QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2

Design and synthesis of new 1,2,4-oxadiazole/quinazoline-4-one hybrids with antiproliferative activity as multitargeted inhibitors

Introduction

The combination of BRAF and tyrosine kinase (TK) inhibitors has been demonstrated to be highly effective in inhibiting tumor development and is an approach for overcoming resistance in clinical trials. Accordingly, a novel series of 1,2,4-oxadiazole/quinazoline-4-one hybrids was developed as antiproliferative multitargeted inhibitors.

Methods

The structures of the newly synthesized compounds 9a-o were validated using IR, NMR, MS, and elemental techniques. 9a-o were tested as antiproliferative agents.

Results and Discussion

The results showed that the majority of the tested compounds showed significant antiproliferative action with 9b, 9c, 9h, 9k, and 9l being the most potent. Compounds 9b, 9c, 9h, 9k, and 9l were tested as EGFR and BRAFV600E inhibitors. These in vitro tests revealed that compounds 9b, 9c, and 9h are strong antiproliferative agents that may act as dual EGFR/BRAFV600E inhibitors. 9b, 9c, and 9h were further investigated for their inhibitory effect on mutant EGFR (EGFRT790M), and the results showed that the tested compounds had considerable inhibitory action. Cell cycle study and apoptosis detection demonstrated that compound 9b exhibits cell cycle arrest at the G2/M transition. Molecular docking simulations reveal the binding mechanism of the most active antiproliferative agents.

Design, synthesis and antimicrobial activity of novel quinoline derivatives: an in silico and in vitro study

A series of new quinoline derivatives has been designed, synthesized and evaluated as antibacterial and antifungal agents functioning as peptide deformylase enzyme (PDF) inhibitors and fungal cell wall disruptors on the basis of computational and experimental methods. The molecular docking and ADMET assessment aided in the synthesis of quinoline derivatives starting from 6-amino-4-methyl-1H-quinoline-2-one substituted with different types of sulfonyl/benzoyl/propargyl moieties. These newly synthesized compounds were evaluated for their in vitro antibacterial and antifungal activity. Antibacterial screening of all compounds showed excellent MIC value (MIC, 50 - 3.12 µg/mL) against bacterial strains, viz. Bacillus cerus, Staphylococcus, Pseudomonas and Escherichia coli. Compounds 2 and 6 showed better activity. Fractional inhibitory concentration (FIC) values of compounds were lowered by 1/2 to 1/128 of the original MIC values when a combinatorial screening with reference drugs was performed. Further, antifungal screening against fungal strains, viz. A. flavus, A. niger, F. oxysporum and C. albicans also showed that all compounds were potentially active and compound 6 being the most potent. Further, the cytotoxicity experiments revealed that compound 6 was the least toxic molecule. The molecular dynamics (MD) simulation investigations elucidated the conformational stability of compound 6-PDF complex with flexible binding pocket residues. The highest number of stable hydrogen bonds with the PDF residues during the entire simulation time illustrated strong binding affinity of compound 6 with PDF.Communicated by Ramaswamy H. Sarma.

Synthesis of a new series of 4-pyrazolylquinolinones with apoptotic antiproliferative effects as dual EGFR/BRAFV600E inhibitors

The current study focuses on developing a single molecule that acts as an antiproliferative agent with dual or multi-targeted action, reducing drug resistance and adverse effects. A new series of 4-pyrazolylquinolin-2-ones (5a-j) with apoptotic antiproliferative effects as dual EGFR/BRAFV600E inhibitors were designed and synthesized. Compounds 5a-j were investigated for their cell viability effect against a normal cell line (MCF-10A). Results showed that none of the compounds were cytotoxic, and all 5a-j demonstrated more than 90% cell viability at 50 μM concentration. Using erlotinib as a reference, the MTT assay investigated the antiproliferative impact of targets 5a-j against four human cancer cell lines. Compounds 5e, 5f, 5h, 5i, and 5j were the most potent antiproliferative agents with GI50 values of 42, 26, 29, 34, and 37 nM, making compounds 5f and 5h more potent than erlotinib (GI50 = 33 nM). Moreover, compounds 5e, 5f, 5h, 5i, and 5j were further investigated as dual EGFR/BRAFV600E inhibitors, and results revealed that compounds 5f, 5h, and 5i are potent antiproliferative agents that act as dual EGFR/BRAFV600E inhibitors. Cell cycle analysis and apoptosis detection revealed that compound 5h displaying cell cycle arrest at the G1 transition could induce apoptosis with a high necrosis percentage. Docking studies revealed that compound 5f exhibited a strong affinity for EGFR and BRAFV600E, with high docking scores of -8.55 kcal mol-1 and -8.22 kcal mol-1, respectively. Furthermore, the ADME analysis of compounds 5a-j highlighted the diversity in their pharmacokinetic properties, emphasizing the importance of experimental validation.

