Modeling and Simulation in Cancer Nanomedicine


Modeling and Simulation
Ion channel
molecular dynamics

How to Cite

Talukdar, K. (2021). Modeling and Simulation in Cancer Nanomedicine. Journal of Basic & Applied Sciences, 17, 54–63.


There is a certain function of ion channels in cancer cell progression and proliferation. The mutation of ion channels is proved to have a clear influence on the same. The progress of nanomedicine research needs the proper concept of the exact role of ion channels in cancer and the cause of the disease. In this work, an ion channel protein residing in our stomach with PDB id 3ux4 is analyzed to get an idea about its structure-function relationship. The disordered region and mutation sensitivity of the channel causing cancer are analyzed in different ways. Eight disordered regions of the protein are found in the study. The pocket in the active site is found along with the position of the miss-sense mutation. The maximum mutation region is also found for a sample disordered region. The engineered ion channel is simulated in the environment of water and ions. The potential energy of the water-ion model of the protein calculated by molecular dynamics simulation is 20,412 kcal/mol after simulating the system for 1,00000 steps.


Muthu MS, Singh S. Targeted nanomedicines: Effective treatment modalities for cancer, AIDS and brain disorders. Nanomedicine 2009; 4(1): 105-118.

Deshpande PP, Biswas S, Torchilin V P. Current trends in the use of liposomes for tumor targeting. Nanomedicine, 2013; 8(9): 1509-28.

Bulbake U, Doppalapudi S, Kommineni N, Khan W. Liposomal Formulations in Clinical Use: An Updated Review. Pharmaceutics 2017; 9(4): 12.

Lamichhane N, Udayakumar T, D’Souza W, Ii CS, Raghavan S, Polf J, Mahmood J. Liposomes: Clinical Applications and Potential for Image-Guided Drug Delivery. Molecules 2018; 23(2): 288.

Mendes LP, Pan J, Torchilin V. Dendrimers as Nanocarriers for Nucleic Acid and Drug Delivery in Cancer Therapy. Molecules 2017; 22(9): 1401.

Franiak-Pietryga I, Ziemba B, Messmer B, Skowronska-Krawczyk D. Dendrimers as Drug Nanocarriers: The Future of Gene Therapy and Targeted Therapies in Cancer. Dendrimers - Fundamentals and Applications 2018, InTech Open.

Li B, Li Q, Mo J, Dai H. Drug-Loaded Polymeric Nanoparticles for Cancer Stem Cell Targeting. Frontiers in Pharmacology 2017; 8: 51.

Ferrari R, Sponchioni M, Morbidelli M, Moscatelli D. Polymer nanoparticles for the intravenous delivery of anticancer drugs: The checkpoints on the road from the synthesis to clinical translation. Nanoscale 2018; 10(48): 22701-22719.

Wu M, Huang S. Magnetic nanoparticles in cancer diagnosis, drug delivery and treatment (Review). Molecular and Clinical Oncology 2017; 7(5): 738-746.

Chang D, Lim M, Goos JA, Qiao R, Ng YY, Mansfeld, FM., . . . Kavallaris, M. Biologically Targeted Magnetic Hyperthermia: Potential and Limitations. Frontiers in Pharmacology 2018; 9: 831.

Son KH, Hong JH, Lee J W. Carbon nanotubes as cancer therapeutic carriers and mediators. International Journal of Nanomedicine 2016; 11: 5163-5185.

Sanginario A, Miccoli B, Demarchi D. Carbon Nanotubes as an Effective Opportunity for Cancer Diagnosis and Treatment. Biosensors 2017; 7(1): 9.

Frieboes H B, Wu M, Lowengrub J, Decuzzi P, Cristini V. A Computational Model for Predicting Nanoparticle Accumulation in Tumor Vasculature. PLOS ONE 2013; 8(2): e56876.

Cai Y, Wu J, Li Z, Long Q. Mathematical Modelling of a Brain Tumour Initiation and Early Development: A Coupled Model of Glioblastoma Growth, Pre-Existing Vessel Co-Option, Angiogenesis and Blood Perfusion. PLOS ONE 2016; 11: e0150296.

