Pharmacokinetic properties and in silico ADME modeling in drug discovery

The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME - absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.

Mass spectrometry-based protein profiling strategies for biomarker discovery in liver and inflammatory bowel diseases

The study of protein expression profiles for biomarker discovery in serum and in mammalian cell populations needs the continuous improvement and combination of proteins/peptides separation techniques, mass spectrometry, statistical and bioinformatic approaches. In this thesis work two different mass spectrometry-based protein profiling strategies have been developed and applied to liver and inflammatory bowel diseases (IBDs) for the discovery of new biomarkers. The first of them, based on bulk solid-phase extraction combined with matrix-assisted laser desorption/ionization - Time of Flight mass spectrometry (MALDI-TOF MS) and chemometric analysis of serum samples, was applied to the study of serum protein expression profiles both in IBDs (Crohn’s disease and ulcerative colitis) and in liver diseases (cirrhosis, hepatocellular carcinoma, viral hepatitis). The approach allowed the enrichment of serum proteins/peptides due to the high interaction surface between analytes and solid phase and the high recovery due to the elution step performed directly on the MALDI-target plate. Furthermore the use of chemometric algorithm for the selection of the variables with higher discriminant power permitted to evaluate patterns of 20-30 proteins involved in the differentiation and classification of serum samples from healthy donors and diseased patients. These proteins profiles permit to discriminate among the pathologies with an optimum classification and prediction abilities. In particular in the study of inflammatory bowel diseases, after the analysis using C18 of 129 serum samples from healthy donors and Crohn’s disease, ulcerative colitis and inflammatory controls patients, a 90.7% of classification ability and a 72.9% prediction ability were obtained. In the study of liver diseases (hepatocellular carcinoma, viral hepatitis and cirrhosis) a 80.6% of prediction ability was achieved using IDA-Cu(II) as extraction procedure. The identification of the selected proteins by MALDITOF/ TOF MS analysis or by their selective enrichment followed by enzymatic digestion and MS/MS analysis may give useful information in order to identify new biomarkers involved in the diseases. The second mass spectrometry-based protein profiling strategy developed was based on a label-free liquid chromatography electrospray ionization quadrupole - time of flight differential analysis approach (LC ESI-QTOF MS), combined with targeted MS/MS analysis of only identified differences. The strategy was used for biomarker discovery in IBDs, and in particular of Crohn’s disease. The enriched serum peptidome and the subcellular fractions of intestinal epithelial cells (IECs) from healthy donors and Crohn’s disease patients were analysed. The combining of the low molecular weight serum proteins enrichment step and the LCMS approach allowed to evaluate a pattern of peptides derived from specific exoprotease activity in the coagulation and complement activation pathways. Among these peptides, particularly interesting was the discovery of clusters of peptides from fibrinopeptide A, Apolipoprotein E and A4, and complement C3 and C4. Further studies need to be performed to evaluate the specificity of these clusters and validate the results, in order to develop a rapid serum diagnostic test. The analysis by label-free LC ESI-QTOF MS differential analysis of the subcellular fractions of IECs from Crohn’s disease patients and healthy donors permitted to find many proteins that could be involved in the inflammation process. Among them heat shock protein 70, tryptase alpha-1 precursor and proteins whose upregulation can be explained by the increased activity of IECs in Crohn’s disease were identified. Follow-up studies for the validation of the results and the in-depth investigation of the inflammation pathways involved in the disease will be performed. Both the developed mass spectrometry-based protein profiling strategies have been proved to be useful tools for the discovery of disease biomarkers that need to be validated in further studies.

