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Identification of natural product as selective PI3Kα inhibitor against NSCLC: multi-ligand pharmacophore modeling, molecular docking, ADME, DFT, and MD simulations

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Abstract

Non-small cell lung cancer (NSCLC) is a widespread and often aggressive form of cancer affecting people worldwide. PIK3CA missense mutations play a significant role in the progression of growth factor signaling in cancer, making PI3Kα an important biological target for inhibition against NSCLC. Natural product molecules with PI3Kα inhibitory activity are promising therapeutic agents for the treatment of NSCLC, owing to their selectivity and potentially lower toxicity compared to synthetic compounds. To discover new natural product molecules, we integrated ligand-based virtual screening with structure-based virtual screening. We developed a multi-ligand pharmacophore hypothesis, validated it with 3D Field-based QSAR, and screened a Natural-Product-Based Library (ChemDiv) containing 3601 molecules. After initial screening, 137 hit molecules were generated and further screened using the extra precision (XP) Glide docking protocol. The best ten molecules were selected for free binding energy (ΔG) analysis using MMGBSA and ADME predictions. For further optimization, the top four hits were subjected to induced fit docking (IFD), quantum chemical descriptors analysis by Frontier Molecular Orbital (FMO) studies, and a 100 ns molecular dynamics (MD) simulation. The compounds—S721-1955, CM4579-5085, S721-1963, and S721-1999—exhibited better results than the PI3Kα selective inhibitor alpelisib. In silico prediction analysis of S721-1955 and alpelisib revealed that the former exhibited superior selectivity theoretically, as evidenced by its higher affinity for the target protein. The selective natural product molecule identified in this study holds promise as a potential anti-cancer drug against NSCLC in the near future, but further in vitro and in vivo studies are necessary to confirm its efficacy.

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Data availability

The data generated in this study is presented in this manuscript and the additional data is also supplied in the supporting file.

Abbreviations

Arg:

Arginine

Asn:

Asparagine

Asp:

Aspartic acid

AI:

Artificial Intelligence

DFT:

Density Function Theory

EGFR:

Epidermal growth factor receptor

FMO:

Frontier Molecular Orbital

Glide:

Grid-based ligand docking with energetics

Gln:

Glutamine

Glu:

Glutamate

NSCLC:

Non-small cell lung cancer

His:

Histidine

HTVS:

High Throughput Virtual Screening

IGFR:

Insulin-like growth factor receptor

IL:

Interleukin

INSR:

Insulin receptor

IFD:

Induced Fit Docking

Ile:

Isoleucine

Lys:

Lysine

MD:

Molecular Dynamics

ML:

Machine Learning

Met:

Methionine

mTOR:

Mammalian target of rapamycin

MMGBSA:

Molecular Mechanics Generalized Born Surface Area

OPLS:

Optimized potentials for liquid simulations

PI3K:

Phosphatidylinositol 3-kinase

Phe:

Phenylalanine

Pro:

Proline

QSAR:

Quantitative Structure Activity Relationship

Ras:

Rat sarcoma virus

RMSD:

Root mean square deviation

Ser:

Serine

STAT:

Signal transducers and activators of transcription

SP:

Standard precision

SPC:

Simple point charge

SID:

Simulation Interaction Diagram

SA:

Synthetic accessibility

Thr:

Threonine

Trp:

Tryptophan

Tyr:

Tyrosine

VEGFA:

Vascular endothelial growth factor A

VSGB:

Surface Generalized Born Model and Variable Dielectric

Val:

Valine

XP:

Extra precision

References

  1. Paul D, Sanap G, Shenoy S et al (2021) Artificial intelligence in drug discovery and development. Drug Discov Today 26:80–93. https://doi.org/10.1016/j.drudis.2020.10.010

    Article  CAS  PubMed  Google Scholar 

  2. Jana S, Ganeshpurkar A, Singh SK (2018) Multiple 3D-QSAR modeling, e-pharmacophore, molecular docking, and in vitro study to explore novel AChE inhibitors. RSC Adv 8:39477–39495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Cancer Facts & Figures (2023) Atlanta: American Cancer Society, Inc. 2022. In: Am. Cancer Soc. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/2023-cancer-facts-figures.html

  4. Halder D, Das S, Joseph A, Jeyaprakash RS (2022) Molecular docking and dynamics approach to in silico drug repurposing for inflammatory bowels disease by targeting TNF alpha. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2022.2050948

