylene) (OPE), and the investigation on the electric field effect associated to OFET operation on pentacene thin film stability. In this dissertation, a novel family of substituted OPE liquid crystals with applications in stimuli-responsive materials is presented. In more detail, simulations can not only provide evidence for the characterization of the liquid crystalline phases of different OPEs, but elucidate the role of charge transfer states in donor-acceptor LCs containing an endohedral metallofullerene moiety. Such systems can be regarded as promising candidates for organic photovoltaics. Furthermore, exciton dynamics simulations are performed as a way to obtain additional information about the degree of order in OPE columnar phases. Finally, ab initio and molecular mechanics simulations are used to investigate the influence of an applied electric field on pentacene reactivity and stability. The reaction path of pentacene thermal dimerization in the presence of an external electric field is investigated; the results can be related to the fatigue effect observed in OFETs, that show significant performance degradation even in the absence of external agents. In addition to this, the effect of the gate voltage on a pentacene monolayer are simulated, and the results are then compared to X-ray diffraction measurements performed for the first time on operating OFETs.

Novel dual D3R/GSK-3β modulators: an innovative multitarget strategy for bipolar disorder

Among the psychiatric diseases, bipolar disorder (BD) is the sixth leading cause of disability with a prevalence up to 4 % worldwide. BD is a complex neuropsychiatric condition which alternates episodes of mania with symptoms of depression. Although the neurobiological pathways are not completely clarified, the dopamine (DA) hypothesis, recognized as the leading theory explaining the pathophysiology of the malady, states that the dramatically compromised homeostatic regulation of dopaminergic circuits leads to alternated changes in DA neurotransmission. Modulation of D2 and D3 receptors (D2/3R) through partial agonists represents the first-line therapeutic strategy for psychiatric diseases. Moreover, a deregulation of the enzyme glycogen synthase kinase-3β (GSK-3β) has been reported as peculiar feature of BD. In this scenario, the concomitant modulation of D3R and GSK-3β, by employing multitarget compounds, could offer promises to achieve an effective cure of this illness. In the light of these findings, we rationally envisaged the pharmacophoric model at the basis of the design of several D3R partial agonists, suitable to be exploited for the dual D3R/GSK-3β ligand design. Thus, synthetic efforts were addressed to develop a first set of hybrid molecules able to concurrently modulate the selected targets. For a chemical structure point of view, we employed different spacers to combine a substituted aryl-piperazine moiety, reported in previously discovered D3R modulators, with a pyrazole-based fragment, already identified in GSK-3β inhibitors. A fluorescent and a cellular functional assays were carried out to assess the activity of all synthetized compounds against GSK-3β and on D3R, respectively. Most of the derivatives proved to effectively modulate both GSK-3β and D3R with potencies in the low-µM and low-nM range, respectively. The consistent biological data allowed us to identify some lead candidates worth to be further modified with the aim to optimize their biological profile and to perform a structure-activity relationship (SAR) study.

Catalytic upgrading of carboxylic acids and esters to bio fuels and bio chemicals

The research activity was focused on the transformation of methyl propionate (MP) into methyl methacrylate (MMA), avoiding the use of formaldehyde (FAL) thanks to a one-pot strategy involving in situ methanol (MeOH) dehydrogenation over the same catalytic bed were the hydroxy-methylation/dehydration of MP with FAL occurs. The relevance of such research line is related to the availability of cheap renewable bio-glycerol from biodiesel production, from which MP can be obtained via a series of simple catalytic reactions. Moreover, the conventional MMA synthesis (Lucite process) suffers from safety issues related to the direct use of carcinogenic FAL and depends on non-renewable MP. During preliminary studies, ketonization of carboxylic acids and esters has been recognized as a detrimental reaction which hinders the selective synthesis of MMA at low temperature, together with H-transfer hydrogenation with FAL or MeOH as the H-donor at higher temperatures. Therefore, ketonization of propionic acid (PA) and MP was investigated over several catalysts (metal oxides and metal phosphates), to obtain a better understanding of the structure-activity relationship governing the reaction and to design a catalyst for MMA synthesis capable to promote the desired reaction while minimizing ketonization and H-transfer. However, ketonization possesses scientific and industrial value itself and represents a strategy for the upgrade of bio oils from fast pyrolysis of lignocellulosic materials, a robust and versatile technology capable to transform the most abundant biomass into liquid biofuels. The catalysts screening showed that ZrO2 and La2O3 are the best catalysts, while MgO possesses low ketonization activity, but still, H-transfer parasitic hydrogenation of MMA reduces its yield over all catalysts. Such study resulted in the design of Mg/Ga mixed oxides that showed enhanced dehydrogenating activity towards MeOH at low temperatures. It was found that the introduction of Ga not only minimize ketonization, but also modulates catalyst basicity reducing H-transfer hydrogenations.

