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Conclusions

TK/TD modeling rests on sound pharmacokinetic and pharmacodynamic principles. While the application of modeling techniques to explain toxicoki- netic and toxicodynamic outcomes is not as widespread and abundant as the description of clinical pharmacology, efficacy, or safety outcomes, it is expected that this area will continue to grow, as regulatory agencies continue to encourage the application of these techniques, and as computer science continues to advance, both in hardware and software. Moreover, while little experimental work has been performed in the area of clinical drug-herb interactions using either experimental or modeling and simulation analyses, the potential effects of drug-herb interactions are significant: reductions in drug levels can cause lack of efficacy which may result in breakthrough symptoms and increases in drug levels can lead to unexpected adverse drug reactions. Here, modeling and simulation could be used to estimate these potential effects.

References

  • 1. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatory- information/guidances/ucm074937.pdf, last accessed January 2016.
  • 2. http://www.fda.gov/downloads/Drugs/./Guidances/UCM078932.pdf, last accessed January 2016.
  • 3. S. Holford, K. Allegaert, B. J. Anderson, B. Kukanich, A. B. Sousa and A. Steinman, et al., Parent-metabolite pharmacokinetic models for tramadol— tests of assumptions and predictions, J. Pharmacol. Clin. Toxicol., 2014, 2(1), 1023.
  • 4. D. H. Zhao, Z. Zhang, C. Y. Zhang, Z. C. Liu, H. Deng and J. J. Yu, et al., Population pharmacokinetics of valnemulin in swine, J. Vet. Pharmacol. Ther., 2014, 37(1), 59-65.
  • 5. http://www.fda.gov/ScienceResearch/SpecialTopics/WomensHealthRe- search/default.htm, last accessed January 2016.
  • 6. http://www.fda.gov/downloads/drugs/guidances/UCM072137.pdf, last accessed January 2017.
  • 7. http://www.fda.gOv/downloads/Drugs/./Guidances/ucm079690.pdf, last accessed January 2016.
  • 8. J. A. Jacquez, Compartmental Analysis in Biology and Medicine, university of Michigan press, Апп arbor, 2nd edn, 1985.
  • 9. J. G. Wagner, Pharmacokinetics for the Pharmaceutical Scientist, Technomic publishing Company, Inc., Lancaster, 1st edn, 1993.
  • 10. M. Rowland and T. N. Tozer, Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications, Lippincott, Williams & Wilkins, Philadelphia, 4th edn, 2010.
  • 11. Q. Huang and J. г. riviere, the application of allometric scaling principles to predict pharmacokinetic parameters across species, Expert Opin. DrugMetab. Toxicol., 2014, 10(9), 1241-1253.
  • 12. D. Liu, L. Pan, H. Yang and J. Wang, A physiologically based toxicoki- netic and toxicodynamic model links the tissue distribution of benzo[a] pyrene and toxic effects in the scallop Chlamys farreri, Environ. Toxicol. Pharmacol., 2014, 37, 493-504.
  • 13. H. J. Clewell, P. R. Gentry, J. M. Gearhart, B. C. Allen and M. E. Andersen, Comparison of cancer risk estimates for vinyl chloride using animal and human data with a PBPK model, Sci. Total Environ., 2001, 274, 37-66.
  • 14. M. Pelekis and C. Emond, Physiological modeling and derivation of the rat to human toxicokinetic uncertainty factor for the carbamate pesticide aldicarb, Environ. Toxicol. Pharmacol., 2009, 28, 179-191.
  • 15. H. Sayama, H. Komura and M. Kogayu, Application of hybrid approach based on empirical and physiological concept for predicting pharmacokinetics in humans—usefulness of exponent on prospective evaluation of predictability, Drug Metab. Dispos., 2013, 41, 498-507.
  • 16. H. Sayama, H. Komura, M. Kogayu and M. Iwaki, Development of a hybrid physiologically based pharmacokinetic model with drug-specific scaling factors in rat to improve prediction of human pharmacokinetics,

