Strategies to hijack MTs dysfunction in neurodegenerative tauopathies: Design and synthesis of novel CNS-disease-modifying tools

Neuronal microtubules assembly and dynamics are regulated by several proteins including (MT)-associated protein tau, whose aberrant hyperphosphorylation promotes its dissociation from MTs and its abnormal deposition into neurofibrillary tangles, a common neurotoxic hallmarks of neurodegenerative tauopathies. To date, no disease-modifying drugs have been approved to combat CNS tau-related diseases. The multifactorial etiology of these conditions represents one of the major limits in the discovery of effective therapeutic options. In addition, tau protein functions are orchestrated by diverse post-translational modifications among which phosphorylation mediated by PKs plays a leading role. In this context, conventional single-target therapies are often inadequate in restoring perturbed networks and fraught with adverse side-effects. This thesis reports two distinct approaches to hijack MT defects in neurons. The first is focused on the rational design and synthesis of first-in-class triple inhibitors of GSK-3β, FYN, and DYRK1A, three close-related PKs, which act as master regulators of aberrant tau hyperphosphorylation. A merged multi-target pharmacophore strategy was applied to simultaneously modulate all three targets and achieve a disease-modifying effect. Optimization of ARN25068 by a computationally and crystallographic driven SAR exploration, allowed to rationalize the key structural modifications to maintain a balanced potency against all three targets and develop a new generation of quite well-balanced analogs exhibiting improved physicochemical properties, a good in vitro ADME profile, and promising cell-based anti-tau phosphorylation activity. In Part II, MT-stabilizing compounds have been developed to compensate MT defects in tau-related pathologies. Intensive chemical effort has been devoted to scaling up BL-0884, identified as a promising MT-normalizing TPD, which exhibited favorable ADME-PK, including brain penetration, oral bioavailability, and brain pharmacodynamic activity. A suitable functionalization of the exposed hydroxyl moiety of BL-0884 was carried out to generate corresponding esters and amides possessing a wide range of applications as prodrugs and active targeting for cancer chemotherapy.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.