Skip to content
2000
Volume 21, Issue 14
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Statistical physics (SP) formalism in medicine involves applying concepts and methods to study biological systems and medical problems. It is an interdisciplinary field that combines physics, mathematics, and biology to analyze complex biological processes at molecular, cellular, and tissue levels. The goal of SP in medicine is to gain insights into biological systems' mechanisms and develop new strategies for diagnosing and treating diseases. SP is used in drug discovery, disease modeling, medical imaging, and the study of pharmaceutical systems in pharmacy. SP is applied to understand the anticoagulant properties of substances by modeling interactions between blood components and studying blood properties affecting coagulation. For antiviral drugs, SP models simulate interactions between antiviral molecules, virus particles, and other biological components to optimize drug efficacy. SP models are also used in studying antifungals, antibiotics, and anticancer drugs to understand drug behavior in complex systems and improve treatments. In PS, mathematical models are used for drug absorption, dosage regimens, target-mediated drug disposition, population pharmacokinetics, and physiological-based pharmacokinetic modeling and simulation (PBPK). In rheology, SP is applied to study the flow and deformation of materials like liquids and semi-solids. In understanding physicochemical principles/processes, SP helps predict and explain the behavior of systems with many particles, such as solutions, solubilization, and adsorption. For drug delivery systems, SP is used to study drug transport and distribution in the body, improving drug efficacy and safety. Metal nanocomposites are studied using SP to understand their behavior as antibacterial agents and anticoagulants. SP models predict the mechanical, electrical, and thermal properties of metal nanocomposites for various applications.

Loading

Article metrics loading...

/content/journals/lddd/10.2174/0115701808265088230922110240
2024-11-01
2024-11-19
Loading full text...

Full text loading...

References

  1. Hernández-LemusE. Random fields in physics, biology and data science.Front. Phys. (Lausanne)2021964185910.3389/fphy.2021.641859
    [Google Scholar]
  2. WadaT. ScarfoneA.M. On the Kaniadakis Distributions Applied in Statistical Physics and Natural Sciences.Entropy (Basel)202325229210.3390/e25020292 36832658
    [Google Scholar]
  3. TeschendorffA.E. FeinbergA.P. Statistical mechanics meets single-cell biology.Nat. Rev. Genet.202122745947610.1038/s41576‑021‑00341‑z 33875884
    [Google Scholar]
  4. CoccoS. FeinauerC. FigliuzziM. MonassonR. WeigtM. Inverse statistical physics of protein sequences: A key issues review.Rep. Prog. Phys.201881303260110.1088/1361‑6633/aa9965 29120346
    [Google Scholar]
  5. RamezanpourA. BeamA.L. ChenJ.H. MashaghiA. Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms.Diagnostics (Basel)2020101197210.3390/diagnostics10110972 33228143
    [Google Scholar]
  6. DaviesAL Galla, T Network meta-analysis: A statistical physics perspective.J Stat Mech Theory Exp202220221111R001
    [Google Scholar]
  7. KiyotaY. YoshidaN. HirataF. A new approach for investigating the molecular recognition of protein: Toward structure-based drug design based on the 3D-RISM theory.J. Chem. Theory Comput.20117113803381510.1021/ct200358h 26598271
