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Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Review Article

Computer Simulation for Effective Pharmaceutical Kinetics and Dynamics: A Review

Author(s): Gaurav Tiwari, Anuja Shukla, Anju Singh and Ruchi Tiwari*

Volume 20, Issue 4, 2024

Published on: 17 April, 2023

Page: [325 - 340] Pages: 16

DOI: 10.2174/1573409919666230228104901

Price: $65

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Abstract

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo, and ex vivo experiments. As a result, numerous in silico computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

Keywords: Computer simulation, pharmacokinetics, pharmacodynamics, whole-cell modeling, cell simulation, pharmaceutical kinetics.

Graphical Abstract
[1]
Anderson, B.J.; Holford, N.H.G. Rectal paracetamol dosing regimens: Determination by computer simulation. Paediatr. Anaesth., 1997, 7(6), 451-455.
[http://dx.doi.org/10.1046/j.1460-9592.1997.d01-125.x] [PMID: 9365970]
[2]
Kuentz, M.; Nick, S.; Parrott, N.; Röthlisberger, D. A strategy for preclinical formulation development using GastroPlus™ as pharmacokinetic simulation tool and a statistical screening design applied to a dog study. Eur. J. Pharm. Sci., 2006, 27(1), 91-99.
[http://dx.doi.org/10.1016/j.ejps.2005.08.011] [PMID: 16219449]
[3]
Scholz, J.; Steinfath, M.; Schulz, M. Clinical pharmacokinetics of alfentanil, fentanyl and sufentanil. An update. Clin. Pharmacokinet., 1996, 31(4), 275-292.
[http://dx.doi.org/10.2165/00003088-199631040-00004] [PMID: 8896944]
[4]
Orsi, M.; Sanderson, W.E.; Essex, J.W. Permeability of small molecules through a lipid bilayer: A multiscale simulation study. J. Phys. Chem. B, 2009, 113(35), 12019-12029.
[http://dx.doi.org/10.1021/jp903248s] [PMID: 19663489]
[5]
Weinshilboum, R.; Wang, L. Pharmacogenomics: Bench to bedside. Nat. Rev. Drug Discov., 2004, 3(9), 739-748.
[http://dx.doi.org/10.1038/nrd1497] [PMID: 15340384]
[6]
Aebersold, R.; Hood, L.E.; Watts, J.D. Equipping scientists for the new biology. Nat. Biotechnol., 2000, 18(4), 359.
[http://dx.doi.org/10.1038/74325] [PMID: 10748470]
[7]
Guyton, A.C.; Hall, J.E. Human physiology and mechanisms of disease; Saunders: Philadelphia, 1997.
[8]
Westerhoff, H.V.; Palsson, B.O. The evolution of molecular biology into systems biology. Nat. Biotechnol., 2004, 22(10), 1249-1252.
[http://dx.doi.org/10.1038/nbt1020] [PMID: 15470464]
[9]
Cawello, W.; Antonucci, T. The correlation between pharmacodynamics and pharmacokinetics: Basics of pharmacokinetics-pharmacodynamics modeling. J. Clin. Pharmacol., 1997, 37(S1), 65S-69S.
[http://dx.doi.org/10.1177/009127009703700124] [PMID: 9048287]
[10]
Crampin, E.J.; Smith, N.P.; Hunter, P.J. Multi-scale modelling and the IUPS physiome project. J. Mol. Histol., 2004, 35(7), 707-714.
[PMID: 15614626]
[11]
Thompson, C.M.; Sonawane, B.; Barton, H.A.; DeWoskin, R.S.; Lipscomb, J.C.; Schlosser, P.; Chiu, W.A.; Krishnan, K. Approaches for applications of physiologically based pharmacokinetic models in risk assessment. J. Toxicol. Environ. Health B Crit. Rev., 2008, 11(7), 519-547.
[http://dx.doi.org/10.1080/10937400701724337] [PMID: 18584453]
[12]
Dourson, M.L.; Andersen, M.E.; Erdreich, L.S.; MacGregor, J.A. Using human data to protect the public’s health. Regul. Toxicol. Pharmacol., 2001, 33(2), 234-256.
[http://dx.doi.org/10.1006/rtph.2001.1469] [PMID: 11350206]
[13]
Seidel, T.; Schuetz, D.A.; Garon, A.; Langer, T. The pharmacophore concept and its applications in computer-aided drug design. Prog. Chem. Org. Nat. Prod., 2019, 110, 99-141.
[http://dx.doi.org/10.1007/978-3-030-14632-0_4] [PMID: 31621012]
[14]
Kellogg, G.E. Computer applications in pharmaceutical research and development. J. Med. Chem., 2006, 49, 26-7923.
[15]
Girard, P.; Cucherat, M.; Guez, D. Clinical trial simulation in drug development. Therapie, 2004, 59(3), 287-295, 297-304.
[http://dx.doi.org/10.2515/therapie:2004056] [PMID: 15559184]
[16]
Bonate, P.L. A brief introduction to Monte Carlo simulation. Clin. Pharmacokinet., 2001, 40(1), 15-22.
[http://dx.doi.org/10.2165/00003088-200140010-00002] [PMID: 11236807]
[17]
Dermody, G.; Whitehead, L.; Wilson, G.; Glass, C. The role of virtual reality in improving health outcomes for community-dwelling older adults: Systematic review. J. Med. Internet Res., 2020, 22(6), e17331.
[http://dx.doi.org/10.2196/17331] [PMID: 32478662]
[18]
Viceconti, M.; Henney, A.; Morley-Fletcher, E. In silico clinical trials: How computer simulation will transform the biomedical industry. Int. J. Clin. Trials, 2016, 3(2), 37-46.
