Skip to main content

Advertisement

Log in

Fractional calculus in pharmacokinetics

  • Original Paper
  • Published:
Journal of Pharmacokinetics and Pharmacodynamics Aims and scope Submit manuscript

Abstract

We are witnessing the birth of a new variety of pharmacokinetics where non-integer-order differential equations are employed to study the time course of drugs in the body: this is dubbed “fractional pharmacokinetics”. The presence of fractional kinetics has important clinical implications such as the lack of a half-life, observed, for example with the drug amiodarone and the associated irregular accumulation patterns following constant and multiple-dose administration. Building models that accurately reflect this behaviour is essential for the design of less toxic and more effective drug administration protocols and devices. This article introduces the readers to the theory of fractional pharmacokinetics and the research challenges that arise. After a short introduction to the concepts of fractional calculus, and the main applications that have appeared in literature up to date, we address two important aspects. First, numerical methods that allow us to simulate fractional order systems accurately and second, optimal control methodologies that can be used to design dosing regimens to individuals and populations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. West BJ, Deering W (1994) Fractal physiology for physicists: Lévy statistics. Phys Rep 246(1):1–100

    Article  Google Scholar 

  2. Ionescu C, Lopes A, Copot D, Machado J, Bates J (2017) The role of fractional calculus in modeling biological phenomena: a review. Commun Nonlinear Sci Numer Simul 51:141–159. doi:10.1016/j.cnsns.2017.04.001

    Article  Google Scholar 

  3. Kopelman R (1988) Fractal reaction kinetics. Science 241(4873):1620–1626

    Article  CAS  PubMed  Google Scholar 

  4. Macheras P (1996) A fractal approach to heterogeneous drug distribution: calcium pharmacokinetics. Pharm Res 13(5):663–670

    Article  CAS  PubMed  Google Scholar 

  5. Pereira L (2010) Fractal pharmacokinetics. Comput Math Methods Med 11:161–184

    Article  PubMed  Google Scholar 

  6. Wise ME (1985) Negative power functions of time in pharmacokinetics and their implications. J Pharmacokinet Biopharm 13(3):309–346

    Article  CAS  PubMed  Google Scholar 

  7. Tucker G, Jackson P, Storey G, Holt D (1984) Amiodarone disposition: polyexponential, power and gamma functions. Eur J Clin Pharmacol 26(5):655–656

    Article  CAS  PubMed  Google Scholar 

  8. Weiss M (1999) The anomalous pharmacokinetics of amiodarone explained by nonexponential tissue trapping. J Pharmacokinet Biopharm 27(4):383–396

    Article  CAS  PubMed  Google Scholar 

  9. Phan G, Le Gall B, Deverre JR, Fattal E, Bénech H (2006) Predicting plutonium decorporation efficacy after intravenous administration of DTPA formulations: study of pharmacokinetic-pharmacodynamic relationships in rats. Pharm Res 23(9):2030–2035

    Article  CAS  PubMed  Google Scholar 

  10. Sokolov IM, Klafter J, Blumen A (2002) Fractional kinetics. Phys Today 55(11):48–54

    Article  CAS  Google Scholar 

  11. Podlubny I (1999) Fractional differential equations, mathematics in science and engineering, vol 198. Academic Publisher, San Diego

    Google Scholar 

  12. Magin RL (2004a) Fractional calculus in bioengineering, part 1. Critical reviews \(^{{TM}}\). Biomed Eng 32(1):1–104

    Google Scholar 

  13. Magin RL (2004b) Fractional calculus in bioengineering, part3. Critical reviews \(^{{TM}}\). Biomed Eng 32(3–4):195–377

    Google Scholar 

  14. Magin RL (2004c) Fractional calculus in bioengineering, part 2. Critical reviews \(^{{TM}}\). Biomed Eng 32(2):105–194

    Google Scholar 

  15. Butera S, Paola MD (2014) A physically based connection between fractional calculus and fractal geometry. Ann Phys 350:146–158. doi:10.1016/j.aop.2014.07.008

    Article  CAS  Google Scholar 

  16. Chen W, Sun H, Zhang X, Korošak D (2010) Anomalous diffusion modeling by fractal and fractional derivatives. Comput Math Appl 59(5):1754–1758. doi:10.1016/j.camwa.2009.08.020

    Article  Google Scholar 

  17. Metzler R, Glöckle WG, Nonnenmacher TF (1994) Fractional model equation for anomalous diffusion. Phys A 211(1):13–24. doi:10.1016/0378-4371(94)90064-7

