Computational and chemical approaches to drug repurposing

Drug repurposing, which entails discovering novel therapeutic applications for already existing drugs, provides numerous benefits compared to conventional drug discovery methods. This strategy can be pursued through two primary approaches: computational and chemical. Computational methods involve the utilization of data mining and bioinformatics techniques to identify potential drug candidates, while chemical approaches involve experimental screens oriented to finding new potential treatments based on existing drugs. Both computational and chemical methods have proven successful in uncovering novel therapeutic uses for established drugs. During my PhD, I participated in several experimental drug repurposing screens based on high-throughput phenotypic approaches. Finally, attracted by the potential of computational drug repurposing pipelines, I decided to contribute and generate a web platform focused on the use of transcriptional signatures to identify potential new treatments for human disease. A summary of these studies follows: In Study I, we utilized the tetracycline repressor (tetR)-regulated mechanism to create a human osteosarcoma cell line (U2OS) with the ability to express TAR DNA-binding protein 43 (TDP-43) upon induction. TDP-43 is a protein known for its association with several neurodegenerative diseases. We implemented a chemical screening with this system as part of our efforts to repurpose approved drugs. While the screening was unsuccessful to identify modulators of TDP-43 toxicity, it revealed compounds capable of inhibiting the doxycyclinedependent TDP-43 expression. Furthermore, a complementary CRISPR/Cas9 screening using the same cell system identified additional regulators of doxycycline-dependent TDP43 expression. This investigation identifies new chemical and genetic modulators of the tetR system and highlights potential limitations of using this system for chemical or genetic screenings in mammalian cells. In Study II, our objective was to reposition compounds that could potentially reduce the toxic effects of a fragment of the Huntingtin (HTT) protein containing a 94 amino acid long glutamine stretch (Htt-Q94), a feature of Huntington's disease (HD). To achieve this, we carried out a high-throughput chemical screening using a varied collection of 1,214 drugs, largely sourced from a drug repurposing library. Through our screening process, we singled out clofazimine, an FDA-approved anti-leprosy drug, as a potential therapeutic candidate. Its effectiveness was validated across several in vitro models as well as a zebrafish model of polyglutamine (polyQ) toxicity. Employing a combination of computational analysis of transcriptional signatures, molecular modeling, and biochemical assays, we deduced that clofazimine is an agonist for the peroxisome proliferator-activated receptor gamma (PPARγ), a receptor previously suggested to be a viable therapeutic target for HD due to its role in promoting mitochondrial biogenesis. Notably, clofazimine was successful in alleviating the mitochondrial dysfunction triggered by the expression of Htt-Q94. These findings lend substantial support to the potential of clofazimine as a viable candidate for drug repurposing in the treatment of polyQ diseases. In Study III, we explored the molecular mechanism of a previously identified repurposing example, the use of diethyldithiocarbamate-copper complex (CuET), a disulfiram metabolite, for cancer treatment. We found CuET effectively inhibits cancer cell growth by targeting the NPL4 adapter of the p97VCP segregase, leading to translational arrest and stress in tumor cells. CuET also activates ribosomal biogenesis and autophagy in cancer cells, and its cytotoxicity can be enhanced by inhibiting these pathways. Thus, CuET shows promise as a cancer treatment, especially in combination therapies. In Study IV, we capitalized on the Molecular Signatures Database (MSigDB), one of the largest signature repositories, and drug transcriptomic profiles from the Connectivity Map (CMap) to construct a comprehensive and interactive drug-repurposing database called the Drug Repurposing Encyclopedia (DRE). Housing over 39.7 million pre-computed drugsignature associations across 20 species, the DRE allows users to conduct real-time drugrepurposing analysis. This can involve comparing user-supplied gene signatures with existing ones in the DRE, carrying out drug-gene set enrichment analyses (drug-GSEA) using submitted drug transcriptomic profiles, or conducting similarity analyses across all database signatures using user-provided gene sets. Overall, the DRE is an exhaustive database aimed at promoting drug repurposing based on transcriptional signatures, offering deep-dive comparisons across molecular signatures and species. Drug repurposing presents a valuable strategy for discovering fresh therapeutic applications for existing drugs, offering numerous benefits compared to conventional drug discovery methods. The studies conducted in this thesis underscore the potential of drug repurposing and highlight the complementary roles of computational and chemical approaches. These studies enhance our understanding of the mechanistic properties of repurposed drugs, such as clofazimine and disulfiram, and reveal novel mechanisms for targeting specific disease pathways. Additionally, the development of the DRE platform provides a comprehensive tool to support researchers in conducting drug-repositioning analyses, further facilitating the advancement of drug repurposing studies

A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

System biology modeling : the insights for computational drug discovery

Indiana University-Purdue University Indianapolis (IUPUI)Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Role of network topology based methods in discovering novel gene-phenotype associations

The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer.La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer

Optimization, random resampling, and modeling in bioinformatics

Quantitative phenotypes regulated by multiple genes are prevalent in nature and many diseases falls into this category. High-throughput sequencing and high-performance computing provides a basis to understand quantitative phenotypes. However, finding a statistical approach correctly model the phenotypes remain a challenging problem. In this work, I present a resampling-based approach to obtain biological functional categories from gene set and apply the approach to analyze lithium-sensitivity of neurological diseases and cancer. Then, the non-parametrical permutation-based approach is applied to evaluate the performance of a GWAS modeling procedure. While the procedure performs well in statistics, search space reduction is required to address the computation challenge

