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Recent Advances in Drug Delivery and Formulation

Author(s): Shivang Dhoundiyal and Md Aftab Alam*

DOI: 10.2174/2667387817666230907093403

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Advances in Pharmacokinetic Modelling and Computational Approaches for Nanoparticles in Drug Delivery Systems

Page: [210 - 227] Pages: 18

  • * (Excluding Mailing and Handling)

Abstract

Generally, therapeutic drugs have issues like poor solubility, rapid removal from the bloodstream, lack of targeting, and an inability to translocate across cell membranes. Some of these barriers can be overcome by using nano drug delivery systems (DDS), which results in more efficient drug delivery to the site of action. Due to their potential application as drug delivery systems, nanoparticles are the main topic of discussion in this article. Experimental and computational investigations have substantially aided in the understanding of how nanocarriers work and how they interact with medications, biomembranes and other biological components. This review explores how computational modelling can aid in the rational design of DDS that has been optimized and improved upon. The most commonly used simulation methods for studying DDS and some of the most important biophysical elements of DDS are also discussed. Then, we conclude by investigating the computational properties of various types of nanocarriers, such as dendrimers and dendrons, polymer-, peptide-, nucleic acid-, lipid-, carbon-based DDS, and gold nanoparticles.

Graphical Abstract

[1]
Baryakova TH, Pogostin BH, Langer R, McHugh KJ. Overcoming barriers to patient adherence: The case for developing innovative drug delivery systems. Nat Rev Drug Discov 2023; 22(5): 387-409.
[http://dx.doi.org/10.1038/s41573-023-00670-0] [PMID: 36973491]
[2]
Adepu S, Ramakrishna S. Controlled drug delivery systems: Current status and future directions. Molecules 2021; 26(19): 5905.
[http://dx.doi.org/10.3390/molecules26195905] [PMID: 34641447]
[3]
Mishra DK, Shandilya R, Mishra PK. Lipid based nanocarriers: A translational perspective. Nanomedicine 2018; 14(7): 2023-50.
[http://dx.doi.org/10.1016/j.nano.2018.05.021] [PMID: 29944981]
[4]
Yusuf A, Almotairy ARZ, Henidi H, Alshehri OY, Aldughaim MS. Nanoparticles as drug delivery systems: A review of the implication of nanoparticles’ physicochemical properties on responses in biological systems. Polymers 2023; 15(7): 1596.
[http://dx.doi.org/10.3390/polym15071596] [PMID: 37050210]
[5]
Shrestha H, Bala R, Arora S. Lipid-based drug delivery systems. Int J Pharm 2014; 2014: 801820.
[http://dx.doi.org/10.1155/2014/801820]
[6]
Siepmann J, Faham A, Clas SD, et al. Lipids and polymers in pharmaceutical technology: Lifelong companions. Int J Pharm 2019; 558: 128-42.
[http://dx.doi.org/10.1016/j.ijpharm.2018.12.080] [PMID: 30639218]
[7]
Shen S, Wu Y, Liu Y, Wu D. High drug-loading nanomedicines: Progress, current status, and prospects. Int J Nanomedicine 2017; 12: 4085-109.
[http://dx.doi.org/10.2147/IJN.S132780] [PMID: 28615938]
[8]
Palanikumar L, Choi ES, Oh JY, et al. Importance of encapsulation stability of nanocarriers with high drug loading capacity for increasing in vivo therapeutic efficacy. Biomacromolecules 2018; 19(7): 3030-9.
[http://dx.doi.org/10.1021/acs.biomac.8b00589] [PMID: 29883544]
[9]
Cao C, Zhao J, Lu M, Garvey CJ, Stenzel MH. Correlation between drug loading content and biological activity: The complexity demonstrated in paclitaxel-loaded glycopolymer micelle system. Biomacromolecules 2019; 20(4): 1545-54.
[http://dx.doi.org/10.1021/acs.biomac.8b01707] [PMID: 30768256]
[10]
Ramezanpour M, Leung SSW, Delgado-Magnero KH, Bashe BYM, Thewalt J, Tieleman DP. Computational and experimental approaches for investigating nanoparticle-based drug delivery systems. Biochim Biophys Acta Biomembr 2016; 1858(7): 1688-709.
[http://dx.doi.org/10.1016/j.bbamem.2016.02.028] [PMID: 26930298]
[11]
Wang W, Ye Z, Gao H, Ouyang D. Computational pharmaceutics - A new paradigm of drug delivery. J Control Release 2021; 338: 119-36.
[http://dx.doi.org/10.1016/j.jconrel.2021.08.030] [PMID: 34418520]
[12]
Yerpude ST, Potbhare AK, Bhilkar PR, et al. Computational analysis of nanofluids-based drug delivery system: Preparation, current development and applications of nanofluids. In: Applications of Nanofluids in Chemical and Bio-medical Process Industry. Elsevier 2022; pp. 335-64.
[13]
Gilad Y, Gellerman G, Lonard DM, O’Malley BW. Drug combination in cancer treatment—From cocktails to conjugated combinations. Cancers 2021; 13(4): 669.
[http://dx.doi.org/10.3390/cancers13040669] [PMID: 33562300]
[14]
Katiyar RS, Jha PK. Molecular simulations in drug delivery: Opportunities and challenges. Wiley Interdiscip Rev Comput Mol Sci 2018; 8(4): e1358.
[http://dx.doi.org/10.1002/wcms.1358]
[15]
Sung B. In silico modeling of endocrine organ-on-a-chip systems. Math Biosci 2022; 352: 108900.
[http://dx.doi.org/10.1016/j.mbs.2022.108900] [PMID: 36075288]
[16]
Balhara A, Kumar AR, Unadkat JD. Predicting human fetal drug exposure through maternal‐fetal PBPK modeling and in vitro or ex vivo studies. J Clin Pharmacol 2022; 62(S1): S94-S114.