[http://dx.doi.org/10.18203/2349-3259.ijct20161408]
[19]
Fuchs, A.; Csajka, C.; Thoma, Y.; Buclin, T.; Widmer, N. Benchmarking therapeutic drug monitoring software: a review of available computer tools. Clin. Pharmacokinet., 2013, 52(1), 9-22.
[http://dx.doi.org/10.1007/s40262-012-0020-y] [PMID: 23196713]
[20]
Chabaud, S.; Girard, P.; Nony, P.; Boissel, J.P. HERapeutic MOdeling and Simulation Group. Clinical trial simulation using therapeutic effect modeling: application to ivabradine efficacy in patients with angina pectoris. J. Pharmacokinet. Pharmacodyn., 2002, 29(4), 339-363.
[http://dx.doi.org/10.1023/A:1020953107162] [PMID: 12518708]
[21]
Kim, J.; Park, S.; Min, D.; Kim, W. Comprehensive survey of recent drug discovery using deep learning. Int. J. Mol. Sci., 2021, 22(18), 9983.
[http://dx.doi.org/10.3390/ijms22189983] [PMID: 34576146]
[22]
Ludden, T.M.; Beal, S.L.; Sheiner, L.B. Comparison of the Akaike Information Criterion, the Schwarz criterion and the F test as guides to model selection. J. Pharmacokinet. Biopharm., 1994, 22(5), 431-445.
[http://dx.doi.org/10.1007/BF02353864] [PMID: 7791040]
[23]
Marshall, S.; Madabushi, R.; Manolis, E.; Krudys, K.; Staab, A.; Dykstra, K.; Visser, S.A.G. Model-informed drug discovery and development: Current industry good practice and regulatory expectations and future perspectives. CPT Pharmacometrics Syst. Pharmacol., 2019, 8(2), 87-96.
[http://dx.doi.org/10.1002/psp4.12372] [PMID: 30411538]
[24]
Rowland, M.; Peck, C.; Tucker, G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu. Rev. Pharmacol. Toxicol., 2011, 51(1), 45-73.
[http://dx.doi.org/10.1146/annurev-pharmtox-010510-100540] [PMID: 20854171]
[25]
Chen, F.; Hu, Z.Y.; Jia, W.W.; Lu, J.T.; Zhao, Y.S. Quantitative evaluation of drug-drug interaction potentials by in vivo information-guided prediction approach. Curr. Drug Metab., 2015, 15(8), 761-766.
[http://dx.doi.org/10.2174/1389200216666150223151758] [PMID: 25705907]
[26]
Hunter, P.J.; Borg, T.K. Integration from proteins to organs: The Physiome Project. Nat. Rev. Mol. Cell Biol., 2003, 4(3), 237-243.
[http://dx.doi.org/10.1038/nrm1054] [PMID: 12612642]
[27]
Nestorov, I.A.; Aarons, L.J.; Rowland, M. Physiologically based pharmacokinetic modeling of a homologous series of barbiturates in the rat: a sensitivity analysis. J. Pharmacokinet. Biopharm., 1997, 25(4), 413-447.
[http://dx.doi.org/10.1023/A:1025740909016] [PMID: 9561487]
[28]
Sheiner, L.B.; Steimer, J.L. Pharmacokinetic/pharmacodynamic modeling in drug development. Annu. Rev. Pharmacol. Toxicol., 2000, 40(1), 67-95.
[http://dx.doi.org/10.1146/annurev.pharmtox.40.1.67] [PMID: 10836128]
[29]
Chan, P.L.S.; Holford, N.H.G. Drug treatment effects on disease progression. Annu. Rev. Pharmacol. Toxicol., 2001, 41(1), 625-659.
[http://dx.doi.org/10.1146/annurev.pharmtox.41.1.625] [PMID: 11264471]
[30]
Jang, G.R.; Harris, R.Z.; Lau, D.T. Pharmacokinetics and its role in small molecule drug discovery research. Med. Res. Rev., 2001, 21(5), 382-396.
[http://dx.doi.org/10.1002/med.1015] [PMID: 11579439]
[31]
Sheiner, L.B.; Ludden, T.M. Population pharmacokinetics/dynamics. Annu. Rev. Pharmacol. Toxicol., 1992, 32(1), 185-209.
[http://dx.doi.org/10.1146/annurev.pa.32.040192.001153] [PMID: 1605567]
[32]
Sheiner, L.; Wakefield, J. Population modelling in drug development. Stat. Methods Med. Res., 1999, 8(3), 183-193.
[http://dx.doi.org/10.1177/096228029900800302] [PMID: 10636334]
[33]
Gieschke, R.; Reigner, B.G.; Steimer, J.L. Exploring clinical study design by computer simulation based on pharmacokinetic/pharmacodynamic modelling. Int. J. Clin. Pharmacol. Ther., 1997, 35(10), 469-474.
[PMID: 9352398]
[34]
Rowland, M. Physiologic pharmacokinetic models: Relevance, experience, and future trends. Drug Metab. Rev., 1984, 15(1-2), 55-74.
[http://dx.doi.org/10.3109/03602538409015057] [PMID: 6378562]
[35]
Di Ventura, B.; Lemerle, C.; Michalodimitrakis, K.; Serrano, L. From in vivo to In silico biology and back. Nature, 2006, 443(7111), 527-533.
[http://dx.doi.org/10.1038/nature05127] [PMID: 17024084]
[36]
Güell, M.; van Noort, V.; Yus, E.; Chen, W.H.; Leigh-Bell, J.; Michalodimitrakis, K.; Yamada, T.; Arumugam, M.; Doerks, T.; Kühner, S.; Rode, M.; Suyama, M.; Schmidt, S.; Gavin, A.C.; Bork, P.; Serrano, L. Transcriptome complexity in a genome-reduced bacterium. Science, 2009, 326(5957), 1268-1271.