    [Google Scholar]
  8. BizzarriM. GiulianiA. Soft statistical mechanics for biology.Methods Mol. Biol.20222449263280
    [Google Scholar]
  9. NakbiA. BouzidM. AyachiF. BouazizN. Ben LamineA. Quantitative characterization of sucrose taste by statistical physics modeling parameters using an analogy between an experimental physicochemical isotherm of sucrose adsorption on β-cyclodextrin and a putative biological sucrose adsorption from sucrose dose-taste response curve (psychophysics and electrophysiology).J. Mol. Liq.202029811195010.1016/j.molliq.2019.111950
    [Google Scholar]
  10. LiZ. YahyaouiS. BouzidM. ErtoA. DottoG.L. Interpretation of diclofenac adsorption onto ZnFe2O4/chitosan magnetic composite via BET modified model by using statistical physics formalism.J. Mol. Liq.202132711485810.1016/j.molliq.2020.114858
    [Google Scholar]
  11. SellaouiL. GuedidiH. KnaniS. ReinertL. DuclauxL. Ben LamineA. Application of statistical physics formalism to the modeling of adsorption isotherms of ibuprofen on activated carbon.Fluid Phase Equilib.201538710311010.1016/j.fluid.2014.12.018
    [Google Scholar]
  12. YazidiA. SellaouiL. DottoG.L. Bonilla-PetricioletA. FröhlichA.C. LamineA.B. Monolayer and multilayer adsorption of pharmaceuticals on activated carbon: Application of advanced statistical physics models.J. Mol. Liq.201928327628610.1016/j.molliq.2019.03.101
    [Google Scholar]
  13. IdreesF. SibtainF. DarM.J. ShahF.H. AlamM. HussainI. KimS.J. IdreesJ. KhanS.A. SalmanS. Copper biosorption over green silver nanocomposite using artificial intelligence and statistical physics formalism.J. Clean. Prod.202237413399110.1016/j.jclepro.2022.133991
    [Google Scholar]
  14. SellaG. HirshA.E. The application of statistical physics to evolutionary biology.Proc. Natl. Acad. Sci. USA2005102279541954610.1073/pnas.0501865102 15980155
    [Google Scholar]
  15. LeiJ HuangK. Protein folding: A perspective from statistical physics.ArXiv100250132010
    [Google Scholar]
  16. PandeV.S. GrosbergA.Y. TanakaT. Statistical mechanics of simple models of protein folding and design.Biophys. J.19977363192321010.1016/S0006‑3495(97)78345‑0 9414231
    [Google Scholar]
  17. DurangX. HenkelM. ParkH. The statistical mechanics of the coagulation–diffusion process with a stochastic reset.J. Phys. A Math. Theor.201447404500210.1088/1751‑8113/47/4/045002
    [Google Scholar]
  18. HussanJ.R. TrewM.L. HunterP.J. Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics.Front. Physiol.20221383702710.3389/fphys.2022.837027 35399281
    [Google Scholar]
  19. WangH. YangH. Statistical Analysis of Inter-attribute Relationships in Unfractionated Heparin Injection Problems 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)Montreal, QC, Canada2020
    [Google Scholar]
  20. DespresC. DiJ. CantrelleF.X. LiZ. HuventI. ChambraudB. ZhaoJ. ChenJ. ChenS. LippensG. ZhangF. LinhardtR. WangC. KlärnerF.G. SchraderT. LandrieuI. BitanG. Smet-NoccaC. Major differences between the self-assembly and seeding behavior of heparin-induced and in vitro phosphorylated tau and their modulation by potential inhibitors.ACS Chem. Biol.20191461363137910.1021/acschembio.9b00325 31046227
    [Google Scholar]
  21. FadhilahF. RahmawatiI. AnggraeniN. Temperature effects on plasma Li-heparin and Transaminase activity in children blood with tetralogy of Fallot.J. Phys. Conf. Ser.202117641012001