[http://dx.doi.org/10.1002/jcph.2117] [PMID: 36106781]
[17]
Li L, He H, Jiang S, et al. Simulation of the in vivo fate of polymeric nanoparticles traced by environment-responsive near-infrared dye: A physiologically based pharmacokinetic modelling approach. Molecules 2021; 26(5): 1271.
[http://dx.doi.org/10.3390/molecules26051271] [PMID: 33652827]
[18]
Omar MH, Razak KA, Ab Wahab MN, Hamzah HH. Recent progress of conductive 3D-printed electrodes based upon polymers/carbon nanomaterials using a fused deposition modelling (FDM) method as emerging electrochemical sensing devices. RSC Advances 2021; 11(27): 16557-71.
[http://dx.doi.org/10.1039/D1RA01987B] [PMID: 35479129]
[19]
Osipova N, Budko A, Maksimenko O, et al. Comparison of compartmental and non-compartmental analysis to detect biopharmaceutical similarity of intravenous nanomaterial-based rifabutin formulations. Pharmaceutics 2023; 15(4): 1258.
[http://dx.doi.org/10.3390/pharmaceutics15041258] [PMID: 37111743]
[20]
Nathanael K, Pico P, Kovalchuk NM, Lavino AD, Simmons MJH, Matar OK. Computational modelling and microfluidics as emerging approaches to synthesis of silver nanoparticles – A review. Chem Eng J 2022; 436: 135178.
[http://dx.doi.org/10.1016/j.cej.2022.135178]
[21]
Yaghini E, Tacconi E, Pilling A, et al. Population pharmacokinetic modelling of indium-based quantum dot nanoparticles: Preclinical in vivo studies. Eur J Pharm Sci 2021; 157: 105639.
[http://dx.doi.org/10.1016/j.ejps.2020.105639] [PMID: 33188925]
[22]
Kheraldine H, Rachid O, Habib AM, Al Moustafa AE, Benter IF, Akhtar S. Emerging innate biological properties of nano-drug delivery systems: A focus on PAMAM dendrimers and their clinical potential. Adv Drug Deliv Rev 2021; 178: 113908.
[http://dx.doi.org/10.1016/j.addr.2021.113908] [PMID: 34390777]
[23]
Shao X, Shi C, Wu S, Wang F, Li W. Review of the pharmacokinetics of nanodrugs. Nanotechnol Rev 2023; 12(1): 20220525.
[http://dx.doi.org/10.1515/ntrev-2022-0525]
[24]
Sadeghi A, Ruponen M, Puranen J, et al. Imaging, quantitation and kinetic modelling of intravitreal nanomaterials. Int J Pharm 2022; 621: 121800.
[http://dx.doi.org/10.1016/j.ijpharm.2022.121800] [PMID: 35533923]
[25]
Xu K, Li S, Zhou Y, Gao X, Mei J, Liu Y. Application of computing as a high-practicability and -efficiency auxiliary tool in nanodrugs discovery. Pharmaceutics 2023; 15(4): 1064.
[http://dx.doi.org/10.3390/pharmaceutics15041064] [PMID: 37111551]
[26]
Li M, Zou P, Tyner K, Lee S. Physiologically based pharmacokinetic (PBPK) modeling of pharmaceutical nanoparticles. AAPS J 2017; 19(1): 26-42.
[http://dx.doi.org/10.1208/s12248-016-0010-3] [PMID: 27834047]
[27]
Yuan D, He H, Wu Y, Fan J, Cao Y. Physiologically based pharmacokinetic modeling of nanoparticles. J Pharm Sci 2019; 108(1): 58-72.
[http://dx.doi.org/10.1016/j.xphs.2018.10.037] [PMID: 30385282]
[28]
des Rieux A, Fievez V, Garinot M, Schneider YJ, Préat V. Nanoparticles as potential oral delivery systems of proteins and vaccines: A mechanistic approach. J Control Release 2006; 116(1): 1-27.
[http://dx.doi.org/10.1016/j.jconrel.2006.08.013] [PMID: 17050027]
[29]
Oberdörster G, Oberdörster E, Oberdörster J. Nanotoxicology: An emerging discipline evolving from studies of ultrafine particles. Environ Health Perspect 2005; 113(7): 823-39.
[http://dx.doi.org/10.1289/ehp.7339] [PMID: 16002369]
[30]
MacCalman L. Development of a bio-mathematical model in rats to describe clearance, retention and translocation of inhaled nano particles throughout the body. J Phys Conf Ser 2009; 151(1): 012028.
[31]
Bachler G, von Goetz N, Hungerbuhler K. Using physiologically based pharmacokinetic (PBPK) modeling for dietary risk assessment of titanium dioxide (TiO2) nanoparticles. Nanotoxicology 2015; 9(3): 373-80.
[http://dx.doi.org/10.3109/17435390.2014.940404] [PMID: 25058655]
[32]
Owens D III, Peppas N. Opsonization, biodistribution, and pharmacokinetics of polymeric nanoparticles. Int J Pharm 2006; 307(1): 93-102.
[http://dx.doi.org/10.1016/j.ijpharm.2005.10.010] [PMID: 16303268]
[33]
Gao H, He Q. The interaction of nanoparticles with plasma proteins and the consequent influence on nanoparticles behavior. Expert Opin Drug Deliv 2014; 11(3): 409-20.
[http://dx.doi.org/10.1517/17425247.2014.877442] [PMID: 24397260]
[34]
Salvati A, Pitek AS, Monopoli MP, et al. Transferrin-functionalized nanoparticles lose their targeting capabilities when a biomolecule corona adsorbs on the surface. Nat Nanotechnol 2013; 8(2): 137-43.
