Drug Repurposing

This book focuses on various aspects and applications of drug repurposing, the understanding of which is important for treating diseases. Due to the high costs and time associated with the new drug discovery process, the inclination toward drug repurposing is increasing for common as well as rare diseases. A major focus of this book is understanding the role of drug repurposing to develop drugs for infectious diseases, including antivirals, antibacterial and anticancer drugs, as well as immunotherapeutics

Finding disease similarity based on implicit semantic similarity

AbstractGenomics has contributed to a growing collection of gene–function and gene–disease annotations that can be exploited by informatics to study similarity between diseases. This can yield insight into disease etiology, reveal common pathophysiology and/or suggest treatment that can be appropriated from one disease to another. Estimating disease similarity solely on the basis of shared genes can be misleading as variable combinations of genes may be associated with similar diseases, especially for complex diseases. This deficiency can be potentially overcome by looking for common biological processes rather than only explicit gene matches between diseases. The use of semantic similarity between biological processes to estimate disease similarity could enhance the identification and characterization of disease similarity. We present functions to measure similarity between terms in an ontology, and between entities annotated with terms drawn from the ontology, based on both co-occurrence and information content. The similarity measure is shown to outperform other measures used to detect similarity. A manually curated dataset with known disease similarities was used as a benchmark to compare the estimation of disease similarity based on gene-based and Gene Ontology (GO) process-based comparisons. The detection of disease similarity based on semantic similarity between GO Processes (Recall=55%, Precision=60%) performed better than using exact matches between GO Processes (Recall=29%, Precision=58%) or gene overlap (Recall=88% and Precision=16%). The GO-Process based disease similarity scores on an external test set show statistically significant Pearson correlation (0.73) with numeric scores provided by medical residents. GO-Processes associated with similar diseases were found to be significantly regulated in gene expression microarray datasets of related diseases

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

Background Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. Methods Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms. Results Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. Conclusions ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.</p

FAIR and bias-free network modules for mechanism-based disease redefinitions

Even though chronic diseases are the cause of 60% of all deaths around the world, the underlying causes for most of them are not fully understood. Hence, diseases are defined based on organs and symptoms, and therapies largely focus on mitigating symptoms rather than cure. This is also reflected in the most commonly used disease classifications. The complex nature of diseases, however, can be better defined in terms of networks of molecular interactions. This research applies the approaches of network medicine – a field that uses network science for identifying and treating diseases – to multiple diseases with highly unmet medical need such as stroke and hypertension. The results show the success of this approach to analyse complex disease networks and predict drug targets for different conditions, which are validated through preclinical experiments and are currently in human clinical trials

A transcriptomic-based drug repositioning approach for the identification of novel muscle-specific therapies for spinal muscular atrophy

Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disorder (NMD) caused by depleted survival of motor neuron (SMN) levels and characterised by neuronal degeneration and progressive muscle atrophy. Although three approved SMN-dependent treatments have significantly halted disease progression, they are unfortunately not cures. Thus, additional musclespecific therapies are most likely also required to synergistically ameliorate symptoms in SMA patients. One useful strategy for the discovery of novel SMA muscle-specific therapies is drug repositioning (or repurposing), as using existing approved pharmacological compounds allows for the development of more costeffective treatments compared to traditional drug discovery. We have previously investigated drug repositioning in SMA and demonstrated that prednisolone, a synthetic glucocorticoid (GC), improved muscle health and survival in SMA mice. However, the adverse effects associated with chronic GC use limit prednisolone’s long-term therapeutic potential in SMA. We thus wanted to discover prednisolone-targeted genes and pathways in SMA skeletal muscle and identify commercially available drugs that similarly modulate these effectors. We initially performed an RNA sequencing, bioinformatics and drug repositioning database pipeline on muscle from symptomatic post-natal day (P)7 prednisolonetreated and untreated Smn-/-;SMN2 SMA mice. These revealed that genes associated with atrophy, metabolism and muscle function pathways were targeted and normalised by prednisolone in SMA skeletal muscle. Furthermore, a total of 223 commercially approved compounds were predicted to similarly target these genes and pathways. We thus selected metformin, a generic antihyperglycaemic biguanide and oxandrolone, an anabolic steroid, for further investigation in SMA, based on their oral bioavailability, safety in infants and previously reported benefits in related conditions. Metformin was predicted to emulate prednisolone’s activity by upregulating Prkag3 and downregulating Forkhead box O (FoxO) expression. We indeed confirmed that Prkag3 was significantly downregulated in muscle from Smn-/- ;SMN2 and Smn2B/- SMA mice. Furthermore, in vitro experiments in C2C12 myoblast-like cells suggest that the dysregulation of metformin’s molecular targets are SMN-independent and linked to atrophy. However, metformin treatment in both C2C12 cells and Smn2B/- SMA mice (200 mg/kg/day) did not improve disease progression. Furthermore, a higher dose of metformin (400 mg/kg/day) significantly exacerbated disease progression in Smn2B/- SMA mice, which were most likely due to mitochondrial marker dysfunction in the spinal cord. On the other hand, oxandrolone was predicted to upregulate the expression of the androgen receptor (Ar) and its downstream components. However, analyses in both C2C12 cells and muscle from Smn-/-;SMN2 and Smn2B/- SMA mice revealed that most of the predicted oxandrolone targets were in fact not dysregulated. Still, oxandrolone treatment rescued canonical atrophy in C2C12 myotubes and slightly improved survival in Smn2B/- SMA mice (4 mg/kg/day). Taken together, our in vitro and in vivo experiments revealed that metformin and oxandrolone did not successfully emulate prednisolone’s activity in SMA, suggesting that our in silico approach requires refinement for a better prediction of valid drug candidates. Nevertheless, our discovery of prednisolone-targeted pathways and extensive list of drug candidates supports the usefulness of a transcriptomic-based drug repositioning strategy, and that with alterations, it can be quite beneficial for future therapeutic endeavours in SMA

Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap)

Accompanied by increased life span, aging-associated diseases, such as metabolic diseases and cancers, have become serious health threats. Recent studies have documented that aging-associated diseases are caused by prolonged cellular stresses such as endoplasmic reticulum (ER) stress, mitochondrial stress, and oxidative stress. Thus, ameliorating cellular stresses could be an effective approach to treat aging-associated diseases and, more importantly, to prevent such diseases from happening. However, cellular stresses and their molecular responses within the cell are typically mediated by a variety of factors encompassing different signaling pathways. Therefore, a target-based drug discovery method currently being used widely (reverse pharmacology) may not be adequate to uncover novel drugs targeting cellular stresses and related diseases. The connectivity map (CMap) is an online pharmacogenomic database cataloging gene expression data from cultured cells treated individually with various chemicals, including a variety of phytochemicals. Moreover, by querying through CMap, researchers may screen registered chemicals in silico and obtain the likelihood of drugs showing a similar gene expression profile with desired and chemopreventive conditions. Thus, CMap is an effective genome-based tool to discover novel chemopreventive drugs. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.1