QSAR-driven rational design of novel DNA methyltransferase 1 inhibitors

Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

Synthesis, biological investigation, and in silico studies of 2-aminothiazole sulfonamide derivatives as potential antioxidants

Antioxidant compounds have gained current interest as potential protective agents for several therapeutic applications. Antimicrobial drug resistance and infectious diseases also still be concerning globally health issues. Accordingly, the discovery of new antioxidative and antimicrobial agents is essential for human well-being. Thiazole and sulfonamide are privileged scaffolds in drug discovery due to their various bioactive properties. In this study, a series of 2-aminothiazole sulfonamide derivatives (1 - 12) were synthesized and investigated for their antioxidant (i.e., DPPH and SOD-mimic) and antimicrobial activities. Among tested compounds, compound 8 was the most promising one with potent DPPH and SOD (%DPPH = 90.09 %, %SOD = 99.02 %). However, none of these compounds are active antimicrobial agents. Quantitative structure-activity relationship (QSAR) modeling was performed in which the key findings were further used to guide the rational design of additional derivatives. Two antioxidant QSAR models (i.e., DPPH and SOD) were constructed using multiple linear regression (MLR) with good predictive performance. An additional set of structurally modified compounds were designed based on QSAR findings to finally obtain 112 newly designed compounds in which their activities (DPPH and SOD) were predicted. Most of the modified compounds performed better activities than their prototypes. Mass, polarizability, electronegativity, the presence of C-F bond, van der Waals volume, and structural symmetry were revealed as key properties influencing antioxidant activities. In summary, this study demonstrated the combination used of chemical synthesis, experimental assays, and computer-aided drug design for developing novel antioxidants for potential medicinal applications. See also the graphical abstract(Fig. 1).

Downregulating DNA methyltransferase 3B by suppressing the PI3K/Akt signaling pathway enhances the chemosensitivity of glioblastoma to temozolomide

Glioblastoma (GBM) is the most common malignant brain tumor and has the poorest prognosis attributed to its chemoresistance to temozolomide (TMZ), the first-line drug for treating GBM. TMZ resistance represents a significant obstacle to successful GBM treatment, necessitating the development of new strategies to overcome this resistance and augment the chemosensitivity of GBM cells to TMZ. This study established a TMZ-resistant U251 (U251-TMZ) cell line by exposing it to increasing doses of TMZ in vitro. We focused on the DNA methyltransferase 3B (DNMT3B) gene, phosphorylated Akt (p-Akt), total Akt (t-Akt), phosphorylated PI3K (p-PI3K), and total PI3K (t-PI3K) protein expression. Results showed that the DNMT3B gene was significantly upregulated in the U251-TMZ cell line. The p-Akt and p-PI3K protein expression in U251-TMZ cells was also significantly elevated. Moreover, we found that DNMT3B downregulation was correlated with the increased chemosensitivity of GBM cells to TMZ. LY294002 suppressed the PI3K/Akt signaling pathway, leading to a notable inhibition of PI3K phosphorylation and a significant decrease in DNMT3B expression in U251-TMZ cells. Given that DNMT3B expression is mediated by the PI3K/Akt signaling pathway, its downregulation further increased the chemosensitivity of GBM cells to TMZ and therefore is a promising therapeutic for GBM treatment. Our results suggested that DNMT3B downregulation can inhibit the proliferation of GBM cells and induce GBM cell apoptosis in vitro. In addition, the PI3K/Akt signaling pathway plays an important role in the chemosensitivity of GBM cells to TMZ by regulating DNMT3B expression.

Computer-guided design of novel nitrogen-based heterocyclic sphingosine-1-phosphate (S1P) activators as osteoanabolic agents

