Statistical molecular design of a focused salicylidene acylhydrazide library and multivariate QSAR of inhibition of type III secretion in the Gram-negative bacterium Yersinia

Dahlgren, Markus; Zetterström, Caroline E.; Gylfe, Åsa; Linusson, Anna; Elofsson, Mikael

doi:10.1016/j.bmc.2010.02.022

Cited by 57 publications

(57 citation statements)

References 57 publications

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“…Similar studies on salicylanilides and salicylidene acylhydrazides showed strong correlations between theoretical QSAR models and experimental T3SS inhibition (17,18). This work demonstrated that statistical molecular design coupled with QSAR and validation can be a successful strategy for lead optimization of T3SS inhibitors (18).…”

Section: Structure-activity Relationship Studiessupporting

confidence: 56%

Chemical Inhibitors of the Type Three Secretion System: Disarming Bacterial Pathogens

Duncan

Linington

Auerbuch

2012

Antimicrob Agents Chemother

104

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The recent and dramatic rise of antibiotic resistance among bacterial pathogens underlies the fear that standard treatments for infectious disease will soon be largely ineffective. Resistance has evolved against nearly every clinically used antibiotic, and in the near future, we may be hard-pressed to treat bacterial infections previously conquered by "magic bullet" drugs. While traditional antibiotics kill or slow bacterial growth, an important emerging strategy to combat pathogens seeks to block the ability of bacteria to harm the host by inhibiting bacterial virulence factors. One such virulence factor, the type three secretion system (T3SS), is found in over two dozen Gram-negative pathogens and functions by injecting effector proteins directly into the cytosol of host cells. Without T3SSs, many pathogenic bacteria are unable to cause disease, making the T3SS an attractive target for novel antimicrobial drugs. Interdisciplinary efforts between chemists and microbiologists have yielded several T3SS inhibitors, including the relatively well-studied salicylidene acylhydrazides. This review highlights the discovery and characterization of T3SS inhibitors in the primary literature over the past 10 years and discusses the future of these drugs as both research tools and a new class of therapeutic agents.

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Section: Structure-activity Relationship Studiessupporting

confidence: 56%

Chemical Inhibitors of the Type Three Secretion System: Disarming Bacterial Pathogens

Duncan

Linington

Auerbuch

2012

Antimicrob Agents Chemother

104

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“…The method uses a fixed amount of training data on the basis that a model should learn from as many sample as possible to exploit all available information. To the best of our knowledge, there were eight other QSAR studies on ensemble of mixed features and mixed algorithms, applied on training set size of 48-816 compounds [33,[50][51][52][53][54][55][56]. The application of ensemble method had improved the final performances in a majority of these studies when compared with the best performing individual model [52,55].…”

Section: Introductionmentioning

confidence: 90%

Mixed learning algorithms and features ensemble in hepatotoxicity prediction

Liew

Lim

Yap

2011

J Comput Aided Mol Des

101

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Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use.

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“…(also known as ME0052) (16,29) and INP0341 (30) were synthesized and purified from commercially available hydrazides and salicylaldehydes, as described previously (31). The SAHs were dissolved in dimethyl sulfoxide (DMSO; Sigma) to a final concentration of 20 mM and stored at room temperature without exposure to light for a maximum of 3 weeks.…”

Section: Chemicals and Inp Compounds The Chemical Compounds Inp0010mentioning

confidence: 99%

Mutations in hemG Mediate Resistance to Salicylidene Acylhydrazides, Demonstrating a Novel Link between Protoporphyrinogen Oxidase (HemG) and Chlamydia trachomatis Infectivity

et al. 2013

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Statistical molecular design of a focused salicylidene acylhydrazide library and multivariate QSAR of inhibition of type III secretion in the Gram-negative bacterium Yersinia

Cited by 57 publications

References 57 publications

Chemical Inhibitors of the Type Three Secretion System: Disarming Bacterial Pathogens

Chemical Inhibitors of the Type Three Secretion System: Disarming Bacterial Pathogens

Mixed learning algorithms and features ensemble in hepatotoxicity prediction

Mutations in hemG Mediate Resistance to Salicylidene Acylhydrazides, Demonstrating a Novel Link between Protoporphyrinogen Oxidase (HemG) and Chlamydia trachomatis Infectivity

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