    Article  PubMed  Google Scholar 

  5. Halder D, Das S, Aiswarya R, Jeyaprakash RS (2022) Molecular docking and dynamics based approach for the identification of kinase inhibitors targeting PI3Kα against non-small cell lung cancer: a computational study. RSC Adv 12:21452–21467. https://doi.org/10.1039/D2RA03451D

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Karakas B, Bachman KE, Park BH (2006) Mutation of the PIK3CA oncogene in human cancers. Br J Cancer 94:455–459. https://doi.org/10.1038/sj.bjc.6602970

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tan AC (2020) Targeting the PI3K/Akt/mTOR pathway in non-small cell lung cancer (NSCLC). Thorac Cancer 11:511–518. https://doi.org/10.1111/1759-7714.13328

    Article  PubMed  PubMed Central  Google Scholar 

  8. Samuels Y, Waldman T (2010) Oncogenic Mutations of PIK3CA in Human Cancers. In: Rommel C, Vanhaesebroeck B, Vogt P. (eds) Phosphoinositide 3-kinase in Health and Disease. Current Topics in Microbiology and Immunology, vol 347. Springer, Berlin, Heidelberg, https://doi.org/10.1007/82_2010_68

  9. Das S, Roy S, Rahaman SB et al (2022) Structure-activity relationship insight of naturally occurring bioactive molecules and their derivatives against non-small cell lung cancer: a comprehensive review. Curr Med Chem. https://doi.org/10.2174/0929867329666220509112423

    Article  PubMed  Google Scholar 

  10. Yin B, Fang D-M, Zhou X-L, Gao F (2019) Natural products as important tyrosine kinase inhibitors. Eur J Med Chem 182:111664. https://doi.org/10.1016/j.ejmech.2019.111664

    Article  CAS  PubMed  Google Scholar 

  11. Staben ST, Ndubaku C, Blaquiere N et al (2013) Discovery of thiazolobenzoxepin PI3-kinase inhibitors that spare the PI3-kinase β isoform. Bioorganic Med Chem Lett 23:2606–2613. https://doi.org/10.1016/j.bmcl.2013.02.102

    Article  CAS  Google Scholar 

  12. (2021) Schrödinger release 2022–4: LigPrep, Schrödinger, LLC, New York

  13. (2023) Schrödinger release 2018–3: Schrödinger, LLC, New York

  14. Lu C, Wu C, Ghoreishi D et al (2021) OPLS4: improving force field accuracy on challenging regimes of chemical space. J Chem Theory Comput 17:4291–4300. https://doi.org/10.1021/acs.jctc.1c00302

    Article  CAS  PubMed  Google Scholar 

  15. Gao Q, Wang Y, Hou J et al (2017) Multiple receptor-ligand based pharmacophore modeling and molecular docking to screen the selective inhibitors of matrix metalloproteinase-9 from natural products. J Comput Aided Mol Des 31:625–641. https://doi.org/10.1007/s10822-017-0028-3

    Article  CAS  PubMed  Google Scholar 

  16. (2021) Schrödinger release 2022–4: field-based QSAR, Schrödinger, LLC, New York

  17. Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring: 1—method and assessment of docking accuracy. J Med Chem 47:1739–1749. https://doi.org/10.1021/jm0306430

    Article  CAS  PubMed  Google Scholar 

  18. Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring: 2—enrichment factors in database screening. J Med Chem 47:1750–1759. https://doi.org/10.1021/jm030644s

    Article  CAS  PubMed  Google Scholar 

  19. (2021) Schrödinger release 2023–1: Epik, Schrödinger, LLC, New York

  20. Heffron TP, Heald RA, Ndubaku C et al (2016) The rational design of selective benzoxazepin inhibitors of the α-isoform of phosphoinositide 3-kinase culminating in the identification of ( S )-2-((2-(1-isopropyl-1 H -1,2,4-triazol-5-yl)-5,6-dihydrobenzo [f ]imidazo [1,2- d ] [1,4]oxazepin-9-yl)oxy)propa. J Med Chem 59:985–1002. https://doi.org/10.1021/acs.jmedchem.5b01483

    Article  CAS  PubMed  Google Scholar 

  21. (2021) Schrödinger release 2021–4: protein preparation wizard; Epik, Schrödinger, LLC, New York; impact, Schrödinger, LLC, New York; Prime, Schrödinger, LLC, New York