Osteoanabolic agents, or drugs that promote bone formation, have gained considerable attention for osteoporosis management due to their curative and preventive potentials. Sphingosine-1-phosphate receptor 2 (S1PR2) is an attractive drug target, in which its activation leads to osteogenesis-promoting effect. Nitrogen-containing heterocyclic scaffolds (i.e., quinoxaline and indole) are promising pharmacophores possessing diverse bioactivities and were reported as S1PR2 activators. Quantitative structure-activity relationship (QSAR) modeling is a computational approach well-known as a fundamental tool for facilitating successful drug development. This study demonstrates the discovery of new S1PR2 activators using computational-driven rational design. Herein, an original dataset of nitrogen-containing S1PR2 activators was collected from ChEMBL database. The retrieved dataset was separated into two datasets according to their core scaffolds (i.e., quinoxaline and indole). QSAR modeling was performed using multiple linear regression (MLR) algorithm to successfully obtain two models with good predictive performance. The constructed models also revealed key properties playing essential roles for potent S1PR2 activation, such as Van der Waals volume (R2v+ and E3v), mass (MATS5m and Km), electronegativity (H3e), and number of 5-membered rings (nR05). Subsequently, the constructed models were further employed to guide rational design and predict S1PR2 activating effects of an additional set of 752 structurally modified compounds. Most of the modified compounds were predicted to have higher potency than their parents, and a set of promising potent newly designed compounds was highlighted. Additionally, drug-likeness prediction was performed to reveal that most of the newly designed compounds are druggable compounds with possibility for further development.

Decoding the Role of Epigenetics in Breast Cancer Using Formal Modeling and Machine-Learning Methods

Breast carcinogenesis is known to be instigated by genetic and epigenetic modifications impacting multiple cellular signaling cascades, thus making its prevention and treatments a challenging endeavor. However, epigenetic modification, particularly DNA methylation-mediated silencing of key TSGs, is a hallmark of cancer progression. One such tumor suppressor gene (TSG) RUNX3 (Runt-related transcription factor 3) has been a new insight in breast cancer known to be suppressed due to local promoter hypermethylation mediated by DNA methyltransferase 1 (DNMT1). However, the precise mechanism of epigenetic-influenced silencing of the RUNX3 signaling resulting in cancer invasion and metastasis remains inadequately characterized. In this study, a biological regulatory network (BRN) has been designed to model the dynamics of the DNMT1–RUNX3 network augmented by other regulators such as p21, c-myc, and p53. For this purpose, the René Thomas qualitative modeling was applied to compute the unknown parameters and the subsequent trajectories signified important behaviors of the DNMT1–RUNX3 network (i.e., recovery cycle, homeostasis, and bifurcation state). As a result, the biological system was observed to invade cancer metastasis due to persistent activation of oncogene c-myc accompanied by consistent downregulation of TSG RUNX3. Conversely, homeostasis was achieved in the absence of c-myc and activated TSG RUNX3. Furthermore, DNMT1 was endorsed as a potential epigenetic drug target to be subjected to the implementation of machine-learning techniques for the classification of the active and inactive DNMT1 modulators. The best-performing ML model successfully classified the active and least-active DNMT1 inhibitors exhibiting 97% classification accuracy. Collectively, this study reveals the underlined epigenetic events responsible for RUNX3-implicated breast cancer metastasis along with the classification of DNMT1 modulators that can potentially drive the perception of epigenetic-based tumor therapy.

Investigations on Anticancer and Antimalarial Activities of Indole-Sulfonamide Derivatives and In Silico Studies

A library of 44 indole-sulfonamide derivatives (1-44) were investigated for their cytotoxic activities against four cancer cell lines (i.e., HuCCA-1, HepG2, A549, and MOLT-3) and antimalarial effect. Most of the studied indoles exhibit anticancer activity against the MOLT-3 cell line, whereas only hydroxyl-containing bisindoles displayed anticancer activities against the other tested cancer cells as well as antimalarial effect. The most promising anticancer compounds were noted to be CF₃, Cl, and NO₂ derivatives of hydroxyl-bearing bisindoles (30, 31, and 36), while the most promising antimalarial compound was an OCH₃ derivative of non-hydroxyl-containing bisindole 11. Five quantitative structure-activity relationship (QSAR) models were successfully constructed, providing acceptable predictive performance (training set: R = 0.6186-0.9488, RMSE = 0.0938-0.2432; validation set: R = 0.4242-0.9252, RMSE = 0.1100-0.2785). QSAR modeling revealed that mass, charge, polarizability, van der Waals volume, and electronegativity are key properties governing activities of the compounds. QSAR models were further applied to guide the rational design of an additional set of 22 compounds (P1-P22) in which their activities were predicted. The prediction revealed a set of promising virtually constructed compounds (P1, P3, P9, P10, and P16) for further synthesis and development as anticancer and antimalarial agents. Molecular docking was also performed to reveal possible modes of bindings and interactions between the studied compounds and target proteins. Taken together, insightful structure-activity relationship information obtained herein would be beneficial for future screening, design, and structural optimization of the related compounds.

Recent progress on cheminformatics approaches to epigenetic drug discovery

The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.