[http://dx.doi.org/10.1038/nnano.2012.237] [PMID: 23334168]
[35]
Kobayashi H, Watanabe R, Choyke PL. Improving conventional enhanced permeability and retention (EPR) effects; what is the appropriate target? Theranostics 2014; 4(1): 81-9.
[http://dx.doi.org/10.7150/thno.7193] [PMID: 24396516]
[36]
Blanco E, Shen H, Ferrari M. Principles of nanoparticle design for overcoming biological barriers to drug delivery. Nat Biotechnol 2015; 33(9): 941-51.
[http://dx.doi.org/10.1038/nbt.3330] [PMID: 26348965]
[37]
Levy G. Pharmacologic target-mediated drug disposition. Clin Pharmacol Ther 1994; 56(3): 248-52.
[http://dx.doi.org/10.1038/clpt.1994.134] [PMID: 7924119]
[38]
Dua P, Hawkins E, van der Graaf PH. A tutorial on target‐mediated drug disposition (TMDD) models. CPT Pharmacometrics Syst Pharmacol 2015; 4(6): 324-37.
[http://dx.doi.org/10.1002/psp4.41] [PMID: 26225261]
[39]
Sadauskas E, Danscher G, Stoltenberg M, Vogel U, Larsen A, Wallin H. Protracted elimination of gold nanoparticles from mouse liver. Nanomedicine 2009; 5(2): 162-9.
[http://dx.doi.org/10.1016/j.nano.2008.11.002] [PMID: 19217434]
[40]
Utembe W, Clewell H, Sanabria N, Doganis P, Gulumian M. Current approaches and techniques in physiologically based pharmacokinetic (PBPK) modelling of nanomaterials. Nanomaterials 2020; 10(7): 1267.
[http://dx.doi.org/10.3390/nano10071267] [PMID: 32610468]
[41]
Longmire M, Choyke PL, Kobayashi H. Clearance properties of nano-sized particles and molecules as imaging agents: Considerations and caveats. Nanomedicine 2008; 3(5): 703-17.
[http://dx.doi.org/10.2217/17435889.3.5.703]
[42]
Soo Choi H, Liu W, Misra P, et al. Renal clearance of quantum dots. Nat Biotechnol 2007; 25(10): 1165-70.
[http://dx.doi.org/10.1038/nbt1340] [PMID: 17891134]
[43]
Almeida JPM, Chen AL, Foster A, Drezek R. In vivo biodistribution of nanoparticles. Nanomedicine 2011; 6(5): 815-35.
[http://dx.doi.org/10.2217/nnm.11.79] [PMID: 21793674]
[44]
Semmler-Behnke M, Kreyling WG, Lipka J, et al. Biodistribution of 1.4- and 18-nm gold particles in rats. Small 2008; 4(12): 2108-11.
[http://dx.doi.org/10.1002/smll.200800922] [PMID: 19031432]
[45]
Lipka J, Semmler-Behnke M, Sperling RA, et al. Biodistribution of PEG-modified gold nanoparticles following intratracheal instillation and intravenous injection. Biomaterials 2010; 31(25): 6574-81.
[http://dx.doi.org/10.1016/j.biomaterials.2010.05.009] [PMID: 20542560]
[46]
Péry ARR, Brochot C, Hoet PHM, Nemmar A, Bois FY. Development of a physiologically based kinetic model for 99 m -Technetium-labelled carbon nanoparticles inhaled by humans. Inhal Toxicol 2009; 21(13): 1099-107.
[http://dx.doi.org/10.3109/08958370902748542] [PMID: 19814607]
[47]
Tsiros P, Bois FY, Dokoumetzidis A, Tsiliki G, Sarimveis H. Population pharmacokinetic reanalysis of a Diazepam PBPK model: A comparison of Stan and GNU MCSim. J Pharmacokinet Pharmacodyn 2019; 46(2): 173-92.
[http://dx.doi.org/10.1007/s10928-019-09630-x] [PMID: 30949914]
[48]
Hendriks BS, Reynolds JG, Klinz SG, et al. Multiscale kinetic modeling of liposomal doxorubicin delivery quantifies the role of tumor and drug-specific parameters in local delivery to tumors. CPT Pharmacometrics Syst Pharmacol 2012; 1(11): 15.
[http://dx.doi.org/10.1038/psp.2012.16] [PMID: 23835797]
[49]
Lankveld DPK, Oomen AG, Krystek P, et al. The kinetics of the tissue distribution of silver nanoparticles of different sizes. Biomaterials 2010; 31(32): 8350-61.
[http://dx.doi.org/10.1016/j.biomaterials.2010.07.045] [PMID: 20684985]
[50]
Moss DM, Siccardi M. Optimizing nanomedicine pharmacokinetics using physiologically based pharmacokinetics modelling. Br J Pharmacol 2014; 171(17): 3963-79.
[http://dx.doi.org/10.1111/bph.12604] [PMID: 24467481]
[51]
Thompson CM, Sonawane B, Barton HA, et al. Approaches for applications of physiologically based pharmacokinetic models in risk assessment. J Toxicol Environ Health B Crit Rev 2008; 11(7): 519-47.
[http://dx.doi.org/10.1080/10937400701724337] [PMID: 18584453]
[52]
Cheng YH, Riviere JE, Monteiro-Riviere NA, Lin Z. Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling. Nanotoxicology 2018; 12(5): 453-69.
[http://dx.doi.org/10.1080/17435390.2018.1459922] [PMID: 29658401]
[53]
Zhao P, Rowland M, Huang S-M. Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther 2012; 92(1): 17-20.
[http://dx.doi.org/10.1038/clpt.2012.68] [PMID: 22713733]
[54]
Kumar M, Kulkarni P, Liu S, Chemuturi N, Shah DK. Nanoparticle biodistribution coefficients: A quantitative approach for understanding the tissue distribution of nanoparticles. Adv Drug Deliv Rev 2023; 194: 114708.