Brocato TA, Coker EN, Durfee P.N. et al. Understanding the Connection between Nanoparticle Uptake and Cancer Treatment Efficacy using Mathematical Modeling. Sci Rep 2018; 8: 7538.

Dogra, P., Butner, J.D., Chuang, Yl. et al. Mathematical modeling in cancer nanomedicine: a review. Biomed Microdevices 2019; 21: 40.

Sahai N, Gogoi M, Ahmad N. Mathematical Modeling and Simulations for Developing Nanoparticle-Based Cancer Drug Delivery Systems: A Review. Curr Pathobiol Rep 2021; 9: 1-8.

Cheng YH, He C, Riviere J E, Monteiro-Riviere NA, Lin Z. Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach. ACS Nano 2020; 14: 3075-3095.

Prevarskaya N, Skryma R, Shuba Y. Ion channels in cancer: Are cancer hallmarks oncochannelopathies? Physiol Rev 2018; 98(2): 559-621.

Leanza L, Managò A, Zoratti M, Gulbins E, Szabo I. Pharmacological targeting of ion channels for cancer therapy: In vivo evidences. Biochim Biophys Acta 2016; 1836(6 pt B): 1385-97.

Bose T, Cieślar-Pobuda A, Wiechec E. Role of ion channels in regulating Ca2+ homeostasis during the interplay between immune and cancer cells. Cell Death Dis 2015; 6(2): e1648.

Litan, A., Langhans, S.A. Cancer as a channelopathy: ion channels and pumps in tumor development and progression. Front. Cell. Neurosci 2015; 9: 86.

Lang F, Stournaras C. Ion channels in cancer: future perspectives and clinical potential. Philos Trans R Soc Lond B Biol Sci 2014; 369(1638): 20130108.

Storm P, Kjaer Klausen T, Trulsson M, Ho, JCS, Dosnon M et al. A Unifying Mechanism for Cancer Cell Death through Ion Channel Activation by HAMLET. PLOS ONE 2013; 8(3): e58578.

Șterbuleac D, Maniu CL. An antiarrhythmic agent as a promising lead compound for targeting the hEAG1 ion channel in cancer therapy: insights from molecular dynamics simulations. Chem Biol Drug Des 2016; 88(5): 683-689.

Cervera J, Alcaraz A, Mafe S. Bioelectrical Signals and Ion Channels in the Modeling of Multicellular Patterns and Cancer Biophysics. Sci Rep 2016; 6: 20403.

Kelley L, Mezuli S, Yate C. et al. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 2015; 10: 845-858.

Wass MN, Kelley LA, Sternberg MJ. 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res 2010; 38(Web Server issue): W469-W473.

Oberholser K, Sussman J L, Hodis E, Decatur W, Livne S, Prilusky J, Richardson JS, Berchansky A. 2013, "Ramachandran Plot", Proteopedia,

Le Guilloux V, Schmidtke P, Tuffery P. Fpocket: An open source platform for ligand pocket detection. BMC Bioinformatics 2009; 10: 168.

Yates CM, Filippis I, Kelley LA, Sternberg MJ. SuSPect: enhanced prediction of single amino acid variant (SAV) phenotype using network features. J Mol Biol 2014; 426(14): 2692-701.

McNulty R, Ulmschneider J, Luecke H. et al. Mechanisms of molecular transport through the urea channel of Helicobacter pylori. Nat Commun 2013; 4: 2900.

Strugatsky D, McNulty R, Munson K. et al. Structure of the proton-gated urea channel from the gastric pathogen Helicobacter pylori. Nature 2013; 493: 255-258.

Suzuki R, Satou K, Shiroma A, Shimoji M, Teruya K, Matsumoto T, Akada J, Hirano T, Yamaoka Y. Genome-wide mutation analysis of Helicobacter pylori after inoculation to Mongolian gerbils. Gut pathogens 2019; 11: 45.

Breed J, Sankararamakrishnan R, Kerr I D, Sansom MS. Molecular dynamics simulations of water within models of ion channels. Biophysical journal 1996; 70(4): 1643-1661.

Copie G, Cleri F, Blossey R. et al. On the ability of molecular dynamics simulation and continuum electrostatics to treat interfacial water molecules in protein-protein complexes. Sci Rep 2016; 6: 38259.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.