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New antibiotics are needed because drug resistance is increasing while the introduction of new antibiotics is decreasing. We discuss here six possible approaches to develop 'resistance-resistant' antibiotics. First, multitarget inhibitors in which a single compound inhibits more than one target may be easier to develop than conventional combination therapies with two new drugs. Second, inhibiting multiple targets in the same metabolic pathway is expected to be an effective strategy owing to synergy. Third, discovering multiple-target inhibitors should be possible by using sequential virtual screening. Fourth, repurposing existing drugs can lead to combinations of multitarget therapeutics. Fifth, targets need not be proteins. Sixth, inhibiting virulence factor formation and boosting innate immunity may also lead to decreased susceptibility to resistance. Although it is not possible to eliminate resistance, the approaches reviewed here offer several possibilities for reducing the effects of mutations and, in some cases, suggest that sensitivity to existing antibiotics may be restored in otherwise drug-resistant organisms.
Drug Design, Animals, Humans, Drug Resistance, Microbial, Molecular Dynamics Simulation, Anti-Bacterial Agents
Drug Design, Animals, Humans, Drug Resistance, Microbial, Molecular Dynamics Simulation, Anti-Bacterial Agents
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 107 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |