Multisampling-based docking reveals Imidazolidinyl urea as a multitargeted inhibitor for lung cancer: an optimisation followed multi-simulation and in-vitro study

Abstract

Lung Cancer is one of the deadliest cancers, responsible for more than 1.80 million deaths annually worldwide, and it is on the priority list of WHO. In the current scenario, when cancer cells become resistant to the drug, making it less effective leaves the patient in vulnerable conditions. To overcome this situation, researchers are constantly working on new drugs and medications that can help fight drug resistance and improve patients’ outcomes. In this study, we have taken five main proteins of lung cancer, namely RSK4 N-terminal kinase, guanylate kinase, cyclin-dependent kinase 2, kinase CK2 holoenzyme, tumour necrosis factor-alpha and screened the prepared Drug Bank library with 1,55,888 compounds against all using three Glide-based docking algorithms namely HTVS, standard precision and extra precise with a docking score ranging from −5.422 to −8.432 Kcal/mol. The poses were filtered with the MM\GBSA calculations, which helped to identify Imidazolidinyl urea C₁₁H₁₆N₈O₈ (DB14075) as a multitargeted inhibitor for lung cancer, validated with advanced computations like ADMET, interaction pattern fingerprints, and optimised the compound with Jaguar, producing satisfied relative energy. All five complexes were performed with MD Simulation for 100 ns with NPT ensemble class, producing cumulative deviation and fluctuations < 2 Å and a web of intermolecular interaction, making the complexes stable. Further, the in-vitro analysis for morphological imaging, Annexin V/PI FACS assay, ROS and MMP analysis caspase3//7 activity were performed on the A549 cell line producing promising results and can be an option to treat lung cancer at a significantly cheaper state.

Communicated by Ramaswamy H. Sarma

Keywords:

• Lung cancer
• multitargeted inhibitor
• imidazolidinyl urea
• molecular dynamics simulation
• in-vitro validation

Data availability statement

The supplementary data can be made public after publication, and the datasets used and/or analysed during the current study (at any specified step) are available upon reasonable request at [email protected].

Acknowledgements

The authors would like to thank Jamia Millia Islamia for providing the space for the research activities and Schrodinger LLC for providing the software license. The author (VS) is grateful to the Indian Council of Medical Research (ICMR), India for providing the research associate fellowship award (2019-5519/CMB-BMS). Author (HKG) acknowledged financial support from the project OLP-2306.

Author’s contributions

SA; Conceptualisation, Methodology, Software, Data collection and curation, Analysis, MD simulation, Investigation, Writing Original draft, Extensive Editing and formatting of the manuscript, figure arrangement and upgradation and helped in In-vitro validation and provided drug compound. VS; conducted the In-vitro experimental study and analysed data, written in vitro section. HKG; Supervision, reviewed and edited the manuscript and experimental resources. KR; Supervision, reviewed and edited the manuscript and computational resources.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.