Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR

Choudhary, Rekha; Walhekar, Vinayak; Muthal, Amol; Kumar, Dilip; Bagul, Chandrakant; Kulkarni, Ravindra

doi:10.1080/07391102.2023.2175263

Cited by 4 publications

(2 citation statements)

References 67 publications

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“…In recent years, XRCC1 gene polymorphisms, including rs1799782, rs25489 and rs25487, have been successively found to be associated with increased risk of LC [16,17]. However, few studies have focused on the molecular mechanism by which the XRCC1 gene polymorphism promotes LC development.…”

Section: Discussionmentioning

confidence: 99%

XRCC1 rs1799782 Promotes DNA Damage Repair in Lung Cancer Cells by Enhancing Its Binding to FOXA1 to Facilitate FOXA1-Mediated Transcription of XRCC1

Xu,

Li,

Pan

et al. 2024

Discovery Medicine

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Background: X-ray repair cross complementing 1 (XRCC1) rs1799782 polymorphism is associated with an increased risk of lung cancer (LC). The aim of this study is to analyze the underlying biological mechanisms. Methods: Dual luciferase reporter assay was utilized to verify the impact of XRCC1 polymorphism upon promoter activity of XRCC1. Cell counting kit-8 (CCK-8) assay, colony formation assay, senescence-associated beta-galactosidase (SA-β-gal) staining, and immunofluorescent staining were used to assess the viability, proliferation, senescence, and DNA damage of LC cells. Senescence-related proteins (cyclin dependent kinase inhibitor 1A (P21) and eukaryotic translation elongation factor 1-alpha (EF1A)) were quantified by Western blot. Chromatin immunoprecipitation was applied to validate the binding affinity of forkhead box A1 (FOXA1) and XRCC1. FOXA1-specific short hairpin RNA (shFOXA1) was used to perform the rescue assay. Results: In LC cells, XRCC1 rs1799782 promoted viability and proliferation, inhibited senescence, and resulted in upregulation of EF1A as well as downregulation of P21 and phosphorylated H2A.X variant histone (γH2AX). XRCC1 rs1799782 promoted FOXA1-mediated transcription of XRCC1 through enhancing its binding to FOXA1. shFOXA1 counteracted the effects of XRCC1 rs1799782 upon the viability, proliferation, and senescence of LC cells. Conclusions: XRCC1 rs1799782 promotes DNA damage repair in LC cells through enhancing its binding to FOXA1, which facilitates FOXA1-mediated transcription of XRCC1.

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Section: Discussionmentioning

confidence: 99%

XRCC1 rs1799782 Promotes DNA Damage Repair in Lung Cancer Cells by Enhancing Its Binding to FOXA1 to Facilitate FOXA1-Mediated Transcription of XRCC1

Xu,

Li,

Pan

et al. 2024

Discovery Medicine

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“…The binding affinity (IC 50 ) and ADMET profile predominantly influence the drug’s effectiveness ( El-Adl et al, 2021 ). Consequently, DL models have been utilized to forecast the IC 50 , pIC 50 , and ADMET characteristics of the most promising candidates obtained via virtual screening ( Al-Jumaili et al, 2023b ; Choudhary et al, 2023 ). This approach aims to facilitate a direct evaluation of the binding affinity of these candidates regarding the established standards of MTX and PTX.…”

Section: Methodsmentioning

confidence: 99%

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Siddique,

Anwaar,

Bashir

et al. 2024

Front. Chem.

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Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs.Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively.Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis.Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

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An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

Ahmad,

Raza

2024

Journal of Drug Targeting

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Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR

Cited by 4 publications

References 67 publications

XRCC1 rs1799782 Promotes DNA Damage Repair in Lung Cancer Cells by Enhancing Its Binding to FOXA1 to Facilitate FOXA1-Mediated Transcription of XRCC1

XRCC1 rs1799782 Promotes DNA Damage Repair in Lung Cancer Cells by Enhancing Its Binding to FOXA1 to Facilitate FOXA1-Mediated Transcription of XRCC1

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

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