[http://dx.doi.org/10.1126/science.1176951] [PMID: 19965477]
[37]
Kühner, S.; van Noort, V.; Betts, M.J.; Leo-Macias, A.; Batisse, C.; Rode, M.; Yamada, T.; Maier, T.; Bader, S.; Beltran-Alvarez, P.; Castaño-Diez, D.; Chen, W.H.; Devos, D.; Güell, M.; Norambuena, T.; Racke, I.; Rybin, V.; Schmidt, A.; Yus, E.; Aebersold, R.; Herrmann, R.; Böttcher, B.; Frangakis, A.S.; Russell, R.B.; Serrano, L.; Bork, P.; Gavin, A.C. Proteome organization in a genome-reduced bacterium. Science, 2009, 326(5957), 1235-1240.
[http://dx.doi.org/10.1126/science.1176343] [PMID: 19965468]
[38]
Yus, E.; Maier, T.; Michalodimitrakis, K.; van Noort, V.; Yamada, T.; Chen, W.H.; Wodke, J.A.H.; Güell, M.; Martínez, S.; Bourgeois, R.; Kühner, S.; Raineri, E.; Letunic, I.; Kalinina, O.V.; Rode, M.; Herrmann, R.; Gutiérrez-Gallego, R.; Russell, R.B.; Gavin, A.C.; Bork, P.; Serrano, L. Impact of genome reduction on bacterial metabolism and its regulation. Science, 2009, 326(5957), 1263-1268.
[http://dx.doi.org/10.1126/science.1177263] [PMID: 19965476]
[39]
Atlas, J.C.; Shuler, M.L.; Browning, S.T.; Nikolaev, E.V. Incorporating genome-wide DNA sequence information into a dynamic whole-cell model of Escherichia coli: Application to DNA replication. IET Syst. Biol., 2008, 2(5), 369-382.
[http://dx.doi.org/10.1049/iet-syb:20070079] [PMID: 19045832]
[40]
Browning, S.T.; Castellanos, M.; Shuler, M.L. Robust control of initiation of prokaryotic chromosome replication: Essential considerations for a minimal cell. Biotechnol. Bioeng., 2004, 88(5), 575-584.
[http://dx.doi.org/10.1002/bit.20223] [PMID: 15470709]
[41]
Castellanos, M.; Wilson, D.B.; Shuler, M.L. A modular minimal cell model: Purine and pyrimidine transport and metabolism. Proc. Natl. Acad. Sci. USA, 2004, 101(17), 6681-6686.
[http://dx.doi.org/10.1073/pnas.0400962101] [PMID: 15090651]
[42]
Castellanos, M.; Kushiro, K.; Lai, S.K.; Shuler, M.L. A genomically/chemically complete module for synthesis of lipid membrane in a minimal cell. Biotechnol. Bioeng., 2007, 97(2), 397-409.
[http://dx.doi.org/10.1002/bit.21251] [PMID: 17149771]
[43]
Domach, M.M.; Leung, S.K.; Cahn, R.E.; Cocks, G.G.; Shuler, M.L. Computer model for glucose-limited growth of a single cell of Escherichia coli B/r-A. Biotechnol. Bioeng., 1984, 26(9), 1140.
[http://dx.doi.org/10.1002/bit.260260925] [PMID: 18553544]
[44]
Feig, M.; Sugita, Y. Whole-cell models and simulations in molecular detail. Annu. Rev. Cell Dev. Biol., 2019, 35(1), 191-211.
[http://dx.doi.org/10.1146/annurev-cellbio-100617-062542] [PMID: 31299173]
[45]
Davidson, E.H.; Rast, J.P.; Oliveri, P.; Ransick, A.; Calestani, C.; Yuh, C.H.; Minokawa, T.; Amore, G.; Hinman, V.; Arenas-Mena, C.; Otim, O.; Brown, C.T.; Livi, C.B.; Lee, P.Y.; Revilla, R.; Rust, A.G.; Pan, Z.; Schilstra, M.J.; Clarke, P.J.C.; Arnone, M.I.; Rowen, L.; Cameron, R.A.; McClay, D.R.; Hood, L.; Bolouri, H. A genomic regulatory network for development. Science, 2002, 295(5560), 1669-1678.
[http://dx.doi.org/10.1126/science.1069883] [PMID: 11872831]
[46]
Thiele, I.; Jamshidi, N.; Fleming, R.M.T.; Palsson, B.Ø. Genome-scale reconstruction of Escherichia coli’s transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLOS Comput. Biol., 2009, 5(3), e1000312.
[http://dx.doi.org/10.1371/journal.pcbi.1000312] [PMID: 19282977]
[47]
Eleins, S.; Wang, B. Eds. Computer applications in pharmaceutical research and development. John Wiley and Sons: Hoboken, 2006; pp. 513-524.
[http://dx.doi.org/10.1002/0470037237]
[48]
Chan, H.C.S.; Shan, H.; Dahoun, T.; Vogel, H.; Yuan, S. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci., 2019, 40(8), 592-604.
[http://dx.doi.org/10.1016/j.tips.2019.06.004] [PMID: 31320117]
[49]
Bassingthwaighte, J.B.; Sparks, H.V. Indicator dilution estimation of capillary endothelial transport. Annu. Rev. Physiol., 1986, 48(1), 321-334.
[http://dx.doi.org/10.1146/annurev.ph.48.030186.001541] [PMID: 3518617]
[50]
Bassingthwaighte, J.B.; Wang, C.Y.; Chan, I.S. Blood-tissue exchange via transport and transformation by capillary endothelial cells. Circ. Res., 1989, 65(4), 997-1020.