[http://dx.doi.org/10.1016/j.addr.2023.114708] [PMID: 36682420]
[55]
Li M, Panagi Z, Avgoustakis K, Reineke J. Physiologically based pharmacokinetic modeling of PLGA nanoparticles with varied mPEG content. Int J Nanomed 2012; 7: 1345-56.
[PMID: 22419876]
[56]
Siepmann J, Siepmann F. Mathematical modeling of drug delivery. Int J Pharm 2008; 364(2): 328-43.
[http://dx.doi.org/10.1016/j.ijpharm.2008.09.004] [PMID: 18822362]
[57]
Haddish-Berhane N, Rickus JL, Haghighi K. The role of multiscale computational approaches for rational design of conventional and nanoparticle oral drug delivery systems. Int J Nanomed 2007; 2(3): 315-31.
[PMID: 18019831]
[58]
Clancy CE, An G, Cannon WR, et al. Multiscale modeling in the clinic: Drug design and development. Ann Biomed Eng 2016; 44(9): 2591-610.
[http://dx.doi.org/10.1007/s10439-016-1563-0] [PMID: 26885640]
[59]
Mansoorinasab A, Morsali A, Heravi MM, Beyramabadi SA. Quantum mechanical study on the noncovalent adsorption of drug gentamicin onto pristine and COOH functionalized carbon nanotubes. J Comput Theor Nanosci 2015; 12(11): 4935-41.
[http://dx.doi.org/10.1166/jctn.2015.4462]
[60]
Gilad Y, Noy E, Senderowitz H, Albeck A, Firer MA, Gellerman G. Synthesis, biological studies and molecular dynamics of new anticancer RGD-based peptide conjugates for targeted drug delivery. Bioorg Med Chem 2016; 24(2): 294-303.
[http://dx.doi.org/10.1016/j.bmc.2015.12.020] [PMID: 26719208]
[61]
Bernini MC, Fairen-Jimenez D, Pasinetti M, Ramirez-Pastor AJ, Snurr RQ. Screening of bio-compatible metal–organic frameworks as potential drug carriers using Monte Carlo simulations. J Mater Chem B Mater Biol Med 2014; 2(7): 766-74.
[http://dx.doi.org/10.1039/C3TB21328E] [PMID: 32261308]
[62]
Durbin EW, Buxton GA. A coarse-grained model of targeted drug delivery from responsive polymer nanoparticles. Soft Matter 2010; 6(4): 762-7.
[http://dx.doi.org/10.1039/b918476g]
[63]
Manshadi MKD, Saadat M, Mohammadi M, et al. Delivery of magnetic micro/nanoparticles and magnetic-based drug/cargo into arterial flow for targeted therapy. Drug Deliv 2018; 25(1): 1963-73.
[http://dx.doi.org/10.1080/10717544.2018.1497106] [PMID: 30799655]
[64]
Moradi Kashkooli F, Soltani M, Souri M, Meaney C, Kohandel M. Nexus between in silico and in vivo models to enhance clinical translation of nanomedicine. Nano Today 2021; 36: 101057.
[http://dx.doi.org/10.1016/j.nantod.2020.101057]
[65]
Oliver M, Bauzá A, Frontera A, Miró M. Fluorescent lipid nanoparticles as biomembrane models for exploring emerging contaminant bioavailability supported by density functional theory calculations. Environ Sci Technol 2016; 50(13): 7135-43.
[http://dx.doi.org/10.1021/acs.est.6b00772] [PMID: 27243463]
[66]
Anigboro AA, Avwioroko OJ, Akeghware O, Tonukari NJ. Anti-obesity, antioxidant and in silico evaluation of Justicia carnea bioactive compounds as potential inhibitors of an enzyme linked with obesity: Insights from kinetics, semi-empirical quantum mechanics and molecular docking analysis. Biophys Chem 2021; 274: 106607.
[http://dx.doi.org/10.1016/j.bpc.2021.106607] [PMID: 33957576]
[67]
Jeon I, Yun T, Yang S. Classical, coarse-grained, and reactive molecular dynamics simulations on polymer nanocomposites. Multiscale Sci Eng 2022; 4(4): 161-78.
[http://dx.doi.org/10.1007/s42493-022-00086-9]
[68]
Shityakov S, Roewer N, Broscheit JA, Förster C. In silico models for nanotoxicity evaluation and prediction at the blood-brain barrier level: A mini-review. Comput Toxicol 2017; 2: 20-7.
[http://dx.doi.org/10.1016/j.comtox.2017.02.003]
[69]
Retif P, Reinhard A, Paquot H, et al. Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles. Int J Nanomed 2016; 11: 6169-79.
[http://dx.doi.org/10.2147/IJN.S111320] [PMID: 27920524]
[70]
Shamsi M, Mohammadi A, Manshadi MKD, Sanati-Nezhad A. Mathematical and computational modeling of nano-engineered drug delivery systems. J Control Release 2019; 307: 150-65.
[http://dx.doi.org/10.1016/j.jconrel.2019.06.014] [PMID: 31229474]
[71]
Valentinuzzi D, Jeraj R. Computational modelling of modern cancer immunotherapy. Phys Med Biol 2020; 65(24): 24TR01.
[http://dx.doi.org/10.1088/1361-6560/abc3fc] [PMID: 33091898]
[72]
Kotzabasaki M, Froudakis GE. Review of computer simulations on anti-cancer drug delivery in MOFs. Inorg Chem Front 2018; 5(6): 1255-72.
[http://dx.doi.org/10.1039/C7QI00645D]
[73]
Improta R, Santoro F, Blancafort L. Quantum mechanical studies on the photophysics and the photochemistry of nucleic acids and nucleobases. Chem Rev 2016; 116(6): 3540-93.
