Polypharmacology: drug discovery for the future.
ABSTRACT In recent years, even with remarkable scientific advancements and a significant increase of global research and development spending, drugs are frequently withdrawn from markets. This is primarily due to their side effects or toxicities. Drug molecules often interact with multiple targets, coined as polypharmacology, and the unintended drug-target interactions could cause side effects. Polypharmacology remains one of the major challenges in drug development, and it opens novel avenues to rationally design the next generation of more effective, but less toxic, therapeutic agents. This review outlines the latest progress and challenges in polypharmacology studies.
Article: Tools for in silico target fishing[Show abstract] [Hide abstract]
ABSTRACT: Computational target fishing methods are designed to identify the most probable target of a query molecule. This process may allow the prediction of the bioactivity of a compound, the identification of the mode of action of known drugs, the detection of drug polypharmacology, drug repositioning or the prediction of the adverse effects of a compound. The large amount of information regarding the bioactivity of thousands of small molecules now allows the development of these types of methods. In recent years, we have witnessed the emergence of many methods for in silico target fishing. Most of these methods are based on the similarity principle, i.e., that similar molecules might bind to the same targets and have similar bioactivities. However, the difficult validation of target fishing methods hinders comparisons of the performance of each method. In this review, we describe the different methods developed for target prediction, the bioactivity databases most frequently used by these methods, and the publicly available programs and servers that enable non-specialist users to obtain these types of predictions. It is expected that target prediction will have a large impact on drug development and on the functional food industry.Methods 09/2014; 71. DOI:10.1016/j.ymeth.2014.09.006 · 3.22 Impact Factor
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ABSTRACT: Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we propose a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process. We have developed DrugNet, a new methodology for drug-disease and disease-drug prioritization. Our approach is based on a network-based prioritization method called ProphNet which has recently been developed by the authors. ProphNet is able to integrate data from complex networks involving a wide range of types of elements and interactions. In this work, we built a network of interconnected drugs, proteins and diseases and applied DrugNet to different types of tests for drug repositioning. We tested the performance of our approach on different validation tests, including cross validation and tests based on real clinical trials. DrugNet achieved a mean AUC value of 0.9552±0.0015 in 5-fold cross validation tests, and a mean AUC value of 0.8364 for tests based on recent clinical trials (phases 0-4) not present in our data. These results suggest that DrugNet could be very useful for discovering new drug uses. We also studied specific cases of particular interest, proving the benefits of heterogeneous data integration in this problem. Our methodology suggests that new drugs can be repositioned by generating ranked lists of drugs based on a given disease query or vice versa. Our study shows that the simultaneous integration of information about diseases, drugs and targets can lead to a significant improvement in drug repositioning tasks. DrugNet is available as a web tool from http://genome2.ugr.es/drugnet/ (accessed 23.09.14). Matlab source code is also available on the website. Copyright © 2015. Published by Elsevier B.V.Artificial Intelligence in Medicine 01/2015; DOI:10.1016/j.artmed.2014.11.003 · 1.36 Impact Factor
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ABSTRACT: In the past decade, scientific advances in network pharmacology have laid the foundations for a polypharmacological approach to discover new drugs for complex diseases. There is now a comprehensive understanding that many incurable diseases are multifactorial in nature and, consequently, conventional drugs directed to a single molecular target are inadequate. To achieve a desired clinical outcome, a polypharmacological approach seeks to intervene in the diseased network using either combinations of multiple drugs or single small molecules modulating multiple targets. Both these approaches are equally feasible from a clinical standpoint. However, for various reasons which will be discussed in this review, the latter approach may be favoured for Alzheimer's disease (AD) and neglected tropical diseases (NTDs). With each passing year, an increasing number of multitarget drugs and drug candidates are being identified, and several proof-of-concepts for treating these two diseases have emerged. Herein, with an awareness of the obstacles and challenges faced, we explore small molecules that seek to modulate multiple targets with the ultimate goal of harnessing network pharmacology for therapeutic applications in AD and NTDs.Medicinal Chemistry Communication 01/2014; 5(7-7):853-861. DOI:10.1039/c4md00069b · 2.63 Impact Factor