Potentials and future perspectives of multi-target drugs in cancer treatment: the next generation anti-cancer agents

Cancer is a major public health problem worldwide with more than an estimated 19.3 million new cases in 2020. The occurrence rises dramatically with age, and the overall risk accumulation is combined with the tendency for cellular repair mechanisms to be less effective in older individuals. Conventional cancer treatments, such as radiotherapy, surgery, and chemotherapy, have been used for decades to combat cancer. However, the emergence of novel fields of cancer research has led to the exploration of innovative treatment approaches focused on immunotherapy, epigenetic therapy, targeted therapy, multi-omics, and also multi-target therapy. The hypothesis was based on that drugs designed to act against individual targets cannot usually battle multigenic diseases like cancer. Multi-target therapies, either in combination or sequential order, have been recommended to combat acquired and intrinsic resistance to anti-cancer treatments. Several studies focused on multi-targeting treatments due to their advantages include; overcoming clonal heterogeneity, lower risk of multi-drug resistance (MDR), decreased drug toxicity, and thereby lower side effects. In this study, we'll discuss about multi-target drugs, their benefits in improving cancer treatments, and recent advances in the field of multi-targeted drugs. Also, we will study the research that performed clinical trials using multi-target therapeutic agents for cancer treatment.

Ligand based-design of potential schistosomiasis inhibitors through QSAR, homology modeling, molecular dynamics, pharmacokinetics, and DFT studies

Objectives

Schistosomiasis, a neglected tropical disease, is a leading cause of mortality in affected geographic areas. Currently, because no vaccine for schistosomiasis is available, control measures rely on widespread administration of the drug praziquantel (PZQ). The mass administration of PZQ has prompted concerns regarding the emergence of drug resistance. Therefore, new therapeutic targets and potential compounds are necessary to combat schistosomiasis.

Methods

Twenty-four potent derivatives of PZQ were optimized via density functional theory (DFT) at the B3LYP/6-31G∗ level. Quantitative structureactivity relationship (QSAR) models were generated and statistically validated, and a lead candidate was selected to develop therapeutic options with improved efficacy against schistosomiasis. The biological and binding energies of the designed compounds were evaluated. In addition, molecular dynamics; drug-likeness; absorption, distribution, metabolism, excretion, and toxicity (ADMET); and DFT studies were performed on the newly designed compounds.

Results

Five QSAR models were generated, among which model 1 had favorable validation parameters (R2train: 0.957, R2adj: 0.941, LOF: 0.101, Q2cv: 0.906, and R2test: 0.783) and was chosen to identify a lead candidate. Other statistical parameters for the chosen model included variance inflation factor values ranging from 1.242 to 1.678, and a Y-scrambling coefficient (cRp2) of 0.747. Five new compounds were designed with improved predicted activity (ranging from 5.081 to 7.022) surpassing those of both the lead compound and PZQ (predicted pEC50 of 5.545). Molecular dynamics simulation revealed high binding affinity of the proposed compounds toward the target receptor. ADMET and drug-likeness assessments indicated adherence to Lipinski's rule of five criteria, thereby suggesting pharmacological and oral safety. In addition, DFT analysis indicated resistance to electronic alteration during chemical reactions.

Conclusion

The proposed compounds exhibited potential drug characteristics, thus indicating their suitability for further investigation to enhance schistosomiasis treatment options.