[http://dx.doi.org/10.1021/acs.chemrev.5b00444] [PMID: 26928320]
[74]
Ullah A, Ullah N, Nawaz T, Aziz T. Molecular mechanisms of Sanguinarine in cancer prevention and treatment. Anticancer Agents Med Chem 2022; 23(7): 765-78.
[PMID: 36045531]
[75]
Thiel W. Semiempirical quantum–chemical methods. Wiley Interdiscip Rev Comput Mol Sci 2014; 4(2): 145-57.
[http://dx.doi.org/10.1002/wcms.1161]
[76]
Peters MB, Raha K, Merz KM Jr. Quantum mechanics in structure-based drug design. Curr Opin Drug Discov Devel 2006; 9(3): 370-9.
[PMID: 16729734]
[77]
Al-Anber MJ. Theoretical semiempirical study of the biomolecules interaction with carbon nanotubes. J Macromol Sci Part B Phys 2011; 50(12): 2481-7.
[http://dx.doi.org/10.1080/00222348.2011.557004]
[78]
Rodríguez-Ropero F, Zanuy D, Alemán C. Electronic characterization of all-thiophene conducting dendrimers: Molecules and assemblies. Polymer 2010; 51(1): 308-15.
[http://dx.doi.org/10.1016/j.polymer.2009.11.007]
[79]
Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol 2011; 9(1): 71.
[http://dx.doi.org/10.1186/1741-7007-9-71] [PMID: 22035460]
[80]
Evoli S, Mobley DL, Guzzi R, Rizzuti B. Multiple binding modes of ibuprofen in human serum albumin identified by absolute binding free energy calculations. Phys Chem Chem Phys 2016; 18(47): 32358-68.
[http://dx.doi.org/10.1039/C6CP05680F] [PMID: 27854368]
[81]
Dubbeldam D, Torres-Knoop A, Walton KS. On the inner workings of Monte Carlo codes. Mol Simul 2013; 39(14-15): 1253-92.
[http://dx.doi.org/10.1080/08927022.2013.819102]
[82]
Elkomy MH, El Menshawe SF, Eid HM, Ali AMA. Development of a nanogel formulation for transdermal delivery of tenoxicam: A pharmacokinetic–pharmacodynamic modeling approach for quantitative prediction of skin absorption. Drug Dev Ind Pharm 2017; 43(4): 531-44.
[http://dx.doi.org/10.1080/03639045.2016.1268153] [PMID: 27910712]
[83]
Diao L, Meibohm B. Pharmacokinetics and pharmacokinetic-pharmacodynamic correlations of therapeutic peptides. Clin Pharmacokinet 2013; 52(10): 855-68.
[http://dx.doi.org/10.1007/s40262-013-0079-0] [PMID: 23719681]
[84]
Yadav R, Bulitta JB, Nation RL, Landersdorfer CB. Optimization of synergistic combination regimens against carbapenem-and aminoglycoside-resistant clinical Pseudomonas aeruginosa isolates via mechanism-based pharmacokinetic/pharmacodynamic modeling. Antimicrob Agents Chemother 2016; 61(1): e01011-6.
[PMID: 27821448]
[85]
Bittig AT, Uhrmacher AM. Spatial modeling in cell biology at multiple levels. Proceedings of the 2010 Winter Simulation Conference 5-8 Dec, 2010. Baltimore, Maryland. 2010; pp. 608-19.
[http://dx.doi.org/10.1109/WSC.2010.5679125]
[86]
Li Y, Stroberg W, Lee TR, et al. Multiscale modeling and uncertainty quantification in nanoparticle-mediated drug/gene delivery. Comput Mech 2014; 53(3): 511-37.
[http://dx.doi.org/10.1007/s00466-013-0953-5]
[87]
Tian Y, Shi C, Sun Y, Zhu C, Sun CC, Mao S. Designing micellar nanocarriers with improved drug loading and stability based on solubility parameter. Mol Pharm 2015; 12(3): 816-25.
[http://dx.doi.org/10.1021/mp5006504] [PMID: 25587749]
[88]
Wu L, Zhang J, Watanabe W. Physical and chemical stability of drug nanoparticles. Adv Drug Deliv Rev 2011; 63(6): 456-69.
[http://dx.doi.org/10.1016/j.addr.2011.02.001] [PMID: 21315781]
[89]
Siepmann J, Siepmann F. Mathematical modeling of drug dissolution. Int J Pharm 2013; 453(1): 12-24.
[http://dx.doi.org/10.1016/j.ijpharm.2013.04.044] [PMID: 23618956]
[90]
Gupta J, Nunes C, Vyas S, Jonnalagadda S. Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations. J Phys Chem B 2011; 115(9): 2014-23.
[http://dx.doi.org/10.1021/jp108540n] [PMID: 21306175]
[91]
Hollingsworth SA, Dror RO. Molecular dynamics simulation for all. Neuron 2018; 99(6): 1129-43.
[http://dx.doi.org/10.1016/j.neuron.2018.08.011] [PMID: 30236283]
[92]
González MA. Force fields and molecular dynamics simulations. Collection SFN 2011; 12(2011): 169-200.
[http://dx.doi.org/10.1051/sfn/201112009]
[93]
Navakoudis E, Stergiannakos T, Daskalakis V. A perspective on the major light-harvesting complex dynamics under the effect of pH, salts, and the photoprotective PsbS protein. Photosynth Res 2023; 156(1): 163-77.
[http://dx.doi.org/10.1007/s11120-022-00935-6] [PMID: 35816266]
[94]
Su CF, Merlitz H, Thalmann F, Marques C, Sommer JU. Coarse-grained model of oxidized membranes and their interactions with nanoparticles of various degrees of hydrophobicity. J Phys Chem C 2019; 123(11): 6839-48.
[http://dx.doi.org/10.1021/acs.jpcc.8b11909]
[95]
Santo KP, Neimark AV. Dissipative particle dynamics simulations in colloid and Interface science: A review. Adv Colloid Interface Sci 2021; 298: 102545.
