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Discovery of GSK2656157: An Optimized PERK Inhibitor Selected for Preclinical Development.

ABSTRACT: We recently reported the discovery of GSK2606414 (1), a selective first in class inhibitor of protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), which inhibited PERK activation in cells and demonstrated tumor growth inhibition in a human tumor xenograft in mice. In continuation of our drug discovery program, we applied a strategy to decrease inhibitor lipophilicity as a means to improve physical properties and pharmacokinetics. This report describes our medicinal chemistry optimization culminating in the discovery of the PERK inhibitor GSK2656157 (6), which was selected for advancement to preclinical development.

SUBMITTER: Axten JM

PROVIDER: S-EPMC4027568 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Discovery of GSK2656157: An Optimized PERK Inhibitor Selected for Preclinical Development.

Axten Jeffrey M JM Romeril Stuart P SP Shu Arthur A Ralph Jeffrey J Medina Jesús R JR Feng Yanhong Y Li William Hoi Hong WH Grant Seth W SW Heerding Dirk A DA Minthorn Elisabeth E Mencken Thomas T Gaul Nathan N Goetz Aaron A Stanley Thomas T Hassell Annie M AM Gampe Robert T RT Atkins Charity C Kumar Rakesh R

ACS medicinal chemistry letters 20130812 10

We recently reported the discovery of GSK2606414 (1), a selective first in class inhibitor of protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), which inhibited PERK activation in cells and demonstrated tumor growth inhibition in a human tumor xenograft in mice. In continuation of our drug discovery program, we applied a strategy to decrease inhibitor lipophilicity as a means to improve physical properties and pharmacokinetics. This report describes our medicinal chemistry optimiza ...[more]

PMID: 24900593

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Project description:Giardiasis, caused by the intestinal parasite Giardia lamblia, is a severe diarrheal disease, endemic in poverty-stricken regions of the world, and also a common infection in developed countries. The available therapeutic options are associated with adverse effects, and G. lamblia resistance to the standard-of-care drugs is spreading. Fumagillin, an antimicrosporidiosis drug, is a therapeutic agent with potential for the treatment of giardiasis. However, it exhibits considerable, albeit reversible, toxicity when used to treat immunocompromised microsporidiosis patients. Fumagillin is also a highly unstable compound. To address these liabilities, we designed and synthesized stable fumagillol derivatives with lower levels of permeation across polarized epithelial Caco-2 cells and better potency against G. lamblia trophozoites than fumagillin. Metronidazole-resistant G. lamblia strains were also susceptible to the new fumagillol derivatives. In addition, these compounds were more potent against the amebiasis-causing parasite Entamoeba histolytica than fumagillin. Two compounds exhibited better thermal and acid stability than fumagillin, which should prolong the drug shelf life and reduce compound degradation in the stomach. Studies with a mouse model of giardiasis with the most stable compound, 4-(((((3R,4S,5S,6R)-5-methoxy-4-((2R,3R)-2-methyl-3-(3-methylbut-2-en-1-yl)oxiran-2-yl)-1-oxaspiro[2.5]octan-6-yl)oxy)carbonyl)amino)benzoic acid (compound 9), revealed that it had better efficacy (effective dose [ED]) than fumagillin at both the fully curative dose (the 100% ED) of 6.6 mg/kg of body weight and a 50% ED of 0.064 mg/kg. Plasma pharmacokinetics revealed the slow absorption of compound 9 through the gut, consistent with the in vitro characterization in Caco-2 cells. An acute-dose study yielded a maximum tolerated dose (MTD) of 1,500 mg/kg, 227-fold higher than the fully curative dose. Thus, along with improved stability, compound 9 also exhibited an excellent therapeutic window.

Project description:BackgroundModern genomic and proteomic profiling methods produce large amounts of data from tissue and blood-based samples that are of potential utility for improving patient care. However, the design of precision medicine tests for unmet clinical needs from this information in the small cohorts available for test discovery remains a challenging task. Obtaining reliable performance assessments at the earliest stages of test development can also be problematic. We describe a novel approach to classifier development designed to create clinically useful tests together with reliable estimates of their performance. The method incorporates elements of traditional and modern machine learning to facilitate the use of cohorts where the number of samples is less than the number of measured patient attributes. It is based on a hierarchy of classification and information abstraction and combines boosting, bagging, and strong dropout regularization.ResultsWe apply this dropout-regularized combination approach to two clinical problems in oncology using mRNA expression and associated clinical data and compare performance with other methods of classifier generation, including Random Forest. Performance of the new method is similar to or better than the Random Forest in the two classification tasks used for comparison. The dropout-regularized combination method also generates an effective classifier in a classification task with a known confounding variable. Most importantly, it provides a reliable estimate of test performance from a relatively small development set of samples.ConclusionsThe flexible dropout-regularized combination approach is able to produce tests tailored to particular clinical questions and mitigate known confounding effects. It allows the design of molecular diagnostic tests addressing particular clinical questions together with reliable assessment of whether test performance is likely to be fit-for-purpose in independent validation at the earliest stages of development.

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Discovery of GSK2656157: An Optimized PERK Inhibitor Selected for Preclinical Development.

Publications

Discovery of GSK2656157: An Optimized PERK Inhibitor Selected for Preclinical Development.

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