Design and characterization of polymorphs and co-crystals of organic molecular semiconductors

In the last decades, organic semiconductors have attracted attention due to their possible employment in solution-processed optoelectronic and electronic devices. One of the advantages of solution processing is the possibility to process into flexible substrates at low cost. Organic molecular materials tend to form polymorphs, which can exhibit very different properties. In most cases, the control of the crystal structure is decisive to maximize the performance of the final device. Although organic electronics have progressed a lot, n-type organic semiconductors still lag behind p-type, presenting challenges such as air instability and poor solubility. NDI derivatives are promising candidates for applications in organic electronics due to their characteristics. Recently, the structure-properties relationship and the polymorphism of these molecules have gained attention. In the first part of this thesis, NDI-C6 thermal behavior was extensively explored which revealed two different behaviors depending on the annealing process. This study allowed to define the stability ranking of the NDI-C6 bulk forms and to determine the crystal structure of Form γ at 54°C. Additionally, the polymorphic and thermal behavior of thin films of NDI-C6 was also explored. It was possible to isolate pure Form α, Form β, Form γ and a new metastable Form ε. It was also possible to determine the stability ranking of the phases in thin films. OFETs were fabricated having different polymorphs as active layer, unfortunately the performance was not ideal. During the second part of this thesis, core-chlorinated NDIs with fluoroalkyl chains were studied. Initially, the focus was on the polymorphism of CF3-NDI that revealed a solvate form with a very interesting molecular arrangement suggesting the possibility to form charge transfer co-crystals. In the last part of the thesis, the synthesis and characterization of CT co-crystal with different NDI derivatives, and acceptor and as donor BTBT and ditBu-BTBT were explored.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

New synthetic bile acid analogue agonists of FXR and TGR5 receptors: Analytical methodologies for the study of their physico-chemical properties, pharmacokinetic activity and metabolism.

This thesis reports an integrated analytical approach for the study of physicochemical and biological properties of new synthetic bile acid (BA) analogues agonists of FXR and TGR5 receptors. Structure-activity data were compared with those previous obtained using the same experimental protocols on synthetic and natural occurring BA. The new synthetic BA analogues are classified in different groups according also to their potency as a FXR and TGR5 agonists: unconjugated and steroid modified BA and side chain modified BA including taurine or glycine conjugates and pseudo-conjugates (sulphonate and sulphate analogues). In order to investigate the relationship between structure and activity the synthetic analogues where admitted to a physicochemical characterization and to a preliminary screening for their pharmacokinetic and metabolism using a bile fistula rat model. Sensitive and accurate analytical methods have been developed for the quali-quantitative analysis of BA in biological fluids and sample used for physicochemical studies. Combined High Performance Liquid Chromatography Electrospray tandem mass spectrometry with efficient chromatographic separation of all studied BA and their metabolites have been optimized and validated. Analytical strategies for the identification of the BA and their minor metabolites have been developed. Taurine and glycine conjugates were identified in MS/MS by monitoring the specific ion transitions in multiple reaction monitoring (MRM) mode while all other metabolites (sulphate, glucuronic acid, dehydroxylated, decarboxylated or oxo) were monitored in a selected-ion reaction (SIR) mode with a negative ESI interface by the following ions. Accurate and precise data where achieved regarding the main physicochemical properties including solubility, detergency, lipophilicity and albumin binding . These studies have shown that minor structural modification greatly affect the pharmacokinetics and metabolism of the new analogues in respect to the natural BA and on turn their site of action, particularly where their receptor are located in the enterohepatic circulation.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

The physical city and the urban structure: detecting amenity zones and applying urban morphology to New York

The city is a collection of built structures and infrastructure embedded in socio-cultural processes: any investigation into a city’s transformations involves considerations on the degree to which its composite elements respond to socio-economical changes. The main purpose of this research is to investigate how transformations in the functional requirements of New York’s society have spurred, since the 1970s, changes in both the city’s urban structure and physical form. The present work examines the rise of Amenity Zones in New York, and investigates the transformations that have occurred in New York’s built environment since the 1970s. By applying qualitative measures and analyzing the relationship between urban amenities and the creative class, the present work has investigated changes in the urban structure and detected a hierarchical series of amenity zones classes, namely, Super Amenity Zones (SAZs), Nodal Amenity Zones (NAZs) and Peripheral Amenity Zones (PAZs). This series allows for a more comprehensive reading of the urban structure in a complex city like New York, bringing advancements to the amenity zone’s methodology. In order to examine the manner in which the other component of the city, the physical form, has changed or adapted to the new socio-economic condition, the present research has applied Conzenian analysis to a select study area, Atlantic Avenue. The results of this analysis reveal that, contrary to the urban structure, which changes rapidly, the physical form of New York is hard to modify completely, due to the resilience of the town plan and its elements, and to preservation laws; the city rather adapts to socio-economical changes through process of adaptive reuses or conversion. Concluding, this research has examined the dialectic between the ever-changing needs of society and the complexity of the built environment and urban structure, showing the different degrees to which the urban landscape modifies, reacts and sometimes adapts to the population’s functional requirements.