[http://dx.doi.org/10.1161/01.RES.65.4.997] [PMID: 2791233]
[51]
Muzikant, A.L.; Penland, R.C. Models for profiling the potential QT prolongation risk of drugs. Curr. Opin. Drug Discov. Devel., 2002, 5(1), 127-135.
[PMID: 11865666]
[52]
Zhong, F.; Xing, J.; Li, X.; Liu, X.; Fu, Z.; Xiong, Z.; Lu, D.; Wu, X.; Zhao, J.; Tan, X.; Li, F.; Luo, X.; Li, Z.; Chen, K.; Zheng, M.; Jiang, H. Artificial intelligence in drug design. Sci. China Life Sci., 2018, 61(10), 1191-1204.
[http://dx.doi.org/10.1007/s11427-018-9342-2] [PMID: 30054833]
[53]
Malone, H.R.; Syed, O.N.; Downes, M.S.; D’Ambrosio, A.L.; Quest, D.O.; Kaiser, M.G. Simulation in neurosurgery: A review of computer-based simulation environments and their surgical applications. Neurosurgery, 2010, 67(4), 1105-1116.
[http://dx.doi.org/10.1227/NEU.0b013e3181ee46d0] [PMID: 20881575]
[54]
Popel, A.S.; Pries, A.R.; Slaaf, D.W. Microcirculation physiome project. J. Vasc. Res., 1999, 36(3), 253-255.
[http://dx.doi.org/10.1159/000025649] [PMID: 10393512]
[55]
Lazebnik, Y. Can a biologist fix a radio? Or, what I learned while studying apoptosis. Cancer Cell, 2002, 2(3), 179-182.
[http://dx.doi.org/10.1016/S1535-6108(02)00133-2] [PMID: 12242150]
[56]
Loew, L.M.; Schaff, J.C. The Virtual Cell: A software environment for computational cell biology. Trends Biotechnol., 2001, 19(10), 401-406.
[http://dx.doi.org/10.1016/S0167-7799(01)01740-1] [PMID: 11587765]
[57]
Slepchenko, B.M.; Schaff, J.C.; Macara, I.; Loew, L.M. Quantitative cell biology with the Virtual Cell. Trends Cell Biol., 2003, 13(11), 570-576.
[http://dx.doi.org/10.1016/j.tcb.2003.09.002] [PMID: 14573350]
[58]
Price, N.D.; Papin, J.A.; Schilling, C.H.; Palsson, B.O. Genome-scale microbial in silico models: The constraints-based approach. Trends Biotechnol., 2003, 21(4), 162-169.
[http://dx.doi.org/10.1016/S0167-7799(03)00030-1] [PMID: 12679064]
[59]
Famili, I.; Förster, J.; Nielsen, J.; Palsson, B.O. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. USA, 2003, 100(23), 13134-13139.
[http://dx.doi.org/10.1073/pnas.2235812100] [PMID: 14578455]
[60]
Ibarra, R.U.; Edwards, J.S.; Palsson, B.O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature, 2002, 420(6912), 186-189.
[http://dx.doi.org/10.1038/nature01149] [PMID: 12432395]
[61]
Schilling, C.H.; Covert, M.W.; Famili, I.; Church, G.M.; Edwards, J.S.; Palsson, B.O. Genome-scale metabolic model of Helicobacter pylori 26695. J. Bacteriol., 2002, 184(16), 4582-4593.
[http://dx.doi.org/10.1128/JB.184.16.4582-4593.2002] [PMID: 12142428]
[62]
Papin, J.A.; Hunter, T.; Palsson, B.O.; Subramaniam, S. Reconstruction of cellular signalling networks and analysis of their properties. Nat. Rev. Mol. Cell Biol., 2005, 6(2), 99-111.
[http://dx.doi.org/10.1038/nrm1570] [PMID: 15654321]
[63]
Tomita, M. Whole-cell simulation: A grand challenge of the 21st century. Trends Biotechnol., 2001, 19(6), 205-210.
[http://dx.doi.org/10.1016/S0167-7799(01)01636-5] [PMID: 11356281]
[64]
Karr, J.R.; Takahashi, K.; Funahashi, A. The principles of whole-cell modeling. Curr. Opin. Microbiol., 2015, 27, 18-24.
[http://dx.doi.org/10.1016/j.mib.2015.06.004] [PMID: 26115539]
[65]
Carrera, J.; Covert, M.W. Why build whole-cell models? Trends Cell Biol., 2015, 25(12), 719-722.
[http://dx.doi.org/10.1016/j.tcb.2015.09.004] [PMID: 26471224]
[66]
McAdams, H.H.; Arkin, A. Stochastic mechanisms in gene expression. Proc. Natl. Acad. Sci. USA, 1997, 94(3), 814-819.
[http://dx.doi.org/10.1073/pnas.94.3.814] [PMID: 9023339]
[67]
Morton-Firth, C.J.; Bray, D. Predicting temporal fluctuations in an intracellular signalling pathway. J. Theor. Biol., 1998, 192(1), 117-128.
[http://dx.doi.org/10.1006/jtbi.1997.0651] [PMID: 9628844]
[68]
Cornish-Bowden, A.; Hofmeyr, J.H.S. MetaModel: A program for modelling and control analysis of metabolic pathways on the IBM PC and compatibles. Bioinformatics, 1991, 7(1), 89-93.
[http://dx.doi.org/10.1093/bioinformatics/7.1.89] [PMID: 2004280]
[69]
Shu, J.; Shuler, M.L. A mathematical model for the growth of a single cell of E. coli on a glucose/glutamine/ammonium medium. Biotechnol. Bioeng., 1989, 33(9), 1117-1126.
