Computer Simulation for Effective Pharmaceutical Kinetics and Dynamics: A Review


Cite item

Full Text

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.

About the authors

Gaurav Tiwari

Department of Pharmaceutical Sciences, PSIT-Pranveer Singh Institute of Technology Pharmacy

Email: info@benthamscience.net

Anuja Shukla

Department of Pharmaceutical Sciences, PSIT-Pranveer Singh Institute of Technology Pharmacy

Email: info@benthamscience.net

Anju Singh

Department of Pharmacy, University Institute of Pharmacy, Chhatrapati Shahu Ji Maharaj University (Formerly Kanpur University)

Email: info@benthamscience.net

Ruchi Tiwari

Department of Pharmaceutical Sciences, PSIT-Pranveer Singh Institute of Technology Pharmacy

Author for correspondence.
Email: info@benthamscience.net

References

  1. Anderson, B.J.; Holford, N.H.G. Rectal paracetamol dosing regimens: Determination by computer simulation. Paediatr. Anaesth., 1997, 7(6), 451-455. doi: 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. doi: 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. doi: 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. doi: 10.1021/jp903248s PMID: 19663489
  5. Weinshilboum, R.; Wang, L. Pharmacogenomics: Bench to bedside. Nat. Rev. Drug Discov., 2004, 3(9), 739-748. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.2515/therapie:2004056 PMID: 15559184
  16. Bonate, P.L. A brief introduction to Monte Carlo simulation. Clin. Pharmacokinet., 2001, 40(1), 15-22. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.1002/1097-0134(20001101)41:23.0.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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.1002/bip.10260 PMID: 12491537
  121. Zasloff, M. Antimicrobial peptides of multicellular organisms. Nature, 2002, 415(6870), 389-395. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2024 Bentham Science Publishers