Linked open drug data for pharmaceutical research and development

There is an abundance of information about drugs available on the Web. Data sources range from medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in clinical trials. These data are typically not connected together, which reduces the ease with which insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web Consortium's (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly available data about drugs, created Linked Data representations of the data sets, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task force provides recommendations for the best practices of exposing data in a Linked Data representation. In this paper, we present past and ongoing work of LODD and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy

Mining the Medical and Patent Literature to Support Healthcare and Pharmacovigilance

Recent advancements in healthcare practices and the increasing use of information technology in the medical domain has lead to the rapid generation of free-text data in forms of scientific articles, e-health records, patents, and document inventories. This has urged the development of sophisticated information retrieval and information extraction technologies. A fundamental requirement for the automatic processing of biomedical text is the identification of information carrying units such as the concepts or named entities. In this context, this work focuses on the identification of medical disorders (such as diseases and adverse effects) which denote an important category of concepts in the medical text. Two methodologies were investigated in this regard and they are dictionary-based and machine learning-based approaches. Futhermore, the capabilities of the concept recognition techniques were systematically exploited to build a semantic search platform for the retrieval of e-health records and patents. The system facilitates conventional text search as well as semantic and ontological searches. Performance of the adapted retrieval platform for e-health records and patents was evaluated within open assessment challenges (i.e. TRECMED and TRECCHEM respectively) wherein the system was best rated in comparison to several other competing information retrieval platforms. Finally, from the medico-pharma perspective, a strategy for the identification of adverse drug events from medical case reports was developed. Qualitative evaluation as well as an expert validation of the developed system's performance showed robust results. In conclusion, this thesis presents approaches for efficient information retrieval and information extraction from various biomedical literature sources in the support of healthcare and pharmacovigilance. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. This can promote the literature-based knowledge discovery, improve the safety and effectiveness of medical practices, and drive the research and development in medical and healthcare arena

The value of semantics in biomedical knowledge graphs

Knowledge graphs use a graph-based data model to represent knowledge of the real world. They consist of nodes, which represent entities of interest such as diseases or proteins, and edges, which represent potentially different relations between these entities. Semantic properties can be attached to these nodes and edges, indicating the classes of entities they represent (e.g. gene, disease), the predicates that indicate the types of relationships between the nodes (e.g. stimulates, treats), and provenance that provides references to the sources of these relationships.Modelling knowledge as a graph emphasizes the interrelationships between the entities, making knowledge graphs a useful tool for performing computational analyses for domains in which complex interactions and sequences of events exist, such as biomedicine. Semantic properties provide additional information and are assumed to benefit such computational analyses but the added value of these properties has not yet been extensively investigated.This thesis therefore develops and compares computational methods that use these properties, and applies them to biomedical tasks. These are: biomarker identification, drug repurposing, drug efficacy screening, identifying disease trajectories, and identifying genes targeted by disease-associated SNPs located on the non-coding part of the genome.In general, we find that methods which use concept classes, predicates, or provenance improves achieve a superior performance over methods that do not use them. We thereby demonstrate the added value of these semantic properties for computational analyses performed on biomedical knowledge graphs.<br/

Integrative bioinformatics and graph-based methods for predicting adverse effects of developmental drugs

Adverse drug effects are complex phenomena that involve the interplay between drug molecules and their protein targets at various levels of biological organisation, from molecular to organismal. Many factors are known to contribute toward the safety profile of a drug, including the chemical properties of the drug molecule itself, the biological properties of drug targets and other proteins that are involved in pharmacodynamics and pharmacokinetics aspects of drug action, and the characteristics of the intended patient population. A multitude of scattered publicly available resources exist that cover these important aspects of drug activity. These include manually curated biological databases, high-throughput experimental results from gene expression and human genetics resources as well as drug labels and registered clinical trial records. This thesis proposes an integrated analysis of these disparate sources of information to help bridge the gap between the molecular and the clinical aspects of drug action. For example, to address the commonly held assumption that narrowly expressed proteins make safer drug targets, an integrative data-driven analysis was conducted to systematically investigate the relationship between the tissue expression profile of drug targets and the organs affected by clinically observed adverse drug reactions. Similarly, human genetics data were used extensively throughout the thesis to compare adverse symptoms induced by drug molecules with the phenotypes associated with the genes encoding their target proteins. One of the main outcomes of this thesis was the generation of a large knowledge graph, which incorporates diverse molecular and phenotypic data in a structured network format. To leverage the integrated information, two graph-based machine learning methods were developed to predict a wide range of adverse drug effects caused by approved and developmental therapies