[http://dx.doi.org/10.1016/j.cis.2021.102545] [PMID: 34757286]
[96]
Vasan R. Computational modeling of cell membrane mechanics from sub-cellular to tissue length scales. San Diego: University of California 2020.
[97]
Engel MC. DNA systems under internal and external forcing: An exploration using coarse-grained modelling. Springer Nature 2019.
[98]
Cong L, Teng B, Bai W, Chen B. A VOS based immersed boundary-lattice boltzmann method for incompressible fluid flows with complex and moving boundaries. Comput Fluids 2023; 255: 105832.
[http://dx.doi.org/10.1016/j.compfluid.2023.105832]
[99]
Shen ZH, Wang JJ, Jiang JY, et al. Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics. Nat Commun 2019; 10(1): 1843.
[http://dx.doi.org/10.1038/s41467-019-09874-8] [PMID: 31015446]
[100]
Guedes R, Serra P, Salvador J, Guedes R. Computational approaches for the discovery of human proteasome inhibitors: An overview. Molecules 2016; 21(7): 927.
[http://dx.doi.org/10.3390/molecules21070927] [PMID: 27438821]
[101]
Torchilin V. Intracellular delivery of protein and peptide therapeutics. Drug Discov Today Technol 2008; 5(2-3): e95-e103.
[http://dx.doi.org/10.1016/j.ddtec.2009.01.002] [PMID: 24981097]
[102]
Mehta CH, Narayan R, Nayak UY. Computational modeling for formulation design. Drug Discov Today 2019; 24(3): 781-8.
[http://dx.doi.org/10.1016/j.drudis.2018.11.018] [PMID: 30502513]
[103]
Aminpour M, Montemagno C, Tuszynski JA. An overview of molecular modeling for drug discovery with specific illustrative examples of applications. Molecules 2019; 24(9): 1693.
[http://dx.doi.org/10.3390/molecules24091693] [PMID: 31052253]
[104]
Hossain S, Kabedev A, Parrow A, Bergström CAS, Larsson P. Molecular simulation as a computational pharmaceutics tool to predict drug solubility, solubilization processes and partitioning. Eur J Pharm Biopharm 2019; 137: 46-55.
[http://dx.doi.org/10.1016/j.ejpb.2019.02.007] [PMID: 30771454]
[105]
Yadav P, Bandyopadhyay A, Chakraborty A, Sarkar K. Enhancement of anticancer activity and drug delivery of chitosan-curcumin nanoparticle via molecular docking and simulation analysis. Carbohydr Polym 2018; 182: 188-98.
[http://dx.doi.org/10.1016/j.carbpol.2017.10.102] [PMID: 29279114]
[106]
Dhanasekaran S, Rameshthangam P, Venkatesan S, Singh SK, Vijayan SR. In vitro and in silico studies of chitin and chitosan based nanocarriers for curcumin and insulin delivery. J Polym Environ 2018; 26(10): 4095-113.
[http://dx.doi.org/10.1007/s10924-018-1282-8]
[107]
Aparna V, Melge AR, Rajan VK, Biswas R, Jayakumar R, Gopi Mohan C. Carboxymethylated ɩ-carrageenan conjugated amphotericin B loaded gelatin nanoparticles for treating intracellular Candida glabrata infections. Int J Biol Macromol 2018; 110: 140-9.
[http://dx.doi.org/10.1016/j.ijbiomac.2017.11.126] [PMID: 29169943]
[108]
Ahmed S, Govender T, Khan I, et al. Experimental and molecular modeling approach to optimize suitable polymers for fabrication of stable fluticasone nanoparticles with enhanced dissolution and antimicrobial activity. Drug Des Devel Ther 2018; 12: 255-69.
[http://dx.doi.org/10.2147/DDDT.S148912] [PMID: 29440875]
[109]
Cheng Y, Wang J, Rao T, He X, Xu T. Pharmaceutical applications of dendrimers: Promising nanocarriers for drug delivery. Front Biosci 2008; 13(13): 1447-71.
[http://dx.doi.org/10.2741/2774] [PMID: 17981642]
[110]
Perche F, Torchilin VP. Recent trends in multifunctional liposomal nanocarriers for enhanced tumor targeting. J Drug Deliv 2013; 2013: 705265.
[http://dx.doi.org/10.1155/2013/705265]
[111]
Yang S, Wang M, Wang T, et al. Self-assembled short peptides: Recent advances and strategies for potential pharmaceutical applications. Mater Today Bio 2023; 20: 100644.
[http://dx.doi.org/10.1016/j.mtbio.2023.100644] [PMID: 37214549]
[112]
Panwar N, Soehartono AM, Chan KK, et al. Nanocarbons for biology and medicine: Sensing, imaging, and drug delivery. Chem Rev 2019; 119(16): 9559-656.
[http://dx.doi.org/10.1021/acs.chemrev.9b00099] [PMID: 31287663]
[113]
Gautier J, Allard-Vannier E, Munnier E, Soucé M, Chourpa I. Recent advances in theranostic nanocarriers of doxorubicin based on iron oxide and gold nanoparticles. J Control Release 2013; 169(1-2): 48-61.
[http://dx.doi.org/10.1016/j.jconrel.2013.03.018] [PMID: 23567046]
[114]
Li J, Zhang W, Gao Y, et al. Near-infrared light and magnetic field dual-responsive porous silicon-based nanocarriers to overcome multidrug resistance in breast cancer cells with enhanced efficiency. J Mater Chem B Mater Biol Med 2020; 8(3): 546-57.
[http://dx.doi.org/10.1039/C9TB02340B] [PMID: 31854435]
[115]
Gillies E, Fréchet J. Dendrimers and dendritic polymers in drug delivery. Drug Discov Today 2005; 10(1): 35-43.
