Chemiluminescence – based bioanalytical devices for astrobiological, food safety and clinical applications

At the intersection of biology, chemistry, and engineering, biosensors are a multidisciplinary innovation that provide a cost-effective alternative to traditional laboratory techniques. Due to their advantages, biosensors are used in medical diagnostics, environmental monitoring, food safety and many other fields. The first part of the thesis is concerned with learning the state of the art of paper-based immunosensors with bioluminescent (BL) and chemiluminescent (CL) detection. The use of biospecific assays combined with CL detection and paper-based technology offers an optimal approach to creating analytical tools for on-site applications and we have focused on the specific areas that need to be considered more in order to ensure a future practical implementation of these methods in routine analyses. The subsequent part of the thesis addresses the development of an autonomous lab-on-chip platform for performing chemiluminescent-based bioassays in space environment, exploiting a CubeSat platform for astrobiological investigations. An origami-inspired microfluidic paper-based analytical device has been developed with the purpose of assesses its performance in space and to evaluate its functionality and the resilience of the (bio)molecules when exposed to a radiation-rich environment. Subsequently, we designed a paper-based assay to detect traces of ovalbumin in food samples, creating a user-friendly immunosensing platform. To this purpose, we developed an origami device that exploits a competitive immunoassay coupled with chemiluminescence detection and magnetic microbeads used to immobilize ovalbumin on paper. Finally, with the aim of exploring the use of biomimetic materials, an hydrogel-based chemiluminescence biosensor for the detection of H2O2 and glucose was developed. A guanosine hydrogel was prepared and loaded with luminol and hemin, miming a DNAzyme activity. Subsequently, the hydrogel was modified by incorporating glucose oxidase enzyme to enable glucose biosensing. The emitted photons were detected using a portable device equipped with a smartphone's CMOS (complementary metal oxide semiconductor) camera for CL emission detection.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.