[http://dx.doi.org/10.1002/bit.260330907] [PMID: 18588029]
[70]
Goldbeter, A. A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. Proc. Natl. Acad. Sci. USA, 1991, 88(20), 9107-9111.
[http://dx.doi.org/10.1073/pnas.88.20.9107] [PMID: 1833774]
[71]
Tyson, J.J. Modeling the cell division cycle: cdc2 and cyclin interactions. Proc. Natl. Acad. Sci. USA, 1991, 88(16), 7328-7332.
[http://dx.doi.org/10.1073/pnas.88.16.7328] [PMID: 1831270]
[72]
Novak, B.; Tyson, J.J. Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte extracts and intact embryos. J. Cell Sci., 1993, 106(4), 1153-1168.
[http://dx.doi.org/10.1242/jcs.106.4.1153] [PMID: 8126097]
[73]
Tomita, M.; Hashimoto, K.; Takahashi, K.; Shimizu, T.; Matsuzaki, Y.; Miyoshi, F.; Saito, K.; Tanida, S.; Yugi, K.; Venter, J.; Hutchison, C. III E-CELL: Software environment for whole-cell simulation. Bioinformatics, 1999, 15(1), 72-84.
[http://dx.doi.org/10.1093/bioinformatics/15.1.72] [PMID: 10068694]
[74]
Varma, A.; Palsson, B.O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol., 1994, 60(10), 3724-3731.
[http://dx.doi.org/10.1128/aem.60.10.3724-3731.1994] [PMID: 7986045]
[75]
McCloskey, D.; Palsson, B.Ø.; Feist, A.M. Basic and applied uses of genome‐scale metabolic network reconstructions of Escherichia coli. Mol. Syst. Biol., 2013, 9(1), 661.
[http://dx.doi.org/10.1038/msb.2013.18] [PMID: 23632383]
[76]
Yilmaz, L.S.; Walhout, A.J.M. Metabolic network modeling with model organisms. Curr. Opin. Chem. Biol., 2017, 36, 32-39.
[http://dx.doi.org/10.1016/j.cbpa.2016.12.025] [PMID: 28088694]
[77]
Mendoza, S.N.; Olivier, B.G.; Molenaar, D.; Teusink, B. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol., 2019, 20(1), 158.
[http://dx.doi.org/10.1186/s13059-019-1769-1] [PMID: 31391098]
[78]
Min Lee, J. J.; Gianchandani, E.P.; Eddy, J.A.; Papin, J.A. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLOS Comput. Biol., 2008, 4(5), e1000086.
[http://dx.doi.org/10.1371/journal.pcbi.1000086] [PMID: 18483615]
[79]
Karr, J.R.; Sanghvi, J.C.; Macklin, D.N.; Gutschow, M.V.; Jacobs, J.M.; Bolival, B., Jr; Assad-Garcia, N.; Glass, J.I.; Covert, M.W. A whole-cell computational model predicts phenotype from genotype. Cell, 2012, 150(2), 389-401.
[http://dx.doi.org/10.1016/j.cell.2012.05.044] [PMID: 22817898]
[80]
King, Z.A.; Lu, J.; Dräger, A.; Miller, P.; Federowicz, S.; Lerman, J.A.; Ebrahim, A.; Palsson, B.O.; Lewis, N.E. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res., 2016, 44(D1), D515-D522.
[http://dx.doi.org/10.1093/nar/gkv1049] [PMID: 26476456]
[81]
Betts, M.J.; Russell, R.B. The hard cell: From proteomics to a whole cell model. FEBS Lett., 2007, 581(15), 2870-2876.
[http://dx.doi.org/10.1016/j.febslet.2007.05.062] [PMID: 17555749]
[82]
Noske, A.B.; Costin, A.J.; Morgan, G.P.; Marsh, B.J. Expedited approaches to whole cell electron tomography and organelle mark-up in situ in high-pressure frozen pancreatic islets. J. Struct. Biol., 2008, 161(3), 298-313.
[http://dx.doi.org/10.1016/j.jsb.2007.09.015] [PMID: 18069000]
[83]
McGuffee, S.R.; Elcock, A.H. Diffusion, crowding & protein stability in a dynamic molecular model of the bacterial cytoplasm. PLOS Comput. Biol., 2010, 6(3), e1000694.
[http://dx.doi.org/10.1371/journal.pcbi.1000694] [PMID: 20221255]
[84]
Yu, I.; Mori, T.; Ando, T.; Harada, R.; Jung, J.; Sugita, Y.; Feig, M. Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm. eLife, 2016, 5, e19274.
[http://dx.doi.org/10.7554/eLife.19274] [PMID: 27801646]
[85]
Ander, M.; Tomás-Oliveira, I.; Ferkinghoff-Borg, J.; Beltrao, P.; Foglierini, M.; Di Ventura, B.; Serrano, L.; Lemerle, C.; Serrano, L. SmartCell, a framework to simulate cellular processes that combines stochastic approximation with diffusion and localisation: analysis of simple networks. Syst. Biol., 2004, 1(1), 129-138.
[http://dx.doi.org/10.1049/sb:20045017] [PMID: 17052123]
[86]
Takahashi, K.; Arjunan, S.N.V.; Tomita, M. Space in systems biology of signaling pathways-towards intracellular molecular crowding in silico. FEBS Lett., 2005, 579(8), 1783-1788.