Una base dati per il knowledge discovery in genetica medica

L'innovazione delle tecnologie di sequenziamento negli ultimi anni ha reso possibile la catalogazione delle varianti genetiche nei campioni umani, portando nuove scoperte e comprensioni nella ricerca medica, farmaceutica, dell'evoluzione e negli studi sulla popolazione. La quantità di sequenze prodotta è molto cospicua, e per giungere all'identificazione delle varianti sono necessari diversi stadi di elaborazione delle informazioni genetiche in cui, ad ogni passo, vengono generate ulteriori informazioni. Insieme a questa immensa accumulazione di dati, è nata la necessità da parte della comunità scientifica di organizzare i dati in repository, dapprima solo per condividere i risultati delle ricerche, poi per permettere studi statistici direttamente sui dati genetici. Gli studi su larga scala coinvolgono quantità di dati nell'ordine dei petabyte, il cui mantenimento continua a rappresentare una sfida per le infrastrutture. Per la varietà e la quantità di dati prodotti, i database giocano un ruolo di primaria importanza in questa sfida. Modelli e organizzazione dei dati in questo campo possono fare la differenza non soltanto per la scalabilità, ma anche e soprattutto per la predisposizione al data mining. Infatti, la memorizzazione di questi dati in file con formati quasi-standard, la dimensione di questi file, e i requisiti computazionali richiesti, rendono difficile la scrittura di software di analisi efficienti e scoraggiano studi su larga scala e su dati eterogenei. Prima di progettare il database si è perciò studiata l’evoluzione, negli ultimi vent’anni, dei formati quasi-standard per i flat file biologici, contenenti metadati eterogenei e sequenze nucleotidiche vere e proprie, con record privi di relazioni strutturali. Recentemente questa evoluzione è culminata nell’utilizzo dello standard XML, ma i flat file delimitati continuano a essere gli standard più supportati da tools e piattaforme online. È seguita poi un’analisi dell’organizzazione interna dei dati per i database biologici pubblici. Queste basi di dati contengono geni, varianti genetiche, strutture proteiche, ontologie fenotipiche, relazioni tra malattie e geni, relazioni tra farmaci e geni. Tra i database pubblici studiati rientrano OMIM, Entrez, KEGG, UniProt, GO. L'obiettivo principale nello studio e nella modellazione del database genetico è stato quello di strutturare i dati in modo da integrare insieme i dati eterogenei prodotti e rendere computazionalmente possibili i processi di data mining. La scelta di tecnologia Hadoop/MapReduce risulta in questo caso particolarmente incisiva, per la scalabilità garantita e per l’efficienza nelle analisi statistiche più complesse e parallele, come quelle riguardanti le varianti alleliche multi-locus.

Development of new tools and devices for CMB and foreground data analysis and future experiments

The discovery of the Cosmic Microwave Background (CMB) radiation in 1965 is one of the fundamental milestones supporting the Big Bang theory. The CMB is one of the most important source of information in cosmology. The excellent accuracy of the recent CMB data of WMAP and Planck satellites confirmed the validity of the standard cosmological model and set a new challenge for the data analysis processes and their interpretation. In this thesis we deal with several aspects and useful tools of the data analysis. We focus on their optimization in order to have a complete exploitation of the Planck data and contribute to the final published results. The issues investigated are: the change of coordinates of CMB maps using the HEALPix package, the problem of the aliasing effect in the generation of low resolution maps, the comparison of the Angular Power Spectrum (APS) extraction performances of the optimal QML method, implemented in the code called BolPol, and the pseudo-Cl method, implemented in Cromaster. The QML method has been then applied to the Planck data at large angular scales to extract the CMB APS. The same method has been applied also to analyze the TT parity and the Low Variance anomalies in the Planck maps, showing a consistent deviation from the standard cosmological model, the possible origins for this results have been discussed. The Cromaster code instead has been applied to the 408 MHz and 1.42 GHz surveys focusing on the analysis of the APS of selected regions of the synchrotron emission. The new generation of CMB experiments will be dedicated to polarization measurements, for which are necessary high accuracy devices for separating the polarizations. Here a new technology, called Photonic Crystals, is exploited to develop a new polarization splitter device and its performances are compared to the devices used nowadays.

High precision tools for slepton pair production processes at hadron colliders

In this thesis, we develop high precision tools for the simulation of slepton pair production processes at hadron colliders and apply them to phenomenological studies at the LHC. Our approach is based on the POWHEG method for the matching of next-to-leading order results in perturbation theory to parton showers. We calculate matrix elements for slepton pair production and for the production of a slepton pair in association with a jet perturbatively at next-to-leading order in supersymmetric quantum chromodynamics. Both processes are subsequently implemented in the POWHEG BOX, a publicly available software tool that contains general parts of the POWHEG matching scheme. We investigate phenomenological consequences of our calculations in several setups that respect experimental exclusion limits for supersymmetric particles and provide precise predictions for slepton signatures at the LHC. The inclusion of QCD emissions in the partonic matrix elements allows for an accurate description of hard jets. Interfacing our codes to the multi-purpose Monte-Carlo event generator PYTHIA, we simulate parton showers and slepton decays in fully exclusive events. Advanced kinematical variables and specific search strategies are examined as means for slepton discovery in experimentally challenging setups.