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Application of QSAR models for acute toxicity of tetrazole compounds administrated orally and intraperitoneally in rat and mouse

Tetrazoles and their derivatives possess various biological activities, such as antibacterial, anti-fungal, and other activities. However, these compounds may induce specific cumulative and toxic effects in living organisms. Therefore, quantitative structure-activity relationship (QSAR) models were constructed to study the acute oral toxicity of tetrazoles in rats and mice. The toxicity data of 111 tetrazole compounds were collected using the ChemIDplus, ChEMBL and ECHA databases as response variables, while the PaDEL-descriptor generated the 2D descriptors as independent variables. The models were developed and validated following the OECD guidelines by the DTC-QSAR tool. Three QSAR models were successfully established for the oral routes of rat and mouse and the intraperitoneal route of mouse, respectively. The scatter plots showed high consistency between the training and test data sets. All the models successfully met the external and internal validation criteria. Most of the descriptors kept in the final models exhibited positive correlations with toxicity, whereas only 6 descriptors exhibited negative associations. Several chemicals were identified as response or structural outliers, based on the standardized residuals and leverage values. In conclusion, the findings of this investigation demonstrate that the proposed QSAR models hold promise in forecasting the acute toxicity of recently developed or synthesized tetrazole compounds, thereby mitigating potential risks to human health and the environment.

Quinazolin-4-one/3-cyanopyridin-2-one Hybrids as Dual Inhibitors of EGFR and BRAFV600E: Design, Synthesis, and Antiproliferative Activity

A novel series of hybrid compounds comprising quinazolin-4-one and 3-cyanopyridin-2-one structures has been developed, with dual inhibitory actions on both EGFR and BRAFV600E. These hybrid compounds were tested in vitro against four different cancer cell lines. Compounds 8, 9, 18, and 19 inhibited cell proliferation significantly in the four cancer cells, with GI50 values ranging from 1.20 to 1.80 µM when compared to Doxorubicin (GI50 = 1.10 µM). Within this group of hybrids, compounds 18 and 19 exhibited substantial inhibition of EGFR and BRAFV600E. Molecular docking investigations provided confirmation that compounds 18 and 19 possess the capability to inhibit EGFR and BRAFV600E. Moreover, computational ADMET prediction indicated that most of the newly synthesized hybrids have low toxicity and minimal side effects.

Design, Synthesis, and Biological Evaluation of Novel 3-Cyanopyridone/Pyrazoline Hybrids as Potential Apoptotic Antiproliferative Agents Targeting EGFR/BRAFV600E Inhibitory Pathways

A series of novel 3-cyanopyridone/pyrazoline hybrids (21-30) exhibiting dual inhibition against EGFR and BRAFV600E has been developed. The synthesized target compounds were tested in vitro against four cancer cell lines. Compounds 28 and 30 demonstrated remarkable antiproliferative activity, boasting GI50 values of 27 nM and 25 nM, respectively. These hybrids exhibited dual inhibitory effects on both EGFR and BRAFV600E pathways. Compounds 28 and 30, akin to Erlotinib, displayed promising anticancer potential. Compound 30 emerged as the most potent inhibitor against cancer cell proliferation and BRAFV600E. Notably, both compounds 28 and 30 induced apoptosis by elevating levels of caspase-3 and -8 and Bax, while downregulating the antiapoptotic Bcl2 protein. Molecular docking studies confirmed the potential of compounds 28 and 30 to act as dual EGFR/BRAFV600E inhibitors. Furthermore, in silico ADMET prediction indicated that most synthesized 3-cyanopyridone/pyrazoline hybrids exhibit low toxicity and minimal adverse effects.

Design, Synthesis, Antiproliferative Actions, and DFT Studies of New Bis-Pyrazoline Derivatives as Dual EGFR/BRAFV600E Inhibitors

Some new Bis-pyrazoline hybrids 8-17 with dual EGFR and BRAFV600E inhibitors have been developed. The target compounds were synthesized and tested in vitro against four cancer cell lines. Compounds 12, 15, and 17 demonstrated strong antiproliferative activity with GI50 values of 1.05 µM, 1.50 µM, and 1.20 µM, respectively. Hybrids showed dual inhibition of EGFR and BRAFV600E. Compounds 12, 15, and 17 inhibited EGFR-like erlotinib and exhibited promising anticancer activity. Compound 12 is the most potent inhibitor of cancer cell proliferation and BRAFV600E. Compounds 12 and 17 induced apoptosis by increasing caspase 3, 8, and Bax levels, and resulted in the downregulation of the antiapoptotic Bcl2. The molecular docking studies verified that compounds 12, 15, and 17 have the potential to be dual EGFR/BRAFV600E inhibitors. Additionally, in silico ADMET prediction revealed that most synthesized bis-pyrazoline hybrids have low toxicity and adverse effects. DFT studies for the two most active compounds, 12 and 15, were also carried out. The values of the HOMO and LUMO energies, as well as softness and hardness, were computationally investigated using the DFT method. These findings agreed well with those of the in vitro research and molecular docking study.