[http://dx.doi.org/10.1016/j.febslet.2005.01.072] [PMID: 15763552]
[87]
Thul, P.J.; Åkesson, L.; Wiking, M.; Mahdessian, D.; Geladaki, A.; Ait Blal, H.; Alm, T.; Asplund, A.; Björk, L.; Breckels, L.M.; Bäckström, A.; Danielsson, F.; Fagerberg, L.; Fall, J.; Gatto, L.; Gnann, C.; Hober, S.; Hjelmare, M.; Johansson, F.; Lee, S.; Lindskog, C.; Mulder, J.; Mulvey, C.M.; Nilsson, P.; Oksvold, P.; Rockberg, J.; Schutten, R.; Schwenk, J.M.; Sivertsson, Å.; Sjöstedt, E.; Skogs, M.; Stadler, C.; Sullivan, D.P.; Tegel, H.; Winsnes, C.; Zhang, C.; Zwahlen, M.; Mardinoglu, A.; Pontén, F.; von Feilitzen, K.; Lilley, K.S.; Uhlén, M.; Lundberg, E. A subcellular map of the human proteome. Science, 2017, 356(6340), eaal3321.
[http://dx.doi.org/10.1126/science.aal3321] [PMID: 28495876]
[88]
Bouhaddou, M.; Barrette, A.M.; Stern, A.D.; Koch, R.J.; DiStefano, M.S.; Riesel, E.A.; Santos, L.C.; Tan, A.L.; Mertz, A.E.; Birtwistle, M.R. A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens. PLOS Comput. Biol., 2018, 14(3), e1005985.
[http://dx.doi.org/10.1371/journal.pcbi.1005985] [PMID: 29579036]
[89]
Singla, J.; McClary, K.M.; White, K.L.; Alber, F.; Sali, A.; Stevens, R.C. Opportunities and challenges in building a spatiotemporal multi-scale model of the human pancreatic β cell. Cell, 2018, 173(1), 11-19.
[http://dx.doi.org/10.1016/j.cell.2018.03.014] [PMID: 29570991]
[90]
Szigeti, B.; Roth, Y.D.; Sekar, J.A.P.; Goldberg, A.P.; Pochiraju, S.C.; Karr, J.R. A blueprint for human whole-cell modeling. Curr. Opin. Syst. Biol., 2018, 7, 8-15.
[http://dx.doi.org/10.1016/j.coisb.2017.10.005] [PMID: 29806041]
[91]
Macklin, D.N.; Ahn-Horst, T.A.; Choi, H.; Ruggero, N.A.; Carrera, J.; Mason, J.C.; Sun, G.; Agmon, E.; DeFelice, M.M.; Maayan, I.; Lane, K.; Spangler, R.K.; Gillies, T.E.; Paull, M.L.; Akhter, S.; Bray, S.R.; Weaver, D.S.; Keseler, I.M.; Karp, P.D.; Morrison, J.H.; Covert, M.W. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science, 2020, 369(6502), eaav3751.
[http://dx.doi.org/10.1126/science.aav3751] [PMID: 32703847]
[92]
Goldberg, A.P.; Szigeti, B.; Chew, Y.H.; Sekar, J.A.P.; Roth, Y.D.; Karr, J.R. Emerging whole-cell modeling principles and methods. Curr. Opin. Biotechnol., 2018, 51, 97-102.
[http://dx.doi.org/10.1016/j.copbio.2017.12.013] [PMID: 29275251]
[93]
Pandit, S.A.; Bostick, D.; Berkowitz, M.L. Mixed bilayer containing dipalmitoylphosphatidylcholine and dipalmitoylphosphatidylserine: lipid complexation, ion binding, and electrostatics. Biophys. J., 2003, 85(5), 3120-3131.
[http://dx.doi.org/10.1016/S0006-3495(03)74730-4] [PMID: 14581212]
[94]
Chiu, S.W.; Jakobsson, E.; Mashl, R.J.; Scott, H.L. Cholesterol-induced modifications in lipid bilayers: A simulation study. Biophys. J., 2002, 83(4), 1842-1853.
[http://dx.doi.org/10.1016/S0006-3495(02)73949-0] [PMID: 12324406]
[95]
Hofsäß, C.; Lindahl, E.; Edholm, O. Molecular dynamics simulations of phospholipid bilayers with cholesterol. Biophys. J., 2003, 84(4), 2192-2206.
[http://dx.doi.org/10.1016/S0006-3495(03)75025-5] [PMID: 12668428]
[96]
Navrátilová, V.; Paloncýová, M.; Kajšová, M.; Berka, K.; Otyepka, M. Effect of cholesterol on the structure of membrane-attached cytochrome P450 3A4. J. Chem. Inf. Model., 2015, 55(3), 628-635.
[http://dx.doi.org/10.1021/ci500645k] [PMID: 25654496]
[97]
Róg, T.; Pasenkiewicz-Gierula, M. Effects of epicholesterol on the phosphatidylcholine bilayer: A molecular simulation study. Biophys. J., 2003, 84(3), 1818-1826.
[http://dx.doi.org/10.1016/S0006-3495(03)74989-3] [PMID: 12609883]
[98]
Tieleman, D.P.; Marrink, S.J.; Berendsen, H.J.C. A computer perspective of membranes: Molecular dynamics studies of lipid bilayer systems. Biochim. Biophys. Acta Rev. Biomembr., 1997, 1331(3), 235-270.
[http://dx.doi.org/10.1016/S0304-4157(97)00008-7] [PMID: 9512654]
[99]
Koubi, L.; Tarek, M.; Bandyopadhyay, S.; Klein, M.L.; Scharf, D. Membrane structural perturbations caused by anesthetics and nonimmobilizers: A molecular dynamics investigation. Biophys. J., 2001, 81(6), 3339-3345.
[http://dx.doi.org/10.1016/S0006-3495(01)75967-X] [PMID: 11720997]
[100]
Tang, P.; Xu, Y. Large-scale molecular dynamics simulations of general anesthetic effects on the ion channel in the fully hydrated membrane: The implication of molecular mechanisms of general anesthesia. Proc. Natl. Acad. Sci., 2002, 99(25), 16035-16040.
