Progettazione e sintesi di nuovi antagonisti CB1 a struttura pirazolica come potenziali radiofarmaci per la PET imaging

Cannabinoid receptors are members of the large family of G-protein coupled receptors. Two types of cannabinoid receptor have been discovered: CB1 and CB2. CB1 receptors are localised predominantly in the brain whereas CB2 receptors are more abundant in peripheral nervous system cells. CB1 receptors have been related with a number of disorders, including depression, anxiety, stress, schizophrenia, chronic pain and obesity. For this reason, several cannabinoid ligands were developed as drug candidates. Among these ligands, a prominent position is occupied by SR141716 (Rimonabant), which is a pyrazole derivative with inverse agonist activity discovered by Sanofi-Synthelabo in 1994. This compound was marketed in Europe as an anti-obesity drug, but subsequently withdrawn due to its side-effects. Since the relationship between the CB1 receptors’ functional modification, density and distribution, and the beginning of a pathological state is still not well understood, the development of radio-ligands suitable for in vivo PET (Positron Emission Tomography) functional imaging of CB1 receptors remains an important area of research in medicine and drug development. To date, a few radiotracers have been synthesised and tested in vivo, but most of them afforded unsatisfactory brain imaging results. A handful of radiolabelled CB1 PET ligands have also been submitted to clinical trials in humans. In this PhD Thesis the design, synthesis and characterization of three new classes of potential high-affinity CB1 ligands as candidate PET tracers is described.

Reti complesse e analisi del segnale elettroencefalografico

The identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analyzed using a phase synchronization based measure, minimum spanning tree and k-core decomposition. The analysis was performed for each classical brain rhythm separately. Furthermore, we aim to provide a network approach insensitive to the effects that epoch length has on functional connectivity (FC) and network reconstruction. Two different measures, the phase lag index (PLI) and the Amplitude Envelope Correlation (AEC), were applied to EEG resting-state recordings for a group of eighteen healthy volunteers. Weighted clustering coefficient (CCw), weighted characteristic path length (Lw) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Results about distinctive functional core, show highest classification rates from k-core decomposition in gamma (EER=0.130, AUC=0.943) and high beta (EER=0.172, AUC=0.905) frequency bands. Results from scalp analysis concerning the influence of epoch length, show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 seconds for PLI and 6 seconds for AEC. Moreover, CCw and Lw show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 seconds versus 4-8 seconds for AEC). At the source-level the results were even more reliable, with stability already at 1 second duration for PLI-based MSTs. Our results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. Regarding epoch length, the present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.