    Article  Google Scholar 

  18. Copot D, Ionescu CM, Keyser RD (2014) Relation between fractional order models and diffusion in the body. IFAC Proc Vol 47:9277–9282. doi:10.3182/20140824-6-ZA-1003.02138

    Article  Google Scholar 

  19. Gmachowski L (2015) Fractal model of anomalous diffusion. Eur Biophys J 44(8):613–621. doi:10.1007/s00249-015-1054-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Klafter J, Sokolov IM (2011) First steps in random walks: from tools to applications. Oxford University Press, Oxford

    Book  Google Scholar 

  21. Eirich FR (1990) The fractal approach to heterogeneous chemistry, surfaces, colloids, polymers. Wiley, New York. doi:10.1002/pol.1990.140280608

    Google Scholar 

  22. Dokoumetzidis A, Macheras P (2011) The changing face of the rate concept in biopharmaceutical sciences: from classical to fractal and finally to fractional. Pharm Res 28(5):1229–1232

    Article  CAS  PubMed  Google Scholar 

  23. Dokoumetzidis A, Macheras P (2009) Fractional kinetics in drug absorption and disposition processes. J Pharmacokinet Pharmacodyn 36(2):165–178

    Article  CAS  PubMed  Google Scholar 

  24. Kytariolos J, Dokoumetzidis A, Macheras P (2010) Power law IVIVC: an application of fractional kinetics for drug release and absorption. Eur J Pharm Sci 41(2):299–304

    Article  CAS  PubMed  Google Scholar 

  25. Popović JK, Atanacković MT, Pilipović AS, Rapaić MR, Pilipović S, Atanacković TM (2010) A new approach to the compartmental analysis in pharmacokinetics: fractional time evolution of diclofenac. J Pharmacokinet Pharmacodyn 37(2):119–134

    Article  PubMed  Google Scholar 

  26. Popović JK, Dolićanin D, Rapaić MR, Popović SL, Pilipović S, Atanacković TM (2011) A nonlinear two compartmental fractional derivative model. Eur J Drug Metabol Pharmacokinet 36(4):189–196

    Article  Google Scholar 

  27. Popović JK, Poša M, Popović KJ, Popović DJ, Milošević N, Tepavčević V (2013) Individualization of a pharmacokinetic model by fractional and nonlinear fit improvement. Eur J Drug Metabol Pharmacokinet 38(1):69–76

    Article  Google Scholar 

  28. Popović JK, Spasić DT, Tošić J, Kolarović JL, Malti R, Mitić IM, Pilipović S, Atanacković TM (2015) Fractional model for pharmacokinetics of high dose methotrexate in children with acute lymphoblastic leukaemia. Commun Nonlinear Sci Numer Simul 22(1):451–471

    Article  Google Scholar 

  29. Copot D, Chevalier A, Ionescu CM, De Keyser R (2013) A two-compartment fractional derivative model for propofol diffusion in anesthesia. In: IEEE International Conference on Control Applications, pp 264–269. doi:10.1109/CCA.2013.6662769

  30. Verotta D (2010) Fractional dynamics pharmacokinetics-pharmacodynamic models. J Pharmacokinet Pharmacodyn 37(3):257–276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Van der Graaf PH, Benson N, Peletier LA (2015) Topics in mathematical pharmacology. J Dyn Differ Equ 28(3–4):1337–1356. doi:10.1007/s10884-015-9468-4

    Google Scholar 

  32. Hennion M, Hanert E (2013) How to avoid unbounded drug accumulation with fractional pharmacokinetics. J Pharmacokinet Pharmacodyn 40(6):691–700. doi:10.1007/s10928-013-9340-2

    Article  CAS  PubMed  Google Scholar 

  33. Samko S, Kilbas A, Marichev O (1993) Fractional integral and derivatives. Gordon & Breach Science Publishers, Philadelphia

    Google Scholar 

  34. Deng J, Deng Z (2014) Existence of solutions of initial value problems for nonlinear fractional differential equations. Appl Math Lett 32:6–12. doi:10.1016/j.aml.2014.02.001

    Article  Google Scholar 

  35. Mainardi F (2014) On some properties of the Mittag-Leffler function \(\cal{E}_\alpha (-t^\alpha )\), completely monotone for \(t> 0\) with \(0<\alpha <1\). Discret Contin Dyn Syst 19(7):2267–2278. doi:10.3934/dcdsb.2014.19.2267