Modelling PIP4K2A inhibitory activity of 1,7-naphthyridine analogues using machine learning and molecular docking studies

PIP4K2A is a type II lipid kinase that catalyzed the rate-limiting step of the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2). PIP4K2A has been intricately linked to the inhibition of various types of tumors via reactive oxygen species-mediated apoptosis, making it an important therapeutic target. In the quest of finding biologically active substances with efficient PIP4K2A inhibitory activity, machine learning algorithms were used to investigate the quantitative relationship between structures and inhibitory activities of 1,7-naphthyridine analogues. Three machine learning algorithms (MLR, ANN, and SVM) were used to develop QSAR models that can effectively predict the PIP4K2A inhibitory activity of a library of 1,7-naphthyridine analogues. The cascaded feature selection method was performed by sequential application of GFA and MP5 algorithms to identify a molecular descriptor subset that can best describe the PIP4K2A inhibitory activity of 1,7-naphthyridine analogues. PIP4K2A inhibitory activities predicted by the ML models were strongly correlated with the experimental values. The QSAR Modelling indicates that the best-performing ML model was SVM with the RBF kernel function. The SVM model performed very well in predicting PIP4K2A inhibitory activity of the 1,7-naphthyridine analogues with RTR and QEX values of 0.9845 and 0.8793 respectively. To further gain more structural insight into the origin of PIP4K2A inhibitory activity of 1,7-naphthyridine analogues, molecular docking studies were performed. The results indicate that five compounds; 15, 25, 13, 09, and 28 were found to have a high binding affinity with the receptor molecules. Hydrogen bonding, pi-pi interaction, and pi-cation interactions were found to modulate the binding interaction of the inhibitors. Although the SVM gives essentially a black-box model which cannot be readily interpreted, using SVM in tandem with MLR and ANN provides a unique perspective in building robust QSAR predictive models. The superior predictive performance of the ML models and the explanatory power of MLR models were combined to provide a unique insight into the structure-activity relationship of 1,7-naphthyridine inhibitors. This is relevant in that it provides information that can be invaluable as guidelines for the design of novel PIP4K2A inhibitors.

Design and prediction of novel pyrazole derivatives as potential anti-cancer compounds based on 2D-2D-QSAR study against PC-3, B16F10, K562, MDA-MB-231, A2780, ACHN and NUGC cancer cell lines

Despite the decades of scientific studies for developing promising new therapies, cancer remains a major cause of illness and mortality, worldwide. Several cancer types are the major topic of research in drug discovery programs due to their global incidence cases and growing frequency. In the present study, using two different statistical approaches PCA (principal component analysis) and PLS (partial least squares), six 2D-QSAR (quantitative structure activity relationship) models have been developed for the set of compounds retrieved against seven cancer cell lines vizPC-3, B16F10, K562, MDA-MB-231, A2780, and ACHN. For the creation and validation of 2D-QSAR models, OECD (Organization for Economic Co-operation and Development) requirements have been strictly followed. All of the generated 2D-QSAR models produce a significant and high correlation coefficient value with several other statistical parameters. Moreover, developed 2D-QSAR models have been used for activity predictions of in-house synthesized 63 pyrazole derivatives compounds. Precisely, most statistically significant and accepted2D-QSAR model generated for each cancer cell line has been used to predict the pIC₅₀ value (anti-cancer activity) of all 63 synthesized pyrazole derivatives. Furthermore, designing of novel pyrazole derivatives has been carried out by substituting the essential functional groups based on the best derived 2D-QSAR models for each cancer cell line, more precisely, based on the most significant molecular descriptors with enhanced anti-cancer activity. Finally, the prediction of the new designed molecules reveals higher pIC₅₀ than the standard compounds.