    [Google Scholar]
  22. JonesB.A. LesslerJ. BiancoS. KaufmanJ.H. Statistical Mechanics and Thermodynamics of Viral Evolution.PLoS One2015109e013748210.1371/journal.pone.0137482 26422205
    [Google Scholar]
  23. WadeR.C. McCammonJ.A. Binding of an antiviral agent to a sensitive and a resistant human rhinovirus. Computer simulation studies with sampling of amino acid side-chain conformations.J. Mol. Biol.1992225369771210.1016/0022‑2836(92)90395‑Z 1318384
    [Google Scholar]
  24. WuJ. YanP. ArchibaldC. Modelling the evolution of drug resistance in the presence of antiviral drugs.BMC Public Health20077130010.1186/1471‑2458‑7‑300 17953775
    [Google Scholar]
  25. GhavasiehA. BontorinS. ArtimeO. VerstraeteN. De DomenicoM. Multiscale statistical physics of the pan-viral interactome unravels the systemic nature of SARS-CoV-2 infections.Commun. Phys.2021418310.1038/s42005‑021‑00582‑8
    [Google Scholar]
  26. DecherchiS. CavalliA. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation.Chem. Rev.202012023127881283310.1021/acs.chemrev.0c00534 33006893
    [Google Scholar]
  27. XieJ. YangQ. HanX. DongY. ZhangT. LiY. JiM. LiuC. CaiY. WangY. Pharmacokinetic/Pharmacodynamic Target Attainment of Different Antifungal Agents in De-escalation Treatment in Critically Ill Patients: A Step toward Dose Optimization Using Monte Carlo Simulation.Antimicrob. Agents Chemother.2022666e00099e2210.1128/aac.00099‑22 35604209
    [Google Scholar]
  28. SinghV. ShrivastavaS. Kumar SinghS. KumarA. SaxenaS. Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.Brief. Bioinform.2022232bbac00810.1093/bib/bbac008 35152278
    [Google Scholar]
  29. AllenR.J. WaclawB. Bacterial growth: A statistical physicist’s guide.Rep. Prog. Phys.201982101660110.1088/1361‑6633/aae546 30270850
    [Google Scholar]
  30. AllenR. WaclawB. Antibiotic resistance: A physicist’s view.Phys. Biol.201613404500110.1088/1478‑3975/13/4/045001 27510596
    [Google Scholar]
  31. KhordadR. Rastegar SedehiH.R. Modeling cancer growth and its treatment by means of statistical mechanics entropy.Eur. Phys. J. Plus2016131829110.1140/epjp/i2016‑16291‑3
    [Google Scholar]
  32. GonzálezJA AcandaM AkhtarZ New combinational therapies for cancer using modern statistical mechanics.ArXiv1902007282019
    [Google Scholar]
  33. DrorR.O. PanA.C. ArlowD.H. BorhaniD.W. MaragakisP. ShanY. XuH. ShawD.E. Pathway and mechanism of drug binding to G-protein-coupled receptors.Proc. Natl. Acad. Sci. USA201110832131181312310.1073/pnas.1104614108 21778406
    [Google Scholar]
  34. SinghD. ChaudhuryS. Statistical properties of fluctuating enzymes with dynamic cooperativity using a first passage time distribution formalism.J. Chem. Phys.20171461414510310.1063/1.4979945 28411619
    [Google Scholar]
  35. StolzenbergS. MichinoM. LeVineM.V. WeinsteinH. ShiL. Computational approaches to detect allosteric pathways in transmembrane molecular machines.Biochim. Biophys. Acta Biomembr.201618587, Part B1652166210.1016/j.bbamem.2016.01.010 26806157
    [Google Scholar]
  36. LaínezJ.M. MockusL. BlauG. A Variational Bayesian Approach for Dosage Regimen IndividualizationComp Aided ChemEng2011291563156710.1016/B978‑0‑444‑54298‑4.50091‑X
    [Google Scholar]
  37. MagerD.E. JuskoW.J. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition.J. Pharmacokinet. Pharmacodyn.200128650753210.1023/A:1014414520282 11999290
    [Google Scholar]