    Article  Google Scholar 

  36. Papadopoulou V, Kosmidis K, Vlachou M, Macheras P (2006) On the use of the weibull function for the discernment of drug release mechanisms. Int J Pharm 309(1):44–50. doi:10.1016/j.ijpharm.2005.10.044

    Article  CAS  PubMed  Google Scholar 

  37. De Hoog FR, Knight J, Stokes A (1982) An improved method for numerical inversion of Laplace transforms. SIAM J Sci Stat Comput 3(3):357–366

    Article  Google Scholar 

  38. Dokoumetzidis A, Magin R, Macheras P (2010a) A commentary on fractionalization of multi-compartmental models. J Pharmacokinet Pharmacodyn 37(2):203–207

    Article  PubMed  Google Scholar 

  39. Dokoumetzidis A, Magin R, Macheras P (2010b) Fractional kinetics in multi-compartmental systems. J Pharmacokinet Pharmacodyn 37(5):507–524

    Article  PubMed  Google Scholar 

  40. Popović JK, Pilipoviá S, Atanackoviá TM (2013) Two compartmental fractional derivative model with fractional derivatives of different order. Commun Nonlinear Sci Numer Simul 18(9):2507–2514. doi:10.1016/j.cnsns.2013.01.004

    Article  Google Scholar 

  41. Petráš I, Magin RL (2011) Simulation of drug uptake in a two compartmental fractional model for a biological system. Commun Nonlinear Sci Numer Simul 16(12):4588–4595. doi:10.1016/j.cnsns.2011.02.012

    Article  PubMed  PubMed Central  Google Scholar 

  42. Kilbas AA, Srivastava HM, Trujillo JJ (2006) Theory and applications of fractional differential equations. Elsevier, Amsterdam

    Google Scholar 

  43. Moloni S (2015) Applications of fractional calculus to pharmacokinetics. Master’s thesis, University of Patras, Department of Mathematics, Patras

  44. Holt DW, Tucker GT, Jackson PR, Storey GC (1983) Amiodarone pharmacokinetics. Am Heart J 106(4):840–847. doi:10.1016/0002-8703(83)90006-6

    Article  CAS  PubMed  Google Scholar 

  45. Kaczorek T (2011) Selected problems of fractional systems theory. Springer, Berlin

    Book  Google Scholar 

  46. Sopasakis P, Ntouskas S, Sarimveis H (2015) Robust model predictive control for discrete-time fractional-order systems. In: IEEE Mediterranean Conference on Control and Automation, pp 384–389

  47. Verotta D (2010) Fractional compartmental models and multi-term Mittag-Leffler response functions. J Pharmacokinet Pharmacodyn 37(2):209–215. doi:10.1007/s10928-010-9155-3

    Article  PubMed  PubMed Central  Google Scholar 

  48. Garrappa R (2015) Numerical evaluation of two and three parameter Mittag-Leffler functions. SIAM J Numer Anal 53:1350–1369. doi:10.1137/140971191

    Article  Google Scholar 

  49. Seybold H, Hilfer R (2009) Numerical algorithm for calculating the generalized mittag-leffler function. SIAM J Numer Anal 47(1):69–88. doi:10.1137/070700280

    Article  Google Scholar 

  50. Gorenflo R, Loutchko J, Luchko Y (2002) Computation of the Mittag-Leffler function and its derivatives. Fract Calc Appl Anal 5:1–26

    Google Scholar 

  51. Silva M, Machado J, Barbosa R (2006) Comparison of different orders Padé fractional order PD0.5 control algorithm implementations. IFAC Proc Vol 39(11):373–378

    Article  Google Scholar 

  52. Matsuda K, Fujii H (1993) H(infinity) optimized wave-absorbing control—analytical and experimental results. J Guid Control Dyn 16(6):1146–1153

    Article  Google Scholar 

  53. Oustaloup A, Levron F, Mathieu B, Nanot FM (2000) Frequency-band complex noninteger differentiator: characterization and synthesis. IEEE Trans Circuits Syst I 47(1):25–39

    Article  Google Scholar 

  54. Petráš I (2011) Fractional derivatives, fractional integrals, and fractional differential equations in matlab. In: Assi A (ed) Engineering Education and Research Using MATLAB, InTech. http://doi.org/10.5772/19412

  55. Charef A, Sun HH, Tsao YY, Onaral B (1992) Fractal system as represented by singularity function. IEEE Trans Autom Control 37(9):1465–1470