The foodomics approach to investigate the impact of the matrix structure on food quality: applications of magnetic resonance spectroscopy, relaxometry and imaging

The aim of this thesis is to explore the possible influence of the food matrix on food quality attributes. Using nuclear magnetic resonance techniques, the matrix-dependent properties of different foods were studied and some useful indices were defined to classify food products based on the matrix behaviour when responding to processing phenomena. Correlations were found between fish freshness indices, assessed by certain geometric parameters linked to the morphology of the animal, i.e. a macroscopic structure, and the degradation of the product structure. The same foodomics approach was also applied to explore the protective effect of modified atmospheres on the stability of fish fillets, which are typically susceptible to oxidation of the polyunsaturated fatty acids incorporated in the meat matrix. Here, freshness is assessed by evaluating the time-dependent change in the fish metabolome, providing an established freshness index, and its relationship to lipid oxidation. In vitro digestion studies, focusing on food products with different matrixes, alone and in combination with other meal components (e.g. seasoning), were conducted to investigate possible interactions between enzymes and food, modulated by matrix structure, which influence digestibility. The interaction between water and the gelatinous matrix of the food, consisting of a network of protein gels incorporating fat droplets, was also studied by means of nuclear magnetic relaxometry, in order to create a prediction tool for the correct classification of authentic and counterfeit food products protected by a quality label. This is one of the first applications of an NMR method focusing on the supramolecular structure of the matrix, rather than the chemical composition, to assess food authenticity. The effect of innovative processing technologies, such as PEF applied to fruit products, has been assessed by magnetic resonance imaging, exploiting information associated with the rehydration kinetics exerted by a modified food structure.

A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning

The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns. The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure.

Spectroscopic investigation on photoreactivity, structure and polymorphism of organic molecular crystals

The study of the spectroscopic phenomena in organic solids, in combination with other techniques, is an effective tool for the understanding of the structural properties of materials based on these compounds. This Ph.D. work was dedicated to the spectroscopic investigation of some relevant processes occurring in organic molecular crystals, with the goal of expanding the knowledge on the relationship between structure, dynamics and photoreactivity of these systems. Vibrational spectroscopy has been the technique of choice, always in combination with X-ray diffraction structural studies and often the support of computational methods. The vibrational study of the molecular solid state reaches its full potential when it includes the low-wavenumber region of the lattice-phonon modes, which probe the weak intermolecular interactions and are the fingerprints of the lattice itself. Microscopy is an invaluable addition in the investigation of processes that take place in the micro-meter scale of the crystal micro-domains. In chemical and phase transitions, as well as in polymorph screening and identification, the combination of Raman microscopy and lattice-phonon detection has provided useful information. Research on the fascinating class of single-crystal-to-single-crystal photoreactions, has shown how the homogeneous mechanism of these transformations can be identified by lattice-phonon microscopy, in agreement with the continuous evolution of their XRD patterns. On describing the behavior of the photodimerization mechanism of vitamin K3, the focus was instead on the influence of its polymorphism in governing the product isomerism. Polymorphism is the additional degree of freedom of molecular functional materials, and by advancing in its control and properties, functionalities can be promoted for useful applications. Its investigation focused on thin-film phases, widely employed in organic electronics. The ambiguities in phase identification often emerging by other experimental methods were successfully solved by vibrational measurements.

Cross-population radio galaxies as a test of the jet-accretion relationship

Radio galaxies (RGs) are extremely relevant in addressing important unknowns concerning the interaction among black hole accretion, radio jets, and the environment. In the classical scheme, their accretion rate and ejection of relativistic jets are directly linked: efficient accretion (HERG) is associated with powerful edge-brightened jets (FRIIs); inefficient accretion (LERG) is associated with weak edge-darkened jets (FRIs). The observation of RGs with an inefficient engine associated with edge-brightened radio emission (FRII-LERGs) broke this scheme. FRII-LERGs constitute a suitable population to explore how accretion and ejection are linked and evaluate the environment's role in shaping jets. To this aim, we performed a multiwavelength study of different RGs catalogs spanning from Jy to mJy flux densities. At first, we investigated the X-ray properties of a sample of 51 FRIIs belonging to the 3CR catalog at z<0.3. Two hypotheses were invoked to explain FRII-LERGs behavior: evolution from classical FRIIs; the role of the environment. Next, we explored the mJy sky by studying the optical-radio properties of hundreds of RGs at z<0.15 (Best & Heckman 2012 sample). FRII-LERGs appear more similar to the old FRI-LERGs than to the young FRII-HERGs. These results point towards an evolutive scenario, however, nuclear time scale changes, star population aging, and kpc-Mpc radio structure modification do not agree. The role of the Mpc environment was then investigated. The Wen et al. 2015 galaxy clusters sample, built exploiting the SDSS survey, allowed us to explore the habitat of 7219 RGs at z<0.3. Most RGs are found to live in outside clusters. For these sources, differences among RG classes are still present. Thus, the environment is not the key parameter, and the possibility of intrinsic differences was reconsidered: we speculated that different black hole properties (spin and magnetic field at its horizon) could determine the observed spread in jet luminosity.