Full genome analysis of microglial activation; ramifications of TREM2

Neuroinflammation is a pathological hallmark of Alzheimer's disease (AD) and it is well established that microglia, the brain's resident phagocytes, are pivotal for the immune response observed in AD. In the healthy brain, microglia attack and remove pathogens and cell debris, but have been shown to become reactive in AD. An apparent link between microglia and AD is Amyloid β (Aβ), which accumulates in the plaques observed in the brains of AD patients and has been reported as a microglia activator. Genome Wide Association Studies (GWAS) have allowed the identification of more than 20 genetic risk associations to AD. Many of these associations highlight the importance of immune pathways (and others) in AD. More recently, the identification of mutations in TREM2 (Triggering Receptor Expressed on Myeloid Cells 2), a gene exclusively expressed by microglia in the brain, has brought microglial activation and dysfunction back to the attention of the AD community. The main focus of this study is to understand microglial activation elicited by different stimuli including Aβ1-42 monomers, oligomers and fibrils- with regards to their inflammatory activation status (M1, M2 or other) and whole-genome expression profile. To this end, the mouse-derived BV2 cell line was used to assess gene expression changes during microglial activation. Data shows that M1 and M2 activators alter gene expression of AD-associated genes in a manner that is potentially detrimental for AD progression. A second objective of this thesis was to use the CRISPR/Cas9 gene editing technology for the generation of Trem2-deficient BV2 cell lines. As a result, Trem2 +/- (haploinsufficient) and Trem2 -/- (knockout) BV2 cell lines were generated. Subsequently, these cell lines were characterised in terms of their phagocytic, proliferation, migration, cytokine release capacities and whole genome expression. In consequence, this study provides new and wellcharacterised in vitro models for the study of Trem2 function

The evaluation and harmonisation of disparate information metamodels in support of epidemiological and public health research

BACKGROUND: Descriptions of data, metadata, provide researchers with the contextual information they need to achieve research goals. Metadata enable data discovery, sharing and reuse, and are fundamental to managing data across the research data lifecycle. However, challenges associated with data discoverability negatively impact on the extent to which these data are known by the wider research community. This, when combined with a lack of quality assessment frameworks and limited awareness of the implications associated with poor quality metadata, are hampering the way in which epidemiological and public health research data are documented and repurposed. Furthermore, the absence of enduring metadata management models to capture consent for record linkage metadata in longitudinal studies can hinder researchers from establishing standardised descriptions of consent. AIM: To examine how metadata management models can be applied to ameliorate the use of research data within the context of epidemiological and public health research. METHODS: A combination of systematic literature reviews, online surveys and qualitative data analyses were used to investigate the current state of the art, identify current perceived challenges and inform creation and evaluation of the models. RESULTS: There are three components to this thesis: a) enhancing data discoverability; b) improving metadata quality assessment; and c) improving the capture of consent for record linkage metadata. First, three models were examined to enhance research data discoverability: data publications, linked data on the World Wide Web and development of an online public health portal. Second, a novel framework to assess epidemiological and public health metadata quality framework was created and evaluated. Third, a novel metadata management model to improve capture of consent for record linkage metadata was created and evaluated. CONCLUSIONS: Findings from these studies have contributed to a set of recommendations for change in research data management policy and practice to enhance stakeholders’ research environment

Intensive Labours, Expansive Visions: Emerging ideals of the ethical subject amidst the rise of cognitive neuroscience

This thesis seeks to trace the escalating shift from mind to brain and resulting changes in understandings of care for the self, emergent in part through growing influence of neuroethics and related calls for ‘neuro-enhancement’ of the ethical subject. This study – propelled largely through a critical discourse analysis of recent disciplinary output and public engagement – is particularly interested in observing the increasing confidence of neuroscience-informed perspectives on humanity, with announcements that we are witnessing a so-called ‘Second Enlightenment’. Such calls for a new ontology of ethics, I argue, amounts to overly ‘expansive’ claims funnelled through increasingly ‘intensive’ gazes. Within the rise of neuroscience more broadly, empirical neuroethics proclaims its epistemic privilege with respect to tracing our moral selfhood, in part through its location of measures of the ethical subject within functionally ascribed activity traced at the neurological level. Once elusive properties of conduct and wellbeing are now sought to be registered in the common currency of this synaptic ledger, exclusively overseen by specialists in this new field of expertise. The thesis then explores the subsequent adoption of this new empirical currency by those practicing a ‘hard’ transhumanism. Advocates of this position urge us to embrace methods of cognitive and moral ‘enhancement,’ lest we find ourselves unfit for the future in a world of ever-escalating risk. However, I argue that dominant framings of care of the self within neuroethics tend to be narrowly construed. I suggest that by failing to recognise the socio-historical contingencies of their claims, neuroethicists risk producing rigid, stultifying, and perhaps even self-defeating constructs of the ideal citizen. The personal ethos advanced by these new technologies of the self creates new forms of personal responsibility, which, consistent with neoliberal ideals of progress, involves a perpetual labour upon one's brain as a mode of accumulation strategy. This threatens to become a cruel labour that ultimately jars with our eventual and inevitable neurodegeneration. In response to this emerging ethos, I attempt to go beyond the constraints of a merely critical discourse to enable a more productive, if cautious, engagement with the claims of the new, applied neuro-disciplines. I consider what kind of differently expansive framing of subjectivity might be better suited to the present, compared with the ‘hyper-cognitive’ subject of certain ‘hard’ neuroscientific and neuroethical discourses. Contributing to the growing interest in the social sciences in the broad movement of ‘neurodiversity’, I turn to fictional accounts of dementia to see what might be learned from these literary sources. I argue that these literary explorations of subjectivity open up novel ways of reconceiving our relation to our neurology, and thus may play an important role in reimagining the self in a manner adequate to the complexity, urgency, and promise of our times. Though grounded primarily within the field of the sociology of science and technology, this thesis also draws extensively on related thought in poststructuralist critical and literary theory, while also maintaining an accessibility acutely attuned to the growing importance of interdisciplinary exchange