    Article  Google Scholar 

  56. Carlson G, Halijak C (1964) Approximation of fractional capacitors \(1/s^{1/n}\) by a regular Newton process. IEEE Trans Circuits Theory 11(2):210–213. doi:10.1109/TCT.1964.1082270

    Article  Google Scholar 

  57. Gao Z, Liao X (2012) Rational approximation for fractional-order system by particle swarm optimization. Nonlinear Dyn 67(2):1387–1395. doi:10.1007/s11071-011-0075-6

    Article  Google Scholar 

  58. Sopasakis P, Sarimveis H (2017) Stabilising model predictive control for discrete-time fractional-order systems. Automatica 75:24–31

    Article  Google Scholar 

  59. Podlubny I (2000) Matrix approach to discrete fractional calculus. Fract Calc Appl Anal 3:359–386

    Google Scholar 

  60. Zainal NH, Kılıçman A (2014) Solving fractional partial differential equations with corrected fourier series method. Abstr Appl Anal 2014:1–5. doi:10.1155/2014/958931

    Article  Google Scholar 

  61. Kumar P, Agrawal OP (2006) An approximate method for numerical solution of fractional differential equations. Signal Process 86(10):2602–2610. doi:10.1016/j.sigpro.2006.02.007

    Article  Google Scholar 

  62. Zayernouri M, Matzavinos A (2016) Fractional Adams-Bashforth/Moulton methods: an application to the fractional Keller–Segel chemotaxis system. J Comput Phys 317:1–14

    Article  CAS  Google Scholar 

  63. Lubich C (1986) Discretized fractional calculus. SIAM J Math Anal 17(3):704–719

    Article  Google Scholar 

  64. Garrappa R (2015) Trapezoidal methods for fractional differential equations: theoretical and computational aspects. Math Comput Simul 110:96–112

    Article  Google Scholar 

  65. Diethelm K, Ford NJ, Freed AD (2002) A predictor-corrector approach for the numerical solution of fractional differential equations. Nonlinear Dyn 29(1):3–22. doi:10.1023/A:1016592219341

    Article  Google Scholar 

  66. Garrappa R (2010) On linear stability of predictor-corrector algorithms for fractional differential equations. Int J Comput Math 87(10):2281–2290. doi:10.1080/00207160802624331

    Article  Google Scholar 

  67. Lubich C (1985) Fractional linear multistep methods for Abel–Volterra integral equations of the second kind. Math Comput 45(172):463–469

    Article  Google Scholar 

  68. Garrappa R (2015) Software for fractional differential equations. https://www.dm.uniba.it/Members/garrappa/Software. Accessed 22 Sept 2017

  69. Herceg D, Ntouskas S, Sopasakis P, Dokoumetzidis A, Macheras P, Sarimveis H, Patrinos P (2017) Modeling and administration scheduling of fractional-order pharmacokinetic systems. In: IFAC World Congress, Toulouse, France

  70. Hollenbeck KJ (1998) INVLAP.M: A MATLAB function for numerical inversion of Laplace transforms by the de Hoog algorithm. http://www.mathworks.com/matlabcentral/answers/uploaded_files/1034/invlap.m. Accessed 22 Sept 2017

  71. Lin SD (2013) Lu CH (2013) Laplace transform for solving some families of fractional differential equations and its applications. Adv Differ Equ 1:137

    Article  Google Scholar 

  72. Kexue L, Jigen P (2011) Laplace transform and fractional differential equations. Appl Math Lett 24(12):2019–2023

    Article  Google Scholar 

  73. Valsa J, Brančik L (1998) Approximate formulae for numerical inversion of Laplace transforms. Int J Numer Model 11(3):153–166

    Article  Google Scholar 

  74. Hassanzadeh H, Pooladi-Darvish M (2007) Comparison of different numerical Laplace inversion methods for engineering applications. Appl Math Comput 189(2):1966–1981

    Google Scholar 

  75. Sopasakis P, Patrinos P, Sarimveis H (2014) Robust model predictive control for optimal continuous drug administration. Comput Methods Progr Biomed 116(3):193–204. doi:10.1016/j.cmpb.2014.06.003

    Article  Google Scholar 

  76. Sopasakis P, Patrinos P, Sarimveis H, Bemporad A (2015) Model predictive control for linear impulsive systems. IEEE Trans Autom Control 60:2277–2282. doi:10.1109/TAC.2014.2380672

