Prediction Of Anticancer Activity Research Articles

3D-QSAR, molecular docking, ADMET, simulation dynamic, and retrosynthesis studies on new styrylquinolines derivatives against breast cancer

Abstract Breast cancer is the most common illness among women, accounting for 25% of all diagnoses, as stated by the American Cancer Society. Current research focuses on 43 compounds of styrylquinoline derivatives as potential inhibitors of tubulin to design a new drug that could potentially be effective against breast cancer cells in humans. The target compounds were subjected to a three-dimensional quantitative structure-activity relationship/comparative molecular similarity indices analysis (CoMSIA) approach, where CoMSIA models were used; the best results obtained are (Q 2 = 0.84, R 2 = 0.97, r ext 2 {r}_{\text{ext}}^{2} = 0.91), H-bond acceptor field was discovered to be important for increasing inhibitory activity by examining the contour maps (54%), and it plays a key role in the prediction of anticancer activity. Based on the contour maps of the CoMSIA models, we obtained information that allows us to propose four new molecules with higher cancer inhibitory than the 43 compounds found in the literature. The molecular docking was applied to determine the likely types of binding between the tubulin protein (PDB ID: 4O2B) and the proposed compounds, and the results show that M1 has a higher total score of 6.53 and two interactions with important conventional hydrogen bond type, followed by compound M2 with a total score of 5.74. Furthermore, the designed molecules showed better pharmacokinetic properties based on absorption, distribution, metabolism, excretion, and toxicity properties. Molecular dynamics simulations at 100 ns were conducted to confirm the binding stability of the selected ligands (M1 and M2) with tubulin protein. The simulation parameters used in the current study are root mean square deviation, root mean square fluctuation, H-bond, Rg, solvent accessible surface area, and binding energy. As a result, the designed compounds (ligands M1 and M2) have shown noteworthy potential as a drug candidate for experimental in vivo and in vitro testing due to their potential inhibition of breast cancer. Finally, the study of retrosynthesis in this work facilitates the synthesis of drug candidates.

Bioinformatics and Molecular Docking Study of Amentoflavone and 3,8-Biapigenin as Inhibitors on Cervical Cancer Proteins

Cervical cancer maintains its second-place ranking for Indonesia's highest number of cancer cases. In 2021, there were 36,633 cases of cervical cancer in Indonesia, with a rising death rate. Commonly, chemotherapy is used to treat cervical cancer and can improve the survival chances of patients, but these therapies imply increased toxicity. Biflavonoid group compounds like amentoflavone and 3,8-Biapigenin have the potential to act as anticancer agents by modulating multiple signaling pathways. This study aims to determine the cervical anticancer potential of amentoflavone and 3,8-Biapigenin based on in silico study. Prediction of anticancer activity in silico using Prediction of Activity Spectra for Active Substances (PASS) online, followed by target protein tracing using STITCH-STRING, then receptor analysis test using Ramachandran plot. A molecular docking test was conducted to determine the binding affinity of the compound with the receptor. Based on the online PASS, the compounds as thought to have low cervical anticancer potential if tested on a laboratory scale. STAT3, EP300, CYP1A1, and AKR1C1 proteins used in this study have met the requirements of a suitable receptor for molecular docking test. The best binding affinity was obtained at the interaction of amentoflavone and STAT3 with a better docking score (-9.3 kcal/mol) than doxorubicin (-7.1 kcal/mol). Overall, the results suggest biflavonoid compounds have the potential to be developed as a chemopreventive agent for cervical cancer.Keywords: bioinformatics, molecular docking, amentoflavone, 3,8-Biapigenin, cervical cancer protein.

Predicting, designing, characterization and evaluation of a new novel anticancer peptide SSVAM-9 against the lung carcinoma, an insilico approach

Several anticancer drugs are getting resisted by the cancer cell and treatment like chemotherapy, radiation causes serious side effects. In immunomodulatory treatment the efficiency is less and CAR-T cells, CAR-NK cells require enormous time to get adopt to the in vitro and may cause seizures, dilemma, concussion in prolonged use against the cancer. Even the production of CAR-T cells and NK cells are tedious process. To overcome this situation, anticancer peptides can be used, as they don’t have any drug resistance and they can be highly potent, with good cell penetration. The advantages of these peptides are easy to modify, produce and formulate. This pandemic showed us that, identifying and characterizing a novel anticancer peptide (ACP) is an extremely time and labor consuming process. To reduce the time and labor, this study uses several in silico tools and algorithms like SVM, RF, XGBoost and KNN to predict a novel anticancer peptide. After several studies, with the collected data, a novel anticancer peptide – SSVAM-9 was predicted, which acts against the lung carcinoma. In this, anticancer activity prediction, cell permeation prediction with all 4 algorithms; stability prediction, allergenicity prediction and activity on lung carcinoma prediction were carried out in in silico model. Considering all the parameters, one best novel ACP was selected (SSVAM-9), and it can be easily formulated as the peptide is a stable one. This approach is an advantageous one as it is cost efficient and less-time consuming which can be studied in vivo and in vitro in future.

Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity.

