QSAR modeling, docking, ADME and reactivity of indazole derivatives as antagonizes of estrogen receptor alpha (ER-α) positive in breast cancer

In silico screening of a series of 1,6-disubstituted 1H-pyrazolo[3,4-d]pyrimidines as potential selective inhibitors of the Janus kinase 3

Rheumatoid arthritis is a common chronic disabling inflammatory disease that is characterized by inflammation of the synovial membrane and leads to discomfort. In the current study, twenty-seven 1,6-disubstituted 1H-pyrazolo[3,4-d]pyrimidines were tested as potential selective inhibitors of the tyrosine-protein kinase JAK3 using a number of molecular modeling methods. The activity of the screened derivatives was statistically quantified using multiple linear regression and artificial neural networks. To assess the quality, robustness, and predictability of the generated models, the leave-one-out cross-validation method was applied with favorable results (Q2 = 0.75) and Y-randomization. In addition, the evaluation of the predictive ability of the established model was confirmed by means of an external validation using a composite test set and an applicability domain approach. The covalent docking indicated that the tested 1H-pyrazolo[3,4-d]pyrimidines containing the acrylic aldehyde moiety had irreversible interaction with the residue Cys909 in the active sites of the tyrosine-protein kinase JAK3 by Michael addition. The molecular dynamics for three selected derivatives (compounds 9, 12, and 18) were used to verify the covalent docking by determining the stability of hydrogen bonding interactions with active sites, which are needed to stop tyrosine-protein kinase JAK3. The results obtained showed that the tested compounds containing acrylic aldehyde moiety had favorable binding free energies, indicating a strong affinity for the JAK3 enzyme. Overall, this current study suggests that the tested compounds containing the acrylic aldehyde moiety have the potential to act as anti-JAK3 inhibitors. They could be explored further to be used as treatment options for rheumatoid arthritis.Communicated by Ramaswamy H. Sarma.

In-silico design of novel potential HDAC inhibitors from indazole derivatives targeting breast cancer through QSAR, molecular docking and pharmacokinetics studies

Latest studies confirmed that abnormal function of histone deacetylase (HDAC) plays a pivotal role in formation of tumors and is a potential therapeutic target for treating breast cancer. In this research, in-silico drug discovery approaches via quantitative structure activity relationship (QSAR) and molecular docking simulations were adapted to 43 compounds of indazole derivatives with HDAC inhibition for anticancer activity against breast cancer. The QSAR models were built from multiple linear regression (MLR), and models predictability was cross-validated by leave-one-out (LOO) method. Based on these results, compounds C32, C26 and C31 from model 3 showed superior inhibitory activity with pIC50 of 9.30103, 9.1549 and 9.1549. We designed 10 novel compounds with molecular docking scores ranging from -7.9 to -9.3 kcal/mol. The molecular docking simulation results reveal that amino acid residues ILE1122 and PRO1123 play a significant role in bonding with 6CE6 protein. Furthermore, newly designed compounds P5, P2 and P7 with high docking scores of -9.3 kcal/mol, -8.9 kcal/mol and -8.8 kcal/mol than FDA-approved drug Raloxifene (-8.5 kcal/mol) and aid in establishment of potential drug candidate for HDAC inhibitors. The in-silico ADME functionality is used in the final phase to evaluate newly designed inhibitors as potential drug candidates. The results suggest that newly designed compounds P5, P2 and P7 can be used as a potential anti-breast cancer drug candidate.

Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures

Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp2-hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.

A computational study of potent series of selective estrogen receptor degraders for breast cancer therapy

A detailed multistep framework combining quantitative structure-activity relationship, global reactivity, absorption, distribution, metabolism and elimination properties, molecular docking and molecular dynamics simulation (MD) on a series of Selective Estrogen Receptor Down-Regulators (SERDs) interacting with Estrogen Receptor α (ERα) has been performed. The partial least squares regression method derived an empirical model with better predictive capability. The results of global reactivity descriptors revealed that all the compounds are considered strong electrophiles, allowing them to participate in polar reactions more easily. The Brain Or IntestinaL EstimateD permeation diagram revealed that compounds 49 and 31 were predicted to be well absorbed by the human gastrointestinal tract and would not enter the brain. The elucidation of the binding mode between the most active compounds that comply with Lipinski's and Veber's rules from the dataset and ERα targets was explored by molecular docking. The MD simulations were performed for 100 ns on the best compounds, which indicated their stability state under dynamics simulations. These findings are expected to help predict the anticancer activities of the studied SERD compounds and better understand their binding mechanism with ERα targets.Communicated by Ramaswamy H. Sarma.