    Article  Google Scholar 

  77. Rivadeneira PS, Ferramosca A, González AH (2015) MPC with state window target control in linear impulsive systems. In: 5th IFAC Conference on Nonlinear Model Predictive Control NMPC 2015, vol 48, pp 507–512. http://dx.doi.org/10.1016/j.ifacol.2015.11.329

  78. Sopasakis P, Sarimveis H (2012) An integer programming approach for optimal drug dose computation. Comput Methods Progr Biomed 108(3):1022–1035. doi:10.1016/j.cmpb.2012.06.008

    Article  Google Scholar 

  79. Krieger A, Pistikopoulos EN (2014) Model predictive control of anesthesia under uncertainty. Comput Chem Eng 71:699–707. doi:10.1016/j.compchemeng.2014.07.025

    Article  CAS  Google Scholar 

  80. Favero SD, Bruttomesso D, Palma FD, Lanzola G, Visentin R, Filippi A, Scotton R, Toffanin C, Messori M, Scarpellini S, Keith-Hynes P, Kovatchev BP, DeVries JH, Renard E, Magni L, Avogaro A (2014) First use of model predictive control in outpatient wearable artificial pancreas. Diabetes Care 37(5):1212–1215. doi:10.2337/dc13-1631

    Article  PubMed  Google Scholar 

  81. Kannikeswaran N, Lieh-Lai M, Malian M, Wang B, Farooqi A, Roback MG (2016) Optimal dosing of intravenous ketamine for procedural sedation in children in the ED—a randomized controlled trial. Am J Emerg Med 34(8):1347–1353. doi:10.1016/j.ajem.2016.03.064

    Article  PubMed  Google Scholar 

  82. Fukudo S, Matsueda K, Haruma K, Ida M, Hayase H, Akiho H, Nakashima Y, Hongo M (2017) Optimal dose of ramosetron in female patients with irritable bowel syndrome with diarrhea: A randomized, placebo-controlled phase II study. Neurogastroenterol Motil 29(6):e13,023. doi:10.1111/nmo.13023

    Article  Google Scholar 

  83. De Ocenda VR, Almeida-Prieto S, Luzardo-Álvarez A, Barja J, Otero-Espinar F, Blanco-Méndez J (2016) Pharmacokinetic model of florfenicol in turbot (scophthalmus maximus): establishment of optimal dosage and administration in medicated feed. J Fish Dis 40(3):411–424. doi:10.1111/jfd.12525

    Article  PubMed  Google Scholar 

  84. Savic R, Weiner M, MacKenzie W, Engle M, Johnson J, Nsubuga P, Nahid P, Nguyen N, Peloquin C, Dooley K, Dorman S (2017) Defining the optimal dose of rifapentine for pulmonary tuberculosis: Exposure-response relations from two phase II clinical trials. Clin Pharm Ther 102(2):321–331. doi:10.1002/cpt.634

    Article  CAS  Google Scholar 

  85. Bertsekas DP (2017) Dynamic programming and optimal control, 4th edn. Athena Scientific, Nashua

    Google Scholar 

  86. Löfberg J (2004) YALMIP: A toolbox for modeling and optimization in MATLAB. In: IEEE International Symposium on Computer Aided Control Systems Design, New Orleans, LA, USA, pp 284–289. http://doi.org/10.1109/CACSD.2004.1393890

  87. Stella L, Themelis A, Patrinos P (2017) Forward–backward quasi-newton methods for nonsmooth optimization problems. Comput Optim Appl 67(3):443–487. doi:10.1007/s10589-017-9912-y, forBES. https://github.com/kul-forbes/ForBES

  88. Diamond S, Boyd S (2016) CVXPY: A Python-embedded modeling language for convex optimization. J Mach Learn Res 17(83):1–5

    Google Scholar 

  89. Bertsekas DP, Shreve SE (1996) Stochastic optimal control: the discrete-time case. Athena Scientific, Nashua

    Google Scholar 

  90. Schumitzky A, Milman M, Katz D, D’Argenio DZ, Jelliffe RW (1983) Stochastic control of pharmacokinetic systems. In: The Seventh Annual Symposium on Computer Applications in Medical Care, 1983. Proceedings., pp 222–225, doi:10.1109/SCAMC.1983.764595

  91. Lago P (1992) Open-loop stochastic control of pharmacokinetic systems: a new method for design of dosing regimens. Comput Biomed Res 25(1):85–100. doi:10.1016/0010-4809(92)90037-b

