Discovery and Optimization of Potent, Selective, and <i>in Vivo</i> Efficacious 2-Aryl Benzimidazole BCATm Inhibitors

Deng, Hongfeng; Zhou, Jia; Sundersingh, Flora; Messer, Jeffrey A.; Somers, D.O.; Ajakane, Myriam; Arico-Muendel, Christopher C.; Beljean, Arthur; Belyanskaya, Svetlana; Bingham, Ryan P.; Blazensky, Emily; Boullay, Anne-Bénédicte; Boursier, Eric; Chai, Jing; Carter, Paul; Chung, Chun‐wa; Daugan, Alain; Ding, Yun; Herry, Kenny; Hobbs, Clare I.; Humphries, Eric; Kollmann, Christopher; Nguyen, Van-Loc; Nicodème, Edwige; Smith, Sarah E.; Dodic, N Sinisa; Ancellin, Nicolas

doi:10.1021/acsmedchemlett.5b00389

Cited by 40 publications

(31 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…49 Another is the enzyme human mitochondrial branched-chain aminotransferase, in which HTS, FBDD, and ELT were applied to identify four distinct series. 50–53 In both examples, the chemical diversity achieved from the complementary screening approaches was greater than that which would have been achieved from one tactic alone. 7-Transmembrane receptor targets have also shown to be tractable to ELT as well as HTS.…”

Section: Deploying Dna-encoded Library Technology In Combination Withmentioning

confidence: 98%

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

et al. 2018

Self Cite

View full text Add to dashboard Cite

Section: Deploying Dna-encoded Library Technology In Combination Withmentioning

confidence: 98%

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

et al. 2018

Self Cite

View full text Add to dashboard Cite

“…S7K). Lastly, to test the involvement of enhanced muscle branched chain amino acid catabolism we took advantage of the fact that muscle, but not liver hepatocytes, expresses branched chain amino acid transaminase 2 (BCAT2; neither express BCAT1) 39 and a novel BCAT2 inhibitor, Co8b 40 . As such, treatment of muscle-liver co-incubates with Co8b reversed the effect of T2D liver on the depression of muscle protein synthesis (Fig.…”

Section: Silencing Of Both But Not Individual Liver Alanine Aminotrmentioning

confidence: 99%

“…This dialyzed media was then immediately used to assess effects on protein synthesis in isolated C57Bl/6J EDL muscles. In addition, studies were conducted where 5mM alanine (Sigma, DEU) or 20µM compound 8b (Co8b; a BCAT2 inhibitor, pIC50 Cell 6.2 40 ; MU-002-BC19 (batch KPS-266-45e) Advanced Molecular Technologies, AUS) in the co-incubation media.…”

Section: Dynamic Metabolic Tests In Vivomentioning

confidence: 99%

An endocrine-hepato-muscular metabolic cycle links skeletal muscle atrophy and hyperglycemia in type 2 diabetes

Okun

Rusu

Chan

et al. 2020

Preprint

View full text Add to dashboard Cite

Both obesity and sarcopenia are frequently associated in ageing, and together may promote the progression of related conditions such as diabetes and frailty. However, little is known about the pathophysiological mechanisms underpinning this association. Here we uncover dysregulated systemic alanine metabolism and hyper-expression of the alanine transaminases (ALT) in the liver of obese/diabetic mice and humans. Hepatocyte-selective silencing of both ALT enzymes revealed a clear role in systemic alanine clearance which related to glycemic control. In obese/diabetic mice, not only did silencing both ALT enzymes retard hyperglycemia, but also reversed skeletal muscle atrophy. This was due to a rescue of depressed skeletal muscle protein synthesis, with a liver-skeletal muscle amino acid metabolic crosstalk exemplified by ex vivo experiments. Mechanistically, chronic liver glucocorticoid and glucagon signaling driven liver alanine catabolism promoted hyperglycemia and skeletal muscle wasting. Taken together, here we reveal an endocrine-hepato-muscular metabolic cycle linking hyperglycemia and skeletal muscle atrophy in type 2 diabetes.

show abstract

“…As such, it has served as basis for interpreting the results of most selection experiments reported in the literature. [16][17][18][19][20][21][22][23] Yet this assumption is, implicitly, subject to a number of constraints, as can be gleaned from previously reported results of computational simulations of DEL affinity selections. 24 Notably, for the count-affinity relationship to robustly hold: (i ) all DEL members must be represented in equimolar amounts at the start of a screen or, alternatively, the starting concentration of each DEL member must be known; (ii ) each tag must unambiguously resolve to a single molecular entity; (iii ) there must be sufficient sequencing coverage and low level of sequencing noise present; (iv ) experimental conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Denoising DNA Encoded Library Screens with Sparse Learning

Kómár¹,

Kalinić²

2020

ACS Comb. Sci.

View full text Add to dashboard Cite

DNA-encoded libraries (DELs) are large, pooled collections of compounds in which every library member is attached to a stretch of DNA encoding its complete synthetic history. DEL-based hit discovery involves affinity selection of the library against a protein of interest, whereby compounds retained by the target are subsequently identified by next-generation sequencing of the corresponding DNA tags. When analyzing the resulting data, one typically assumes that sequencing output (i.e. read counts) is proportional to the binding affinity of a given compound, thus enabling hit prioritization and elucidation of any underlying structure-activity relationships (SAR). This assumption, though, tends to be severely confounded by a number of factors, including variable reaction yields, presence of incomplete products masquerading as their intended counterparts, and sequencing noise. In practice, these confounders are often ignored, potentially contributing to low hit validation rates, and universally leading to loss of valuable information. To address this issue, we have developed a method for comprehensively denoising DEL selection outputs. Our method, dubbed "deldenoiser", is based on sparse learning and leverages inputs that are commonly available within a DEL generation and screening workflow. Using simulated and publicly available DEL affinity selection data, we show that "deldenoiser" is not only able to recover and rank true binders much more robustly than read count-based approaches, but also that it yields scores which accurately capture the underlying SAR. The proposed method can, thus, be of significant utility in hit prioritization following DEL screens. File list (2) download file view on ChemRxiv deldenoiser_manuscript.pdf (2.78 MiB) download file view on ChemRxiv deldenoiser-SupportingInformation.pdf (10.59 MiB

show abstract

Discovery and Optimization of Potent, Selective, and in Vivo Efficacious 2-Aryl Benzimidazole BCATm Inhibitors

Cited by 40 publications

References 13 publications

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

An endocrine-hepato-muscular metabolic cycle links skeletal muscle atrophy and hyperglycemia in type 2 diabetes

Denoising DNA Encoded Library Screens with Sparse Learning

Contact Info

Product

Resources

About