  38. KaralisV.D. On the Interplay between Machine Learning, Population Pharmacokinetics, and Bioequivalence to Introduce Average Slope as a New Measure for Absorption Rate.Appl. Sci. (Basel)2023134225710.3390/app13042257
    [Google Scholar]
  39. KrstevskaA. ĐurišJ. IbrićS. CvijićS. In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools.Pharmaceutics202215110710.3390/pharmaceutics15010107 36678737
    [Google Scholar]
  40. PotterC.B. DavisM.T. AlbadarinA.B. WalkerG.M. Investigation of the Dependence of the Flory–Huggins Interaction Parameter on Temperature and Composition in a Drug–Polymer System.Mol. Pharm.201815115327533510.1021/acs.molpharmaceut.8b00797 30259745
    [Google Scholar]
  41. BabuM.A. NithyaR. Sankar, V Molecular Dynamic Approach to Predict the Miscibility of Excipients for Lipid-based Formulations.Research Square202210.21203/rs.3.rs‑1287799/v1
    [Google Scholar]
  42. FerrarJ.A. SellersB.D. ChanC. LeungD.H. Towards an improved understanding of drug excipient interactions to enable rapid optimization of nanosuspension formulations.Int. J. Pharm.202057811909410.1016/j.ijpharm.2020.119094 32006625
    [Google Scholar]
  43. MendykA. GüresS. JachowiczR. From heuristic to mathematical modeling of drugs dissolution profiles: Application of artificial neural networks and genetic programming.Comput. Math. Methods Med.2015201586387410.1155/2015/863874
    [Google Scholar]
  44. Del GadoE. MorrisJ.F. Preface: Physics of dense suspensions.J. Rheol. (N.Y.N.Y.)202064222322510.1122/8.0000016
    [Google Scholar]
  45. DottoG.L. SellaouiL. LimaE.C. LamineA.B. Physicochemical and thermodynamic investigation of Ni(II) biosorption on various materials using the statistical physics modeling.J. Mol. Liq.201622012913510.1016/j.molliq.2016.04.075
    [Google Scholar]
  46. SugimotoI. SudaY. TakahashiK. Physicochemical and statistical characterization of gas-sensing behaviors of resonator sensors with carbonaceous films prepared by rf-sputtering of aromatic and hydrophilic biomolecules.Results in Chemistry2022410042610.1016/j.rechem.2022.100426
    [Google Scholar]
  47. WjihiS. AouainiF. AlmuqrinA.H. LamineA.B. Physicochemical assessment of prednisone adsorption on two molecular composites using statistical physics formalism in cosmetics.Arab. J. Chem.20201386876688610.1016/j.arabjc.2020.06.040
    [Google Scholar]
  48. ChenQ. JiY. GeK. Influence of excipients on thermodynamic phase behavior of pharmaceutical/solvent systems: Molecular thermodynamic model prediction.Chem. Eng. Sci.202124411679810.1016/j.ces.2021.116798
    [Google Scholar]
  49. Ben KhemisI. SagaamaA. IssaouiN. Ben LamineA. Steric and energetic characterizations of mouse and human musk receptors activated by nitro musk smelling compounds at molecular level: Statistical physics treatment and molecular docking analysis.Int. J. Biol. Macromol.202118833334210.1016/j.ijbiomac.2021.08.042 34389381
    [Google Scholar]
  50. KnoppM.M. OlesenN.E. HuangY. HolmR. RadesT. Statistical Analysis of a Method to Predict Drug–Polymer Miscibility.J. Pharm. Sci.2016105136236710.1002/jps.24704 26539792
    [Google Scholar]
  51. LopesL.M. de MoraesM.A. BeppuM.M. Phase diagram and estimation of flory-huggins parameter of interaction of silk fibroin/sodium alginate blends.Front. Bioeng. Biotechnol.2020897310.3389/fbioe.2020.00973 33014999
    [Google Scholar]
