Despite great strides in revealing and understanding the physiological and molecular bases of cardiovascular disease, efforts to translate this understanding into needed therapeutic interventions continue to lag far behind the initial discoveries. Although pharmaceutical companies continue to increase investments into research and development, the number of drugs gaining federal approval is in decline. Many factors underlie these trends, and a vast number of technological and scientific innovations are being sought through efforts to reinvigorate drug discovery pipelines. Recent advances in molecular profiling technologies and development of sophisticated computational approaches for analyzing these data are providing new, systems-oriented approaches towards drug discovery. Unlike the traditional approach to drug discovery which is typified by a one-drug-one-target mindset, systems-oriented approaches to drug discovery leverage the parallelism and high-dimensionality of the molecular data to construct more comprehensive molecular models that aim to model broader bimolecular systems. These models offer a means to explore complex molecular states (e.g., disease) where thousands to millions of molecular entities comprising multiple molecular data types (e.g., proteomics and gene expression) can be evaluated simultaneously as components of a cohesive biomolecular system. In this paper, we discuss emerging approaches towards systems-oriented drug discovery and contrast these efforts with the traditional, unidimensional approach to drug discovery. We also highlight several applications of these system-oriented approaches across various aspects of drug discovery, including target discovery, drug repositioning and drug toxicity. When available, specific applications to cardiovascular drug discovery are highlighted and discussed.
"Since drug repositioning can benefit not only pharmaceutical companies but also patients, various efforts, including traditionally blind screening methods of chemical libraries against specific cell lines  or cellular organisms  , and serial testing of animal models , have been made to search new indications for existing drugs. Meanwhile, to reduce cost and time of in vivo and in vitro experiments, many computational methods have been published for drug repositioning           . These methods can be classified as " drug based " or " disease based " and they mainly take similarity measures (chemical similarity, molecular activity similarity, or side effect similarity), molecular docking, or shared molecular pathology to reveal potential repurposing opportunities . "
[Show abstract][Hide abstract] ABSTRACT: Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies.
Computational and Mathematical Methods in Medicine 05/2015; 2015:1-7. DOI:10.1155/2015/130620 · 0.77 Impact Factor
"Identifying these networks may ultimately aid in decision making (e.g., personnel placement and disposition, treatment planning) and treatment development (Califano et al., 2012; Dudley, Schadt, Sirota, Butte, & Ashley, 2010; Schadt, 2009; Schadt & Bjorkegren, 2012; Schadt et al., 2009). "
[Show abstract][Hide abstract] ABSTRACT: Posttraumatic stress disorder (PTSD) and other deployment-related outcomes originate from a complex interplay between constellations of changes in DNA, environmental traumatic exposures, and other biological risk factors. These factors affect not only individual genes or bio-molecules but also the entire biological networks that in turn increase or decrease the risk of illness or affect illness severity. This review focuses on recent developments in the field of systems biology which use multidimensional data to discover biological networks affected by combat exposure and post-deployment disease states. By integrating large-scale, high-dimensional molecular, physiological, clinical, and behavioral data, the molecular networks that directly respond to perturbations that can lead to PTSD can be identified and causally associated with PTSD, providing a path to identify key drivers. Reprogrammed neural progenitor cells from fibroblasts from PTSD patients could be established as an in vitro assay for high throughput screening of approved drugs to determine which drugs reverse the abnormal expression of the pathogenic biomarkers or neuronal properties.
European Journal of Psychotraumatology 08/2014; 5. DOI:10.3402/ejpt.v5.23938 · 2.40 Impact Factor
"Large medical databases link people's genotype, phenotype and prescription drug records. Researchers are mining these databases to identify unanticipated drug side effects and repurpose drugs for new indications (Dudley et al., 2010). Human behavioral phenomics is a powerful way to approach drug repurposing, but it cannot be used for chemical screening . "
[Show abstract][Hide abstract] ABSTRACT: Most neuroactive drugs were discovered through unexpected behavioral observations. Systematic behavioral screening is inefficient in most model organisms. But, automated technologies are enabling a new phase of discovery-based research in central nervous system (CNS) pharmacology. Researchers are using large-scale behavior-based chemical screens in zebrafish to discover compounds with new structures, targets, and functions. These compounds are powerful tools for understanding CNS signaling pathways. Substantial differences between human and zebrafish biology will make it difficult to translate these discoveries to clinical medicine. However, given the molecular genetic similarities between humans and zebrafish, it is likely that some of these compounds will have translational utility. We predict that the greatest new successes in CNS drug discovery will leverage many model systems, including in vitro assays, cells, rodents, and zebrafish.
Frontiers in Pharmacology 07/2014; 5:153. DOI:10.3389/fphar.2014.00153 · 3.80 Impact Factor
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