Synthesis, Antitumor Evaluation, Molecular Modeling and Quantitative Structure-Activity Relationship (QSAR) of Novel 2-[(4-Amino-6-N-substituted-1,3,5-triazin-2-yl)methylthio]-4-chloro-5-methyl-N-(1H-benzo[d]imidazol-2(3H)-ylidene)Benzenesulfonamides

Design, Structure-Activity Relationships, and Computational Modeling Studies of a Series of α-Helix Biased, Ultra-Short Glucagon-like Peptide-1 Receptor Agonists

A systematic structure-activity and computational modeling analysis of a series of glucagon-like peptide-1 receptor (GLP-1R) agonists based upon an ultra-short GLP-1 peptide, H-His-Aib-Glu-Gly-Thr-Phe-Thr-Ser-Asp-Bip-Bip-NH2, was conducted. This highly potent 11-mer peptide led to a deeper understanding of the α-helical bias of strategic α-methylation within the linear parent template as well as optimization of GLP-1R agonist potency by 1000-fold. These data were correlated with previously reported co-structures of both full-length GLP-1 analogs and progenitor N-terminal GLP-1 fragment analogs related to such ultra-short GLP-1R agonist peptides. Furthermore, the development of a quantitative structure-activity relationship (QSAR) model to analyze these findings is described in this study.

Recent updates on 1,2,3-, 1,2,4-, and 1,3,5-triazine hybrids (2017-present): The anticancer activity, structure-activity relationships, and mechanisms of action

Cancer is one of the leading causes of death across the world, and the prevalence and mortality rates of cancer will continue to grow. Chemotherapeutics play a critical role in cancer therapy, but drug resistance and side effects are major hurdles to effective treatment, evoking an immediate need for the discovery of new anticancer agents. Triazines including 1,2,3-, 1,2,4-, and 1,3,5-triazine have occupied a propitious place in drug design and development due to their excellent pharmacological profiles. Mechanistically, triazine derivatives could interfere with various signaling pathways to induce cancer cell death. Hence, triazine derivatives possess potential in vitro and in vivo efficacy against diverse cancers. In particular, triazine hybrids are able to overcome drug resistance and reduce side effects. Moreover, several triazine hybrids such as brivanib (indole-containing pyrrolo[2,1-f][1,2,4]triazine), gedatolisib (1,3,5-triazine-urea hybrid), and enasidenib (1,3,5-triazine-pyridine hybrid) have already been available in the market. Accordingly, triazine hybrids are useful scaffolds for the discovery of novel anticancer chemotherapeutics. This review focuses on the anticancer activity of 1,2,3-, 1,2,4-, and 1,3,5-triazine hybrids, together with the structure-activity relationships and mechanisms of action developed from 2017 to the present. The enriched structure-activity relationships may be useful for further rational drug development of triazine hybrids as potential clinical candidates.

New 2-[(4-Amino-6-N-substituted-1,3,5-triazin-2-yl)methylthio]-N-(imidazolidin-2-ylidene)-4-chloro-5-methylbenzenesulfonamide Derivatives, Design, Synthesis and Anticancer Evaluation

In the search for new compounds with antitumor activity, new potential anticancer agents were designed as molecular hybrids containing the structures of a triazine ring and a sulfonamide fragment. Applying the synthesis in solution, a base of new sulfonamide derivatives 20–162 was obtained by the reaction of the corresponding esters 11–19 with appropriate biguanide hydrochlorides. The structures of the compounds were confirmed by spectroscopy (IR, NMR), mass spectrometry (HRMS or MALDI-TOF/TOF), elemental analysis (C,H,N) and X-ray crystallography. The cytotoxic activity of the obtained compounds toward three tumor cell lines, HCT-116, MCF-7 and HeLa, was examined. The results showed that some of the most active compounds belonged to the R¹ = 4-trifluoromethylbenzyl and R¹ = 3,5-bis(trifluoromethyl)benzyl series and exhibited IC₅₀ values ranging from 3.6 µM to 11.0 µM. The SAR relationships were described, indicating the key role of the R² = 4-phenylpiperazin-1-yl substituent for the cytotoxic activity against the HCT-116 and MCF-7 lines. The studies regarding the mechanism of action of the active compounds included the assessment of the inhibition of MDM2-p53 interactions, cell cycle analysis and apoptosis induction examination. The results indicated that the studied compounds did not inhibit MDM2-p53 interactions but induced G0/G1 and G2/M cell cycle arrest in a p53-independent manner. Furthermore, the active compounds induced apoptosis in cells harboring wild-type and mutant p53. The compound design was conducted step by step and assisted by QSAR models that correlated the activity of the compounds against the HCT-116 cell line with molecular descriptors.

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy

Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully translated into clinical cancer treatment. Here we developed a Quantitative Structure-Activity Relationships (QSAR) classification models using empirical molecular descriptors and fingerprints to predict the activity against the p53 protein, using the potency value with the active or inactive label, were developed. These models were built using in total 10,505 molecules that were extracted from the ChEMBL, ZINC and Reaxys® databases, and recent literature. Three machine learning (ML) techniques e.g., Random Forest, Support Vector Machine, Convolutional Neural Network were explored to build models for p53 inhibitor prediction. The performances of the models were successfully evaluated by internal and external validation. Moreover, based on the best in silico p53 model, a virtual screening campaign was carried out using 1443 FDA-approved drugs that were extracted from the ZINC database. A list of virtual screening hits was assented on base of some limits established in this approach, such as: (1) probability of being active against p53; (2) applicability domain; (3) prediction of the affinity between the p53, and ligands, through molecular docking. The most promising according to the limits established above was dihydroergocristine. This compound revealed cytotoxic activity against a p53-expressing CRC cell line with an IC₅₀ of 56.8 µM. This study demonstrated that the computer-aided drug design approach can be used to identify previously unknown molecules for targeting p53 protein with anti-cancer activity and thus pave the way for the study of a therapeutic solution for CRC.

Identification of Dipeptidyl Peptidase (DPP) Family Genes in Clinical Breast Cancer Patients via an Integrated Bioinformatics Approach

Breast cancer is a heterogeneous disease involving complex interactions of biological processes; thus, it is important to develop therapeutic biomarkers for treatment. Members of the dipeptidyl peptidase (DPP) family are metalloproteases that specifically cleave dipeptides. This family comprises seven members, including DPP3, DPP4, DPP6, DPP7, DPP8, DPP9, and DPP10; however, information on the involvement of DPPs in breast cancer is lacking in the literature. As such, we aimed to study their roles in this cancerous disease using publicly available databases such as cBioportal, Oncomine, and Kaplan–Meier Plotter. These databases comprise comprehensive high-throughput transcriptomic profiles of breast cancer across multiple datasets. Furthermore, together with investigating the messenger RNA expression levels of these genes, we also aimed to correlate these expression levels with breast cancer patient survival. The results showed that DPP3 and DPP9 had significantly high expression profiles in breast cancer tissues relative to normal breast tissues. High expression levels of DPP3 and DPP4 were associated with poor survival of breast cancer patients, whereas high expression levels of DPP6, DPP7, DPP8, and DPP9 were associated with good prognoses. Additionally, positive correlations were also revealed of DPP family genes with the cell cycle, transforming growth factor (TGF)-beta, kappa-type opioid receptor, and immune response signaling, such as interleukin (IL)-4, IL6, IL-17, tumor necrosis factor (TNF), and interferon (IFN)-alpha/beta. Collectively, DPP family members, especially DPP3, may serve as essential prognostic biomarkers in breast cancer.