New molecular detection tools adapted to emerging rhinoviruses and enteroviruses

Human rhinoviruses (HRV), and to a lesser extent human enteroviruses (HEV), are important respiratory pathogens. Like other RNA viruses, these picornaviruses have an intrinsic propensity to variability. This results in a large number of different serotypes as well as the incessant discovery of new genotypes. This large and growing diversity not only complicates the design of real-time PCR assays but also renders immunofluorescence unfeasible for broad HRV and HEV detection or quantification in cells. In this study, we used the 5' untranslated region, the most conserved part of the genome, as a target for the development of both a real-time PCR assay (Panenterhino/Ge/08) and a peptide nucleic acid-based hybridization oligoprobe (Panenterhino/Ge/08 PNA probe) designed to detect all HRV and HEV species members according to publicly available sequences. The reverse transcription-PCR assay has been validated, using not only plasmid and viral stocks but also quantified RNA transcripts and around 1,000 clinical specimens. These new generic detection PCR assays overcame the variability of circulating strains and lowered the risk of missing emerging and divergent HRV and HEV. An additional real-time PCR assay (Entero/Ge/08) was also designed specifically to provide sensitive and targeted detection of HEV in cerebrospinal fluid. In addition to the generic probe, we developed specific probes for the detection of HRV-A and HRV-B in cells. This investigation provides a comprehensive toolbox for accurate molecular identification of the different HEV and HRV circulating in humans.

Discovery of "isolated" co-moving T Tauri stars in Cepheus

Context. During the course of a large spectroscopic survey of X-ray active late-type stars in the solar neighbourhood, we discovered four lithium-rich stars packed within just a few degrees on the sky. Although located in a sky area rich in CO molecular regions and dark clouds, the Cepheus-Cassiopeia complex, these very young stars are projected several degrees away from clouds in front of an area void of interstellar matter. As such, they are very good "isolated" T Tauri star candidates. Aims. We present optical observations of these stars conducted with 1-2 m class telescopes. We acquired high-resolution optical spectra as well as photometric data allowing us to investigate in detail their nature and physical parameters with the aim of testing the "runaway" and "in-situ" formation scenarios. Their kinematical properties are also analyzed to investigate their possible connection to already known stellar kinematic groups. Methods. We use the cross-correlation technique and other tools developed by us to derive accurate radial and rotational velocities and perform an automatic spectral classification. The spectral subtraction technique is used to infer chromospheric activity level in the Hα line core and clean the spectra of photospheric lines before measuring the equivalent width of the lithium absorption line. Results. Both physical (lithium content, chromospheric, and coronal activities) and kinematical indicators show that all stars are very young, with ages probably in the range 10-30 Myr. In particular, the spectral energy distribution of TYC4496-780-1 displays a strong near-and far-infrared excess, typical of T Tauri stars still surrounded by an accretion disc. They also share the same Galactic motion, proving that they form a homogeneous moving group of stars with the same origin. Conclusions. The most plausible explanation of how these "isolated" T Tauri stars formed is the "in-situ" model, although accurate distances are needed to clarify their connection with the Cepheus-Cassiopeia complex. The discovery of this loose association of "isolated" T Tauri stars can help to shed light on atypical formation processes of stars and planets in low-mass clouds.

The Effect of Discovery Systems on Online Journal Usage: A Longitudinal Study

Many academic libraries are implementing discovery services as a way of giving their users a single comprehensive search option for all library resources. These tools are designed to change the research experience, yet very few studies have investigated the impact of discovery service implementation. This study examines one aspect of that impact by asking whether usage of publisher-hosted journal content changes after implementation of a discovery tool. Libraries that have begun using the four major discovery services have seen an increase in usage of this content, suggesting that for this particular type of material, discovery services have a positive impact on use. Though all discovery services significantly increased usage relative to a no discovery service control group, some had a greater impact than others, and there was extensive variation in usage change among libraries using the same service. Future phases of this study will look at other types of content.

Conotoxins as research tools and drug leads

The complex mixture of biologically active peptides that constitute the venom of Conus species provides a rich source of ion channel neurotoxins. These peptides, commonly known as conotoxins, exhibit a high degree of selectivity and potency for different ion channels and their subtypes making them invaluable tools for unravelling the secrets of the nervous system. Furthermore, several conotoxin molecules have profound applications in drug discovery, with some examples currently undergoing clinical trials. Despite their relatively easy access by chemical synthesis, rapid access to libraries of conotoxin analogues for use in structure-activity relationship studies still poses a significant limitation. This is exacerbated in conotoxins containing multiple disulfide bonds, which often require synthetic strategies utilising several steps. This review will examine the structure and activity of some of the known classes of conotoxins and will highlight their potential as neuropharmacological tools and as drug leads. Some of the classical and more recent approaches to the chemical synthesis of conotoxins, particularly with respect to the controlled formation of disulfide bonds will be discussed in detail. Finally, some examples of structure-activity relationship studies will be discussed, as well as some novel approaches for designing conotoxin analogues.