J. Pharm. Sci., 2013, 102(11), 4193-4204.

  • 17. C. S. Kim, J. A. Sandberg, W. Slikker, Z. Binienda, P. M. Schlosser and T. A. patterson, Quantitative exposure assessment: application of physiologically-based pharmacokinetic (PBPK) modeling of low-dose, long-term exposures of organic acid toxicant in the brain, Environ. Toxicol. Pharmacol., 2001, 9(4), 153-160.
  • 18. L. M. Sweeney, C. R. Kirman, S. A. Gannon, K. D. Thrall, M. L. Gargas and J. H. Kinzell, Development of a physiologically based pharmacokinetic (PBPK) model for methyl iodide in rats, rabbits, and humans, Inhalation Toxicol., 2009, 21(6), 552-582.
  • 19. C. Timchalk, R. J. Nolan, A. L. Mendrala, D. A. Dittenber, K. A. Brzak and J. L. Mattsson, A Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model for the organophosphate insecticide chlorpy- rifos in rats and humans, Toxicol. Sci., 2002, 66(1), 34-53.
  • 20. A. E. Loccisano, M. P. Longnecker, J. L. Campbell, M. E. Andersen and H. J. Clewell, Development of PBPK models for PFOA and PFOS for human pregnancy and lactation life stages, J. Toxicol. Environ. Health, Part A, 2013, 76(1), 25-57.
  • 21. M. Yoon, J. D. Schroeter, A. Nong, M. D. Taylor, D. C. Dorman and M. E. Andersen, et al., physiologically based pharmacokinetic modeling of fetal and neonatal manganese exposure in humans: describing manganese homeostasis during development, Toxicol. Sci., 2011, 122(2), 297-316.
  • 22. D. G. Kleinbaum, L. L. Kupper and K. E. Muller, Applied Regression Analysis and Other Multivariate Methods, PWS-KENT Publishing Company, Boston, 2nd edn, 1988.
  • 23. S. Beal, L. B. Sheiner, A. Boeckmann and R. J. Bauer, NONMEM User's Guides (1989-2009), Icon Development Solutions, Ellicott City, 2009.
  • 24. S. Donnet and A. Samson, A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models, Adv. Drug Delivery Rev., 2013, 65(7), 929-939.
  • 25. J. Leander, J. Almquist, C. Ahlstrom, J. Gabrielsson and M. Jirstrand, Mixed effects modeling using stochastic differential equations: illustrated by pharmacokinetic data of nicotinic acid in obese Zucker rats, AAPS J., 2015, 17(3), 586-596.
  • 26. F.-R. Yan, P. Zhang, J.-L. Liu, Y.-X. Tao, X. Lin and T. Lu, et al., Parameter Estimation of population pharmacokinetic Models with Stochastic Differential Equations: Implementation of an Estimation Algorithm, J. Probab. Stat., 2014, 2014, 836518.
  • 27. S. Agatonovic-Kustrin and R. Beresford, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, J. Pharm. Biomed. Anal., 2000, 22, 717-727.
  • 28. K. M. Tolle, H. Chen and H.-H. Chow, Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets, Decis. Support Syst., 2000, 30, 139-151.
  • 29. J. V. S. Gobburu and E. P. Chen, Artificial Neural Networks As a Novel Approach to Integrated Pharmacokinetic-Pharmacodynamic Analysis, J. Pharm. Sci., 1996, 85(5), 505-510.
  • 30. S. H. Haidar, S. B. Johnson, M. J. Fossler and A. S. Hussain, Modeling the Pharmacokinetics and Pharmacodynamics of a Unique Oral Hypoglycemic Agent Using Neural Networks, Pharm. Res., 2002, 19(1), 87-91.
  • 31. A. Belie, I. Grabnar, I. Belie, R. Karba and A. Mrhar, Predicting the anti-hypertensive effect of nitrendipine plasma concentration profiles using artificial neural networks, Comput. Biol. Med., 2005, 35, 892-904.
  • 32. G. Levy, Relationship between elimination rate of drugs and rate of decline of their pharmacologic effects,J. Pharm. Sci., 1964, 53, 342-343.
  • 33. G. Levy, Kinetics of pharmacologic effects, Clin. Pharmacol. Ther., 1966, 7(3), 362-372.
  • 34. R. Nagashima, R. A. O'Reilly and G. Levy, Kinetics of pharmacologic effects in man: the anticoagulant action of warfarin, Clin. Pharmacol. Ther., 1969, 10(1), 22-35.
  • 35. C. Louizos, J. A. Yanez, M. L. Forrest and N. M. Davies, Understanding the hysteresis loop conundrum in pharmacokinetic/pharmacodynamic relationships,J. Pharm. Pharm. Sci., 2014, 17(1), 34-91.
  • 36. B. C. Bender, F. Schaedeli-Stark, R. Koch, A. Joshi, Y. W. Chu and H. Rugo, et al., a population pharmacokinetic/pharmacodynamic model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer, Cancer Chemother. Pharmacol., 2012, 70(4), 591-601.
  • 37. S. Hayes, P. N. Mudd, Jr., D. Ouellet, B. M. Johnson, D. Williams and E. Gibiansky, Population PK/PD modeling of eltrombopag in subjects with advanced solid tumors with chemotherapy-induced thrombocytopenia, Cancer Chemother. Pharmacol., 2013, 71, 1507-1520.
  • 38. http://www.ecotoxmodels.org/hot-topics/toxicokinetic-toxicodynamic- models/, last accessed February 2016.
  • 39. E. D. Lobo and J. P. Balthasar, Pharmacokinetic-pharmacodynamic modeling of methotrexate-induced toxicity in mice, J. Pharm. Sci., 2003, 92(8), 1654-1664.
  • 40. R. Ashauer and C. D. Brown, Toxicodynamic assumptions in ecotoxicological hazard models, Environ. Toxicol. Chem., 2008, 27(8), 1817-1821.
  • 41. J. Stadnicka-Michalak, K. Schirmer and R. Ashauer, Toxicology across scales: Cell population growth in vitro predicts reduced fish growth, Sci. Adv., 2015, 1(7), e1500302.
  • 42. K. K. Rozman and J. Doull, Dose and time as variables of toxicity, Toxicology, 2000, 144(1-3), 169-178.
  • 43. J. M. Frazier, Predictive toxicodynamics: Empirical/mechanistic approaches, Toxicol. In Vitro, 1997, 11(5), 465-472.
  • 44. T. Jager, C. Albert, T. G. Preuss and R. Ashauer, General unified threshold model of survival-a toxicokinetic-toxicodynamic framework for ecotoxicology, Environ. Sci. Technol., 2011, 1;45(7), 2529-2540.
  • 45. P. Jacqmin, E. Snoeck, E. A. van Schaick, R. Gieschke, P. Pillai and J. L. Steimer, et al., Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model, J. Pharmacokinet. Pharmacodyn., 2007, 34(1), 57-85.
  • 46. http://www.accessdata.fda.gov/drugsatfda_docs/label/2 009/ 020702s056lbl.pdf, last accessed February 2016.
  • 47. S. Zhou, E. Chan, S. Q. Pan, M. Huang and E. J. Lee, Pharmacokinetic interactions of drugs with St. John’s wort, J. Psychopharmacol., 2004, 18(2), 262-276.
  • 48. http://www.drugs.com, last accessed February 2016.
 
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