Current target-based drug discovery platforms are not able to predict drug efficacy and the full spectrum of drug effects in organisms. Hence, many experimental drugs do not survive the lengthy and costly process of drug development. Understanding how drugs affect cellular network structures and how the resulting signals are translated into drug effects is extremely important for the discovery of new medicines. This requires a greater understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular level. There is a growing recognition that this information must be integrated into discovery paradigms, but a 'road map' for obtaining and integrating information about heterogeneous networks into drug-discovery platforms currently does not exist. This review explores recent network-centered approaches developed to investigate the genesis of medicine and disease effects, specifically highlighting protein-protein interaction network models and their use in cause-effect analyses in medicine.
"From a network perspective, the entity that needs to be targeted and modulated must shift from single proteins to entire disease molecular networks [61, 62]. The efficacy of such therapies can be explained by the fact that drugs targeting different proteins in the disease network or pathway could trigger a synergistic response, and their combinations can eliminate compensatory reactions and feedback controls, thereby overcoming the robustness of diseases [63, 64]. These perspectives illuminate that the level of complexity of the proposed therapies should be increased. "
[Show abstract][Hide abstract] ABSTRACT: The scientific understanding of traditional Chinese medicine (TCM) has been hindered by the lack of methods that can explore the complex nature and combinatorial rules of herbal formulae. On the assumption that herbal ingredients mainly target a molecular network to adjust the imbalance of human body, here we present a-self-developed TCM network pharmacology platform for discovering herbal formulae in a systematic manner. This platform integrates a set of network-based methods that we established previously to catch the network regulation mechanism and to identify active ingredients as well as synergistic combinations for a given herbal formula. We then provided a case study on an antirheumatoid arthritis (RA) formula, Qing-Luo-Yin (QLY), to demonstrate the usability of the platform. We revealed the target network of QLY against RA-related key processes including angiogenesis, inflammatory response, and immune response, based on which we not only predicted active and synergistic ingredients from QLY but also interpreted the combinatorial rule of this formula. These findings are either verified by the literature evidence or have the potential to guide further experiments. Therefore, such a network pharmacology strategy and platform is expected to make the systematical study of herbal formulae achievable and to make the TCM drug discovery predictable.
Evidence-based Complementary and Alternative Medicine 04/2013; 2013(343):456747. DOI:10.1155/2013/456747 · 1.88 Impact Factor
"The lower evolutionary pressure on allosteric sites, and in particular the sensitivity of the conformational consequences – agonist or antagonist – to slight changes in the drug, or the environment, including even changes in protonation states, make the results of preclinical and animal studies less predictive with regard to the effects of the drug in humans. This is especially challenging when considering a highly diverse patient population       . "
[Show abstract][Hide abstract] ABSTRACT: Allosteric drugs bind to sites which are usually less conserved evolutionarily as compared to orthosteric sites. As such, they can discriminate between closely related proteins, have fewer side effects, and a consequent lower concentration can convey a lesser likelihood of receptor desensitization. However, an allosteric mode of action may also make the results of preclinical and animal experiments less predictive. The sensitivity of the allosteric consequences to the environment further increases the importance of accounting for patient population diversity. Even subtle differences in protein sequence, in cellular metabolic states or in target tissues, can result in different outcomes. This mini-hot-topic issue of CTMC showcases some successes and challenges of allosteric drug development through the examples of seventransmembrane (GPCR), AMPA, NMDA and metabotropic glutamate receptors, as well as the morpheein model of allosterism involved in inherent metabolic errors. Finally, the development of allo-network drugs, which are allosteric drugs acting indirectly on the neighborhood of the pharmacological target in protein-protein interaction or signaling networks, is described.
Current topics in medicinal chemistry 01/2013; 13(1). DOI:10.2174/1568026611313010002 · 3.40 Impact Factor
"Drug development often fails because the development process does not always take into account the vast complexity of the cell and the robustness of its networks. In recent years, systems level and network analyses have become increasingly applied methods in drug design              . "
[Show abstract][Hide abstract] ABSTRACT: Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design.
Current topics in medicinal chemistry 01/2013; 13(1). DOI:10.2174/1568026611313010007 · 3.40 Impact Factor
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