Structural optimization of pyrrolopyrimidine BTK inhibitors based on molecular simulation

CONTEXT

BTK is a critical regulator involved in the proliferation, differentiation, and apoptosis of B cells. BTK inhibitors can effectively alleviate various diseases such as tumors, leukemia, and asthma. During this study, a range of novel BTK inhibitors were designed using 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation.

METHODS

We selected 41 pyrrolopyrimidine derivatives as BTK inhibitors to structure a 3D-QSAR model. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were adopted to research the connection between the pharmacological activities and chemical structures of the compounds. The CoMFA model (q2 = 0.519, R2 = 0.971), CoMSIA model (q2 = 0.512, R2 = 0.990), and external validation demonstrated excellent predictive performance and reliability of the 3D-QSAR model. We designed eight novel molecules with higher inhibitory activities according to the three-dimensional equipotential fields and explored the interactions between the compounds and BTK by molecular docking, which showed that the novel molecules had higher binding affinities with BTK than the template molecule 18. Then, the results of molecular docking were further verified by MD simulation, which showed that amino acid residues such as Leu528, Val416, and Met477 played vital parts in the interaction, and the binding free energy analysis showed that the novel molecules had higher stability with BTK. Finally, the ADME/T properties were predicted for all of the novel compounds, and the results showed that the majority of them had favorable pharmacokinetic properties. Therefore, this study provides strong support for the development of novel BTK inhibitors.

MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors

Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study presents a framework for analyzing the quantitative structure-activity relationship (QSAR) of estrogen receptor alpha inhibitors. Our approach utilizes supervised learning, integrating self-attention Transformer and molecular graph information, to predict estrogen receptor alpha inhibitors. We established five classification models for predicting these inhibitors in breast cancer. Among these models, our proposed MATH model achieved remarkable precision, recall, F1 score, and specificity, with values of 0.952, 0.972, 0.960, and 0.922, respectively, alongside an ROC AUC of 0.977. MATH exhibited robust performance, suggesting its potential to assist pharmaceutical and health researchers in identifying candidate compounds for estrogen alpha inhibitors and guiding drug discovery pathways.

Research progress on tamoxifen and its analogs associated with nuclear receptors

Tamoxifen, a triphenylethylene-based selective estrogen-receptor modulator, is a landmark drug for the treatment of breast cancer and is also used for treating liver cancer and osteoporosis. Structural studies of tamoxifen have led to the synthesis of more than 20 novel tamoxifen analogs as receptor modulators, including 16 ERα modulators 2-17, an ERRβ inverse agonist 19 and six ERRγ inverse agonists 20-25. This paper summarizes the research progress and structure-activity relationships of tamoxifen analogs modulating these three nuclear receptors reported in the literature, and introduces the relationship between these three nuclear receptor-mediated diseases and tamoxifen analogs to guide the research of novel tamoxifen analogs.

Antioxidant Properties of Curcumin Analogues to Inhibit Thermal Degradation of Low-Density Polyethylene: Experimental and DFT Study

Curcumin can be isolated from plants (Curcuma longa) and it belongs to the ginger family. It exhibits many useful properties and acts as an antioxidant. The aim of the current study was to prepare eight curcumin analogues and investigate their antioxidant activities to inhibit the thermal degradation of low-density polyethylene (LDPE). The carbonyl index (CI) was measured to test the effectiveness of the curcumin analogues. Various doses (0.5, 1, 2, 4, and 6% wt/wt) of a mixture containing LDPE and curcumin analogues were prepared, and the CI was measured. The eight curcumin analogues were found to have good to excellent antioxidant activity against the degradation of LDPE. It was clear that the curcumin analogue derived from vanillin and acetone has the highest antioxidant activity. The density functional theory study was conducted for the eight curcumin analogues to test their reactivity and stability. Again, the global reactivity descriptors analysis showed that compound derived from vanillin and acetone was the most reactive compound to inhibit thermal degradation of LDPE.

