Innovative analytical methods for Central Nervous System Drug analysis in biological fluids

During recent years a consistent number of central nervous system (CNS) drugs have been approved and introduced on the market for the treatment of many psychiatric and neurological disorders, including psychosis, depression, Parkinson disease and epilepsy. Despite the great advancements obtained in the treatment of CNS diseases/disorders, partial response to therapy or treatment failure are frequent, at least in part due to poor compliance, but also genetic variability in the metabolism of psychotropic agents or polypharmacy, which may lead to sub-therapeutic or toxic plasma levels of the drugs, and finally inefficacy of the treatment or adverse/toxic effects. With the aim of improving the treatment, reducing toxic/side effects and patient hospitalisation, Therapeutic Drug Monitoring (TDM) is certainly useful, allowing for a personalisation of the therapy. Reliable analytical methods are required to determine the plasma levels of psychotropic drugs, which are often present at low concentrations (tens or hundreds of nanograms per millilitre). The present PhD Thesis has focused on the development of analytical methods for the determination of CNS drugs in biological fluids, including antidepressants (sertraline and duloxetine), antipsychotics (aripiprazole), antiepileptics (vigabatrin and topiramate) and antiparkinsons (pramipexole). Innovative methods based on liquid chromatography or capillary electrophoresis coupled to diode-array or laser-induced fluorescence detectors have been developed, together with the suitable sample pre-treatment for interference removal and fluorescent labelling in case of LIF detection. All methods have been validated according to official guidelines and applied to the analysis of real samples obtained from patients, resulting suitable for the TDM of psychotropic drugs.

Multi-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

Algal wastewater treatment and biomass producing potential: nutrient removal efficiency and cell physiological responses

Microalgae are sun - light cell factories that convert carbon dioxide to biofuels, foods, feeds, and other bioproducts. The concept of microalgae cultivation as an integrated system in wastewater treatment has optimized the potential of the microalgae - based biofuel production. These microorganisms contains lipids, polysaccharides, proteins, pigments and other cell compounds, and their biomass can provide different kinds of biofuels such as biodiesel, biomethane and ethanol. The algal biomass application strongly depends on the cell composition and the production of biofuels appears to be economically convenient only in conjunction with wastewater treatment. The aim of this research thesis was to investigate a biological wastewater system on a laboratory scale growing a newly isolated freshwater microalgae, Desmodesmus communis, in effluents generated by a local wastewater reclamation facility in Cesena (Emilia Romagna, Italy) in batch and semi - continuous cultures. This work showed the potential utilization of this microorganism in an algae - based wastewater treatment; Desmodesmus communis had a great capacity to grow in the wastewater, competing with other microorganisms naturally present and adapting to various environmental conditions such as different irradiance levels and nutrient concentrations. The nutrient removal efficiency was characterized at different hydraulic retention times as well as the algal growth rate and biomass composition in terms of proteins, polysaccharides, total lipids and total fatty acids (TFAs) which are considered the substrate for biodiesel production. The biochemical analyses were coupled with the biomass elemental analysis which specified the amount of carbon and nitrogen in the algal biomass. Furthermore photosynthetic investigations were carried out to better correlate the environmental conditions with the physiology responses of the cells and consequently get more information to optimize the growth rate and the increase of TFAs and C/N ratio, cellular compounds and biomass parameter which are fundamental in the biomass energy recovery.