The merits of ethnomedicine-led approach to identify and prioritize anticancer medicinal plants have been challenged as cancer is more likely to be poorly understood in traditional medicine practices. Nonetheless, it is also believed that useful data can be generated by combining ethnobotanical findings with available scientific studies. Thus, this study combined an ethnobtanical study with ligand based in silico screening to identify relevant medical plants and predict their anticancer potential based on their phytoconstiutents reported in scientific literatures. First, relevant medicinal plants were identified through an ethnobotanical survey. A list of phytochemicals was prepared based on literature review of articles which reported on the natural products of identified medicinal plants. Then, their phytochemicals were subjected to in silico evaluation, which included a hybrid score similarity measure, rule of five, Ghose-Viswanadhan-Wendoloski (GVW)-indices and structural features criteria, to predict their anticancer activity and drugability. A total of 18 medicinal plants and 265 phytoconstituents were identified. The natural product pool constituted 109(41.13%) terpenoids, 67(25.28%) phenolics, 29(10.94%) simple and functionalized hydrocarbons, 26(9.81%) alkaloids, 25(9.43%) glycosides and 9(3.40%) compounds belonging to different phytochemical classes. The similarity measure using CDRUG identified 34(12.73%) phytochemicals with high (p-Value < 0.05) and 35(13.21%) with moderate possibility (p-Value < 0.1) of anticancer activity. In fact, three of the predicted compounds had the same structure with known anticancer compounds (HSCORE=1). The 80% GVW-indices based antineoplastic drugabilityranges were all mate by 25 of the predicted compounds. Predicted compounds were also shown to have ring structures and functional groups deemed important for anticancer activity. Given the findings, there is a promising anticancer activity by the traditionally used medicinal plants and a potential for the predicted phytochemicals to be pursued as possible hits or me-too drugs.

How to rank and discriminate artificial neural networks? Case study: prediction of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives

Model discrimination is still not a resolved task. The classical statistical approaches lead to different results (for the same models) and at the same time a lot of models seem to be statistically equivalent. The authors deliberately select such conditions when their algorithm is superior. Hence, it is better to apply different approaches to compare and rank the models fairly. This paper presents the application of methodology called sum of ranking differences (SRD) to rank the artificial neural network models [quantitative structure–activity relationship (QSAR) models] designed for prediction of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives toward androgen receptor negative prostate cancer cells (AR-, PC-3). The SRD method suggests the consistent models, in terms of compounds order and proximity to the golden standard, which should preferably be used in the prediction of anticancer activity of studied androstane derivatives.

Chemometric modelling of triphenylmethyl derivatives as potent anticancer agents

The increase in number of cancer-affected patients worldwide has amplified the need for the development of new anticancer drug-like molecules. In this background, the quantitative structure–activity relationship (QSAR) has proved to be an efficient tool for the drug-designing process. In this work, the in silico technique is applied to study the anticancer activity of two different sets of triphenyl methyl (TPM) derivatives on SK-MEL 5 cell line. Chemometric tools such as genetic partial least squares (G/PLS) algorithm was used for phenethylamine and d-phenylalanine TPM derivatives, whereas genetic function approximation technique was utilised for phosphonate and phosphonochloridate TPM derivatives in development of QSAR models. The calculated descriptors represent the influence of geometry, topology, molecular shape and polarisability of the molecules on the anticancer activity of TPM derivatives. Internal and external validation tests of the models provide satisfactory results (R2 = 0.823, 0.880, Q2 = 0.732, 0.771, = 0.644, 0.716) proving the fitness and predictivity of the model in compliance with Organization for Economic Co-operation and Development principles. The developed models can be effectively utilised further for the prediction of anticancer activity of new compounds that fall within the domain of applicability of the referred models.

Guest editors’ introduction: special issue on mining and learning with graphs

Research in machine learning and data mining acknowledges more and more the fact that most data occurring in real world problems is inherently structured. Graphs and particular subclasses of graphs are commonly used representations for such structured data. They are present in important application areas ranging from biology and chemistry to the World Wide Web and beyond. Following the ever increasing number of successful workshops on topics like mining and learning with graphs and motivated by the ever increasing amount of high quality submissions to these workshops, this special issue gathers some of the progress in the field over the recent years. One of the major trends has been to investigate how the success of kernel methods on data which is readily embedded in a Euclidean space, can be carried over to structured data. In this spirit, the paper “Graph kernels based on tree patterns for molecules” by Pierre Mahe and Jean-Philippe Vert investigates kernel functions based on co-occurrence of particular subtree patterns in graphs. They extend and use these kernels for toxicity and anti-cancer activity prediction with support vector machines on graphs representing the 2D structure of molecules. The paper “On the properties of von Neumann kernels for link analysis” by Masashi Shimbo, Takahiko Ito, Daichi Mochihashi, and Yuji Matsumoto investigates the relationship between link analysis measures and kernel functions defined on the vertices of a single huge graph. Having identified common issues of both these kernels and these link analysis measures, the authors proceed by proposing a modified kernel and demonstrate its effectiveness by analysing a citation network of scientific papers.

Prediction of anticancer activity by tumor uptake of radiolabeled monoclonal antibody

A novel and sensitive method is described for rapid detection of anticancer drug efficacy against human tumor xenografts in nude mice. CA or BRO human melanoma cells were inoculated i.m. in nude mice, which then were treated with saline or cytotoxic agents. After injection of 111In-labeled 9.2.27 antimelanoma monoclonal antibody, the site of tumor inoculation and the contralateral non-tumor site were excised from sacrificed mice. Relatively lower 111In uptake in the area of tumor inoculation correlated with prevention or delay of tumor growth in identically treated counterpart mice.