[http://dx.doi.org/10.1073/pnas.252522299] [PMID: 12438684]
[101]
Mukhopadhyay, P.; Vogel, H.J.; Tieleman, D.P. Distribution of pentachlorophenol in phospholipid bilayers: A molecular dynamics study. Biophys. J., 2004, 86(1), 337-345.
[http://dx.doi.org/10.1016/S0006-3495(04)74109-0] [PMID: 14695275]
[102]
Feller, S.E.; Brown, C.A.; Nizza, D.T.; Gawrisch, K. Nuclear Overhauser enhancement spectroscopy cross-relaxation rates and ethanol distribution across membranes. Biophys. J., 2002, 82(3), 1396-1404.
[http://dx.doi.org/10.1016/S0006-3495(02)75494-5] [PMID: 11867455]
[103]
Grossfield, A.; Sachs, J.; Woolf, T.B. Dipole lattice membrane model for protein calculations. Proteins, 2000, 41(2), 211-223.
[http://dx.doi.org/10.1002/1097-0134(20001101)41:2<211:AID-PROT60>3.0.CO;2-9] [PMID: 10966574]
[104]
Im, W.; Feig, M.; Brooks, C.L., III An implicit membrane generalized born theory for the study of structure, stability, and interactions of membrane proteins. Biophys. J., 2003, 85(5), 2900-2918.
[http://dx.doi.org/10.1016/S0006-3495(03)74712-2] [PMID: 14581194]
[105]
Kessel, A.; Haliloglu, T.; Ben-Tal, N. Interactions of the M2delta segment of the acetylcholine receptor with lipid bilayers: A continuum-solvent model study. Biophys. J., 2003, 85(6), 3687-3695.
[http://dx.doi.org/10.1016/S0006-3495(03)74785-7] [PMID: 14645060]
[106]
Lazaridis, T. Effective energy function for proteins in lipid membranes. Proteins, 2003, 52(2), 176-192.
[http://dx.doi.org/10.1002/prot.10410] [PMID: 12833542]
[107]
Feller, S.E.; Gawrisch, K.; Woolf, T.B. Rhodopsin exhibits a preference for solvation by polyunsaturated docosohexaenoic acid. J. Am. Chem. Soc., 2003, 125(15), 4434-4435.
[http://dx.doi.org/10.1021/ja0345874] [PMID: 12683809]
[108]
de Planque, M.R.R.; Killian, J.A. Protein-lipid interactions studied with designed transmembrane peptides: Role of hydrophobic matching and interfacial anchoring. Mol. Membr. Biol., 2003, 20(4), 271-284.
[http://dx.doi.org/10.1080/09687680310001605352] [PMID: 14578043]
[109]
Petrache, H.I.; Grossfield, A.; MacKenzie, K.R.; Engelman, D.M.; Woolf, T.B. Modulation of glycophorin A transmembrane helix interactions by lipid bilayers: molecular dynamics calculations. J. Mol. Biol., 2000, 302(3), 727-746.
[http://dx.doi.org/10.1006/jmbi.2000.4072] [PMID: 10986130]
[110]
Valiyaveetil, F.I.; Zhou, Y.; MacKinnon, R. Lipids in the structure, folding, and function of the KcsA K+ channel. Biochemistry, 2002, 41(35), 10771-10777.
[http://dx.doi.org/10.1021/bi026215y] [PMID: 12196015]
[111]
Edholm, O.; Berger, O.; Jähnig, F. Structure and fluctuations of bacteriorhodopsin in the purple membrane: A molecular dynamics study. J. Mol. Biol., 1995, 250(1), 94-111.
[http://dx.doi.org/10.1006/jmbi.1995.0361] [PMID: 7602600]
[112]
Knecht, V.; Grubmüller, H. Mechanical coupling via the membrane fusion SNARE protein syntaxin 1A: A molecular dynamics study. Biophys. J., 2003, 84(3), 1527-1547.
[http://dx.doi.org/10.1016/S0006-3495(03)74965-0] [PMID: 12609859]
[113]
Escrive, C.; Laguerre, M. Molecular dynamics simulations of the insertion of two ideally amphipathic lytic peptides LK15 and LK9 in a 1,2-dimyristoylphosphatidylcholine monolayer. Biochim. Biophys. Acta Biomembr., 2001, 1513(1), 63-74.
[http://dx.doi.org/10.1016/S0005-2736(01)00343-1] [PMID: 11427195]
[114]
Sun, F. Molecular dynamics simulation of human immunodeficiency virus protein U (Vpu) in lipid/water Langmuir monolayer. J. Mol. Model., 2003, 9(2), 114-123.
[http://dx.doi.org/10.1007/s00894-003-0123-3] [PMID: 12687433]
[115]
Freites, J.A.; Choi, Y.; Tobias, D.J. Molecular dynamics simulations of a pulmonary surfactant protein B peptide in a lipid monolayer. Biophys. J., 2003, 84(4), 2169-2180.
[http://dx.doi.org/10.1016/S0006-3495(03)75023-1] [PMID: 12668426]
[116]
Nordgren, C.E.; Tobias, D.J.; Klein, M.L.; Blasie, J.K. Molecular dynamics simulations of a hydrated protein vectorially oriented on polar and nonpolar soft surfaces. Biophys. J., 2002, 83(6), 2906-2917.
[http://dx.doi.org/10.1016/S0006-3495(02)75300-9] [PMID: 12496067]
[117]
Engelman, D.M.; Chen, Y.; Chin, C.N.; Curran, A.R.; Dixon, A.M.; Dupuy, A.D.; Lee, A.S.; Lehnert, U.; Matthews, E.E.; Reshetnyak, Y.K.; Senes, A.; Popot, J.L. Membrane protein folding: beyond the two stage model. FEBS Lett., 2003, 555(1), 122-125.
