Evidence map›Paper›PMID 42672009›Full record

ArticlePloS one2026

Computational identification and evaluation of Adenium obesum phytochemicals as potential EGFR inhibitors for targeted therapy against non-small-cell lung cancer.

Md Naziur Rahman, Abu Yousuf Hossin, Shahriar Mannan Imon Talukder, Mt Sumaiya Siddika, Md Harun-Ur-Rashid, Shahanaz Parveen, Md Ridwanul Islam, Md Yeamin Hossain, Mahfuj Ahmed, Brototi Chakraborty Dyuti and 2 more

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Md Naziur RahmanCollege of Agricultural Sciences, IUBAT-International University of Business Agriculture and Technology, Dhaka, Bangladesh.ORCID https://orcid.org/0009-0003-3443-0180
Abu Yousuf HossinCollege of Agricultural Sciences, IUBAT-International University of Business Agriculture and Technology, Dhaka, Bangladesh.
Shahriar Mannan Imon TalukderCollege of Agricultural Sciences, IUBAT-International University of Business Agriculture and Technology, Dhaka, Bangladesh.ORCID https://orcid.org/0009-0002-5634-5908
Mt Sumaiya SiddikaBarind Medical College & Hospital, Rajshahi, Bangladesh.
Md Harun-Ur-RashidDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.
Shahanaz ParveenDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.
Md Ridwanul IslamDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.
Md Yeamin HossainDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.
Mahfuj AhmedDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.ORCID https://orcid.org/0009-0001-7019-4467
Brototi Chakraborty DyutiCollege of Agricultural Sciences, IUBAT-International University of Business Agriculture and Technology, Dhaka, Bangladesh.
Zarin TabassumCollege of Agricultural Sciences, IUBAT-International University of Business Agriculture and Technology, Dhaka, Bangladesh.
Ajoy KumerDepartment of General Medicine, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, India.ORCID https://orcid.org/0000-0001-5136-6166

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-small-cell lung cancer (NSCLC) remains the leading cause of lung cancer-related mortality, largely driven by aberrant activation of the epidermal growth factor receptor (EGFR). Despite the clinical success of EGFR tyrosine kinase inhibitors (TKIs), intrinsic and acquired resistance, coupled with safety concerns, highlight the need for novel, safer inhibitors. Natural products represent an underexplored source of structurally diverse bioactive compounds with favorable biocompatibility. In this study, a comprehensive in silico approach is used to evaluate phytochemicals from Adenium obesum as potential candidate EGFR-targeting compound. Initially, sixteen phytochemicals were first assessed for predicted antineoplastic activity using PASS. High-scoring molecules were docked against the EGFR kinase domain (PDB ID: 1M17), besides performed detailed protein-ligand interaction analysis, drug-likeness and ADMET profiling, toxicity prediction and 100-ns molecular dynamics (MD) simulations. PASS-based bioactivity prediction revealed strong anticancer potential among the sixteen screened compounds, with consistently high antineoplastic and antiproliferative activity probabilities (Pa > 0.79) and low inactivity scores, supporting their selection for subsequent docking, ADMET, and molecular dynamics analyses. Next, several phytochemicals exhibited strong docking affinities, with Cardenolide achieving the highest binding score (-9.9 kcal/mol) and forming stable interactions with key catalytic residues. A 100-ns MD simulation confirmed the structural stability, persistent binding, and dynamic integrity of the EGFR-Cardenolide complex under physiological conditions. Importantly, interaction mapping revealed that Cardenolide engages conserved and functionally critical regions of the EGFR kinase domain associated with catalytic activity and structural stability, supporting its mechanistic relevance as an ATP-competitive scaffold. Additionally, predicted pharmacokinetic and toxicity profiles further supported Cardenolide's suitability as a drug-like candidate. Collectively, these results identify Cardenolide as a computationally prioritized candidate with favorable predicted EGFR-binding characteristics, structural stability, and physicochemical and toxicity profiles. However, as the present study is based entirely on computational analyses, these findings should be considered hypothesis-generating and do not establish EGFR inhibitory activity or therapeutic efficacy. Experimental validation, including biochemical kinase inhibition and cellular assays, is therefore required to determine the actual EGFR inhibitory potential and anticancer activity of Cardenolide. Nevertheless, the findings provide a rational basis for prioritizing Cardenolide for further experimental investigation and illustrate the potential of Adenium obesum phytochemicals as a source of candidate EGFR-targeting compounds for future NSCLC drug discovery.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsPhytochemicalsProtein Kinase InhibitorsCell Line, TumorCell ProliferationErbB ReceptorsHumansMolecular Docking SimulationMolecular Dynamics SimulationEGFR protein, humanErbB ReceptorsPhytochemicalsProtein Kinase Inhibitors

Identifiers

PMID42672009
PMCPMC13529004

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.