Novel diaryl ureas with efficacy in a mouse model of malaria

Anderson, John W.; Sarantakis, Dimitri; Terpinski, Jacek; Kumar, T. R. Santha; Tsai, Han Chun; Kuo, Mack; Ager, Arba L.; Jacobs, William R.; Schiehser, Guy A.; Ekins, Sean; Sacchettini, James C.; Jacobus, David P.; Fidock, David A.; Freundlich, Joel S.

doi:10.1016/j.bmcl.2012.12.022

Cited by 24 publications

(18 citation statements)

References 26 publications

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“…We have witnessed this first hand during our work on different projects developing small molecules for the treatment of tuberculosis[56] or malaria. [77] While the mouse is not an absolutely predictive model for translating these[1–4] or other diseases[7,8,11,12,14] to human studies, it is relatively cheap and widely accessible. One of the key criteria for obtaining sufficient in vivo efficacy is displaying high metabolic stability (or low to moderate clearance of the compound).…”

Section: Discussionmentioning

confidence: 99%

“…We constructed these “Laplacian-corrected Naïve Bayesian classifier” models using our previously published protocols. [4,53,55,56,60,73,76,77] Models were internally validated using 5-fold cross-validation in Pipeline Pilot, which involves leaving out a random selection of 20% of the compounds, building a model with the remaining 80% of the dataset, and evaluating the model with the set of 20% that was initially left out. This process was repeated five times, and the following “internal” statistics for the best model were calculated by Pipeline Pilot[50]: ROC rating (Receiver Operator Characteristic curve’s quality), ROC score (the area under the curve of the ROC plot), sensitivity, specificity, and concordance.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

et al. 2015

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Purpose Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Methods Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). Results “Pruning” out the moderately unstable/moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 hour. Conclusions Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

et al. 2015

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“…The Horner−Wadsworth−Emmons (HWE) olefination of 3-nitrobenzaldehyde with triethyl phosphonoacetate in the presence of lithium hydroxide in THF at room temperature led to the formation of ethyl 3-(3-nitrophenyl)acrylate (9) in quantitative yield. The latter on chemoselective reduction of nitro group with SnCl 2 · 2H 2 O (10.0 equiv) as above the respective aminophenyl derivative 10 in good yield.…”

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Identification of Novel Phenyl Butenonyl C-Glycosides with Ureidyl and Sulfonamidyl Moieties as Antimalarial Agents

Ramakrishna

Gunjan

Shukla

et al. 2014

ACS Med. Chem. Lett.

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A new series of C-linked phenyl butenonyl glycosides bearing ureidyl(thioureidyl) and sulfonamidyl moieties in the phenyl rings were designed, synthesized, and evaluated for their in vitro antimalarial activities against Plasmodium falciparum 3D7 (CQ sensitive) and K1 (CQ resistant) strains. Among all the compounds screened the Clinked phenyl butenonyl glycosides bearing sulfonamidyl moiety (5a) and ureidyl moiety in the phenyl ring (7d and 8c) showed promising antimalarial activities against both 3D7 and K1 strains with IC 50 values in micromolar range and low cytotoxicity offering new HITS for further exploration.

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“…The complexity of these problems suggests that their solution will require the concerted effort of the community of stem cell researchers, rather than individual piecemeal solutions. As seen with other technologies relevant to healthcare and drug discovery, bottlenecks that can hamper progress can be caused by gaps in collaboration or software, which can be readily addressed [44,45].…”

Section: Impediments To Progress In Stem Cell Researchmentioning

confidence: 99%

Databases and collaboration require standards for human stem cell research

Litterman

Ekins

2015

Drug Discovery Today

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Stem cell research is at an important juncture: despite significant potential for human health and several countries with key initiatives to expedite commercialization, there are gaps in capturing and exploiting the results of past and current research. Here, we propose a concerted plan that could be taken to foster a more collaborative approach and ensure that all research efforts can be leveraged across the community. The creation of a definitive centralized database repository, or at least harmonized data repositories, for stem cell groups in academia and industry, enabling secure selective sharing of data when needed, could provide the core structure that is sought globally and protect intellectual property. The development of minimum information about stem cell experiments (MIASCE) could be key to this development.

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Novel diaryl ureas with efficacy in a mouse model of malaria

Cited by 24 publications

References 26 publications

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

Identification of Novel Phenyl Butenonyl C-Glycosides with Ureidyl and Sulfonamidyl Moieties as Antimalarial Agents

Databases and collaboration require standards for human stem cell research

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