
Journal of Medicinal Chemistry p. 8903 - 8925,23 (2012)
Update date:2022-08-04
Topics:
Wong, Jason C.
Tang, Guozhi
Wu, Xihan
Liang, Chungen
Zhang, Zhenshan
Guo, Lei
Peng, Zhenghong
Zhang, Weixing
Lin, Xianfeng
Wang, Zhanguo
Mei, Jianghua
Chen, Junli
Pan, Song
Zhang, Nan
Liu, Yongfu
Zhou, Mingwei
Feng, Lichun
Zhao, Weili
Li, Shijie
Zhang, Chao
Zhang, Meifang
Rong, Yiping
Jin, Tai-Guang
Zhang, Xiongwen
Ren, Shuang
Ji, Ying
Zhao, Rong
She, Jin
Ren, Yi
He, Yun
Chen, Li
Xu, Chunping
Chen, Dawei
Cai, Jie
Chen, Taiping
Shan, Song
Pan, Desi
Ning, Zhiqiang
Lu, Xianping
Herein, we describe the pharmacokinetic optimization of a series of class-selective histone deacetylase (HDAC) inhibitors and the subsequent identification of candidate predictive biomarkers of hepatocellular carcinoma (HCC) tumor response for our clinical lead using patient-derived HCC tumor xenograft models. Through a combination of conformational constraint and scaffold hopping, we lowered the in vivo clearance (CL) and significantly improved the bioavailability (F) and exposure (AUC) of our HDAC inhibitors while maintaining selectivity toward the class I HDAC family with particular potency against HDAC1, resulting in clinical lead 5 (HDAC1 IC50 = 60 nM, mouse CL = 39 mL/min/kg, mouse F = 100%, mouse AUC after single oral dose at 10 mg/kg = 6316 h·ng/mL). We then evaluated 5 in a biomarker discovery pilot study using patient-derived tumor xenograft models, wherein two out of the three models responded to treatment. By comparing tumor response status to compound tumor exposure, induction of acetylated histone H3, candidate gene expression changes, and promoter DNA methylation status from all three models at various time points, we identified preliminary candidate response prediction biomarkers that warrant further validation in a larger cohort of patient-derived tumor models and through confirmatory functional studies.
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Doi:10.1080/15533174.2012.680161
(2012)Doi:10.1002/pola.26202
(2012)Doi:10.1002/pola.26208
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(2020)Doi:10.1021/jm300878g
(2012)