A systematic pathway-based network approach for in silico drug repositioning

Drug repositioning, the method of finding new uses for existing drugs, holds the potential to reduce the cost and time of drug development. Successful drug repositioning strategies depend heavily on the availability and aggregation of different drug and disease databases. Moreover, to yield greater understanding of drug prioritisation approaches, it is necessary to objectively assess (benchmark) and compare different methods. Data aggregation requires extensive curation of non-standardised drug nomenclature. To overcome this, we used a graph-theoretic approach to construct a drug synonym resource that collected drug identifiers from a range of publicly available sources, establishing missing links between databases. Thus, we could systematically assess the performance of available in silico drug repositioning methodologies with increased power for scoring true positive drug-disease pairs. We developed a novel pathway-based drug repositioning pipeline, based on a bipartite network of pathway- and drug-gene set correlations that captured functional relationships. To prioritise drugs, we used our bipartite network and the differentially expressed pathways in a given disease that formed a disease signature. We then took the cumulative network correlation between disease pathway and drug signatures to generate a drug prioritisation score. We prioritised drugs for three case studies: juvenile idiopathic arthritis, Alzheimer's and Parkinson's disease. We explored the use of different true positive lists in the evaluation of drug repositioning performance, providing insight into the most appropriate benchmark designs. We have identified several promising drug candidates and showed that our method successfully prioritises disease-modifying treatments over drugs offering symptomatic relief. We have compared the pipeline’s performance to an alternative well-established method and showed that our method has increased sensitivity to current treatment trends. The successful translation of drug candidates identified in this thesis has the potential to speed up the drug-discovery pipeline and thus more rapidly and efficiently deliver disease-modifying treatments to patients

Genetic alterations defining human primary melanoma and mechanisms of immune evasion

The somatic mutations found in melanomas reflect the biological processes that govern tumour development. They also help shape how tumours evolve and escape immune regulation. Studying these changes can therefore help to refine our understanding of melanoma progression with the added potential to offer new perspectives for disease management. Most prior melanoma sequencing studies have focused on advanced disease. Thus, somatic alterations that influence the behaviour of early-stage tumours have not been fully explored. Consequently, in this thesis I study a collection of 524 primary melanomas on which extensive clinical data have been collected for almost two decades. I describe the mutational landscape of these tumours including driver genes, new recurrent variants, mutually exclusive genetic interactions and copy number alterations. I discuss and associate these features with aspects of tumour pathology, sun exposure, immunogenicity and patient outcomes. To identify genes required for melanoma survival, I intersect my genomic analysis with a dataset of CRISPR-Cas9 dropout screens and discover a melanoma-associated genetic vulnerability mediated by Interferon Regulatory Factor 4 (IRF4). I then begin to experimentally validate and explore the biological pathway by which IRF4 may function in the context of melanoma. Checkpoint inhibitors have revolutionised melanoma care, yet only a minority of patients respond to these treatments and our comprehension of the mechanisms governing PD-L1 expression on melanoma cells is still limited. In the second part of this thesis, I examine the regulation of the key checkpoint receptor PD-L1, which is often upregulated in melanoma to facilitate tumour escape. To improve our understanding of the processes controlling PD-L1 expression, and how the PD-1/PD-L1 axis can be targeted to overcome immune evasion, I employ a genome-wide CRISPR-Cas9 screening approach. I identify genes which elicit downregulation of PD-L1 when disrupted in melanoma cells, capturing several central processes including basal transcription, N-linked glycosylation and intracellular transport. A second extensive screen in eight cancer cell lines of melanoma, bladder and lung cancer origin validate these findings and link novel candidate genes, including Sphingolipid Transporter 1 (SPNS1), to the control of PD-L1 cell surface expression. Additional work is required to further validate and understand the regulation of PD-L1 through SPNS1, as well as its contribution to immune surveillance. In summary, I present the first comprehensive evaluation into the somatic alteration landscape of primary melanomas, gaining insight into the molecular architecture of these tumours. Additionally, I introduce novel genes and processes regulating PD-L1 gene transcription, processing and presentation on the cell surface. Collectively, these results improve our understanding of the genetic processes that govern primary melanomas, as well as providing valuable insights into the mechanism of PD-L1 regulation