ALBERT

Description: Guided by the structural optimization principle and the promising anticancer effect of the quinoxaline nucleus, a new series of novel HDAC inhibitors were designed and synthesized. The synthesized compounds were designed to bear the reported pharmacophoric features of the HDAC inhibitors in addition to an extra moiety to occupy the non-used vacant deep pocket of the HDAC receptor. The newly prepared compounds were evaluated for their in vitro anti-proliferative activities against HepG-2 and HuH-7 liver cancer cell lines. The tested compounds showed promising anti-proliferative activities against both cell lines. The most active ten candidates (6c, 6d, 6f, 6g, 6k, 6l, 7b, 8, 10h, and 12) were further evaluated for their effect on the gene expression levels of Bax as an apoptotic marker and Bcl-2 as an anti-apoptotic one. Moreover, they were evaluated for their ability to inhibit histone deacetylase (HDAC1, HDAC4, and HDAC6) activities. Compound 6c achieved the best cytotoxic activities on both HepG-2 and HuH-7 cell lines with IC50 values of 1.53 and 3.06 µM, respectively, and also it showed the most inhibitory activities on HDAC1, HDAC4, and HDAC6 with IC50 values of 1.76, 1.39, and 3.46 µM, respectively, compared to suberoylanilide hydroxamic acid (SAHA) as a reference drug (IC50 = 0.86, 0.97, and 0.93 µM, respectively). Furthermore, it achieved a more characteristic arrest in the growth of cell population of HepG-2 at both G0/G1 and S phases with 1.23-, and 1.18-fold, respectively, compared to that of the control, as determined by cell cycle analysis. Also, compound 6c showed a marked elevation in the AnxV-FITC apoptotic HepG-2 cells percentage in both early and late phases increasing the total apoptosis percentage by 9.98-, and 10.81-fold, respectively, compared to the control. Furthermore, docking studies were carried out to identify the proposed binding mode of the synthesized compounds towards the prospective target (HDAC4). In silico ADMET and toxicity studies revealed that most of the synthesized compounds have accepted profiles of drug-likeness with low toxicity. Finally, an interesting SAR analysis was concluded to help the future design of more potent HDACIs in the future by medicinal chemists.

Description: The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.