In silico docking and ADME study of deketene curcumin derivatives (DKC) as an aromatase inhibitor or antagonist to the estrogen-alpha positive receptor (Erα+): potent application of breast cancer

Regardless of many extensive studies, hormonal-based breast cancer is the most common cause of cancer-related mortality of females worldwide. Indeed, estrogen receptor-positive (ER +) is the communal subtype in breast cancer. To treat this, three types of medications are typically used: selective estrogen receptor modulators (SERMs), selective estrogen receptor down modulators (SERDMs), and aromatase inhibitors (AIs), all of which directly interact with the activation of the estrogen signaling pathway and its formation. Despite their effectiveness, the development of new treatments is required since clinical efficacy is restricted owing to resistance. As a result, in silico studies for drug discovery are booming over the decades because of their affordability and less time-consuming features. Here, 25 deketene curcumin derivatives have been selected for docking studies through MVD software over the positive type of breast cancer through both the treatment hosts Erα + receptor and aromatase. DKC compounds are used because they have several pharmacological uses, including anti-cancer, anti-diabetic, anti-viral, anti-fungal, and anti-bacterial properties. Moreover, an ADME study was carried out for DKC derivatives that reveal the optimum drug-likeness profile. From 25 derivatives, the results showed a better MolDock score, hydrogen bonding, and steric interaction between compounds DKC-10, DKC-20, and DKC-21 with Erα + and aromatase. Although the study was done on both the treatable path hosts, better results were obtained with Erα + as an antagonist. Therefore, it is proposed that three selected DKC derivatives would be better therapeutic agents against breast cancer.

Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2

The recent new contagion coronavirus 2019 (COVID-19) disease is a new generation of severe acute respiratory syndrome coronavirus-2 SARS-CoV-2 which infected millions confirmed cases and hundreds of thousands death cases around the world so far. Molecular docking combined with molecular dynamics is one of the most important tools of drug discovery and drug design, which it used to examine the type of binding between the ligand and its protein enzyme. Global reactivity has important properties, which enable chemists to understand the chemical reactivity and kinetic stability of compounds. In this study, molecular docking and reactivity were applied for eighteen drugs, which are similar in structure to chloroquine and hydroxychloroquine, the potential inhibitors to angiotensin-converting enzyme (ACE2). Those drugs were selected from DrugBank. The reactivity, molecular docking and molecular dynamics were performed for two receptors ACE2 and [SARS-CoV-2/ACE2] complex receptor in two active sites to find a ligand, which may inhibit COVID-19. The results obtained from this study showed that Ramipril, Delapril and Lisinopril could bind with ACE2 receptor and [SARS-CoV-2/ACE2] complex better than chloroquine and hydroxychloroquine. This new understanding should help to improve predictions of the impact of such alternatives on COVID-19.Communicated by Ramaswamy H. Sarma.

Ligand based 3D-QSAR model, pharmacophore, molecular docking and ADME to identify potential fibroblast growth factor receptor 1 inhibitors

The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby significantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. In this study, a combination of structure-based drug carriers and molecular docking-based virtual screening was used to screen new potential FGFR1 inhibitors. Forty eight known inhibitors were collected to establish 3 D-QSAR models and pharmacophore models, investigate the relationship between the activity and conformation of compounds, and verify the efficiency of pharmacophore. In Accelrys Discovery Studio 2016, the ZINC database was filtered by Lipinski's Rule of Five and SMART's filtration. Then, Hypo01 was used for virtual screening of ZINC database. Compounds with predicted activity values less than 1 μM were molecularly docked with FGFR1 protein crystals, the docking results were observed, and the interaction between compounds and targets was studied. The absorption, distribution, metabolism and excretion (ADME) and toxicity of potential inhibitors were studied, and a compound with new structural scaffolds were obtained. It could be further studied to explore their better therapeutic effects.Communicated by Ramaswamy H. Sarma.