Carcinogenicity prediction using the index of ideality of correlation

Carcinogenicity testing is necessary to protect human health and comply with regulations, but testing it with the traditionally used two-year rodent studies is time-consuming and expensive. In certain cases, such as for impurities, alternative methods may be convenient. Thus there is an urgent need for alternative approaches for reliable and robust assessments of carcinogenicity. The Monte Carlo technique with CORAL software is a tool to tackle this task for unknown compounds using available experimental data for a representative set of compounds. The models can be constructed with the simplified molecular input line entry system without additional physicochemical descriptors. We describe here a model based on a data set of 1167 substances. Matthew's correlation coefficient values for calibration and validation sets are 0.747 and 0.577, respectively. Double bonds between carbon atoms and double bonds of oxygen atoms are the molecular features that indicate the carcinogenic potential of a compound.

Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power

The trade-off between a machine learning (ML) and deep learning (DL) model's predictability and its interpretability has been a rising concern in central nervous system-related quantitative structure-activity relationship (CNS-QSAR) analysis. Many state-of-the-art predictive modeling failed to provide structural insights due to their black box-like nature. Lack of interpretability and further to provide easy simple rules would be challenging for CNS-QSAR models. To address these issues, we develop a protocol to combine the power of ML and DL to generate a set of simple rules that are easy to interpret with high prediction power. A data set of 940 market drugs (315 CNS-active, 625 CNS-inactive) with support vector machine and graph convolutional network algorithms were used. Individual ML/DL modeling methods were also constructed for comparison. The performance of these models was evaluated using an additional external dataset of 117 market drugs (42 CNS-active, 75 CNS-inactive). Fingerprint-split validation was adopted to ensure model stringency and generalizability. The resulting novel hybrid ensemble model outperformed other constituent traditional QSAR models with an accuracy of 0.96 and an F1 score of 0.95. With the power of the interpretability provided with this protocol, our model laid down a set of simple physicochemical rules to determine whether a compound can be a CNS drug using six sub-structural features. These rules displayed higher classification ability than classical guidelines, with higher specificity and more mechanistic insights than just for blood-brain barrier permeability. This hybrid protocol can potentially be used for other drug property predictions.

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Melanoma is recognized as the most dangerous type of skin cancer, with high mortality and resistance to currently used treatments. To overcome the limitations of the available therapeutic options, the discovery and development of new, more effective, and safer therapies is required. In this review, the different research steps involved in the process of antimelanoma drug evaluation and selection are explored, including information regarding in silico, in vitro, and in vivo experiments, as well as clinical trial phases. Details are given about the most used cell lines and assays to perform both two- and three-dimensional in vitro screening of drug candidates towards melanoma. For in vivo studies, murine models are, undoubtedly, the most widely used for assessing the therapeutic potential of new compounds and to study the underlying mechanisms of action. Here, the main melanoma murine models are described as well as other animal species. A section is dedicated to ongoing clinical studies, demonstrating the wide interest and successful efforts devoted to melanoma therapy, in particular at advanced stages of the disease, and a final section includes some considerations regarding approval for marketing by regulatory agencies. Overall, considerable commitment is being directed to the continuous development of optimized experimental models, important for the understanding of melanoma biology and for the evaluation and validation of novel therapeutic strategies.

Computational evaluation of potent 2-(1H-imidazol-2-yl) pyridine derivatives as potential V600E-BRAF inhibitors

Background

V600E-BRAF is a major protein target involved in various types of human cancers. However, the acquired resistance of the V600E-BRAF kinase to the vemurafenib and the side effects of other identified drugs initiate the search for efficient inhibitors. In the current paper, virtual docking screening combined with drug likeness and ADMET properties predictions were jointly applied to evaluate potent 2-(1H-imidazol-2-yl) pyridines as V600E-BRAF kinase inhibitors.

Results

Most of the studied compounds showed better docking scores and favorable interactions with theiV600E-BRAF target. Among the screened compounds, the two most potent (14 and 30) with good rerank scores (−124.079 and − 122.290) emerged as the most effective, and potent V600E-BRAF kinase inhibitors which performed better than vemurafenib (−116.174), an approved V600E-BRAF kinase inhibitor. Thus, the docking studies exhibited that these compounds have shown competing inhibition of V600E-BRAF kinase with vemurafenib at the active site and revealed better pharmacological properties based on Lipinski’s and Veber’s drug-likeness rules for oral bioavailability and ADMET properties.

Conclusion

The docking result, drug-likeness rules, and ADMET parameters identified compounds (14 and 30) as the best hits against V600E-BRAF kinase with better pharmacological properties. This suggests that these compounds may be developed as potent V600E-BRAF inhibitors.