    Article  CAS  PubMed  Google Scholar 

  92. Bayard D, Milman M, Schumitzky A (1994) Design of dosage regimens: a multiple model stochastic control approach. Int J Bio-Med Comput 36(1):103–115. doi:10.1016/0020-7101(94)90100-7

    Article  CAS  Google Scholar 

  93. Campi MC, Garatti S, Prandini M (2009) The scenario approach for systems and control design. Annu Rev Control 33(2):149–157. doi:10.1016/j.arcontrol.2009.07.001

    Article  Google Scholar 

  94. Shapiro A, Dentcheva D, Ruszczyński (2009) Lectures on stochastic programming: modeling and theory. SIAM

  95. Herceg D, Sopasakis P, Bemporad A, Patrinos P (2017) Risk-averse model predictive control. https://arxiv.org/abs/1704.00342

  96. Gaweda AE, Jacobs AA, Aronoff GR, Brier ME (2008) Model predictive control of erythropoietin administration in the anemia of ESRD. Am J Kidney Dis 51(1):71–79. doi:10.1053/j.ajkd.2007.10.003

    Article  CAS  PubMed  Google Scholar 

  97. Ionescu CM, Keyser RD, Torrico BC, Smet TD, Struys MM, Normey-Rico JE (2008) Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia. IEEE Trans Biomed Eng 55(9):2161–2170. doi:10.1109/TBME.2008.923142

    Article  PubMed  Google Scholar 

  98. Schaller S, Lippert J, Schaupp L, Pieber TR, Schuppert A, Eissing T (2016) Robust PBPK/PD-based model predictive control of blood glucose. IEEE Trans Biomed Eng 63(7):1492–1504. doi:10.1109/TBME.2015.2497273

    Article  PubMed  Google Scholar 

  99. Hovorka R, Canonico V, Chassin L, Haueter U, Massi-Benedetti M, Federici M, Pieber T, Schaller H, Schaupp L, Vering T, Wilinska M (2004) Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas 25(4):905–920. doi:10.1088/0967-3334/25/4/010

    Article  PubMed  Google Scholar 

  100. Toffanin C, Messori M, Palma FD, Nicolao GD, Cobelli C, Magni L (2013) Artificial pancreas: model predictive control design from clinical experience. J Diabetes Sci Technol 7(6):1470–1483. doi:10.1177/193229681300700607

    Article  PubMed  PubMed Central  Google Scholar 

  101. Parker RS, Doyle FJ, Peppas NA (1999) A model-based algorithm for blood glucose control in type I diabetic patients. IEEE Trans Biomed Eng 46(2):148–157. doi:10.1109/10.740877

    Article  CAS  PubMed  Google Scholar 

  102. Rawlings J, Mayne D (2009) Model predictive control: theory and design. Nob Hill Publishing, Madison

    Google Scholar 

  103. Sopasakis P, Sarimveis H (2014) Controlled drug administration by a fractional PID. IFAC World Congress, Cape Town, pp 8421–8426

    Google Scholar 

  104. Patrinos P, Sopasakis P, Sarmiveis H, Bemporad A (2014) Stochastic model predictive control for constrained discrete-time Markovian switching systems. Automatica 50(10):2504–2514. doi:10.1016/j.automatica.2014.08.031

    Article  Google Scholar 

  105. Sopasakis P, Herceg D, Patrinos P, Bemporad A (2017) Stochastic economic model predictive control for Markovian switching systems. In: IFAC World Congress

  106. Patek SD, Breton MD, Chen Y, Solomon C, Kovatchev B (2007) Linear quadratic gaussian-based closed-loop control of type 1 diabetes. J Diabetes Sci Technol 1(6):834–841

    Article  PubMed  PubMed Central  Google Scholar 

  107. Wang Q, Molenaar P, Harsh S, Freeman K, Xie J, Gold C, Rovine M, Ulbrecht J (2014) Personalized state-space modeling of glucose dynamics for type 1 diabetes using continuously monitored glucose, insulin dose, and meal intake. J Diabetes Sci Technol 8(2):331–345. doi:10.1177/1932296814524080

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aristides Dokoumetzidis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sopasakis, P., Sarimveis, H., Macheras, P. et al. Fractional calculus in pharmacokinetics. J Pharmacokinet Pharmacodyn 45, 107–125 (2018). https://doi.org/10.1007/s10928-017-9547-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10928-017-9547-8

Keywords

Navigation