Drug repositioning : a machine-learning approach through data integration

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses

Solving the Problem of New Uses

One of the most dramatic public-policy failures in biomedical research is the lack of incentives for industry to develop new therapeutic uses (“indications”) for existing drugs once generics are available. Policymakers and commentators are well aware of this “problem of new uses,” but fail to appreciate its magnitude. Over the past decade, this gap in the incentives for pharmaceutical R&D has become one of the greatest barriers to medical progress. Recent technological advances have allowed researchers to identify hundreds of potential new indications for older drugs that could address critical unmet medical needs. And researchers are poised to discover hundreds more. Developing new indications for existing drugs is much faster, cheaper, and less risky than developing new drugs, and therefore offers the single most promising avenue for delivering new medical treatments to the public. However, pharmaceutical companies invariably lose interest in developing new uses for existing drugs when patients have access to low-cost generics. This article explores the nature and source of this gap in the incentives for developing new medical treatments, showing that it ultimately stems from a simple information problem. At present, the government encourages drug development by granting firms temporary monopoly rights that block generic manufacturers from making or selling imitations of their drugs. The government also makes available an alternative type of monopoly protection for new indications that applies to the act of taking or administering a drug for a new therapeutic use. The latter monopoly rights could provide the appropriate incentives for developing new uses of existing drugs. However, pharmaceutical companies cannot enforce these rights without knowing when physicians prescribe the drug for the patented indication as opposed to some other use. If the government established an infrastructure for pharmaceutical companies to monitor the prescribed indications when pharmacists fill a prescription, those firms would possess the information necessary to enforce patents on new indications, thereby solving the problem of new uses. This article argues that the government could easily create such an infrastructure through the expanding use of eprescribing software and electronic medical records

Predicting potential drugs and drug-drug interactions for drug repositioning

The purpose of drug repositioning is to predict novel treatments for existing drugs. It saves time and reduces cost in drug discovery, especially in preclinical procedures. In drug repositioning, the challenging objective is to identify reasonable drugs with strong evidence. Recently, benefiting from various types of data and computational strategies, many methods have been proposed to predict potential drugs. Signature-based methods use signatures to describe a specific disease condition and match it with drug-induced transcriptomic profiles. For a disease signature, a list of potential drugs is produced based on matching scores. In many studies, the top drugs on the list are identified as potential drugs and verified in various ways. However, there are a few limitations in existing methods: (1) For many diseases, especially cancers, the tissue samples are often heterogeneous and multiple subtypes are involved. It is challenging to identify a signature from such a group of profiles. (2) Genes are treated as independent elements in many methods, while they may associate with each other in the given condition. (3) The disease signatures cannot identify potential drugs for personalized treatments. In order to address those limitations, I propose three strategies in this dissertation. (1) I employ clustering methods to identify sub-signatures from the heterogeneous dataset, then use a weighting strategy to concatenate them together. (2) I utilize human protein complex (HPC) information to reflect the dependencies among genes and identify an HPC signature to describe a specific type of cancer. (3) I use an HPC strategy to identify signatures for drugs, then predict a list of potential drugs for each patient. Besides predicting potential drugs directly, more indications are essential to enhance my understanding in drug repositioning studies. The interactions between biological and biomedical entities, such as drug-drug interactions (DDIs) and drug-target interactions (DTIs), help study mechanisms behind the repurposed drugs. Machine learning (ML), especially deep learning (DL), are frontier methods in predicting those interactions. Network strategies, such as constructing a network from interactions and studying topological properties, are commonly used to combine with other methods to make predictions. However, the interactions may have different functions, and merging them in a single network may cause some biases. In order to solve it, I construct two networks for two types of DDIs and employ a graph convolutional network (GCN) model to concatenate them together. In this dissertation, the first chapter introduces background information, objectives of studies, and structure of the dissertation. After that, a comprehensive review is provided in Chapter 2. Biological databases, methods and applications in drug repositioning studies, and evaluation metrics are discussed. I summarize three application scenarios in Chapter 2. The first method proposed in Chapter 3 considers the issue of identifying a cancer gene signature and predicting potential drugs. The k-means clustering method is used to identify highly reliable gene signatures. The identified signature is used to match drug profiles and identify potential drugs for the given disease. The second method proposed in Chapter 4 uses human protein complex (HPC) information to identify a protein complex signature, instead of a gene signature. This strategy improves the prediction accuracy in the experiments of cancers. Chapter 5 introduces the signature-based method in personalized cancer medicine. The profiles of a given drug are used to identify a drug signature, under the HPC strategy. Each patient has a profile, which is matched with the drug signature. Each patient has a different list of potential drugs. Chapter 6 propose a graph convolutional network with multi-kernel to predict DDIs. This method constructs two DDI kernels and concatenates them in the GCN model. It achieves higher performance in predicting DDIs than three state-of-the-art methods. In summary, this dissertation has proposed several computational algorithms for drug repositioning. Experimental results have shown that the proposed methods can achieve very good performance

Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach

Background: The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs. Methods: We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line. Findings: First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability. Interpretation: These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine. Funding: This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA

Integration of multi-scale protein interactions for biomedical data analysis

With the advancement of modern technologies, we observe an increasing accumulation of biomedical data about diseases. There is a need for computational methods to sift through and extract knowledge from the diverse data available in order to improve our mechanistic understanding of diseases and improve patient care. Biomedical data come in various forms as exemplified by the various omics data. Existing studies have shown that each form of omics data gives only partial information on cells state and motivated jointly mining multi-omics, multi-modal data to extract integrated system knowledge. The interactome is of particular importance as it enables the modelling of dependencies arising from molecular interactions. This Thesis takes a special interest in the multi-scale protein interactome and its integration with computational models to extract relevant information from biomedical data. We define multi-scale interactions at different omics scale that involve proteins: pairwise protein-protein interactions, multi-protein complexes, and biological pathways. Using hypergraph representations, we motivate considering higher-order protein interactions, highlighting the complementary biological information contained in the multi-scale interactome. Based on those results, we further investigate how those multi-scale protein interactions can be used as either prior knowledge, or auxiliary data to develop machine learning algorithms. First, we design a neural network using the multi-scale organization of proteins in a cell into biological pathways as prior knowledge and train it to predict a patient's diagnosis based on transcriptomics data. From the trained models, we develop a strategy to extract biomedical knowledge pertaining to the diseases investigated. Second, we propose a general framework based on Non-negative Matrix Factorization to integrate the multi-scale protein interactome with multi-omics data. We show that our approach outperforms the existing methods, provide biomedical insights and relevant hypotheses for specific cancer types

Towards Personalized Medicine: Computational Approaches For Drug Repurposing And Cell Type Identification

The traditional drug discovery process is extremely slow and costly. More than 90% of drugs fail to pass beyond the early stage of development and toxicity tests, and many of the drugs that go through early phases of the clinical trials fail because of adverse reactions, side effects, or lack of efficiency. In spite of unprecedented investments in research and development (R&D), the number of new FDA-approved drugs remains low, reflecting the limitations of the current R&D model. In this context, finding new disease indications for existing drugs sidesteps these issues and can therefore increase the available therapeutic choices at a fraction of the cost of new drug development. In this thesis, we introduce a drug repurposing approach that takes advantage of prior knowledge of drug targets, disease-related genes, and signaling pathways to construct a drug-disease network composed of the genes that are most likely perturbed by a drug. Systems biology can be used as an effective platform in drug discovery and development by leveraging the understanding of interactions between the different system components. By performing a system-level analysis on this network, our approach estimates the amount of perturbation caused by drugs and diseases and discovers drugs with the potential desired effects on the given disease. Next, we develop a stable clustering method that employs a bootstrap approach to identify the stable clusters of cells. We show that strong patterns in single cell data will remain despite small perturbations. The results, that are validated based on well-known metrics, show that using this approach yields improvement in correctly identifying the cell types, compared to other existing methods

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

The Drug Repurposing Ecosystem: Intellectual Property Incentives, Market Exclusivity, and the Future of New Medicines

The pharmaceutical industry is in a state of fundamental transition. New drug approvals have slowed, patents on blockbuster drugs are expiring, and costs associated with developing new drugs are escalating and yielding fewer viable drug candidates. As a result, pharmaceutical firms have turned to a number of alternative strategies for growth. One of these strategies is drug repurposing -finding new ways to deploy approved drugs or abandoned clinical candidates in new disease areas. Despite the efficiency advantages of repurposing drugs, there is broad agreement that there is insufficient repurposing activity because of numerous intellectual property protection and market failures. This Article examines the system that surrounds drug repurposing, including serendipitous discovery, the application of big data methods to prioritize promising repurposing candidates, the unorthodoxly regulated off-label prescription practices of providers, and related prohibitions on pharmaceutical firms\u27 off-label marketing. The Article argues that there is a complex ecosystem in place and that additional or disruptive IP or market exclusivity incentives may harm as much as help in promoting repurposing activity. To illustrate this threat, the Article traces the trajectory of metformin, a common diabetes drug that shows promise for conditions ranging from polycystic ovary syndrome to breast cancer. From the initial reasons for Bristol-Myers Squibb to refuse to invest in promising alternative uses, to the institutions, researchers, and regulators who identified possibilities for metformin treatment, this Article aims to map the role of intellectual property protection, market exclusivity, and search for capital that led to metformin\u27s ascent as a repurposed drug. The Article contributes a concrete understanding to an important problem in pharmaceutical law and policy, one for which scholars have quickly suggested more powerful patent and market exclusivity protection when doing so may undermine the very processes now leading to effective alternative uses for existing drugs

The Drug Repurposing Ecosystem: Intellectual Property Incentives, Market Exclusivity, and the Future of New Medicines

The pharmaceutical industry is in a state of fundamental transition. New drug approvals have slowed, patents on blockbuster drugs are expiring, and costs associated with developing new drugs are escalating and yielding fewer viable drug candidates. As a result, pharmaceutical firms have turned to a number of alternative strategies for growth. One of these strategies is drug repurposing -finding new ways to deploy approved drugs or abandoned clinical candidates in new disease areas