[http://dx.doi.org/10.1016/S0014-5793(03)01106-2] [PMID: 14630331]
[118]
White, S.H.; Wimley, W.C. Membrane protein folding and stability. Physical Principles. Annu. Rev. Biophys. Biomol. Struct., 1999, 28(1), 319-365.
[http://dx.doi.org/10.1146/annurev.biophys.28.1.319] [PMID: 10410805]
[119]
Ash, W.L.; Zlomislic, M.R.; Oloo, E.O.; Tieleman, D.P. Computer simulations of membrane proteins. Biochim. Biophys. Acta Biomembr., 2004, 1666(1-2), 158-189.
[http://dx.doi.org/10.1016/j.bbamem.2004.04.012] [PMID: 15519314]
[120]
Shai, Y. Mode of action of membrane active antimicrobial peptides. Biopolymers, 2002, 66(4), 236-248.
[http://dx.doi.org/10.1002/bip.10260] [PMID: 12491537]
[121]
Zasloff, M. Antimicrobial peptides of multicellular organisms. Nature, 2002, 415(6870), 389-395.
[http://dx.doi.org/10.1038/415389a] [PMID: 11807545]
[122]
La Rocca, P.; Biggin, P.C.; Tieleman, D.P.; Sansom, M.S.P. Simulation studies of the interaction of antimicrobial peptides and lipid bilayers. Biochim. Biophys. Acta Biomembr., 1999, 1462(1-2), 185-200.
[http://dx.doi.org/10.1016/S0005-2736(99)00206-0] [PMID: 10590308]
[123]
Khandelia, H.; Ipsen, J.H.; Mouritsen, O.G. The impact of peptides on lipid membranes. Biochim. Biophys. Acta Biomembr., 2008, 1778(7-8), 1528-1536.
[http://dx.doi.org/10.1016/j.bbamem.2008.02.009] [PMID: 18358231]
[124]
Biggin, P.C.; Sansom, M.S.P. Interactions of α-helices with lipid bilayers: A review of simulation studies. Biophys. Chem., 1999, 76(3), 161-183.
[http://dx.doi.org/10.1016/S0301-4622(98)00233-6] [PMID: 10074693]
[125]
Shepherd, C.M.; Vogel, H.J.; Tieleman, D.P. Interactions of the designed antimicrobial peptide MB21 and truncated dermaseptin S3 with lipid bilayers: Molecular-dynamics simulations. Biochem. J., 2003, 370(1), 233-243.
[http://dx.doi.org/10.1042/bj20021255] [PMID: 12423203]
[126]
Shepherd, C.M.; Schaus, K.A.; Vogel, H.J.; Juffer, A.H. Molecular dynamics study of peptide-bilayer adsorption. Biophys. J., 2001, 80(2), 579-596.
[http://dx.doi.org/10.1016/S0006-3495(01)76039-0] [PMID: 11159427]
[127]
Monticelli, L.; Pedini, D.; Schievano, E.; Mammi, S.; Peggion, E. Interaction of bombolitin II with a membrane-mimetic environment: An NMR and molecular dynamics simulation approach. Biophys. Chem., 2002, 101-102, 577-591.
[http://dx.doi.org/10.1016/S0301-4622(02)00174-6] [PMID: 12488028]
[128]
Huang, W.N.; Sue, S.C.; Wang, D.S.; Wu, P.L.; Wu, W. Peripheral binding mode and penetration depth of cobra cardiotoxin on phospholipid membranes as studied by a combined FTIR and computer simulation approach. Biochemistry, 2003, 42(24), 7457-7466.
[http://dx.doi.org/10.1021/bi0344477] [PMID: 12809502]
[129]
Kamath, S.; Wong, T.C. Membrane structure of the human immunodeficiency virus gp41 fusion domain by molecular dynamics simulation. Biophys. J., 2002, 83(1), 135-143.
[http://dx.doi.org/10.1016/S0006-3495(02)75155-2] [PMID: 12080106]
[130]
Wong, T.C. Membrane structure of the human immunodeficiency virus gp41 fusion peptide by molecular dynamics simulation. Biochim. Biophys. Acta Biomembr., 2003, 1609(1), 45-54.
[http://dx.doi.org/10.1016/S0005-2736(02)00652-1] [PMID: 12507757]
[131]
Aliste, M.P.; MacCallum, J.L.; Tieleman, D.P. Molecular dynamics simulations of pentapeptides at interfaces: Salt bridge and cation-pi interactions. Biochemistry, 2003, 42(30), 8976-8987.
[http://dx.doi.org/10.1021/bi027001j] [PMID: 12885230]
[132]
Dolan, E.A.; Venable, R.M.; Pastor, R.W.; Brooks, B.R. Simulations of membranes and other interfacial systems using P2(1) and Pc periodic boundary conditions. Biophys. J., 2002, 82(5), 2317-2325.
[http://dx.doi.org/10.1016/S0006-3495(02)75577-X] [PMID: 11964222]
[133]
Zhang, J.; Lei, Y.K.; Zhang, Z.; Chang, J.; Li, M.; Han, X.; Yang, L.; Yang, Y.I.; Gao, Y.Q. A perspective on deep learning for molecular modeling and simulations. J. Phys. Chem. B, 2020, 124(34), 6745-6763.
[http://dx.doi.org/10.1063/5.0026836] [PMID: 32663004]
[134]
Basak, S.C.; Zhu, Q.; Mills, D. Prediction of anticancer activity of 2-phenylindoles: Comparative molecular field analysis versus ridge regression using mathematical molecular descriptors. Acta Chim. Slov., 2010, 57(3), 541-550.
[PMID: 24061798]

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