Sintesi di nuovi derivati tri-componente per target photodynamic therapy della neoplasia prostatica

Therapies for the treatment of prostate cancer show several limitations, especially when the cancer metastasizes or acquires resistance to treatment. In addition, most of the therapies currently used entails the occurrence of serious side effects. A different therapeutic approach, more selective and less invasive with respect either to radio or to chemotherapy, is represented by the photodynamic therapy (PDT). The PDT is a treatment that makes use of photosensitive drugs: these agents are pharmacologically inactive until they are irradiated with light at an appropriate wavelength and in the presence of oxygen. The drug, activated by light, forms singlet oxygen, a highly reactive chemical species directly responsible for DNA damage, thus of cell death. In this thesis we present two synthetic strategies for the preparation of two new tri-component derivatives for photodynamic therapy of advanced prostate cancer, namely DRPDT1 and DRPDT2. Both derivatives are formed by three basic elements covalently bounded to each other: a specific ligand with high affinity for the androgen receptor, a suitably chosen spacer molecule and a photoactivated molecule. In particular, DRPDT2 differs from DRPDT1 from the nature of the AR ligand. In fact, in the case of DRPDT2 we used a synthetically engineered androgen receptor ligand able to photo-react even in the absence of oxygen, by delivering NO radical. The presence of this additional pharmacophore, together with the porphyrin, may ensure an additive/synergistic effect to the photo-stimulated therapy, which than may act both in the presence of oxygen and in hypoxic conditions. This approach represents the first example of multimodal photodynamic therapy for prostate cancer.

Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results

The central objective of research in Information Retrieval (IR) is to discover new techniques to retrieve relevant information in order to satisfy an Information Need. The Information Need is satisfied when relevant information can be provided to the user. In IR, relevance is a fundamental concept which has changed over time, from popular to personal, i.e., what was considered relevant before was information for the whole population, but what is considered relevant now is specific information for each user. Hence, there is a need to connect the behavior of the system to the condition of a particular person and his social context; thereby an interdisciplinary sector called Human-Centered Computing was born. For the modern search engine, the information extracted for the individual user is crucial. According to the Personalized Search (PS), two different techniques are necessary to personalize a search: contextualization (interconnected conditions that occur in an activity), and individualization (characteristics that distinguish an individual). This movement of focus to the individual's need undermines the rigid linearity of the classical model overtaken the ``berry picking'' model which explains that the terms change thanks to the informational feedback received from the search activity introducing the concept of evolution of search terms. The development of Information Foraging theory, which observed the correlations between animal foraging and human information foraging, also contributed to this transformation through attempts to optimize the cost-benefit ratio. This thesis arose from the need to satisfy human individuality when searching for information, and it develops a synergistic collaboration between the frontiers of technological innovation and the recent advances in IR. The search method developed exploits what is relevant for the user by changing radically the way in which an Information Need is expressed, because now it is expressed through the generation of the query and its own context. As a matter of fact the method was born under the pretense to improve the quality of search by rewriting the query based on the contexts automatically generated from a local knowledge base. Furthermore, the idea of optimizing each IR system has led to develop it as a middleware of interaction between the user and the IR system. Thereby the system has just two possible actions: rewriting the query, and reordering the result. Equivalent actions to the approach was described from the PS that generally exploits information derived from analysis of user behavior, while the proposed approach exploits knowledge provided by the user. The thesis went further to generate a novel method for an assessment procedure, according to the "Cranfield paradigm", in order to evaluate this type of IR systems. The results achieved are interesting considering both the effectiveness achieved and the innovative approach undertaken together with the several applications inspired using a local knowledge base.