Investigation of healthy and infected newborn saliva: the role of proteins and lipids as potential inflammatory diagnostic tools

We have carried out a discovery proteomics investigation aimed at identifying disease biomarkers present in saliva, and, more specifically, early biomarkers of inflammation. The proteomic characterization of saliva is possible due to the straightforward and non-invasive sample collection that allows repetitive analyses for pharmacokinetic studies. These advantages are particularly relevant in the case of newborn patients. The study was carried out with samples collected during the first 48 hours of life of the newborns according to an approved Ethic Committee procedure. In particular, the salivary samples were collected from healthy and infected (n=1) newborns. Proteins were extracted through cycles of sonication, precipitated in ice cold acetone, resuspended and resolved by 2D-electrophoresis. MALDI TOF/TOF mass spectrometry analysis was performed for each spot obtaining the proteins’ identifications. Then we compared healthy newborn salivary proteome and an infected newborn salivary proteome in order to investigate proteins differently expressed in inflammatory condition. In particular the protein alpha-1-antitrypsin (A1AT), correlated with inflammation, was detected differently expressed in the infected newborn saliva. Therefore, in the second part of the project we aimed to develop a robust LC-MS based method that identifies and quantifies this inflammatory protein within saliva that might represent the first relevant step to diagnose a condition of inflammation with a no-invasive assay. The same LC-MS method is also useful to investigate the presence of the F allelic variant of the A1AT in biological samples, which is correlated with the onset of pulmonary diseases. In the last part of the work we analysed newborn saliva samples in order to investigate how phospholipids and mediators of inflammation (eicosanoids) are subject to variations under inflammatory conditions and a trend was observed in lysophosphatidylcholines composition according to the inflammatory conditions.

Trepanation:history, discovery, theory

This volume will look at the history of trepanation, the identification of skulls, the tools used to make the cranial openings, and theories as to why trepanation might have been performed many thousands of years ago.

Visualisation of quality information for geospatial and remote sensing data:providing the GIS community with the decision support tools for geospatial dataset quality evaluation

The evaluation of geospatial data quality and trustworthiness presents a major challenge to geospatial data users when making a dataset selection decision. The research presented here therefore focused on defining and developing a GEO label – a decision support mechanism to assist data users in efficient and effective geospatial dataset selection on the basis of quality, trustworthiness and fitness for use. This thesis thus presents six phases of research and development conducted to: (a) identify the informational aspects upon which users rely when assessing geospatial dataset quality and trustworthiness; (2) elicit initial user views on the GEO label role in supporting dataset comparison and selection; (3) evaluate prototype label visualisations; (4) develop a Web service to support GEO label generation; (5) develop a prototype GEO label-based dataset discovery and intercomparison decision support tool; and (6) evaluate the prototype tool in a controlled human-subject study. The results of the studies revealed, and subsequently confirmed, eight geospatial data informational aspects that were considered important by users when evaluating geospatial dataset quality and trustworthiness, namely: producer information, producer comments, lineage information, compliance with standards, quantitative quality information, user feedback, expert reviews, and citations information. Following an iterative user-centred design (UCD) approach, it was established that the GEO label should visually summarise availability and allow interrogation of these key informational aspects. A Web service was developed to support generation of dynamic GEO label representations and integrated into a number of real-world GIS applications. The service was also utilised in the development of the GEO LINC tool – a GEO label-based dataset discovery and intercomparison decision support tool. The results of the final evaluation study indicated that (a) the GEO label effectively communicates the availability of dataset quality and trustworthiness information and (b) GEO LINC successfully facilitates ‘at a glance’ dataset intercomparison and fitness for purpose-based dataset selection.

Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery

Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common approach and focuses on confidence first by retaining only 100% confidence rules. The classification tasks in the field of cultural heritage usually deal with data sets with many class labels. This variety is caused by the richness of accumulated culture during the centuries. Comparisons of classifier PGN with other classifiers, such as OneR, JRip and J48, show the competitiveness of PGN in recognizing multi-class datasets on collections of masterpieces from different West and East European Fine Art authors and movements.

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^