  22. (2021) Schrödinger release 2023–1: prime, Schrödinger, LLC, New York

  23. Li H, Robertson AD, Jensen JH (2005) Very fast empirical prediction and rationalization of protein pKa values. Proteins Struct Funct Bioinforma 61:704–721. https://doi.org/10.1002/prot.20660

    Article  CAS  Google Scholar 

  24. (2021) Schrödinger release 2023–1: glide, Schrödinger, LLC, New York

  25. Li J, Abel R, Zhu K et al (2011) The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling. Proteins Struct Funct Bioinfo 79:2794–2812. https://doi.org/10.1002/prot.23106

    Article  CAS  Google Scholar 

  26. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:1–13. https://doi.org/10.1038/srep42717

    Article  Google Scholar 

  27. Daina A, Michielin O, Zoete V (2014) ILOGP: A simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. J Chem Inf Model 54:3284–3301. https://doi.org/10.1021/CI500467K

    Article  CAS  PubMed  Google Scholar 

  28. (2021) Schrödinger release 2022–4: Jaguar, Schrödinger, LLC, New York

  29. Ye N, Yang Z, Liu Y (2022) Applications of density functional theory in COVID-19 drug modeling. Drug Discov Today 27:1411–1419. https://doi.org/10.1016/j.drudis.2021.12.017

    Article  CAS  PubMed  Google Scholar 

  30. Bouzina A, Berredjem M, Bouacida S et al (2022) Synthesis, in silico study (DFT, ADMET) and crystal structure of novel sulfamoyloxy-oxazolidinones: interaction with SARS-CoV-2. J Mol Struct 1257:132579. https://doi.org/10.1016/j.molstruc.2022.132579

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. (2021) Schrödinger release 2022–4: QikProp, Schrödinger, LLC, New York

  32. (2021) Schrödinger release 2021–4: desmond molecular dynamics system, D. E. Shaw Research, New York; Maestro-desmond interoperability tools, Schrödinger, New York

  33. Daina A, Michielin O, Zoete V (2019) SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res 47:W357–W364. https://doi.org/10.1093/nar/gkz382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nath V, Ramchandani M, Kumar N et al (2021) Computational identification of potential dipeptidyl peptidase (DPP)-IV inhibitors: structure based virtual screening, molecular dynamics simulation and knowledge based SAR studies. J Mol Struct 1224:129006. https://doi.org/10.1016/j.molstruc.2020.129006

    Article  CAS  Google Scholar 

  35. Sándor M, Kiss R, Keserű GM (2010) Virtual fragment docking by glide: a validation study on 190 protein−fragment complexes. J Chem Inf Model 50:1165–1172. https://doi.org/10.1021/ci1000407

    Article  CAS  PubMed  Google Scholar 

  36. Zhu Y-F (2012) PI3K expression and PIK3CA mutations are related to colorectal cancer metastases. World J Gastroenterol 18:3745. https://doi.org/10.3748/wjg.v18.i28.3745

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Volinia S, Hiles I, Ormondroyd E et al (1994) Molecular cloning, cDNA sequence, and chromosomal localization of the human phosphatidylinositol 3-kinase p110α (PIK3CA) gene. Genomics 24:472–477. https://doi.org/10.1006/geno.1994.1655

    Article  CAS  PubMed  Google Scholar 

  38. Haider K, Ahmad K, Najmi AK et al (2022) Design, synthesis, biological evaluation, and in silico studies of 2-aminobenzothiazole derivatives as potent PI3Kα inhibitors. Arch Pharm (Weinheim). https://doi.org/10.1002/ardp.202200146

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to express their gratitude to the Manipal-Schrödinger Centre for Molecular Simulations. The authors wish to also thank the Manipal College of Pharmaceutical Sciences for providing the necessary resources for this study. The authors also thank ChemDraw and BioRender.com.

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Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India

    Debojyoti Halder, Subham Das & R. S. Jeyaprakash

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  1. Debojyoti Halder
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  2. Subham Das
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  3. R. S. Jeyaprakash
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Contributions

D.H. wrote the main manuscript text and prepared figures and tables. S.D. wrote the main manuscript text, prepared figures, and tables and supervised and reviewed the manuscript. J.P. reviewed the manuscript.

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Correspondence to Subham Das or R. S. Jeyaprakash.

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Halder, D., Das, S. & Jeyaprakash, R.S. Identification of natural product as selective PI3Kα inhibitor against NSCLC: multi-ligand pharmacophore modeling, molecular docking, ADME, DFT, and MD simulations. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10727-2

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