Two diseases, one approach: multitarget drug discovery in Alzheimer's and neglected tropical diseases

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Abstract

Multitarget drug discovery may represent a promising therapeutic approach to treat Alzheimer's and neglected tropical diseases.

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.

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Network pharmacology: the next paradigm in drug discovery.

Andrew Hopkins (2008)

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

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STRING v9.1: protein-protein interaction networks, with increased coverage and integration

Andrea Franceschini, Damian Szklarczyk, Sune P. Frankild … (2012)

Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.