  52. BansalK. BaghelU.S. ThakralS. Construction and Validation of Binary Phase Diagram for Amorphous Solid Dispersion Using Flory–Huggins Theory.AAPS PharmSciTech201617231832710.1208/s12249‑015‑0343‑8 26092302
    [Google Scholar]
  53. SharmaP. Applications of statistical tools for optimization and development of smart drug delivery system.LondonIntechopen202218310.5772/intechopen.99632
    [Google Scholar]
  54. Gomes-FilhoMS BarbosaMAA OliveiraFA A statistical mechanical model for drug release: Relations between release parameters and porosity. Phys A Stat Mech Its Appl.2020540123165
    [Google Scholar]
  55. SiepmannJ. SiepmannF. Modeling of diffusion controlled drug delivery.J. Control. Release2012161235136210.1016/j.jconrel.2011.10.006 22019555
    [Google Scholar]
  56. Gomes-FilhoM.S. OliveiraF.A. BarbosaM.A.A. Modeling the diffusion-erosion crossover dynamics in drug release.Phys. Rev. E2022105404411010.1103/PhysRevE.105.044110 35590597
    [Google Scholar]
  57. Urbina-VillalbaG. An algorithm for emulsion stability simulations: Account of flocculation, coalescence, surfactant adsorption and the process of Ostwald ripening.Int. J. Mol. Sci.200910376180410.3390/ijms10030761 19399220
    [Google Scholar]
  58. JiangL. RahnamaM. ZhangB. ZhuX. SuiP-C. YeD-D. DjilaliN. Predicting the interaction between nanoparticles in shear flow using lattice Boltzmann method and Derjaguin–Landau–Verwey–Overbeek (DLVO) theory.Phys. Fluids202032404330210.1063/1.5142669
    [Google Scholar]
  59. KayesJ.B. Pharmaceutical suspensions: Relation between zeta potential, sedimentation volume and suspension stability.J. Pharm. Pharmacol.1977294199204 17667
    [Google Scholar]
  60. MobarakM. MohamedE.A. SelimA.Q. MohamedF.M. SellaouiL. Bonilla-PetricioletA. SeliemM.K. Statistical physics modeling and interpretation of methyl orange adsorption on high–order mesoporous composite of MCM–48 silica with treated rice husk.J. Mol. Liq.201928567868710.1016/j.molliq.2019.04.116
    [Google Scholar]
  61. PalR. Modeling of sedimentation and creaming in suspensions and pickering emulsions.Fluids20194418610.3390/fluids4040186
    [Google Scholar]
  62. GhazzyA. NaikR.R. ShakyaA.K. Metal–Polymer Nanocomposites: A Promising Approach to Antibacterial Materials.Polymers (Basel)2023159216710.3390/polym15092167 37177313
    [Google Scholar]
  63. PalzaH. Antimicrobial polymers with metal nanoparticles.Int. J. Mol. Sci.20151612099211610.3390/ijms16012099 25607734
    [Google Scholar]
  64. RazaS. AnsariA. SiddiquiN.N. IbrahimF. AbroM.I. AmanA. Biosynthesis of silver nanoparticles for the fabrication of non cytotoxic and antibacterial metallic polymer based nanocomposite system.Sci. Rep.20211111050010.1038/s41598‑021‑90016‑w 34006995
    [Google Scholar]
  65. KhdaryN.H. GhanemM.A. Metal–organic–silica nanocomposites: Copper, silver nanoparticles–ethylenediamine–silica gel and their CO2 adsorption behaviour.J. Mater. Chem.20122224120321203810.1039/c2jm31104f
    [Google Scholar]
  66. PintoR.J.B. DainaS. SadoccoP. Antibacterial activity of nanocomposites of copper and cellulose.BioMed Res. Int.2013201328051210.1155/2013/280512
    [Google Scholar]
  67. AbebeB. ZereffaE.A. TadesseA. MurthyH.C.A. A Review on Enhancing the Antibacterial Activity of ZnO: Mechanisms and Microscopic Investigation.Nanoscale Res. Lett.202015119010.1186/s11671‑020‑03418‑6 33001404
    [Google Scholar]