[http://dx.doi.org/10.1016/S1359-6446(04)03276-3] [PMID: 15676297]
[116]
Pavan GM. Modeling the interaction between dendrimers and nucleic acids: A molecular perspective through hierarchical scales. ChemMedChem 2014; 9(12): 2623-31.
[http://dx.doi.org/10.1002/cmdc.201402280] [PMID: 25209748]
[117]
Shcharbin D, Shakhbazau A, Bryszewska M. Poly(amidoamine) dendrimer complexes as a platform for gene delivery. Expert Opin Drug Deliv 2013; 10(12): 1687-98.
[http://dx.doi.org/10.1517/17425247.2013.853661] [PMID: 24168461]
[118]
Molla MR, Rangadurai P, Pavan GM, Thayumanavan S. Experimental and theoretical investigations in stimuli responsive dendrimer-based assemblies. Nanoscale 2015; 7(9): 3817-37.
[http://dx.doi.org/10.1039/C4NR04563G] [PMID: 25260107]
[119]
Ficici E, Andricioaei I, Howorka S. Dendrimers in nanoscale confinement: The interplay between conformational change and nanopore entrance. Nano Lett 2015; 15(7): 4822-8.
[http://dx.doi.org/10.1021/acs.nanolett.5b01960] [PMID: 26053678]
[120]
Wang YL, Lu ZY, Laaksonen A. Specific binding structures of dendrimers on lipid bilayer membranes. Phys Chem Chem Phys 2012; 14(23): 8348-59.
[http://dx.doi.org/10.1039/c2cp40700k] [PMID: 22585181]
[121]
Nandy B, Maiti PK, Bunker A. Force biased molecular dynamics simulation study of effect of dendrimer generation on interaction with DNA. J Chem Theory Comput 2013; 9(1): 722-9.
[http://dx.doi.org/10.1021/ct300678r] [PMID: 26589067]
[122]
Kavyani S, Amjad-Iranagh S, Modarress H. Aqueous poly(amidoamine) dendrimer G3 and G4 generations with several interior cores at pHs 5 and 7: A molecular dynamics simulation study. J Phys Chem B 2014; 118(12): 3257-66.
[http://dx.doi.org/10.1021/jp409195c] [PMID: 24588382]
[123]
Duncan R. The dawning era of polymer therapeutics. Nat Rev Drug Discov 2003; 2(5): 347-60.
[http://dx.doi.org/10.1038/nrd1088] [PMID: 12750738]
[124]
Kataoka K, Harada A, Nagasaki Y. Block copolymer micelles for drug delivery: Design, characterization and biological significance. Adv Drug Deliv Rev 2012; 64: 37-48.
[http://dx.doi.org/10.1016/j.addr.2012.09.013] [PMID: 11251249]
[125]
Rösler A, Vandermeulen GWM, Klok HA. Advanced drug delivery devices via self-assembly of amphiphilic block copolymers. Adv Drug Deliv Rev 2012; 64: 270-9.
[http://dx.doi.org/10.1016/j.addr.2012.09.026] [PMID: 11733119]
[126]
Kang M, Lam D, Discher DE, Loverde SM. Molecular modeling of block copolymer self‐assembly and micellar drug delivery. In:Ouyang D. Smith, SC Eds. Computational Pharmaceutics. 2015; pp. 53-80.
[http://dx.doi.org/10.1002/9781118573983.ch4]
[127]
Cai C, Wang L, Lin J, Zhang X. Morphology transformation of hybrid micelles self-assembled from rod-coil block copolymer and nanoparticles. Langmuir 2012; 28(9): 4515-24.
[http://dx.doi.org/10.1021/la204941w] [PMID: 22299635]
[128]
Guo H, Qiu X, Zhou J. Self-assembled core-shell and Janus microphase separated structures of polymer blends in aqueous solution. J Chem Phys 2013; 139(8): 084907.
[http://dx.doi.org/10.1063/1.4817003] [PMID: 24007037]
[129]
Taresco V, Gontrani L, Crisante F, et al. Self-assembly of catecholic moiety-containing cationic random acrylic copolymers. J Phys Chem B 2015; 119(26): 8369-79.
[http://dx.doi.org/10.1021/acs.jpcb.5b05022] [PMID: 26075948]
[130]
Wang T, Chipot C, Shao X, Cai W. Structural characterization of micelles formed of cholesteryl-functionalized cyclodextrins. Langmuir 2011; 27(1): 91-7.
[http://dx.doi.org/10.1021/la103288j] [PMID: 21141954]
[131]
Durzyńska J, Przysiecka Ł, Nawrot R, et al. Viral and other cell-penetrating peptides as vectors of therapeutic agents in medicine. J Pharmacol Exp Ther 2015; 354(1): 32-42.
[http://dx.doi.org/10.1124/jpet.115.223305] [PMID: 25922342]
[132]
Krishnan Y, Simmel FC. Nucleic acid based molecular devices. Angew Chem Int Ed 2011; 50(14): 3124-56.
[http://dx.doi.org/10.1002/anie.200907223] [PMID: 21432950]
[133]
Li Z, Ding H, Ma Y. Translocation of polyarginines and conjugated nanoparticles across asymmetric membranes. Soft Matter 2013; 9(4): 1281-6.
[http://dx.doi.org/10.1039/C2SM26519B]
[134]
Todorova N, Chiappini C, Mager M, et al. Surface presentation of functional peptides in solution determines cell internalization efficiency of TAT conjugated nanoparticles. Nano Lett 2014; 14(9): 5229-37.
[http://dx.doi.org/10.1021/nl5021848] [PMID: 25157643]
[135]
Li Y, Feng D, Zhang X, Cao D. Design strategy of cell-penetrating copolymers for high efficient drug delivery. Biomaterials 2015; 52: 171-9.
