Development of multitargeted inhibitors of both the insulin-like growth factor receptor (IGF-IR) and members of the epidermal growth factor family of receptor tyrosine kinases

Recent Progress of Benzimidazole Hybrids for Anticancer Potential

This review presents the detailed account of factors leading to cancer and design strategy for the synthesis of benzimidazole derivatives as anticancer agents. The recent survey for cancer treatment in Cancer facts and figures 2017 American Chemical Society has shown progressive development in fighting cancer. Researchers all over the world in both developed and developing countries are in a continuous effort to tackle this serious concern. Benzimidazole and its derivatives showed a broad range of biological activities due to their resemblance with naturally occurring nitrogenous base i.e. purine. The review discussed benzimidazole derivatives showing anticancer properties through a different mechanism viz. intercalation, alkylating agents, topoisomerases, DHFR enzymes, and tubulin inhibitors. Benzimidazole derivatives act through a different mechanism and the substituents reported from the earlier and recent research articles are prerequisites for the synthesis of targeted based benzimidazole derivatives as anticancer agents. The review focuses on an easy comparison of the substituent essential for potency and selectivity through SAR presented in figures. This will further provide a better outlook or fulfills the challenges faced in the development of novel benzimidazole derivatives as anticancer.

SAR and QSAR study on the bioactivities of human epidermal growth factor receptor-2 (HER2) inhibitors

In this paper, structure-activity relationship (SAR, classification) and quantitative structure-activity relationship (QSAR) models have been established to predict the bioactivity of human epidermal growth factor receptor-2 (HER2) inhibitors. For the SAR study, we established six SAR (or classification) models to distinguish highly and weakly active HER2 inhibitors. The dataset contained 868 HER2 inhibitors, which was split into a training set including 580 inhibitors and a test set including 288 inhibitors by a Kohonen's self-organizing map (SOM), or a random method. The SAR models were performed using support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP) methods. Among the six models, SVM models obtained superior results compared with other models. The prediction accuracy of the best model (model 1A) was 90.27% and the Matthews correlation coefficient (MCC) was 0.80 on the test set. For the QSAR study, we chose 286 HER2 inhibitors to establish six quantitative prediction models using MLR, SVM and MLP methods. The correlation coefficient (r) of the best model (model 4B) was 0.92 on the test set. The descriptors analysis showed that HAccN, lone pair electronegativity and π electronegativity were closely related to the bioactivity of HER2 inhibitors.

QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and mutant EGFR

Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC₅₀) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs.

Modulation of insulin-like growth factor-1 receptor and its signaling network for the treatment of cancer: current status and future perspectives

Based on over three decades of pre-clinical data, insulin-like growth factor-1 receptor (IGF-1R) signaling has gained recognition as a promoter of tumorogenesis, driving cell survival and proliferation in multiple human cancers. As a result, IGF-1R has been pursued as a target for cancer treatment. Early pioneering efforts targeting IGF-1R focused on highly selective monoclonal antibodies, with multiple agents advancing to clinical trials. However, despite some initial promising results, recent clinical disclosures have been less encouraging. Moreover, recent studies have revealed that IGF-1R participates in a dynamic and complex signaling network, interacting with additional targets and pathways thereof through various crosstalk and compensatory signaling mechanisms. Such mechanisms of bypass signaling help to shed some light on the decreased effectiveness of selective IGF-1R targeted therapies (e.g. monoclonal antibodies) and suggest that targeting multiple nodes within this signaling network might be necessary to produce a more effective therapeutic response. Additionally, such findings have led to the development of small molecule IGF-1R inhibitors which also co-inhibit additional targets such as insulin receptor and epidermal growth factor receptor. Such findings have helped to guide the design rationale of numerous drug combinations that are currently being evaluated in clinical trials.

Discovery of benzimidazole derivatives as novel multi-target EGFR, VEGFR-2 and PDGFR kinase inhibitors

Multi-target EGFR, VEGFR-2 and PDGFR inhibitors are highly useful anticancer agents with improved therapeutic efficacies. In this work, we used two virtual screening methods, support vector machines (SVM) and molecular docking, to identify a novel series of benzimidazole derivatives, 2-aryl benzimidazole compounds, as multi-target EGFR, VEGFR-2 and PDGFR inhibitors. 2-Aryl benzimidazole compounds were synthesized and their biological activities against a tumor cell line HepG-2 and specific kinases were evaluated. Among these compounds, compounds 5a and 5e exhibited high cytotoxicity against HepG-2 cells with IC₅₀ values at ∼2 μM. Further kinase assay study showed that compound 5a have good EGFR inhibitory activity and moderate VEGFR-2 and PDGFR inhibitory activities, while 5e have moderate EGFR inhibitory activity and slightly weaker VEGFR-2 and PDGFR inhibitory activities. Molecular docking analysis suggested that compound 5a more tightly interacts with EGFR and PDGFR than compound 5e. Our study discovered a novel series of benzimidazole derivatives as multi-target EGFR, VEGFR-2 and PDGFR kinases inhibitors.