  68. YinI.X. ZhangJ. ZhaoI.S. MeiM.L. LiQ. ChuC.H. The antibacterial mechanism of silver nanoparticles and its application in dentistry.Int. J. Nanomedicine2020152555256210.2147/IJN.S246764 32368040
    [Google Scholar]
  69. MaX. ZhouS. XuX. DuQ. Copper-containing nanoparticles: Mechanism of antimicrobial effect and application in dentistry-a narrative review.Front. Surg.2022990589210.3389/fsurg.2022.905892 35990090
    [Google Scholar]
  70. MendesC.R. DilarriG. ForsanC.F. SapataV.M.R. LopesP.R.M. de MoraesP.B. MontagnolliR.N. FerreiraH. BidoiaE.D. Antibacterial action and target mechanisms of zinc oxide nanoparticles against bacterial pathogens.Sci. Rep.2022121265810.1038/s41598‑022‑06657‑y 35173244
    [Google Scholar]
  71. ZareY. ShabaniI. Polymer/metal nanocomposites for biomedical applications.Mater. Sci. Eng. C20166019520310.1016/j.msec.2015.11.023 26706522
    [Google Scholar]
  72. GarciaC.V. ShinG.H. KimJ.T. Metal oxide-based nanocomposites in food packaging: Applications, migration, and regulations.Trends Food Sci. Technol.201882213110.1016/j.tifs.2018.09.021
    [Google Scholar]
  73. WongW.K. LaiC.H.N. ChengW.Y. TungL-H. ChangR.C-C. LeungF.K-C. Polymer–Metal Composite Healthcare Materials: From Nano to Device Scale.Journal of Composites Science20226821810.3390/jcs6080218
    [Google Scholar]
  74. AsgharM.A. YousufR.I. ShoaibM.H. AsgharM.A. Antibacterial, anticoagulant and cytotoxic evaluation of biocompatible nanocomposite of chitosan loaded green synthesized bioinspired silver nanoparticles.Int. J. Biol. Macromol.202016093494310.1016/j.ijbiomac.2020.05.197 32470586
    [Google Scholar]
  75. SellaouiL. AliJ. BadawiM. Bonilla-PetricioletA. ChenZ. Understanding the adsorption mechanism of Ag+ and Hg2+ on functionalized layered double hydroxide via statistical physics modeling.Appl. Clay Sci.202019810582810.1016/j.clay.2020.105828
    [Google Scholar]
  76. SellaouiL. Mendoza-CastilloD.I. Reynel-ÁvilaH.E. Bonilla-PetricioletA. Ben LamineA. ErtoA. A new statistical physics model for the ternary adsorption of Cu2+, Cd2+ and Zn2+ ions on bone char: Experimental investigation and simulations.Chem. Eng. J.201834354455310.1016/j.cej.2018.03.033
    [Google Scholar]
  77. VasileiadisT. NoualA. WangY. GraczykowskiB. Djafari-RouhaniB. YangS. FytasG. Optomechanical Hot-Spots in Metallic Nanorod–Polymer Nanocomposites.ACS Nano20221612204192042910.1021/acsnano.2c06673 36475620
    [Google Scholar]
  78. AryanfarA. MedlejS. TarhiniA. DamadiS.R. TehraniB. A.R.; Goddard, W.A., III 3D percolation modeling for predicting the thermal conductivity of graphene-polymer composites.Comput. Mater. Sci.202119711065010.1016/j.commatsci.2021.110650
    [Google Scholar]
  79. LiX. ParkW. ChenY.P. RuanX. Effect of particle size and aggregation on thermal conductivity of metal–polymer nanocomposite.J. Heat Transfer2017139202240110.1115/1.4034757
    [Google Scholar]
  80. ReigD.S. HummelP. WangZ. RosenfeldtS. GraczykowskiB. RetschM. FytasG. Well-defined metal-polymer nanocomposites: The interplay of structure, thermoplasmonics, and elastic mechanical properties.Phys. Rev. Mater.201821212360510.1103/PhysRevMaterials.2.123605
    [Google Scholar]
  81. FarhaA.H. Al NaimA.F. MansourS.A. Thermal Degradation of Polystyrene (PS) Nanocomposites Loaded with Sol Gel-Synthesized ZnO Nanorods.Polymers (Basel)2020129193510.3390/polym12091935 32867070
    [Google Scholar]
/content/journals/lddd/10.2174/0115701808265088230922110240
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test