[http://dx.doi.org/10.1016/j.biomaterials.2015.01.046] [PMID: 25818423]
[136]
Sanchez-Sanchez A, Akbari S, Moreno AJ, et al. Design and preparation of single-chain nanocarriers mimicking disordered proteins for combined delivery of dermal bioactive cargos. Macromol Rapid Commun 2013; 34(21): 1681-6.
[http://dx.doi.org/10.1002/marc.201300562] [PMID: 24115236]
[137]
Barzan G, Kokalari I, Gariglio G, et al. Molecular aspects of the interaction with gram-negative and gram-positive bacteria of hydrothermal carbon nanoparticles associated with Bac8c 2,5Leu antimicrobial peptide. ACS Omega 2022; 7(19): 16402-13.
[http://dx.doi.org/10.1021/acsomega.2c00305] [PMID: 35601297]
[138]
Sun X, Feng Z, Hou T, Li Y. Computational simulation of inorganic nanoparticle drug delivery system at the molecular level 2015.
[http://dx.doi.org/10.1002/9781118573983.ch8]
[139]
Panczyk T, Jagusiak A, Pastorin G, Ang WH, Narkiewicz-Michalek J. Molecular dynamics study of cisplatin release from carbon nanotubes capped by magnetic nanoparticles. J Phys Chem C 2013; 117(33): 17327-36.
[http://dx.doi.org/10.1021/jp405593u]
[140]
Saikia N, Jha AN, Deka RC. Dynamics of fullerene-mediated heat-driven release of drug molecules from carbon nanotubes. J Phys Chem Lett 2013; 4(23): 4126-32.
[http://dx.doi.org/10.1021/jz402231p]
[141]
Chaban VV, Savchenko TI, Kovalenko SM, Prezhdo OV. Heat-driven release of a drug molecule from carbon nanotubes: A molecular dynamics study. J Phys Chem B 2010; 114(42): 13481-6.
[http://dx.doi.org/10.1021/jp104507g] [PMID: 20831145]
[142]
Panczyk T, Warzocha TP, Camp PJ. A magnetically controlled molecular nanocontainer as a drug delivery system: The effects of carbon nanotube and magnetic nanoparticle parameters from Monte Carlo simulations. J Phys Chem C 2010; 114(49): 21299-308.
[http://dx.doi.org/10.1021/jp1088405]
[143]
Chehel Amirani M, Tang T. Binding of nucleobases with graphene and carbon nanotube: A review of computational studies. J Biomol Struct Dyn 2015; 33(7): 1567-97.
[http://dx.doi.org/10.1080/07391102.2014.954315] [PMID: 25118044]
[144]
Wu N, Wang Q, Arash B. Ejection of DNA molecules from carbon nanotubes. Carbon 2012; 50(13): 4945-52.
[http://dx.doi.org/10.1016/j.carbon.2012.06.026]
[145]
Chen BD, Yang CL, Yang JS, Wang MS, Ma XG. Dynamic mechanism of HIV replication inhibitor peptide encapsulated into carbon nanotubes. Curr Appl Phys 2013; 13(6): 1001-7.
[http://dx.doi.org/10.1016/j.cap.2013.02.004]
[146]
Pattni BS, Chupin VV, Torchilin VP. New developments in liposomal drug delivery. Chem Rev 2015; 115(19): 10938-66.
[http://dx.doi.org/10.1021/acs.chemrev.5b00046] [PMID: 26010257]
[147]
Allen TM, Cullis PR. Liposomal drug delivery systems: From concept to clinical applications. Adv Drug Deliv Rev 2013; 65(1): 36-48.
[http://dx.doi.org/10.1016/j.addr.2012.09.037] [PMID: 23036225]
[148]
Klibanov AL, Maruyama K, Torchilin VP, Huang L. Amphipathic polyethyleneglycols effectively prolong the circulation time of liposomes. FEBS Lett 1990; 268(1): 235-7.
[http://dx.doi.org/10.1016/0014-5793(90)81016-H] [PMID: 2384160]
[149]
Dan N. Nanostructured lipid carriers: Effect of solid phase fraction and distribution on the release of encapsulated materials. Langmuir 2014; 30(46): 13809-14.
[http://dx.doi.org/10.1021/la5030197] [PMID: 25375259]
[150]
Dan N. Drug release through liposome pores. Colloids Surf B Biointerfaces 2015; 126: 80-6.
[http://dx.doi.org/10.1016/j.colsurfb.2014.11.042] [PMID: 25546834]
[151]
Ghosh P, Han G, De M, Kim C, Rotello V. Gold nanoparticles in delivery applications. Adv Drug Deliv Rev 2008; 60(11): 1307-15.
[http://dx.doi.org/10.1016/j.addr.2008.03.016] [PMID: 18555555]
[152]
Lee KH, Ytreberg FM. Effect of gold nanoparticle conjugation on peptide dynamics and structure. Entropy 2012; 14(4): 630-41.
[http://dx.doi.org/10.3390/e14040630]
[153]
Brancolini G, Kokh DB, Calzolai L, Wade RC, Corni S. Docking of ubiquitin to gold nanoparticles. ACS Nano 2012; 6(11): 9863-78.
[http://dx.doi.org/10.1021/nn303444b] [PMID: 23033917]
[154]
Van Lehn RC, Alexander-Katz A. Structure of mixed-monolayer-protected nanoparticles in aqueous salt solution from atomistic molecular dynamics simulations. J Phys Chem C 2013; 117(39): 20104-15.
[http://dx.doi.org/10.1021/jp406035e]
[155]
Van Lehn RC, Alexander-Katz A. Free energy change for insertion of charged, monolayer-protected nanoparticles into lipid bilayers. Soft Matter 2014; 10(4): 648-58.
[http://dx.doi.org/10.1039/C3SM52329B] [PMID: 24795979]